TRENDS IN MATERNAL MORTALITY 2000 to 2017 Estimates by WHO, UNICEF, UNFPA, World Bank Group and the United Nations Population Division LAUNCH VERSION TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 Estimates by WHO, UNICEF, UNFPA, World Bank Group and the United Nations Population Division Trends in maternal mortality 2000 to 2017: estimates by WHO, UNICEF, UNFPA, World Bank Group and the United Nations Population Division ISBN 978-92-4-151648-8 © World Health Organization 2019 Some rights reserved. This work is available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO licence (CC BY-NC-SA 3.0 IGO; https://creativecommons.org/licenses/by-nc-sa/3.0/igo). Under the terms of this licence, you may copy, redistribute and adapt the work for non-commercial purposes, provided the work is appropriately cited, as indicated below. In any use of this work, there should be no suggestion that WHO endorses any specific organization, products or services. 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Design and layout: Anne-Marie Labouche CONTENTS Acknowledgments................................................................................................................................................. vi Executive summary................................................................................................................................................ ix 1. Introduction.......................................................................................................................................................1 2. Definitions and measures....................................................................................................................................7 2.1 Definitions for key terms used in this report...................................................................................................8 2.2 Measures of maternal mortality used in this report........................................................................................9 3. Methods ...........................................................................................................................................................13 3.1 Data inputs for the estimation process.........................................................................................................14 3.1.1 Data sources.......................................................................................................................................14 3.1.2 Uncertainty associated with observations and adjustments.....................................................................16 3.2. Other data inputs to the model....................................................................................................................17 3.2.1 Data on all deaths to women aged 15–49 years and HIV-related mortality.........................................................17 3.2.2 Live births data....................................................................................................................................18 3.2.3 Predictor variables in the maternal mortality model.................................................................................18 3.3. Statistical methods....................................................................................................................................18 3.3.1 Bayesian CRVS adjustment model to account for errors in reporting of maternal death in the CRVS system (the CRVS model)...........................................................................................................19 3.3.2 Bayesian maternal mortality estimation model (the BMat model).............................................................24 3.3.3 Maternal mortality indicators estimated by the model.............................................................................28 4. Maternal Mortality estimates and trends: 2000 to 2017 .........................................................................................31 4.1 Maternal mortality estimates for 2017...........................................................................................................32 4.1.1 Regional-level estimates......................................................................................................................33 4.1.2 Country-level estimates.......................................................................................................................34 4.2 Trends in maternal mortality: 2000 to 2017....................................................................................................39 4.2.1 Regional-level trends...........................................................................................................................39 4.2.2 Country-level trends............................................................................................................................40 4.3 Comparison with previous maternal mortality estimates.................................................................................42 5. Assessing progress and setting a trajectory towards ending preventable maternal mortality and achieving SDG target 3.1..................................................................................................................................43 5.1 Transition from MDG to SDG reporting.........................................................................................................44 5.2. Strategies for improving maternal health: 2016 to 2030.................................................................................46 5.2.1 Specialized population groups: humanitarian and crisis settings, vulnerable populations and late maternal deaths....................................................................................................................................46 5.2.2 Challenges remain: need for improved civil registration and vital statistics (CRVS) systems and other data sources.......................................................................................................................................47 6. Conclusions.......................................................................................................................................................51 Annexes................................................................................................................................................................55 Additional relevant materials including links to the full database, country profiles and all model specification codes, as well as language editions of this report (when available) can be found at: www.who.int/reproductivehealth/publications/maternal-mortality-2017/en/ iii LIST OF TABLES Table 3.1. Maternal mortality data records by source type used in generating maternal mortality ratio estimates (MMR, maternal deaths per 100 000 live births) for 2017 Table 4.1. Estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), number of maternal deaths, lifetime risk and proportion of deaths among women of reproductive age that are due to maternal causes (PM), by United Nations Sustainable Development Goal (SDG) region, subregion and other grouping, 2017 Table 4.2. Estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), number of maternal deaths and HIV-related indirect maternal deaths, by United Nations Sustainable Development Goal (SDG) region, subregion and other grouping, 2017 Table 4.3. Comparison of maternal mortality ratio (MMR, maternal deaths per 100 000 live births) and number of maternal deaths, by United Nations Sustainable Development Goal (SDG) region, subregion and other grouping, 2000 and 2017 iv LIST OF ANNEXES Annex 1. Summary description of the country consultations 2019 Annex 2. Measuring maternal mortality Annex 3. Calculation of maternal mortality during crisis years Annex 4. Methods used to derive a complete series of annual estimates for each predictor variable Annex 5. Estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), number of maternal deaths, lifetime risk, percentage of HIV-related indirect maternal deaths and proportion of deaths among women of reproductive age that are due to maternal causes (PM), by country and territory, 2017 Annex 6. Estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), number of maternal deaths, and lifetime risk, by World Health Organization (WHO) region, 2017 Annex 7. Trends in estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), by WHO region, 2000–2017 Annex 8. Estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), number of maternal deaths, and lifetime risk, by United Nations Children’s Fund (UNICEF) region, 2017 Annex 9. Trends in estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), by UNICEF region, 2000–2017 Annex 10. Estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), number of maternal deaths, and lifetime risk, by United Nations Population Fund (UNFPA) region, 2017 Annex 11. Trends in estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), by UNFPA region, 2000–2017 Annex 12. Estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), number of maternal deaths, and lifetime risk, by World Bank Group region and income group, 2017 Annex 13. Trends in estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), by World Bank Group region and income group, 2000–2017 Annex 14. Estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), number of maternal deaths, and lifetime risk, by United Nations Population Division (UNPD) region, 2017 Annex 15. Trends in estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), by UNPD region, 2000–2017 Annex 16. Trends in estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), by United Nations Sustainable Development Goal (SDG) region, subregion and other grouping, 2000–2017 Annex 17. Trends in estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), by country and territory, 2000–2017 v ACKNOWLEDGMENTS The United Nations Maternal Mortality Estimation Inter-Agency Group (UN MMEIG), together with its independent external Technical Advisory Group (TAG), collaborated in developing these maternal mortality estimates. From each of the constituent agencies that form the UN MMEIG, the following individuals worked on the compilation of this report:1 • World Health Organization (WHO): Doris Chou, Ann-Beth Moller and Lale Say • United Nations Children’s Fund (UNICEF): Liliana Carvajal-Aguirre and Jennifer Requejo • United Nations Population Fund (UNFPA): Tapiwa Jhamba • United Nations Population Division (UNPD, a division of the United Nations Department of Economic and Social Affairs [UN DESA]): Kirill Andreev, Lina Bassarsky, Victor Gaigbe-Togbe and Patrick Gerland • The World Bank Group: Charles Kouame, Samuel Mills and Emi Suzuki. The members of the TAG provided independent technical advice: • Saifuddin Ahmed, of Johns Hopkins Bloomberg School of Public Health, United States of America (USA) • Peter Byass, of the Umeå Centre for Global Health Research, Umeå University, Sweden • Thomas W. Pullum, of the Demographic and Health Surveys (DHS) Program, ICF, USA. In addition, independent expert consultants for this project were: • Tim Colbourn, of University College London, United Kingdom of Great Britain and Northern Ireland • Jeff Eaton, of Imperial College London, United Kingdom • Alison Gemmill and Stéphane Helleringer, of Johns Hopkins University, USA • Marie Klingberg Alvin, of Dalarna University/Högskolan Dalarna, Sweden • Laina Mercer, of PATH, USA • Helena Nordenstedt, of the Karolinska Institutet, Sweden • Jon Wakefield, of the University of Washington, USA. The TAG is grateful for the review and support of a working group on maternal mortality in censuses. The work was supported by funding from the United States Agency for International Development (USAID) through MEASURE Evaluation (cooperative agreement AID-OAA-L-14-00004). The members of the working group were: • Liliana Carvajal-Aguirre, of UNICEF • Doris Chou, of WHO • Patrick Gerland, of UNPD • Peter Johnson (retired), Nobuko Mizoguchi and Loraine West (retired) of the United States Census Bureau, USA • Qingfeng Li, of Johns Hopkins Bloomberg School of Public Health, USA • Kavita Singh Ongechi, of the University of North Carolina at Chapel Hill, USA. We are also grateful to the WHO Department of Governing Bodies and External Relations. Country offices for WHO, UNICEF, UNFPA and the World Bank Group are all gratefully acknowledged for facilitating the country consultations. All lists of names are given in alphabetical order by last name. 1 vi Thanks are also due to the following WHO regional office staff: • Regional Office for Africa: Elongo Lokombe, Triphonie Nkurunziza, Léopold Ouedraogo and Prosper Tumusiime • Regional Office for the Americas (Pan American Health Organization [PAHO]): Adrienne Lavita Cox, Bremen de Mucio, Patricia Lorena Ruiz Luna, Antonio Sanhueza and Suzanne Serruya • Regional Office for South-East Asia: C. Anoma Jayathilaka, Mark Landry and Neena Raina • Regional Office for Europe: Nino Berdzuli, Kristina Mauer-Stender, David Novillo and Claudia Stein • Regional Office for the Eastern Mediterranean: Karima Gholbzouri, Ramez Khairi Mahaini and Arash Rashidian • Regional Office for the Western Pacific: Jun Gao, Priya Mannava and Howard Sobel. In addition, WHO provided translation services for documents disseminated during the country consultations. Thanks to Patricia Lorena Ruiz Luna, Antonio Sanhueza and Rosina Romero, of PAHO, for all their translation support for communications during the country consultations. Thank you to all government technical focal persons for maternal mortality and the Sustainable Development Goal (SDG) focal points who reviewed the preliminary maternal mortality estimates and provided valuable feedback and input. Financial support was provided by WHO, through the Department of Reproductive Health and Research and HRP (the UNDP-UNFPA-UNICEF-WHO-World Bank Special Programme of Research, Development and Research Training in Human Reproduction), USAID and the University of Massachusetts, Amherst, USA. Thanks also go to Alison Gemmill and Kerry Wong for helping with the country profiles; to Jenny Cresswell, Carolin Ekman and Doris Hanappi for helping with data review; to Florence Rusciano for assistance with creation of maps; and to Catherine Hamill, Svetlin Kolev and Christine Meynent for assistance with related webpages. This report was prepared by Doris Chou, Ann-Beth Moller and Lale Say of the WHO Department of Reproductive Health and Research; Leontine Alkema and Emily Peterson of the University of Massachusetts, USA; and Jane Patten of Green Ink, United Kingdom. For any further information relating to this report, you may contact Doris Chou (email: choud@who.int) and Lale Say (email: sayl@who.int) of the WHO Department of Reproductive Health and Research. vii ACRONYMS AND ABBREVIATIONS ARR annual rate of reduction ASFR age-specific fertility rates BMat Bayesian maternal mortality estimation model CEMD confidential enquiry into maternal deaths CRVS civil registration and vital statistics DHS Demographic and Health Survey EPMM ending preventable maternal mortality F+/F– false positive/false negative GDP gross domestic product per capita based on PPP conversion GFR general fertility rate ICD International statistical classification of diseases and related health problems2 ICD-MM ICD-maternal mortality (refers to WHO publication: Application of ICD-10 to deaths during pregnancy, childbirth and the puerperium: ICD-MM) MDG Millennium Development Goal MDSR maternal death surveillance and response MICS Multiple Indicator Cluster Survey MMR maternal mortality ratio MMRate maternal mortality rate PM proportion maternal (i.e. proportion of deaths among women of reproductive age that are due to maternal causes) PPP purchasing power parity SBA skilled birth attendant SDG Sustainable Development Goal T+/T– true positive/true negative TAG technical advisory group UI uncertainty interval UNAIDS Joint United Nations Programme on HIV/AIDS UNFPA United Nations Population Fund UNICEF United Nations Children’s Fund UN MMEIG United Nations Maternal Mortality Estimation Inter-Agency Group UNPD United Nations Population Division (in the Department of Economic and Social Affairs) WHO World Health Organization ICD-9, ICD-10 and ICD-11 are all referred to in this document; the numbers indicate the revision (edition) number. 2 viii TRENDS IN MATERNAL MORTALITY EXECUTIVE SUMMARY The Sustainable Development Goals (SDGs) were launched on 25 September 2015 and came into force on 1 January 2016 for the 15-year period until 31 December 2030. Among the 17 SDGs, the direct health-related targets come under SDG 3: Ensure healthy lives and promote well-being for all at all ages. With the adoption of the SDGs, the United Nations Member States extended the global commitments they had made in 2000 to the Millennium Development Goals (MDGs), which covered the period until 2015. In anticipation of the launch of the SDGs, the World Health Organization (WHO) and partners released a consensus statement and full strategy paper on ending preventable maternal mortality (EPMM). The EPMM target for reducing the global maternal mortality ratio (MMR) by 2030 was adopted as SDG target 3.1: reduce global MMR to less than 70 per 100 000 live births by 2030. Having targets for mortality reduction is important, but accurate measurement of maternal mortality remains challenging and many deaths still go uncounted. Many countries still lack well functioning civil registration and vital statistics (CRVS) systems, and where such systems do exist, reporting errors – whether incompleteness (unregistered deaths, also known as “missing”) or misclassification of cause of death – continue to pose a major challenge to data accuracy. ix Methods and interpretation due to modifications in methodology and data availability, differences between these and The United Nations Maternal Mortality previous estimates should not be interpreted Estimation Inter-Agency Group (UN MMEIG) – as representing time trends. In addition, when comprising WHO, the United Nations Children’s interpreting changes in MMRs over time, one Fund (UNICEF), the United Nations Population should take into consideration that it is easier Fund (UNFPA), the World Bank Group and the to reduce the MMR when the level is high United Nations Population Division (UNPD) of than when the MMR level is already low. The the Department of Economic and Social Affairs full database, country profiles and all model – has collaborated with external technical specification codes used are available online.5 experts on a new round of estimates for 2000–2017. To provide increasingly accurate Global estimates for 2017 and MMR estimates, the previous estimation trends for 2000–2017 methods have been refined to optimize use of country-level data. Consultations with The global estimates for the year 2017 indicate countries were carried out during May and that there were 295 000 (UI 279 000 to June 2019. This process generated additional 340 000)6 maternal deaths; 35% lower than in data for inclusion in the maternal mortality 2000 when there were an estimated 451 000 estimation model, demonstrating widespread (UI 431 000 to 485 000) maternal deaths. The expansion of in-country efforts to monitor global MMR in 2017 is estimated at 211 (UI 199 maternal mortality. to 243) maternal deaths per 100 000 live births, representing a 38% reduction since 2000, This report presents internationally comparable when it was estimated at 342. The average global, regional and country-level estimates annual rate of reduction (ARR) in global MMR and trends for maternal mortality between during the 2000–2017 period was 2.9%; this 2000 and 2017. Countries and territories 3 means that, on average, the global MMR included in the analyses are WHO Member declined by 2.9% every year between 2000 States with populations over 100 000, plus two and 2017. The global lifetime risk of maternal territories (Puerto Rico, and the West Bank mortality for a 15-year-old girl in 2017 was and Gaza Strip) . The results described in 4 estimated at 1 in 190; nearly half of the level of this report are the first available estimates for risk in 2000: 1 in 100. The overall proportion of maternal mortality in the SDG reporting period; deaths to women of reproductive age (15–49 but since two years (2016 and 2017) is not years) that are due to maternal causes (PM) sufficient to show trends, estimates have been was estimated at 9.2% (UI 8.7% to 10.6%) in developed and presented covering the period 2017 – down by 26.3% since 2000. This means 2000 to 2017. The new estimates presented in that compared with other causes of death this report supersede all previously published to women of reproductive age, the fraction estimates for years that fall within the same attributed to maternal causes is decreasing. In time period. Care should be taken to use only addition, the effect of HIV on maternal mortality these estimates for the interpretation of trends in 2017 appears to be less pronounced than in in maternal mortality from 2000 to 2017; earlier years; HIV-related indirect maternal 3 Estimates have been computed to ensure comparability 5 Available at: www.who.int/reproductivehealth/ across countries, thus they are not necessarily the same as publications/maternal-mortality-2017/en/ official statistics of the countries, which may use alternative 6 All uncertainty intervals (UIs) reported are 80% UI. The rigorous methods. data can be interpreted as meaning that there is an 80% 4 Puerto Rico is an Associate Member, and the West Bank chance that the true value lies within the UI, a 10% chance and Gaza Strip is a member in the regional committee for the that the true value lies below the lower limit and a 10% WHO Eastern Mediterranean Region. chance that the true value lies above the upper limit. x TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 deaths now account for approximately 1% of Three countries are estimated to have had all maternal deaths compared with 2.5% in extremely high MMR in 2017 (defined as over 2005, at the peak of the epidemic. 1000 maternal deaths per 100 000 live births): South Sudan (1150; UI 789 to 1710), Chad Regional and country-level (1140; UI 847 to 1590) and Sierra Leone (1120; estimates for 2017 UI 808 to 1620). Sixteen other countries, all also in sub-Saharan Africa except for one MMR in the world’s least developed countries (Afghanistan), had very high MMR in 2017 (LDCs) is high,7 estimated at 415 maternal (i.e. estimates ranging between 500 and 999). deaths per 100 000 live births (UI 396 to 477), Only three countries in sub-Saharan Africa had which is more than 40 times higher than that low MMR: Mauritius (61; UI 46 to 85), Cabo for MMR the in Europe (10; UI 9 to 11), and Verde (58; UI 45 to 75) and Seychelles (53; almost 60 times higher than in Australia and UI 26 to 109). Only one country outside the New Zealand (7; UI 6 to 8). In the world’s LDCs, sub-Saharan African region had high MMR: where an estimated 130 000 maternal deaths Haiti (480; UI 346 to 718). Ninety countries occurred in 2017, the estimated lifetime risk were estimated to have MMR of 50 or less in of maternal death was 1 in 56. Sub-Saharan 2017. Africa is the only region with very high MMR for 2017, estimated at 542 (UI 498 to 649), Nigeria and India had the highest estimated while the lifetime risk of maternal death was 1 numbers of maternal deaths, accounting for in 37, compared with just 1 in 7800 in Australia approximately one third (35%) of estimated and New Zealand. Moderate MMR (100–299) global maternal deaths in 2017, with was estimated in Northern Africa, Oceania approximately 67 000 and 35 000 maternal (excluding Australia and New Zealand), deaths (23% and 12% of global maternal Southern Asia, South-Eastern Asia and in small deaths), respectively. Three other countries island developing states. Four subregions also had 10 000 maternal deaths or more: the (Australia and New Zealand, Central Asia, Democratic Republic of the Congo (16 000), Eastern Asia, Western Asia) and two regions Ethiopia (14 000) and the United Republic (Latin America and the Caribbean, and Europe of Tanzania (11 000). Sixty-one countries and Northern America) have low MMR (< 100 were estimated to have had just 10 or fewer maternal deaths per 100 000 live births). maternal deaths in 2017. Sub-Saharan Africa and Southern Asia In 2017, according to the Fragile States Index, accounted for approximately 86% (254 000) of 15 countries were considered to be “very the estimated global maternal deaths in 2017 high alert” or “high alert”8 (from highest to with sub-Saharan Africa alone accounting for lowest: South Sudan, Somalia, Central African roughly 66% (196 000), while Southern Asia Republic, Yemen, Syrian Arab Republic, accounted for nearly 20% (58 000). South- Sudan, the Democratic Republic of the Eastern Asia, in addition, accounted for over Congo, Chad, Afghanistan, Iraq, Haiti, Guinea, 5% of global maternal deaths (16 000). 8 The Fragile States Index is an assessment of 178 countries based on 12 cohesion, economic, social and political indicators, resulting in a score that indicates their susceptibility to instability. Further information about indicators and methodology is available at: https:// fragilestatesindex.org/. At the top of the range (most fragile), 7 For the purpose of categorization, MMR is considered to the scores are categorized as follows: > 110 = very high be low if it is less than 100, moderate if it is 100–299, high if it alert; 100–110 = high alert. These two categories include the is 300–499, very high if it is 500–999 and extremely high if it 15 most fragile countries mentioned here. There are 10 other is equal to or higher than 1000 maternal deaths per 100 000 categories ranging from “very sustainable” to “alert”, which live births. include the remaining 163 countries. xi Executive summary Nigeria, Zimbabwe and Ethiopia), and these 15 quality health services must be considered in countries had MMRs in 2017 ranging from 31 crisis and other unstable situations. (Syrian Arab Republic) to 1150 (South Sudan). Countries that achieved the highest ARRs Regional and country-level trends, between 2000 and 2017 (an average ARR of 2000–2017 7% or above), starting with the highest, were Belarus, Kazakhstan, Timor-Leste, Rwanda, Between 2000 and 2017, the subregion of Turkmenistan, Mongolia, Angola and Estonia. Southern Asia achieved the greatest overall In considering the uncertainty intervals around percentage reduction in MMR: 59% (from 384 their average ARRs, we can only be very sure to 157). This equates to an average ARR of about this high level of acceleration in Belarus, 5.3%. Four other subregions roughly halved Kazakhstan, Timor-Leste and Rwanda. In 13 their MMRs during this period: Central Asia countries, MMR increased in the same period. (52%), Eastern Asia (50%), Europe (53%) In considering the uncertainty around the rate and Northern Africa (54%). MMR in LDCs and direction of change, we believe there have also declined by 46%. Despite its very high been true MMR increases in the United States MMR in 2017, sub-Saharan Africa as a region of America and the Dominican Republic. These also achieved a substantial reduction in MMR findings must be considered in context – as of roughly 38% since 2000. Notably, one many factors may drive positive and negative subregion with very low MMR (12) in 2000 – trends in maternal mortality. Northern America – had an increase in MMR of almost 52% during this period, rising to 18 Conclusions in 2017. This is likely related to already low levels of MMR, as well as improvements in The SDGs include a direct emphasis on data collection, changes in life expectancy reducing maternal mortality while also and/or changes in disparities between highlighting the importance of moving subpopulations. beyond survival. Despite the ambition to end preventable maternal deaths by 2030, The greatest declines in proportion of deaths the world will fall short of this target by more among women of reproductive age that are than 1 million lives with the current pace of due to maternal causes (PM) occurred in two progress. There is a continued urgent need for regions: Central and Southern Asia (56.4%), maternal health and survival to remain high on and Northern Africa and Western Asia (42.6%). the global health and development agenda; Almost no change was seen in PM in Europe the state of maternal health interacts with and and Northern America. reflects efforts to improve the accessibility and quality of care. The 2018 Declaration The 10 countries with the highest MMRs in of Astana repositioned primary health care 2017 (in order from highest to lowest: South as the most (cost) effective and inclusive Sudan, Chad, Sierra Leone, Nigeria, Central means of delivering health services to achieve African Republic, Somalia, Mauritania, Guinea- the SDGs. Primary health care is thereby Bissau, Liberia, Afghanistan) all have ARRs considered the cornerstone for achieving between 2000 and 2017 of less than 5%. universal health coverage (UHC), which only When comparing the ARRs between the year exists when all people receive the quality health ranges of 2000–2010 and 2010–2017, these services they need without suffering financial 10 countries have also had stagnant or slowing hardship. Health services that are unavailable/ levels of ARR and therefore remain at greatest inaccessible or of poor quality, however, risk. The impact of interruptions or loss of will not support the achievement of UHC, as xii TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 envisioned. Efforts to increase the provision of skilled and competent care to more women, before, during and after childbirth, must also be seen in the context of external forces including but not limited to climate change, migration and humanitarian crises – not only because of the environmental risks presented, but also because of their contribution to health complications. In addition, governments are called upon to establish well functioning CRVS systems with accurate attribution of cause of death. Improvements in measurement must be driven by action at the country level, with governments creating systems to capture data specific to their information needs; systems that must also meet the standards required for international comparability. Globally, standardized methods for preventing errors in CRVS reporting (i.e. incompleteness and misclassification) should be established to enhance international comparability. In consideration of the above, it must be noted that this report on the levels and trends of maternal mortality provides just one critical facet of information, which synthesizes and draws from the available data, to assess one aspect of global progress towards achieving global goals for improved health and sustainable development. In the context of efforts to achieve UHC, improving maternal health is critical to fulfilling the aspiration to reach SDG 3. One can only hope that the global community will not be indifferent to the shortfalls that are expected if we cannot improve the current rate of reduction in maternal mortality. Ultimately, we need to expand horizons beyond a sole focus on mortality, to look at the broader aspects – country and regional situations and trends including health systems, UHC, quality of care, morbidity levels and socioeconomic determinants of women’s empowerment and education – and ensure that appropriate action is taken to support family planning, healthy pregnancy and safe childbirth. xiii Executive summary 0 © WHO /Jim Holmes xiv TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 01 TRENDS IN MATERNAL MORTALITY INTRODUCTION The Sustainable Development Goals (SDGs) were launched on 25 September 2015 with the adoption of the General Assembly resolution Transforming our world: the 2030 Agenda for Sustainable Development (1), and they came into force on 1 January 2016 for the 15-year period until 31 December 2030. Among the 17 SDGs, the direct health-related targets come under SDG 3: Ensure healthy lives and promote well-being for all at all ages (2). With the adoption of the SDGs, the United Nations Member States extended the global commitments they had made in 2000 to the Millennium Development Goals (MDGs), which were established after the Millennium Declaration in September 2000, and covered the period until 2015 (3). Among the eight MDGs, MDG 5 was “Improve maternal health”, and MDG target 5.A was to reduce the 1990 maternal mortality ratio (MMR) by three quarters by 2015 (4). The previous report, published in November 2015, provided estimates and trends for maternal mortality for the period 1990 to 2015 (5); the estimates reported in this new edition supersede those and all earlier estimates. In 2014, in anticipation of the launch of the SDGs, the World Health Organization (WHO) released a consensus statement on Targets and strategies for ending preventable maternal mortality (EPMM) (6), followed by a full strategy paper in 2015 (7), endorsed 1 by the United Nations Children’s Fund Health (2016–2030), which is aligned with and (UNICEF), the United Nations Population Fund builds upon the SDG 3 targets and time frame, (UNFPA), the World Bank Group, the United and its five key indicators for the “survive” States Agency for International Development objective are MMR (SDG indicator 3.1.1), (USAID), and a number of international under-five mortality rate (SDG indicator 3.2.1), professional organizations and maternal health neonatal mortality rate (SDG indicator 3.2.2), programmes. The EPMM target for MMR stillbirth rate and adolescent mortality rate (the for 2030 was adopted as the SDG updated last two are not SDG indicators) (11). MMR target: reduce global MMR to less than 70 by 2030 (SDG target 3.1) (2,7,8). Meeting Having targets for mortality reduction is this target will require average reductions important, but it must be acknowledged that of about three times the annual rate of accurate measurement of maternal mortality reduction achieved during the MDG era (5) remains challenging and many deaths still go – an enormous challenge. A supplementary uncounted. Planning and accountability for national target was also set in the EPMM improving maternal health, and assessment strategy paper: By 2030, no country should of SDG target 3.1, require accurate and have an MMR greater than 140, a number internationally comparable measures of twice the global target (7). Collective action by maternal mortality. Many countries have made all countries will be needed to reduce national notable progress in collecting data through civil MMR levels in order to bring the global MMR registration and vital statistics (CRVS) systems, down to less than 70 by 2030. Guided by this surveys, censuses and specialized studies EPMM and SDG target, countries have been over the past decade. This laudable increase in setting their own national targets for 2030, efforts to document maternal deaths provides depending on whether their baseline level of valuable new data, but the diversity of methods MMR in 2010 was greater or less than 420; used to assess maternal mortality in the if greater than 420, their target is to reach absence of well functioning CRVS systems MMR of 140 or less by 2030; if less than 420, continues to prevent direct comparisons their target is to reduce MMR by at least two among the data generated. Further country- thirds by 2030 (7). Countries are also called driven efforts are still needed to establish and upon to achieve equity in MMR for vulnerable strengthen CRVS systems so that all births, populations within each country (7). deaths and causes of death are accurately recorded. The updated Global Strategy calls A major initiative established to galvanize for expansion of CRVS systems to increase efforts in the years counting down to the access to services and entitlements, and in conclusion of the MDGs was the United February 2018, UNICEF and WHO committed Nations Secretary-General’s Global Strategy to working with governments and partners to for Women’s and Children’s Health (“the Global strengthen CRVS systems (12). As of March Strategy”), launched in 2010 (9). At the end of 2018, the World Bank Group reported that the MDG era, the Global Strategy was updated over 110 low- and middle-income countries to include adolescents; the Global Strategy had deficient CRVS systems (13). One of the for Women’s, Children’s and Adolescents’ cross-cutting actions called for in the 2015 Health (2016–2030) has as its objectives EPMM strategy paper was to “Improve metrics, “survive, thrive and transform” and is aligned measurement systems and data quality” to with the timeline and priorities of the SDGs ensure that all maternal and newborn deaths (10). In 2016, WHO published the Indicator and are counted: “Counting every maternal and monitoring framework for the Global Strategy perinatal death through the establishment for Women’s, Children’s and Adolescents’ of effective national surveillance and civil 2 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 registration systems in every country … is a that nominated technical focal persons for priority” (7). As tools for this, the strategy paper maternal mortality or that had existing SDG pointed to standard definitions for causes of focal points were provided with estimates death available in the current International for their country and a detailed description statistical classification of diseases and related of the UN MMEIG processes and methods health problems (ICD) manual along with for estimating levels and trends of maternal guidance in The WHO application of ICD-10 mortality. These consultations gave countries to deaths during pregnancy, childbirth and the opportunity to review the draft country puerperium: ICD-MM (14), as well as use of estimates, data sources and methods; maternal death surveillance and response to provide the UN MMEIG with additional (MDSR) systems, perinatal death surveillance, primary data sources that may not have been confidential enquiries into maternal deaths previously reported or used in the analyses; to (CEMD), and other sources of data. However, build shared understanding of the strengths many countries still lack functional CRVS and weaknesses of the available data and systems, and where such systems do exist, the estimation process; and to establish a reporting errors – whether incompleteness broad sense of ownership of the results. (i.e. unregistered deaths, which are also known These country consultations generated as “missing”) or misclassification of cause of additional data for inclusion in the estimation death – continue to pose a major challenge to model, demonstrating widespread expansion data accuracy (15). of in-country efforts to monitor maternal mortality. Annex 1 presents a summary The United Nations Maternal Mortality of the process and results of the country Estimation Inter-Agency Group (UN MMEIG) consultations. – comprising WHO, UNICEF, UNFPA, the World Bank Group and the United Nations This report presents global, regional and Population Division (UNPD) of the Department country-level estimates and trends for of Economic and Social Affairs – has maternal mortality between 2000 and 2017. collaborated with external technical experts Chapter 2 provides the definitions of key terms on a new round of country-level estimates of and describes the key measures relevant to maternal mortality between 2000 and 2017. An maternal mortality. Chapter 3 describes in independent technical advisory group (TAG), detail the methodology employed to develop composed of demographers, epidemiologists the estimates. Chapter 4 presents the and statisticians, provides technical advice. estimates and trends at the global, regional The estimates for 2000–2017 presented in and country levels. Chapter 5 assesses this report are the ninth in a series of analyses performance so far towards SDG target 3.1, by WHO, UNICEF and other United Nations discusses the implications of the estimates partner agencies to examine global, regional for future efforts towards achieving the target, and country progress in reducing maternal and underlines the importance of improved mortality (5,16–22). To provide increasingly data quality for estimating maternal mortality. accurate estimates of MMR, the previous Chapter 6 presents conclusions. The first four estimation methods have been refined to annexes to this report describe the country optimize use of country-level data. consultation process, present an overview of the common approaches for measuring Consultations with countries were carried maternal mortality, describe the methods used out during May and June 2019, following the to derive a complete series of annual estimates development of preliminary MMR estimates for each predictor variable, and to calculate for the years 2000–2017. WHO Member States maternal mortality during crisis years. Finally, 3 Introduction 8. Boldosser-Boesch A, Brun M, Carvajal L, Annexes 5–17 present the MMR estimates and Chou D, de Bernis L, Fogg K, et al. Setting trends for the different regional groupings for maternal mortality targets for the SDGs. Lancet. 2017;389(10070):696-697. doi:10.1016/S0140- SDG reporting and for WHO, UNICEF, UNFPA, 6736(17)30337-9. the World Bank Group and UNPD, as well as 9. Ki-moon B. Global strategy for women’s the country-level estimates and trends. and children’s health. New York (NY): United Nations; 2010 (http://www.who.int/pmnch/ References knowledge/publications/fulldocument_ globalstrategy/en/, accessed 3 December 2015). 1. Transforming our world: the 2030 Agenda for Sustainable Development 2015. Resolution 10. Global strategy for women’s, children’s and adopted by the General Assembly on 25 adolescents’ health (2016–2030). New York September 2015. United Nations General (NY): Every Woman Every Child; 2015 (http:// Assembly, Seventieth session. New York (NY): globalstrategy.everywomaneverychild.org/, United Nations; 2015 (A/RES/70/1; http://www. accessed 10 June 2019). un.org/ga/search/view_doc.asp?symbol=A/ RES/70/1, accessed 28 May 2019). 11. Indicator and monitoring framework for the Global Strategy for Women’s, Children’s and 2. Sustainable Development Goal 3. In: Adolescents’ Health (2016–2030). Geneva: Sustainable Development Goals Knowledge World Health Organization; 2016 (http://www. Platform [website]. New York (NY): United who.int/life-course/publications/gs-Indicator- Nations; 2019 (https://sustainabledevelopment. and-monitoring-framework.pdf, accessed 25 un.org/SDG3, accessed 10 June 2019). July 2019). 3. Conferences, meetings and events: Millennium 12. The future for women and children: UNICEF Summit (6–8 September 2000). In: United and WHO joint statement on strengthening Nations [website]. New York (NY): United civil registration and vital statistics (CRVS). Nations; undated (https://www.un.org/en/ New York (NY) and Geneva: United Nations events/pastevents/millennium_summit.shtml, Children’s Fund and World Health Organization; accessed 5 June 2019). 2018 (https://www.who.int/healthinfo/ civil_registration/WHO_UNICEF_Statement_ 4. Goal 5: Improve maternal health. In: United CRVS_2018.pdf, accessed 29 August 2019). Nations [website]. undated (https://www. un.org/millenniumgoals/maternal.shtml, 13. Global civil registration and vital statistics: accessed 5 June 2019). about CRVS. In: World Bank: Brief [website]. The World Bank Group; 2018 (https://www. 5. World Health Organization (WHO), United worldbank.org/en/topic/health/brief/global- Nations Children’s Fund (UNICEF), United civil-registration-and-vital-statistics, accessed Nations Population Fund (UNFPA), World Bank 29 August 2019). Group, United Nations Population Division. Trends in maternal mortality: 1990 to 2015: 14. The WHO application of ICD-10 to deaths estimates by WHO, UNICEF, UNFPA, World during pregnancy, childbirth and puerperium: Bank Group and the United Nations Population ICD-MM. Geneva: World Health Organization; Division. Geneva: World Health Organization; 2012 (https://www.who.int/reproductivehealth/ 2015 (https://www.who.int/reproductivehealth/ publications/monitoring/9789241548458/en/, publications/monitoring/maternal- accessed 5 June 2019). mortality-2015/en/, accessed 4 September 2019). 15. World Bank Group, World Health Organization. Global civil registration and vital statistics: 6. Targets and strategies for ending preventable scaling up investment plan 2015–2024. Geneva: maternal mortality: consensus statement. World Health Organization; 2014 (https://www. Geneva: World Health Organization; 2014 who.int/healthinfo/civil_registration/WB-WHO_ (https://www.who.int/reproductivehealth/ ScalingUp_InvestmentPlan_2015_2024.pdf, publications/maternal_perinatal_health/ accessed 5 June 2019). consensus-statement/en/, accessed 5 June 2019). 16. World Health Organization (WHO) Maternal Health and Safe Motherhood Programme, 7. Strategies towards ending preventable United Nations Children’s Fund (UNICEF). maternal mortality (EPMM). Geneva: World Revised 1990 estimates of maternal mortality: Health Organization; 2015 (http://www. a new approach by WHO and UNICEF. everywomaneverychild.org/images/EPMM_ Geneva: WHO; 1996 (http://apps.who.int/ final_report_2015.pdf, accessed 5 November iris/bitstream/10665/63597/1/WHO_FRH_ 2015). MSM_96.11.pdf, accessed 28 May 2019). 4 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 17. World Health Organization (WHO), United Nations Children’s Fund (UNICEF), United Nations Population Fund (UNFPA). Maternal mortality in 1995: estimates developed by WHO, UNICEF and UNFPA. Geneva: World Health Organization; 2001 (https://apps.who.int/iris/ handle/10665/66837, accessed 28 May 2019). 18. World Health Organization (WHO), United Nations Children’s Fund (UNICEF), United Nations Population Fund (UNFPA). Maternal mortality in 2000: estimates developed by WHO, UNICEF and UNFPA. Geneva: World Health Organization; 2004 (http://apps.who. int/iris/bitstream/10665/68382/1/a81531.pdf, accessed 5 November 2015). 19. World Health Organization (WHO), United Nations Children’s Fund (UNICEF), United Nations Population Fund (UNFPA), World Bank. Maternal mortality in 2005: estimates developed by WHO, UNICEF, UNFPA and the World Bank. Geneva: WHO; 2007 (https://www. who.int/whosis/mme_2005.pdf, accessed 28 May 2019). 20. World Health Organization (WHO), United Nations Children’s Fund (UNICEF), United Nations Population Fund (UNFPA), World Bank. Trends in maternal mortality: 1990 to 2008: estimates developed by WHO, UNICEF, UNFPA and the World Bank. Geneva: WHO; 2010 (https://apps.who.int/iris/bitstream/handle/106 65/44423/9789241500265_eng.pdf, accessed 28 May 2019). 21. World Health Organization (WHO), United Nations Children’s Fund (UNICEF), United Nations Population Fund (UNFPA), World Bank. Trends in maternal mortality: 1990 to 2010: WHO, UNICEF, UNFPA and the World Bank estimates. Geneva: WHO; 2012 (http://apps.who.int/iris/bitst ream/10665/44874/1/9789241503631_eng.pdf, accessed 28 May 2019). 22. World Health Organization (WHO), United Nations Children’s Fund (UNICEF), United Nations Population Fund (UNFPA), World Bank, United Nations Population Division. Trends in maternal mortality: 1990 to 2013: estimates by WHO, UNICEF, UNFPA, the World Bank and the United Nations Population Division. Geneva: WHO; 2014 (http://apps.who.int/iris/bitst ream/10665/112682/2/9789241507226_eng. pdf, accessed 28 May 2019). 5 Introduction 6 © H6 Partners / Abbie Trayler-Smith 0 02 CONTENT 8 Definitions for key terms used in this report 9 Measures of maternal mortality used in this report TRENDS IN MATERNAL MORTALITY DEFINITIONS AND MEASURES 7 2.1 Definitions for key terms used “resulting from previous existing disease or in this report disease that developed during pregnancy and not due to direct obstetric causes but In the International statistical classification were aggravated by the physiologic effects of diseases and related health problems of pregnancy” (1). For example, deaths due (ICD)  (1), WHO defines maternal death as: 9 to aggravation (by pregnancy) of an existing cardiac or renal disease are considered the death of a woman while pregnant or indirect maternal deaths. within 42 days of termination of pregnancy, irrespective of the duration and site of A late maternal death is “the death of the pregnancy, from any cause related a woman from direct or indirect obstetric to or aggravated by the pregnancy or its causes, more than 42 days but less than one management but not from unintentional or year after termination of pregnancy” (1). Like incidental causes. 10 maternal deaths, late maternal deaths also include both direct and indirect maternal/ This definition allows identification of a obstetric deaths. Complications of pregnancy maternal death, based on the cause of the or childbirth can lead to death beyond the death being identified as either a direct or six-week (42-day) postpartum period, and the indirect maternal cause. increased availability of modern life-sustaining procedures and technologies enables more Direct obstetric deaths (or direct women to survive adverse outcomes of maternal deaths) are those “resulting from pregnancy and delivery, and also delays obstetric complications of the pregnant state some deaths beyond that postpartum period. (pregnancy, labour and puerperium), and from Specific codes for “late maternal deaths” interventions, omissions, incorrect treatment, are included in the ICD-10 (O96 and O97) to or from a chain of events resulting from any capture these delayed maternal deaths, which of the above” (1). Deaths due to obstetric may not be categorized as maternal deaths haemorrhage or hypertensive disorders in in CRVS systems despite being caused by pregnancy, for example, or those due to pregnancy-related events (2). complications of anaesthesia or caesarean section are classified as direct maternal Maternal deaths and late maternal deaths deaths. are combined in the 11th revision of the ICD under the new grouping of “comprehensive Indirect obstetric deaths (or indirect maternal deaths” (1). maternal deaths) are those maternal deaths A death occurring during pregnancy, childbirth and puerperium (also known 9 ICD-11 (the 11th revision of the ICD) was adopted by the as a pregnancy-related death) is defined World Health Assembly in May 2019 and comes into effect on 1 January 2022. Further information is available at: as: “the death of a woman while pregnant or www.who.int/classifications/icd/en/. The coding rules within 42 days of termination of pregnancy, related to maternal mortality are being edited to fully match the new structure of ICD-11, but without changing the irrespective of the cause of death (obstetric resulting statistics. At the time of this writing, therefore, information about ICD codes relates to ICD-10 (the 10th and non-obstetric)” (1); this definition includes revision of the ICD) (2). The ICD-11 rules can be accessed in unintentional/accidental and incidental the reference guide of ICD-11, at https://icd.who.int. 10 Care has been taken to ensure that the definition of causes. This definition allows measurement of maternal death used for international comparison of deaths that occur during pregnancy, childbirth mortality statistics remains stable over time, but the word “unintentional” has been used in the ICD-11 definition (1) in and puerperium while acknowledging that place of the word “accidental” which was previously used, in ICD-10 (2). such measurements do not strictly conform 8 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 to the standard “maternal death” concept in and for the calculation of maternal mortality settings where accurate information about ratios and rates (i.e. excluding late maternal causes of death based on medical certification deaths).12,13 is unavailable. For instance, in maternal mortality surveys (such as those employing the The number of maternal deaths in a sisterhood method), relatives of a woman of population (during a specified time period, reproductive age who has died are asked about usually one calendar year) reflects two factors: her pregnancy status at the time of death (i) the risk of mortality associated with a single without eliciting any further information on the pregnancy or a single birth (whether live birth cause or circumstances of the death. These or stillbirth); and (ii) the fertility level (i.e. the surveys usually measure deaths to women number of pregnancies or births that are during pregnancy, childbirth and puerperium experienced by women of reproductive age, (pregnancy-related deaths) rather than i.e. age 15–49 years). maternal deaths. The maternal mortality ratio (MMR) is HIV-related indirect maternal deaths are defined as the number of maternal deaths deaths to HIV-positive women caused by the during a given time period per 100 000 live aggravating effect(s) of pregnancy on HIV; births during the same time period; thus, it where the interaction between pregnancy and quantifies the risk of maternal death relative HIV becomes the underlying cause of death, to the number of live births, and essentially these are counted as indirect maternal deaths. captures the first factor mentioned above. There is an ICD code – O98.7 (HIV disease complicating pregnancy, childbirth and the By contrast, the maternal mortality rate puerperium) – for identifying HIV-related (MMRate) is defined and calculated as the indirect maternal deaths.11 number of maternal deaths divided by person- years lived by women of reproductive age in a Incidental HIV deaths are deaths caused by population. The MMRate captures both the risk HIV/AIDS which occur to women who happen of maternal death per pregnancy or per birth to be pregnant, in labour or postpartum (also (whether live birth or stillbirth), and the level defined as “HIV-related deaths to women of fertility in the population (i.e. both factors during pregnancy, delivery or puerperium” [3]); mentioned above). these are not maternal deaths and would not be included in the calculation of MMR. In addition, it is possible to calculate the adult lifetime risk of maternal death for women in All the types and definitions of deaths the population, defined as the probability that described above (as used in this report) are a 15-year-old girl (in the year of the estimate) summarized in Table 2.1. will eventually die from a maternal cause. This indicator takes into account competing causes 2.2 Measures of maternal mortality used in this report 12 ICD-11, Part 2, section 2.28.5.7: “International reporting of maternal mortality: For the purpose of the international reporting of maternal mortality, only those maternal deaths As indicated in the ICD-11 (and previously in occurring before the end of the 42-day reference period the ICD-10), only maternal deaths occurring up should be included in the calculation of the various ratios and rates, although the recording of later deaths is useful for to 42 days postpartum are considered relevant national analytical purposes” (1). for the purposes of international reporting 13 Late maternal deaths coded to O96 (late maternal deaths) and O97 (late maternal deaths due to sequalae of complications) are also of interest for national- and Search for O98.7 at the current (2016) version of ICD-10: 11 international-level analysis, but are not reported in this https://icd.who.int/browse10/2016/en. publication. 9 Definitions and measures Table 2.1. Types and definitions of deaths occurring during pregnancy, childbirth and puerperium (also known as “pregnancy-related deaths”) Maternal deaths Non-maternal deaths Non-HIV- Non-HIV-related maternal deaths: Non-HIV-related, non- related deaths • Maternal death – the death of a woman while pregnant or within 42 days maternal deaths – deaths (the woman may to pregnant and postpartum or may not have of termination of pregnancy, irrespective of the duration and site of the women from unintentional/ had HIV) pregnancy, from any cause related to or aggravated by the pregnancy or accidental or incidental its management but not from unintentional or incidental causes causes other than HIV —— Direct obstetric/maternal deaths – deaths resulting from complications of pregnancy/delivery/postpartum (up to 42 days), from interventions, omissions or incorrect treatment, or from a chain of events resulting from any of the above —— Indirect obstetric/maternal deaths – deaths due to a disease (other than HIV) aggravated by the effects of pregnancy • Late maternal deaths – direct or indirect maternal deaths occurring from 42 days to 1 year after termination of pregnancy HIV-related HIV-related maternal deaths: HIV-related, non- deaths maternal deaths: • HIV-related indirect maternal deaths – deaths to HIV-positive women (the woman was caused by the aggravating effects of pregnancy on HIV • Incidental HIV deaths known to have – deaths caused by HIV/ had HIV) • HIV-related indirect late maternal deaths – deaths to HIV-positive AIDS which occur to women 42 days to 1 year after termination of pregnancy, caused by the women who happen to aggravating effects of pregnancy on HIV be pregnant, in labour or postpartum of death (4). The formula for calculating this such that the model has to account for the measure is given in Chapter 3, section 3.3.3. difference in definitions (see Chapter 3, section 3.3.2: BMat model). An alternative measure of maternal mortality, the proportion maternal (PM), is the For further information on ICD coding and proportion of deaths among women of approaches to measuring maternal mortality, reproductive age that are due to maternal see Annex 2. causes; PM is calculated as the number of maternal deaths in a given time period divided by the total deaths among women aged 15–49 years in that time period. Although by definition PM refers strictly to maternal deaths (and the estimation model described in Chapter 3 is based on this definition), some observed (documented) PMs actually use a “pregnancy-related” definition (and not all pregnancy-related deaths are maternal deaths, as defined in section 2.1 above), 10 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 Box A2.1. STATISTICAL MEASURES OF MATERNAL MORTALITY Maternal mortality ratio (MMR): 000 live births Number of maternal deaths during a given time period per 100  during the same time period (5). Maternal mortality rate (MMRate): Number of maternal deaths during a given time period divided by person-years lived by women of reproductive age (age 15–49 years) in a population during the same time period (6). Adult lifetime risk of maternal death: The probability that a 15-year-old woman will eventually die from a maternal cause (4). The proportion of deaths among women of reproductive age that are due to maternal causes (proportion maternal; PM): The number of maternal deaths divided by the total deaths among women aged 15–49 years (5). References 1. 2.28.5 Standards and reporting requirements 5. World Health Organization (WHO), United related for maternal mortality. In: ICD-11 Nations Children’s Fund (UNICEF), United Reference guide, Part 2. Geneva: World Nations Population Fund (UNFPA), World Bank Health Organization; 2019 (https://icd.who. Group, United Nations Population Division. int/icd11refguide/en/index.html#2.28.5Sta Trends in maternal mortality: 1990 to 2015: ndardsMarternalMortaltiy|standards-and- estimates by WHO, UNICEF, UNFPA, World reporting-requirements-related-for-maternal- Bank Group and the United Nations Population mortality|c2-28-5, accessed 12 July 2019). Division. Geneva: WHO; 2015 (https://www. who.int/reproductivehealth/publications/ 2. International statistical classification of monitoring/maternal-mortality-2015/en/, diseases and related health problems, 10th accessed 4 September 2019). revision. Volume 2: Instruction manual. Geneva; World Health Organization; 2010 6. Wilmoth J, Mizoguchi N, Oestergaard M, (https://www.who.int/classifications/icd/ Say L, Mathers C, Zureick-Brown S, et al. A ICD10Volume2_en_2010.pdf, accessed 10 new method for deriving global estimates June 2019). of maternal mortality. Stat Politics Policy. 2012;3(2):2151-7509.1038. 3. The WHO application of ICD-10 to deaths during pregnancy, childbirth and puerperium: ICD-MM. Geneva: World Health Organization; 2012 (https://www. who.int/reproductivehealth/publications/ monitoring/9789241548458/en/, accessed 4 September 2019). 4. Wilmoth J. The lifetime risk of maternal mortality: concept and measurement. Bull World Health Organ. 2009;87:256-62. doi:10.2471/BLT.07.048280. 11 Definitions and measures 12 © WHO PAHO 0 03 CONTENT 14 Data inputs for the estimation process 17 Other data inputs to the model 18 Statistical methods TRENDS IN MATERNAL MORTALITY METHODS Previously, in 2010, 2012, 2014 and 2015, the United Nations Maternal Mortality Estimation Inter-Agency Group (UN MMEIG) published reports on maternal mortality trends (including data up to 2008, 2010, 2013 and 2015, respectively) with advice from an external technical advisory group (TAG) (1–4). The methods described here for developing estimates of levels and trends of maternal mortality between 2000 and 2017 build upon the methods used in those previous rounds (5,6,7). The key change to the estimation methodology and resulting estimates in this round is described in section 3.3 (Statistical methods) and concerns the adjustment of data from countries’ civil registration and vital statistics (CRVS) systems (section 3.3.1). CRVS data have been adjusted in previous rounds to account for unregistered and/or misclassified maternal deaths (see definitions in Box 3.1). The UN MMEIG has considered concerns from Member States about how this adjustment was calculated, and how it may or may not have reflected improvements in data collection and data quality related to maternal mortality over time. Combined with the updated global maternal mortality database,14 the UN MMEIG Bayesian 14 WHO Mortality Database: https://www.who.int/healthinfo/ mortality_data/en/ (select indicator for “pregnancy, childbirth and the puerperium”). 13 Box 3.1. DEFINITIONS OF INCOMPLETENESS (UNREGISTERED) AND MISCLASSIFICATION OF MATERNAL DEATHS* Incompleteness Incompleteness refers to unregistered deaths (also known as “missing”) – i.e. deaths not registered in the CRVS system – resulting in an incomplete CRVS system. This can arise due to both incomplete identification/registration of individual deaths in each country and incomplete coverage of the national CRVS system within each country. We distinguish between non-maternal deaths not registered in the CRVS system (U–), and maternal deaths not registered in the CRVS system (U+) (see section 3.3.1.a). Misclassification Misclassification refers to incorrect coding of deaths registered within the CRVS system, due either to error in the medical certification of cause of death or error in applying the ICD code. We distinguish between maternal deaths incorrectly classified as non-maternal deaths (false negatives; F–), and non-maternal deaths incorrectly classified as maternal deaths (false positives, F+) (see section 3.3.1.a). * Incompleteness and misclassification are often referred to collectively or individually as “underreporting”, but we suggest not to use this term and instead to be clear about exactly which issue is being referred to, whether incompleteness (unregistered), misclassification, or both. maternal mortality estimation (BMat) model 3.1 Data inputs for the estimation (see section 3.3.2) provides the most up-to- process date maternal mortality estimates yet for the entire 2000–2017 timespan. These results 3.1.1 Data sources supersede all previously published estimates for years within that time period, and due Maternal mortality ratio (MMR) estimates to modifications in methodology and data are based on a variety of data sources – availability, differences between these and including data from CRVS systems, which previous estimates should not be interpreted are the preferred data source (considered as representing time trends. The full database, to be the gold standard for mortality data), country profiles and all model specification population-based household surveys using the codes used are available online.15 sisterhood method, reproductive-age mortality studies (RAMOS), confidential enquires into maternal deaths (CEMD), verbal autopsies, censuses and other specialized maternal mortality studies conducted at the national level. What is needed for the country-level Available at: www.who.int/reproductivehealth/ 15 estimates is a robust, accurate, nationally publications/maternal-mortality-2017/en/. 14 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 representative data source, for which there is • If the ratio is less than 0.95 for one or more clear information about the data collection and years, the completeness is given by the checking methods; this data source may or ratio for each individual year. may not be the national CRVS system. The UN • After obtaining an estimate of MMEIG global maternal mortality estimation completeness, we combine this estimate input database has been updated since the with the proportion of deaths that have last round of estimates in 2015. The new been assigned to an ill defined code. draft estimates were shared with countries We exclude observations for which the during the 2019 country consultation period estimated percentage of deaths that May–June 2019 (see Annex 1), after which are assigned to a well defined code the estimates and the database were updated is lower than 60%. In other words, if again in July 2019 prior to the final run of the completeness proportion*(1 – proportion UN MMEIG BMat model. ill defined)*100% > 60%, the observation is a. Civil registration and vital statistics (CRVS) included (4). b. Specialized studies on maternal mortality For countries that routinely register deaths and apply the medical certificate of cause of death Over recent decades, efforts have been (MCCD), maternal deaths may be incorrectly undertaken in certain settings to measure reported due to unregistered deaths and/or maternal mortality using CRVS data in deaths that are misclassified in terms of ICD combination with further data collection on coding. To account for potential unregistered maternal deaths, sometimes also enhancing deaths as well as misclassification in CRVS the quality of the CRVS systems. In some data, an adjustment is calculated for each cases, a specialized study is conducted CRVS input data point (see section 3.3.1) for the purpose of assessing the extent of before it is included in the BMat model (see misclassification within the CRVS system (i.e. section 3.3.2). independent assessment of cause of death classification among the deaths that were For each country with CRVS data, the level registered as maternal deaths – to check if of completeness of the CRVS, in terms they are “true positives” – and among other of registration of all deaths to females of registered deaths to women of reproductive reproductive age (i.e. fewer unregistered age that were not registered as maternal deaths means the CRVS data are more deaths but which might have been “false complete), is estimated as follows. negatives”). CEMD is an example of a method used for these types of studies. In other cases, • We calculate the annual ratio of female a specialized study is conducted to assess the deaths reported in the CRVS system extent of “missingness” of maternal deaths in divided by female deaths estimated by the CRVS system, by using other methods to WHO for all years with CRVS data, based on document additional unregistered maternal a moving window of five-year periods (five- deaths that have occurred in a specified year periods are used to obtain smoothed geographic area (e.g. RAMOS). estimates of completeness) (8). • If the ratio (in particular, the upper bound These data sources typically expand the of the 80% uncertainty interval on the ratio) scope of their reviews to the entire number is greater than 0.95 for all years with CRVS of deaths among women of reproductive age data, we assume that the CRVS is complete (15–49 years) in a country and triangulate in the country. information from sources including, but not 15 Methods limited to: medical/hospital records, police MMR was available from the data source, records, surveillance systems, national the observed MMR was converted into a PM, registries, death certificates, census, medical again using estimates of all-cause deaths autopsy, and administrative reviews between among females aged 15–49 and live births. national statistical offices and ministries of An upward adjustment of 10% was applied to health. The information reported by these all observations that were not obtained from specialized studies varies greatly, and includes CRVS or specialized studies, to account for any combination of the following: total deaths early in pregnancy that might not have number of deaths to women of reproductive been captured (4). age and/or total number of maternal deaths; all causes of death correctly documented The available data sources provide calculated among all women of reproductive age and/ PMs according to two definitions: “maternal” or or all causes of maternal deaths; unregistered “pregnancy-related” deaths (see Chapter 2). deaths to women of reproductive age and/ PMs for pregnancy-related deaths excluding or unregistered maternal deaths. In these accidents were taken as measures of maternal situations, it is agreed that no adjustment PM without further adjustment. Based on an factor needs to be applied, and so analysis of measured levels of maternal versus observations from specialized studies are pregnancy-related death from sources where included in the BMat model (see section 3.3.2) both quantities were reported, and of injury without adjustment. death rates among women of reproductive age using WHO estimates of cause-specific c. Other data sources for maternal mortality mortality for Member States, the UN MMEIG/ TAG agreed to estimate “maternal” deaths Other available data sources include data from from the PM for “pregnancy-related” deaths, surveillance sites or systems, population- based on assumptions that incidental or based surveys and censuses. From these data accidental deaths (i.e. not maternal deaths) sources, for the purposes of estimation, the comprise 10% of pregnancy-related deaths observed proportion of maternal deaths (PM) (excluding HIV-related deaths) in sub-Saharan among all deaths to women aged 15–49 years African countries, and 15% in other low- and was taken as the preferred indicator for use in middle-income countries (1). estimating maternal mortality. Table 3.1 gives an overview of data used The PM is preferred over observed MMRs or to produce maternal mortality estimates. other summary outcomes because it is less Further information about sources of maternal affected by unregistered deaths: deaths to mortality data is provided in Annex 2. women aged 15–49 that are unregistered would potentially affect the numerator and 3.1.2 Uncertainty associated with the denominator of the PM proportionately observations and adjustments if causes of death are not unregistered differentially. Therefore, in processing data All observed death counts and PMs are subject related to maternal mortality, observed PMs to random error, in the form of sampling error took priority over observed MMRs, and for (for PMs obtained from surveys), stochastic each observed PM, the corresponding MMR error (for PMs obtained from a small number is calculated based on the United Nations of deaths) and/or non-sampling error (i.e. Population Division (UNPD) estimates of random errors that may occur at any point live births (9) and all-cause deaths among during the data-collection process). females aged 15–49 (WHO estimates) (8) for the respective country-period. If only the 16 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 Table 3.1. Maternal mortality data records by source type used in generating the 2000–2017 estimates for maternal mortality Number of Number of Source type records country-years Civil registration and vital statistics (CRVS) 2204 2204 Specialized studies on maternal mortality 376 534 Other sources – reporting on maternal mortality 188 216 Other sources – reporting on pregnancy-related mortality 207 1169 All 2975 4123a a The sum of country-years of data has been rounded. To account for the uncertainty associated a subset of all deaths was accounted for with with these errors, and thus the uncertainty regard to data from incomplete CRVS systems, associated with the PM, error variances were and specialized studies with study populations calculated. For observations from CRVS that were limited to a subset of all-cause or confidential enquiries, stochastic error deaths. variances were obtained, which quantify the uncertainty associated with the true risk of a The WHO life tables (8) include “mortality maternal death, based on the available data. shocks”. Annex 3 describes how these are For observed PMs from surveys and other dealt with in the context of maternal mortality. maternal mortality studies, the error variance was a combination of the sampling variance 3.2. Other data inputs to the model associated with the survey and an additional non-sampling error. The non-sampling error 3.2.1 Data on all deaths to women aged was estimated based on the UN MMEIG 15–49 years and HIV-related mortality maternal mortality database (5). For all observed PMs, the error variances were taken We used a set of consistent external estimates into account when obtaining PM and thus for deaths due to HIV from the Joint United MMR estimates: observations with smaller Nations Programme on HIV/AIDS (UNAIDS) error variances are more informative of the (10) and estimates for deaths among females true PM and will thus carry a greater weight aged 15–49 years from WHO life tables (8). in determining the estimates compared with These agencies revise their estimates on a observations with larger error variances. regular basis to take into account new data and Additionally, uncertainty associated with improved methods. Any comments regarding adjustments (e.g. the CRVS adjustment as per these input indicators should be addressed to the new approach described in section 3.3.1, the respective agencies.16 and adjustment of observations which report “pregnancy-related” deaths) was accounted for. Lastly, uncertainty due to capturing only 16 For UNAIDS mortality estimates: aidsinfo@unaids.org; for WHO life tables: healthstat@who.int. 17 Methods 3.2.2 Live births data of live births with a skilled birth attendant (SBA) at the time of delivery serves as a direct For the preliminary MMR estimates shared measure of the conditions under which births during the 2019 country consultations, inputs occur in a given population (6). for live births were taken from the UNPD’s 2019 revision of World population prospects (9). In Time series of annual estimates for the this publication, the UNPD produced estimates following three predictor variables (covariates) of population and related indicators (e.g. births were constructed from 1990 to 2017. and deaths) for countries or areas, covering five-year periods from 1950–1955 through to • Gross domestic product (GDP) per capita, 2010–2015, as well as projections covering measured in purchasing power parity (PPP) five-year periods from 2015–2020 through to equivalent US dollars using 2011 as the 2095–2100. For countries with well functioning baseline, was generated based on data CRVS systems, UNPD used data on births by from the World Bank Group (11). age of the mother together with population • General fertility rate (GFR) was computed data by age and sex from censuses and official from data on live births and population statistics to estimate age-specific fertility size (number of women aged 15–49) from rates (ASFR) for each historical and future UNPD’s 2019 revision of World population five-year period. The population estimation prospects (9). and projection procedure used the ASFR and other inputs such as age- and sex-specific • Skilled birth attendant (SBA) data consist of mortality rates to generate a consistent time time series derived using all available data series of population size, age distribution, and from population-based national household the demographic components of population survey data and countries’ routine change (births, deaths and migration). reporting mechanisms (WHO and UNICEF Annual estimates of births are obtained by Joint Skilled Birth Attendant database [12]). interpolating the five-year estimates of the For further details related to the predictor number of births output, using the population variables, please refer to Annex 4. estimation and projection procedure. As a result, the annually interpolated national 3.3. Statistical methods estimates do not necessarily match the annual numbers of births reported in the individual We use two models, for different purposes. countries’ CRVS systems.17 1. The CRVS model: For countries that have 3.2.3 Predictor variables in the maternal a CRVS system, we use a Bayesian CRVS mortality model adjustment model to account for errors in reporting of maternal death in the CRVS to The predictor variables used in the BMat obtain the CRVS adjustment factors. model fall into three categories: indicators of socioeconomic development, measures 2. The BMat model: For all countries, we of fertility and process variables. In the final use a Bayesian maternal mortality estimation model, the gross domestic product per model to estimate the MMR for each country- capita (GDP) represents socioeconomic year of interest. development, fertility is measured by the general fertility rate (GFR), and the proportion To estimate MMR for country-years, we first use the CRVS model to obtain the CRVS Any comments regarding the estimates of live births from 17 adjustment factors. These adjustment factors UNPD should be addressed to: population@un.org. 18 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 are then applied in the BMat model to estimate This section explains: the MMR for each country-year of interest (see a. Types of reporting errors encountered in Figure 3.1). The CRVS model is described in CRVS systems section 3.3.1, followed by the description of the BMat model in section 3.3.2. b. Summary metrics for reporting errors c. Deriving sensitivity, specificity and CRVS 3.3.1 Bayesian CRVS adjustment model adjustments from the CRVS model to account for errors in reporting of maternal death in the CRVS system d. Comparison with previous UN MMEIG (the CRVS model) approach to estimate CRVS adjustment factors. Relying on maternal deaths as reported in the The model used to estimate the CRVS data- CRVS system means there is a potential for quality parameters, and corresponding error due to unregistered maternal deaths and/ adjustment factors for CRVS data in BMat are or misclassification of the cause of death within summarized here below (subsections a–d) and the CRVS system. Therefore, an adjustment described in detail in a separate publication by factor is obtained for CRVS data before it is Peterson et al. (13). included in the BMat model (section 3.3.2). Figure 3.1. Overview of modelling steps for MMR estimation Specialized Other data CRVS data sources study data CRVS model: CRVS BMat model: MMR estimate CRVS adjustments estimate MMR estimates adjustments BMat: Bayesian maternal mortality estimation (model); CRVS: civil registration and vital statistics; MMR: maternal mortality ratio BMat: Bayesian maternal mortality estimation (model); CRVS: civil registration and vital statistics; MMR: maternal mortality ratio 19 Methods a. Types of reporting errors encountered in T+ (true positive) = maternal deaths CRVS systems correctly classified in the CRVS system as maternal deaths Definitions of reporting errors (incomplete/ unregistered and misclassification) are T– (true negative) = non-maternal deaths provided earlier in this chapter in Box 3.1 and correctly classified in the CRVS system are discussed further below. as non-maternal deaths. i.Reporting errors within the CRVS system The four-box diagram in Figure 3.2 summarizes (misclassification) what is correctly classified and what is misclassified in the CRVS system, using the Within the CRVS system, incorrect reporting notation provided above. of maternal deaths can be attributed to misclassification in two ways, using the The observed PM – the proportion of deaths following notation: among women of reproductive age that are due to maternal causes – reported in the CRVS F+ (false positive) = non-maternal deaths misclassified in the CRVS system as is given by while the true PM maternal deaths from CRVS data is . F– (false negative) = maternal deaths misclassified in the CRVS system as The UN MMEIG approach to adjust for non-maternal deaths. this potential difference between true and observed PM is explained in section 3.3.1, The remaining deaths are those that have been subsections b and c, below. correctly classified within the CRVS system; these can also be assigned to two groups, using the following notation: Figure 3.2. Four-box diagram of breakdown of the total number of deaths to females of reproductive age (15–49 years) as reported in the CRVS, by CRVS cause-of-death classification T+ F- True maternal deaths in CRVS True non-maternal F+ T- deaths in CRVS CRVS maternal deaths CRVS non-maternal deaths 20 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 ii. Deaths that are not reported in the CRVS (1) Sensitivity (Se): proportion of correctly (incompleteness) classified maternal deaths out of all true maternal deaths, and In cases where the CRVS system does not capture all deaths to females of reproductive (2) Specificity (Sp): proportion of correctly age (i.e. the CRVS is incomplete), we refer to classified non-maternal deaths out of all true these maternal and non-maternal deaths as non-maternal deaths. unregistered (U) deaths. We distinguish two types of unregistered deaths among females of These metrics combined summarize the ability reproductive age, using the following notation: of the CRVS system to correctly identify a true maternal and true non-maternal death. The U– = non-maternal deaths not registered in formulas, using the notation introduced in the CRVS system, and subsection a above, are as follows: U+ = maternal deaths not registered in the Sensitivity = CRVS system. We extend the four-box representation to incorporate these unregistered maternal Specificity = (U+) and non-maternal (U–) deaths (six-box diagram), as shown in Figure 3.3. b. Summary metrics for reporting errors The third metric related to reporting errors in the CRVS is the adjustment factor: i. Reporting within the CRVS (3) CRVS adjustment factor: adjustment factor We summarize the occurrence of associated with CRVS-reported PM, to account misclassification errors in the CRVS into the for the difference between CRVS-reported PM following two metrics: and true PM. Figure 3.3. Six-box diagram of breakdown of the total number of deaths to females of reproductive age (15–49 years), by CRVS cause-of-death classification (T/F) and reporting status (U) T+ F- U+ True maternal deaths in CRVS True non-maternal F+ T- U- deaths in CRVS CRVS CRVS Missed deaths maternal deaths non-maternal deaths 21 Methods For country-years with complete CRVS (i.e. PM-out and accounted for additional uncertainty all maternal deaths are registered in that related to the unknown true ratio when deriving the country’s CRVS system for those years), CRVS CRVS adjustment for country-years with incomplete adjustment factors can be calculated for all CRVS. country-years using their respective estimates of Se, Sp, and true proportional maternal (true c. Deriving sensitivity, specificity and CRVS PM), based on the following relation: adjustments from the CRVS model Expected CRVS-reported PM = i. CRVS model estimates of sensitivity and Se * true PM + (1 - Sp) * (1 – true PM), specificity such that the CRVS adjustment factor is given by The CRVS model obtains estimates of sensitivity and specificity for all country-years with CRVS data. Based CRVS adjustment factor = on these estimates, corresponding estimates of the true PM/ (Se * true PM + (1 - Sp) * (1 – true PM)) adjustment factor for country-years with complete CRVS can be obtained. ii. Reporting in incomplete CRVS systems For all countries with specialized studies to inform Se Reporting errors related to unregistered and Sp, we model Se as well as Sp with a country- maternal deaths (i.e. incomplete CRVS data) specific intercept in the midyear of their respective are summarized in terms of the ratio between: observation period. The country-specific intercept is estimated with a multilevel model, such that estimates • true PM in (PM-in) = the true PM among for countries with specialized studies are informed by deaths captured in the CRVS (so the true those data while estimates for countries with limited number of maternal deaths in the CRVS or no data are informed by information from other over the total number of deaths captured in countries. Se and Sp values for the remaining years the CRVS); before and after the reference year were obtained through a so-called random walk model set-up. In • true PM out (PM-out) = the PM among the random walk set-up, point estimates of Se and deaths not captured in the CRVS. Sp are kept constant unless country-specific data such that: suggest a change. For countries with specialized studies, the estimates are data driven and informed True PM among all deaths = by the combinations of Se and Sp as indicated by the COM*PM-in + (1 - COM)*PM-out studies. where COM stands for completeness of the In the model for Se and Sp, Se is constrained to CRVS data (in terms of reporting all female be between 0.1 and 1 and Sp is constrained to be deaths of reproductive age) as discussed in between 0.95 and 1. These bounds were chosen to section 3.1.1(a). avoid extrapolations for countries with limited data to values that are more extreme than those observed in For country-years with incomplete CRVS the data. (i.e. not all maternal deaths are registered in that country’s CRVS system for those years; We considered predictor variables to capture changes COM < 100%), we investigated the feasibility in sensitivity and specificity over time within countries, of estimating the odds ratio of the two PMs, and differences across countries. The following but data were too limited for inference on this predictor variables were considered as candidate ratio. Instead, we assumed that PM-in equals predictor variables: 22 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 • GFR = 1/Se, hence lower Se results in a higher adjustment, conversely higher Se results GDP per capita • in a lower adjustment. When Sp < 1, while CRVS completeness (COM) • keeping Se fixed, the adjustment factor decreases with decreasing true PM. This effect proportion of causes in the CRVS that are ill • is due to an increasing share of false positive defined (“R” codes in CRVS) maternal deaths among all deaths, and a ICD coding (use of ICD-9 or ICD-10) • decreasing share of false negative deaths, or, in other words, as the true PM decreases, the proportion of CRVS deaths that fall under • proportion of non-maternal deaths reported noncommunicable disease causes of as maternal increases while the proportion of death. maternal deaths reported as non-maternal However, none of the candidate predictor decreases. This relationship implies that variables showed a substantively meaningful keeping specificity and sensitivity constant in relationship with the parameters of interest, extrapolations in countries with specialized hence no predictor variables were used. studies, or for countries without any studies, will result in changing adjustment factors as ii. CRVS model estimates of CRVS the true PM changes. adjustment factors The CRVS model was fitted to specialized Figure 3.4. CRVS adjustment based on study data, collected by review (13), and CRVS the CRVS model for different values of specificity, calculated at different levels of data for the corresponding periods. The CRVS true PM when sensitivity is fixed at 0.586a yields estimates of sensitivity and specificity based on two scenarios. 1.8 • For countries with data from specialized studies, the model is fitted to those data, and the estimates for the CRVS 1.6 adjustment in the corresponding years will be consistent with the empiric country-level CRVS adjustment data. 1.4 • For countries without specialized studies, the estimates for sensitivity and specificity Spec 0.999 Spec 0.9993 are equivalent to global estimates of Spec 0.9999 Spec 1 sensitivity and specificity, obtained from 1.2 fitting the model to the global database (the envelope of all specialized studies). The resulting estimates of Se and Sp are 1.0 constant with time, as global estimates are also constant with time. 0.00 0.01 0.02 0.03 0.04 0.05 True PM Figure 3.4 shows the relationship between true PM and the estimated CRVS adjustment Spec: specificity factors, for specific values of Sp to illustrate PM: proportion maternal their effect on the CRVS adjustment factor. a Based on the CRVS model, we estimated that 58.6% of When Sp = 1, the CRVS adjustment factor maternal deaths are identified correctly in the CRVS. 23 Methods d. Comparison with previous UN MMEIG multiple observations from the same approach to estimate CRVS adjustment countries (so the 1.5 is not the median factors across countries). The CRVS adjustment model, described in The new approach improves upon these subsection c (immediately above), yields limitations through an assessment of variability estimates of sensitivity, specificity and CRVS across countries and within countries adjustments for all country-years without over time, in terms of the sensitivity and specialized study data. In the previous specificity of maternal death classification, round of estimates, the UN MMEIG CRVS extrapolations that are based on Se and Sp, adjustment was set to 1.5 for countries without and an assessment of uncertainty associated specialized studies. For countries with at with these metrics and the resulting CRVS least one specialized study, the adjustment adjustment factor. We also explored the use was calculated for countries with specialized of predictor variables to obtain more country- studies by the ratio of true PM reported in specific adjustments for countries with limited the study to CRVS-based PM, i.e. the ratio data, although, ultimately, no predictor of the proportion of true maternal deaths variables were used (13). out of all female deaths to the proportion of CRVS-reported maternal deaths out of all CRVS-reported female deaths. The CRVS 3.3.2 Bayesian maternal mortality adjustment ratio was kept constant in forward estimation model (the BMat model) extrapolations. Estimation and projection of maternal mortality Limitations of the previous approach include indicators was undertaken using the BMat the following. model. This model is intended to ensure that the MMR estimation approach is consistent • The use of a constant CRVS adjustment across all countries but remains flexible in that factor in extrapolations results in an it is based on covariate-driven trends to inform overestimation of the adjustment factor estimates in countries or country-periods if, in reality, specificity is constant and the with limited information; captures observed true PM decreases (as illustrated in Figure trends in countries with longer time series 3.4 for adjustments based on the CRVS of observations; and takes into account the model). differences in stochastic and sampling errors across observations. • The uncertainty in the adjustment factor had not been assessed. Instead, the In the BMat, the MMR for each country-year uncertainty of the adjustment factor is modelled as the sum of the HIV MMR (i.e. was assumed to be around 50% of the the portion of MMR that is due to HIV-related point estimate for all country-years. maternal deaths) and the non-HIV MMR (i.e. The uncertainty is likely to vary across the portion of MMR that is due to non-HIV- countries and with time, depending on data related maternal deaths): availability and the country-specific setting. • The value of 1.5 was based on the median MMR = Non-HIV MMR + HIV MMR, of a set of studies. The assessment did not account for differences that may be due to where non-HIV-related maternal deaths refer different settings (i.e. high-fertility settings to maternal deaths due to direct obstetric versus low-fertility settings, completeness causes or to indirect causes other than HIV, of CRVS). The set of studies included while HIV-related maternal deaths are those 24 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 HIV-related deaths for which pregnancy was not adjusted. Other data are adjusted as a substantial aggravating factor (also known described in section 3.1.1, subsection b. In as HIV-related indirect maternal deaths) (see the model, standard and stochastic errors for definitions in Chapter 2). observations, which reflect the uncertainty associated with observations, are taken into The estimation of the HIV-related indirect account when obtaining PM and thus MMR maternal deaths follows the same procedure estimates (see section 3.1.1, subsection c). as used in the previous edition of this Observations with smaller error variances publication, as summarized in subsection b (4). are more informative of the true PM and will thus carry a greater weight in determining the In the BMat model, the non-HIV MMR is estimates as compared to observations with estimated as follows: larger error variances. Non-HIV MMR(t) = Expected non-HIV MMR(t) * In countries with high-quality data with little Data-driven multiplier(t) uncertainty, the final BMat estimates will closely track the country data. However, in where the expected non-HIV MMR(t) is the absence of data, or when data are very estimated from a hierarchical regression model uncertain, the predictor variables play an using covariates (predictor variables) and important role and inform the estimated trend country-specific intercepts (described below in MMR. in subsection a). The data-driven multiplier(t) allows for deviations away from the rate of a. Estimation of expected non-HIV-related change in MMR implied by the expected maternal deaths non-HIV MMR, as indicated by country-year- specific data points. For example, if data A hierarchical regression model was used suggested that the non-HIV MMR decreased to obtain the expected number of non-HIV- (or increased) much faster in year t than related maternal deaths for each country- expected based on predictor variables, year and associated non-HIV MMR. The the data-driven multiplier for that year is model predicts the proportion of deaths to estimated to be greater (or smaller) than 1. women of reproductive age that are due to This data-driven multiplier is modelled with a maternal causes (PM) using three predictor flexible time-series model, which fluctuates variables: the GDP per capita, the GFR, and around 1, such that the predictor variables in the presence of a skilled birth attendant the regression model determine the estimated (SBA) as a proportion of live births. These change when data are absent. specific predictor variables were chosen from a broader list of potential predictor variables The estimation of the non-HIV MMR follows which fell into three groups: indicators of social from the estimation of the number of non-HIV and economic development (such as GDP, maternal deaths, explained in subsection b. human development index, life expectancy), process variables (SBA, antenatal care, The model is fitted to all data available in the proportion of institutional births, etc.) and risk country (see Figure 3.1), taking into account exposure (fertility level). adjustments and uncertainty associated with the data points. CRVS observations are adjusted using the estimates of sensitivity and specificity as described earlier, in section 3.3.1. Specialized studies are 25 Methods Box 3.2. ILLUSTRATION OF THE BMAT MODEL Country 1 Country 2 Expected MMR Expected MMR 3000 Estimated MMR 3000 Estimated MMR 2500 2500 MMR (per 100 000 live births) MMR (per 100 000 live births) 2000 2000 1500 1500 1000 1000 500 500 Data (adjusted) 0 0 Data (unadjusted) 1985 2017 1985 2017 Year Year The figure in this box illustrates MMR estimates for Country 1, a country without any observed MMR data, and Country 2, which has data. For both countries, the red dashed line illustrates the final estimates for the MMR, and red shaded areas illustrate the uncertainty associated with the estimates. The blue dashed line illustrates the covariate-driven “expected MMR” that would be estimated by the model if a country did not have data to inform its trend. Black dots illustrate MMR data points (usually obtained from observed PMs as explained in the data section). For each data point, its corresponding “adjusted value”, which is the data after accounting for biases, is plotted in purple, together with associated uncertainty about the true PM (purple vertical lines). For countries such as Country 1 without data points, the country-specific multiplier for the change in the non-HIV MMR is equal to 1 for the entire period, and so the final MMR estimate is given by the expected MMR estimate (the red and blue lines are identical). For Country 2, the available data points suggest a different trend in the MMR as compared to the trend suggested by the covariates (predictor variables) in the regression model (blue line). The final estimates in red better reflect the observed trend in the country’s data. Projections beyond the most recent observation for all countries are determined by the rate of change in the expected MMR (blue line) and the country-specific multiplier: the latter converges slowly to one, thus the rate of change in the projections converges to the rate of change in the expected MMR. 26 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 The model is summarized as follows: cause of death during pregnancy and post- delivery. There is also some evidence from community studies that women with HIV infection have a higher risk of maternal death, where although this may be offset by lower fertility. If HIV is prevalent, there will also be more = the expected proportion of incidental HIV deaths among pregnant and non-HIV-related deaths to women aged postpartum women. When estimating maternal 15–49 years that are due to maternal mortality in these countries, it is, thus, causes [NA = non-HIV; formerly it important to differentiate between incidental referred to “non-AIDS”] HIV deaths (non-maternal deaths) and HIV-related indirect maternal deaths (maternal GDP = gross domestic product per capita deaths caused by the aggravating effects (in 2011 PPP US dollars) of pregnancy on HIV) among HIV-positive GFR = general fertility rate (live births per pregnant and postpartum women who have woman aged 15–49 years) died (i.e. among all HIV-related deaths occurring during pregnancy, childbirth and SBA = proportion of births attended by puerperium).18 skilled health personnel = random intercept term for country j The number of HIV-related indirect maternal deaths , is estimated by: = random intercept term for region k. For countries with data available on maternal mortality, the expected proportion of non-HIV- related maternal deaths was based on country where and regional random effects, whereas for countries with no data available, predictions a*E = the total number of HIV-related deaths were derived using regional random effects among all deaths to women aged 15–49. only. The resulting estimates of the were = is the proportion of HIV-related used to obtain the expected non-HIV MMR deaths to women aged 15–49 that through the following relationship: occur during pregnancy. The value of v can be computed as follows: Expected non-HIV MMR = *(1-a)*E/B, where GFR where is the general fertility rate, and where c is the average exposure time (in years) a = the proportion of HIV-related deaths among to the risk of pregnancy-related mortality all deaths to women aged 15–49 years per live birth (set equal to 1 for this E = the total number of deaths to women of analysis), and where k is the relative risk reproductive age of dying from AIDS for a pregnant versus B = the number of births. a non-pregnant woman (reflecting both the decreased fertility of HIV-positive b. Estimation of HIV-related indirect maternal women and the increased mortality risk deaths of HIV-positive pregnant women). The value of k was set at 0.3 (14). For countries with generalized HIV epidemics and high HIV prevalence, HIV/AIDS is a leading 18 See definitions in Chapter 2. 27 Methods = is the fraction of pregnancy-related population, as estimated by UNPD in the 2019 AIDS deaths assumed to be indirect revision of World population prospects (9). maternal deaths. The UN MMEIG/TAG reviewed available study data on AIDS The MMRate was used to calculate the adult deaths among pregnant women and lifetime risk of maternal mortality (i.e. the recommended using = 0.3 (14). probability that a 15-year-old girl will die eventually from a maternal cause). In countries For observed PMs, we assumed that the total where there is a high risk of maternal death, reported maternal deaths are a combination there is also an elevated likelihood of girls of the proportion of reported non-HIV-related dying before reaching reproductive age. For maternal deaths and the proportion of reported this reason, it makes sense to consider the HIV-related (indirect) maternal deaths, where lifetime risk of maternal mortality conditional the latter is given by a*v for observations with on a girl’s survival to adulthood. The formula a “pregnancy-related death” definition and used yields an estimate of the lifetime risk that a*v*u for observations with a “maternal death” takes into account competing causes of death: definition. Lifetime risk of maternal mortality = (T15–T50)/ x MMRate 3.3.3 Maternal mortality indicators estimated by the model where equals the probability of survival from birth until age 15 years, and The immediate outputs of the BMat model were (T15 – T50)/ equals the average number of estimates in the form of PMs. These values years lived between ages 15 and 50 years (up were then converted to estimates of the MMR19 to a maximum of 35 years) among survivors to as follows: age 15 years. The values for , T15 and T50 are life-table quantities for the female population MMR = PM(D/B) during the period in question (15). The ratio where D is the number of deaths in women (T15 – T50)/ was taken from life tables that aged 15–49 years and B is the number of live include deaths due to mortality shocks, i.e. the births for the country-year corresponding to ratio represents the average number of years the estimate. lived between ages 15 and 50 years among survivors to age 15 years in the presence of Based on MMR estimates, the annual rate the mortality shock. Hence the lifetime risk in of MMR reduction (ARR) and the maternal years with mortality shocks represents the risk mortality rate (MMRate; the number of of dying from a maternal cause in the presence maternal deaths divided by person-years of the mortality shock (see Annex 3 for more lived by women of reproductive age) were information about mortality shocks). calculated. The ARR was calculated as follows: Regional maternal mortality estimates ARR = log(MMRt2/MMRt1)/(t1–t2) (according to the United Nations SDG, where t1 and t2 refer to different years with UNFPA, UNICEF, UNPD, WHO and the World t1 < t2. Bank Group regional groupings) were also computed. The MMR in a given region was The MMRate was calculated by using the computed as the estimated total number of number of maternal deaths divided by maternal deaths divided by the number of live the number of women aged 15–49 in the births for that region. Additionally, the lifetime risk of maternal mortality was based on the weighted average of (T15–T50)/ for a given Definitions of all the measures are provided in Chapter 2. 19 region, multiplied by the MMRate of that region. 28 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 7. Wilmoth J, Mizoguchi N, Oestergaard M, For all outcomes of interest, uncertainty was Say L, Mathers C, Zureick-Brown S, et al. A new assessed and reported in terms of uncertainty method for deriving global estimates of maternal mortality: supplemental intervals. So-called “80% credible intervals” report. 2012:1–31 (https://www.who.int/ are used, which have an 80% probability of reproductivehealth/publications/monitoring/ containing the truth. supplemental_rpt.pdf, accessed 20 June 2019). 8. Life tables. In: Global Health Observatory References (GHO) data [website]. Geneva: World Health Organization; 2019 (https://www.who.int/gho/ mortality_burden_disease/life_tables/ 1. World Health Organization (WHO), United life_tables/en/, accessed 18 June 2019). Nations Children’s Fund (UNICEF), United Nations Population Fund (UNFPA), World Bank. 9. World population prospects: the 2019 revision. Trends in maternal mortality: 1990 to 2008: New York (NY): United Nations Population estimates developed by WHO, UNICEF, UNFPA Division, Department of Economic and Social and the World Bank. 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Zaba B, Calvert C, Marston M, Isingo R, Group, United Nations Population Division. Nakiyingi-Miiro J, Lutalo T, et al. Effect of Trends in maternal mortality: 1990 to 2015: HIV infection on pregnancy-related mortality in estimates by WHO, UNICEF, UNFPA, World sub-Saharan Africa: secondary analyses Bank Group and the United Nations Population of pooled community-based data from the Division. Geneva: WHO; 2015 (https://www.who. network for Analysing Longitudinal Population- int/reproductivehealth/publications/monitoring/ based HIV/AIDS data on Africa (ALPHA). Lancet. maternal-mortality-2015/en/, accessed 4 2013;381(9879):1763-71. doi:10.1016/ September 2019). S0140-6736(13)60803-X. 5. Alkema L, Zhang S, Chou D, Gemmill A, Moller 15. Wilmoth J. The lifetime risk of maternal mortality: AB, Ma Fat D, et al. A Bayesian approach to the concept and measurement. Bull World Health global estimation of maternal mortality. 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(https://www.ncbi. nlm.nih.gov/pubmed/24416714, accessed 18 August 2019). 29 Methods 30 © AMISOM Public Information 0 04 CONTENT 32 Maternal mortality estimates for 2017 39 Trends in maternal mortality: 2000 to 2017 42 Comparison with previous TRENDS maternal mortality estimates IN MATERNAL MORTALITY MATERNAL MORTALITY ESTIMATES AND TRENDS: 2000 TO 2017 This chapter presents and describes estimated maternal mortality ratios (MMRs), numbers of maternal deaths, the proportion of maternal deaths among all deaths to women of reproductive age (PM), and the adult lifetime risk of maternal mortality (i.e. the probability that a 15-year-old girl will die eventually from a maternal cause).20 This chapter also presents and examines trends in these indicators since 2000. Countries and territories included in all the tables presented in this report are limited to WHO Member States with populations over 100 000 in 2019 (i.e. excluding: Andorra, Cook Islands, Dominica, Marshall Islands, Monaco, Nauru, Niue, Palau, Saint Kitts and Nevis, San Marino, Tuvalu), plus two territories (Puerto Rico, and the West Bank and Gaza Strip).21 20 See Chapter 2 for definitions. 21 Puerto Rico is an Associate Member, and the West Bank and Gaza Strip is a member in the regional committee for the WHO Eastern Mediterranean Region (EM/RC40/R.2: https://apps.who.int/iris/bitstream/handle/10665/121332/ em_rc40_r2_en.pdf). The WHO governing bodies use the name “West Bank and Gaza Strip”. 31 This results in a total of 185 countries and territories included in the data presented in these tables (including Annexes 5–17). Box 4.1. ACCURATELY INTERPRETING POINT ESTIMATES AND UNCERTAINTY INTERVALS The numbers provided are the most accurate All maternal mortality indicators derived from the 2017 point estimates possible given the available estimation round include a point estimate and an 80% data. However, these calculations still contain uncertainty interval (UI). For those indicators where only a level of uncertainty that varies depending on point estimates are reported in the text or tables, UIs can be obtained from supplementary material online.23 the amount and quality of available data used to produce them. The range that an estimated The 80% UIs computed for all the estimates provide the indicator’s true value most likely falls within is 10th and 90th percentiles of the posterior distributions. captured by its 80% uncertainty interval (UI); This was chosen rather than the more standard 95% UIs because of the substantial uncertainty inherent in more information about how to interpret the maternal mortality outcomes. estimates and UIs is provided in Box 4.1. Both point estimates and 80% UIs should be taken into The new estimates presented in this report account when assessing estimates. Here we can look at one example and how to interpret it: supersede all previously published estimates for years that fall within the same time period, The estimated 2017 global MMR is 211(UI 193 to 243). and due to modifications in methodology and This means: data availability, differences between these and previous estimates should not be interpreted • The point estimate is 211 and the 80% UI ranges as representing time trends. The full database, from 193 to 243. country profiles and all model specification • There is a 50% chance that the true 2017 global codes used are available online.22 MMR lies above 211, and a 50% chance that the true value lies below 211. Section 4.1 presents global-, regional- and country-level estimates for 2017, while • There is an 80% chance that the true 2017 global MMR lies between 193 and 243. section 4.2 presents trends between 2000 and 2017. • There is a 10% chance that the true 2017 global MMR lies above 243, and a 10% chance that the true value lies below 199. 4.1 Maternal mortality estimates for 2017 Other accurate interpretations include: Globally, an estimated 295 000 (UI 279 000 to • We are 90% certain that the true 2017 global MMR is at least 193. 340 000) maternal deaths occurred in 2017, yielding an overall MMR of 211 (UI 199 to 243) • We are 90% certain that the true 2017 global MMR maternal deaths per 100 000 live births for the is 243 or less. 185 countries and territories covered in this The amount of data available for estimating an indicator analysis. and the quality of that data determine the width of an indicator’s UI. As data availability and quality improve, For 2017, the global lifetime risk of maternal the certainty increases that an indicator’s true value lies close to the point estimate. mortality was estimated at 1 in 190; the overall proportion of deaths to women of reproductive age that are due to maternal causes (PM) was estimated at 9.2% (UI 8.7% to 10.6%). 23 Available at: www.who.int/reproductivehealth/publications/maternal- 22 Available at: www.who.int/reproductivehealth/ mortality-2017/en/ publications/maternal-mortality-2017/en/ 32 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 An estimated 3600 HIV-related indirect Sub-Saharan Africa has a very high MMR25 with maternal deaths occurred in 2017. The global a 2017 point estimate of 542 (UI 498 to 649), HIV-related indirect MMR was estimated at and the lifetime risk of maternal death was 3 maternal deaths per 100 000 live births. estimated at 1 in 37, compared with just 1 in HIV and pregnancy interaction accounted for 7800 in Australia and New Zealand. The PM in 1.22% of maternal deaths globally. sub-Saharan Africa is 18.2%, compared with just 0.5% in Europe. Table 4.1 provides 2017 point estimates of maternal mortality indicators as well as Five subregions/groups of counties have the numbers of maternal deaths by United moderate MMR, with 2017 estimates as Nations Sustainable Development Goal follows: Northern Africa 112 (UI 91 to 145), (SDG) region, subregion and three other Oceania (excluding Australia and New Zealand) groupings (landlocked developing countries, 129 (UI 69 to 267), South-Eastern Asia 137 least developed countries, and small island (UI 115 to 173), Southern Asia 157 (UI 136 to developing States), discussed in section 189) and small island developing States 210 4.1.1. It also presents the range of uncertainty (UI 178 to 277). Four subregions (Australia for each MMR point estimate. Country-level and New Zealand, Central Asia, Eastern Asia, estimates for 2017 are provided in Annex 5, Western Asia) and two regions (Latin America and discussed in section 4.1.2. and the Caribbean, and Europe and Northern America) were estimated to have low MMR For the purpose of categorization, MMR is (< 100 maternal deaths per 100 000 live births). considered to be low if it is less than 100, moderate if it is 100–299, high if it is 300–499, Sub-Saharan Africa and Southern Asia very high if it is 500–999 and extremely high accounted for approximately 86% of the if it is greater than or equal to 1000 maternal estimated global number of maternal deaths deaths per 100 000 live births. in 2017 (254 000) with sub-Saharan Africa alone accounting for roughly 66% (196 4.1.1 Regional-level estimates 000), while Southern Asia accounted for nearly 20% (58 000). South-Eastern Asia, The overall estimate for MMR in the world’s in addition, accounted for over 5% of global least developed countries (LDCs) in 2017 is maternal deaths (16 000). The rest of the world high at 415 (UI 396 to 477) maternal deaths per accounted for the remaining 8.5% of maternal 100 000 live births, which is more than 40 times deaths, with the lowest estimated count being higher than that of the subregion Europe (10; 24 in Australia and New Zealand (just 26 maternal UI 9 to 11), and almost 60 times higher than in deaths). In Europe, there were an estimated the subregion Australia and New Zealand (7; 740 maternal deaths in 2017. UI 6 to 8) (see Table 4.1). In the world’s LDCs, where an estimated 130 000 maternal deaths With regard to the proportion of deaths to occurred in 2017, the estimated lifetime risk of women of reproductive age that are due to maternal death was 1 in 56. maternal causes (PM), in 2017 this was below 10% in all regions and subregions except for sub-Saharan Africa (18.2%), but was high in landlocked developing countries (17.4%) and in LDCs (17.5%). Fifty-nine countries had a 24 SDG regions and subregions are shown in Tables 4.1, 4.2 Extremely high MMR is considered to be ≥ 1000, very 25 and 4.3. The subregions are indented and listed beneath high MMR is 500–999, high MMR is 300–499, moderate their regions. MMR is 100–299, and low MMR is < 100 maternal deaths per 100 000 live births. 33 Assessing progress and setting a trajectory towards ending preventable maternal mortality PM of 1% or less; with the exception of Japan, 4.1.2 Country-level estimates Turkmenistan and the United Arab Emirates, all the other countries with PM less than 1% are in Annex 5 provides 2017 point estimates and Europe. uncertainty intervals for each country’s maternal mortality indicators (MMR and Table 4.2 shows the HIV-related indirect PM), as well as the estimates for numbers MMR and the number and percentage of of maternal deaths, lifetime risk of maternal HIV-related indirect maternal deaths 26 by SDG death, and percentage of HIV-related indirect region, subregion and other grouping in 2017. maternal deaths. Figure 4.1 displays a map Sub-Saharan Africa accounts for the largest with all countries shaded according to MMR proportion (89%) of global HIV-related indirect levels in 2017. maternal deaths: 3200 out of 3600. Europe, however, has by far the highest proportion Three countries are estimated to have had of HIV-related maternal deaths as a subset extremely high maternal mortality in 2017 of all maternal deaths in that subregion, at (defined as over 1000 maternal deaths per 8.9%, with the next highest being 1.6% in 100 000 live births), with the highest MMR sub-Saharan Africa, compared with just 0.13% being in South Sudan, at 1150 (UI 789 to in Western Asia, and no HIV-related maternal 1710) maternal deaths per 100 000 live births, deaths at all in Australia and New Zealand followed by Chad (1140; UI 847 to 1590) and in 2017. The HIV-related indirect MMR for Sierra Leone (1120; UI 808 to 1620). Sixteen sub-Saharan Africa in 2017 is high, estimated other countries, all also in sub-Saharan Africa at 9 maternal deaths per 100 000 live births, except for one, are estimated to have very high compared with 1 in South-Eastern Asia, MMR in 2017 (i.e. ranging between 500 and Latin America and the Caribbean, Oceania 999): Nigeria (917; UI 658 to 1320), Central (excluding Australia and New Zealand), and African Republic (829; UI 463 to 1470), Somalia Europe, and 0 (zero) in all other subregions. (829; UI 385 to 1590), Mauritania (766; UI 528 Without HIV-related indirect maternal deaths, to 1140), Guinea-Bissau (667; UI 457 to 995), the MMR for sub-Saharan Africa in 2017 would Liberia (661; UI 481 to 943), Afghanistan (638; be 533 maternal deaths per 100 000 live births, UI 427 to 1010), Côte d’Ivoire (617; UI 426 to instead of 542. Two subregions are estimated 896), Gambia (597; UI 440 to 808), Guinea to have had more than 100 HIV-related indirect (576; UI 437 to 779), Mali (562; UI 419 to 784), maternal deaths in 2017: Southern Asia and Burundi (544; UI 413 to 728), Lesotho (548; South-Eastern Asia (both 110). UI 391 to 788), Cameroon (529; UI 376 to 790), the United Republic of Tanzania (524; UI 399 Annexes 6–15 present the MMR point to 712) and Niger (509; UI 368 to 724). Only estimates, range of uncertainty, numbers of three countries in sub-Saharan Africa have maternal deaths and lifetime risk of maternal low MMR: Mauritius (61; UI 46 to 85), Cabo death in 2017, as well as the trends in the Verde (58; UI 45 to 75) and Seychelles (53; estimates of MMR between 2000 and 2017, for UI 26 to109). Only one country outside the WHO, UNICEF, UNFPA, World Bank Group and sub-Saharan African region has high MMR: UNPD regions, respectively. Haiti (480; UI 346 to 718). Ninety countries are estimated to have MMR of 50 or less. Nigeria and India had the highest numbers of maternal deaths, and accounted for approximately one third (35%) of all estimated global maternal deaths in 2017, with 26 See definitions in Chapter 2. 34 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 Table 4.1. Estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), number of maternal deaths, lifetime risk and proportion of deaths among women of reproductive age that are due to maternal causes (PM), by United Nations Sustainable Development Goal (SDG) region, subregion and other grouping, 2017 MMRa point estimate and range of uncertainty interval (UI: 80%) Number of Lifetime risk PMd SDG region maternal of maternal MMR deathsb deathc (%) Lower Upper point UI UI estimate World 199 211 243 295 000 190 9.2 Sub-Saharan Africa e 498 542 649 196 000 37 18.2 Northern Africa and Western Asia 73 84 104 9 700 380 5.9 Northern Africa f 91 112 145 6 700 260 8.4 Western Asiag 45 55 69 3 000 650 3.6 Central and Southern Asia 131 151 181 58 000 260 6.6 Central Asia h 21 24 28 390 1 400 1.7 Southern Asia i 136 157 189 58 000 250 6.8 Eastern and South-Eastern Asia 61 69 85 21 000 790 3.3 Eastern Asia j 22 28 35 5 300 2 200 1.5 South-Eastern Asia k 115 137 173 16 000 320 5.5 Latin America and the Caribbean l 70 74 81 7 800 630 3.8 Oceania 34 60 120 400 690 4.1 Australia and New Zealand 6 7 8 26 7 800 0.6 Oceania (excl. Australia and New 69 129 267 380 210 6.5 Zealand)m Europe and Northern America 12 12 14 1 500 4 800 0.6 Europe n 9 10 11 740 6 500 0.5 Northern America o 16 18 20 760 3 100 0.9 Landlocked developing countries p 378 408 484 65 000 57 17.4 Least developed countriesq 396 415 477 130 000 56 17.5 Small island developing States r 178 210 277 2 600 190 8.5 UI: uncertainty interval. a MMR estimates have been rounded according to the following scheme: < 100 rounded to nearest 1; 100–999 rounded to nearest 1; and ≥ 1000 rounded to nearest 10. b Numbers of maternal deaths have been rounded according to the following scheme: < 100 rounded to nearest 1; 100–999 rounded to nearest 10; 1000–9999 rounded to nearest 100; and ≥ 10 000 rounded to nearest 1000. c Lifetime risk numbers have been rounded according to the following scheme: < 100 rounded to nearest 1; 100–999 rounded to nearest 10; and ≥ 1000 rounded to nearest 100. d The number of maternal deaths in a given time period divided by the total deaths among women aged 15–49 years. e Angola, Benin, Botswana, Burkina Faso, Burundi, Cabo Verde, Cameroon, Central African Republic, Chad, Comoros, Congo, Côte d’Ivoire, Democratic Republic of the Congo, Djibouti, Equatorial Guinea, Eritrea, Eswatini, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mozambique, Namibia, Niger, Nigeria, Rwanda, Sao Tome and Principe, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, South Sudan, Togo, Uganda, United Republic of Tanzania, Zambia, Zimbabwe. f Algeria, Egypt, Morocco, State of Libya, Sudan, Tunisia. g Armenia, Azerbaijan, Bahrain, Cyprus, Georgia, Iraq, Israel, Jordan, Kuwait, Lebanon, Oman, Qatar, Saudi Arabia, Syrian Arab Republic, Turkey, United Arab Emirates, West Bank and Gaza Strip, Yemen. h Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, Uzbekistan. i Afghanistan, Bangladesh, Bhutan, India, Iran (Islamic Republic of), Maldives, Nepal, Pakistan, Sri Lanka. j China, Democratic People’s Republic of Korea, Japan, Mongolia, Republic of Korea. 35 Assessing progress and setting a trajectory towards ending preventable maternal mortality k Brunei Darussalam, Cambodia, Indonesia, Lao People’s Democratic Republic, Malaysia, Myanmar, Philippines, Singapore, Thailand, Timor-Leste, Viet Nam. l Antigua and Barbuda, Argentina, Bahamas, Barbados, Belize, Bolivia (Plurinational State of), Brazil, Chile, Colombia, Costa Rica, Cuba, Dominican Republic, Ecuador, El Salvador, Grenada, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Puerto Rico, Saint Lucia, Saint Vincent and the Grenadines, Suriname, Trinidad and Tobago, Uruguay, Venezuela (Bolivarian Republic of). m Fiji, Kiribati, Micronesia (Federated States of), Papua New Guinea, Samoa, Solomon Islands, Tonga, Vanuatu. n Albania, Austria, Belarus, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Montenegro, Netherlands, Norway, Poland, Portugal, Republic of Moldova, Republic of North Macedonia, Romania, Russian Federation, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Ukraine, United Kingdom of Great Britain and Northern Ireland. o Canada, United States of America. p Afghanistan, Armenia, Azerbaijan, Bhutan, Bolivia (Plurinational State of), Botswana, Burkina Faso, Burundi, Central African Republic, Eswatini, Ethiopia, Kazakhstan, Kyrgyzstan, Lao People’s Democratic Republic, Lesotho, Malawi, Mali, Mongolia, Nepal, Niger, Paraguay, Republic of Moldova, Republic of North Macedonia, Rwanda, South Sudan, Tajikistan, Turkmenistan, Uganda, Uzbekistan, Zambia, Zimbabwe. q Afghanistan, Angola, Bangladesh, Benin, Bhutan, Burkina Faso, Burundi, Cambodia, Central African Republic, Chad, Comoros, Democratic Republic of the Congo, Djibouti, Eritrea, Ethiopia, Gambia, Guinea, Guinea-Bissau, Haiti, Kiribati, Lao People’s Democratic Republic, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mozambique, Myanmar, Nepal, Niger, Rwanda, Sao Tome and Principe, Senegal, Sierra Leone, Solomon Islands, Somalia, South Sudan, Sudan, Timor-Leste, Togo, Uganda, United Republic of Tanzania, Vanuatu, Yemen, Zambia. r Antigua and Barbuda, Bahamas, Barbados, Belize, Cabo Verde, Comoros, Cuba, Dominican Republic, Fiji, Grenada, Guinea-Bissau, Guyana, Haiti, Jamaica, Kiribati, Maldives, Mauritius, Micronesia (Federated States of), Papua New Guinea, Puerto Rico, Saint Lucia, Saint Vincent and the Grenadines, Samoa, Sao Tome and Principe, Seychelles, Singapore, Solomon Islands, Suriname, Timor-Leste, Tonga, Trinidad and Tobago, Vanuatu. 36 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 Table 4.2. Estimates of maternal mortality ratio (MMR, maternal deaths per 100 000 live births), number of maternal deaths and HIV-related indirect maternal deaths, by United Nations Sustainable Development Goal (SDG) region, subregion and other grouping, 2017 Percentage Number of of HIV-related Number of HIV-related HIV-related MMR point indirect SDG region maternal indirect indirect estimatea maternal deathsb MMR maternal deathsd deathsc (%) World 211 295 000 3 3 600 1.2 Sub-Saharan Africa e 542 196 000 9 3 200 1.6 Northern Africa and Western Asia 84 9 700 0 20 0.2 Northern Africaf 112 6 700 0 16 0.2 Western Asiag 55 3 000 0 4 0.1 Central and Southern Asia 151 58 000 0 110 0.2 Central Asia h 24 390 0 4 1.0 Southern Asiai 157 58 000 0 110 0.2 Eastern and South-Eastern Asia 69 21 000 0 130 0.6 Eastern Asia j 28 5 300 0 13 0.3 South-Eastern Asia k 137 16 000 1 110 0.7 Latin America and the Caribbean l 74 7 800 1 69 0.9 Oceania 60 400 1 4 1.0 Australia and New Zealand 7 26 0 0 0.0 Oceania (exc. Australia and New 129 380 1 4 1.1 Zealand)m Europe and Northern America 12 1 500 1 71 4.7 Europe n 10 740 1 66 8.9 Northern America o 18 760 0 5 0.7 Landlocked developing countries p 408 65 000 5 840 1.2 Least developed countriesq 415 130 000 5 1 500 1.2 Small island developing Statesr 210 2 600 3 37 1.4 a MMR estimates have been rounded according to the following scheme: < 100 rounded to nearest 1; 100–999 rounded to nearest 1; and ≥ 1000 rounded to nearest 10. b Numbers of maternal deaths have been rounded according to the following scheme: < 100 rounded to nearest 1; 100–999 rounded to nearest 10; 1000–9999 rounded to nearest 100; and ≥ 10 000 rounded to nearest 1000. c According to the Joint United Nations Programme on HIV/AIDS (UNAIDS), HIV-related deaths (including HIV-related indirect maternal deaths) include the estimated number of deaths related to HIV infection, including deaths that occur before reaching the clinical stage classified as AIDS. d Percentage of HIV-related indirect maternal deaths (see note c), calculated as a percentage of all maternal deaths. e–r See footnotes for Table 4.1. 37 Assessing progress and setting a trajectory towards ending preventable maternal mortality approximately 67 000 (UI 48 000 to 96 000) 26%), Guinea-Bissau and Mali (both 24%), the and 35 000 (UI 28 000 to 43 000) maternal Democratic Republic of the Congo and Nigeria deaths (23% and 12% of global maternal (both 23%), the United Republic of Tanzania deaths), respectively. Three other countries (22%), and Burundi, Senegal and Sierra Leone also had 10 000 maternal deaths or more: the (all 21%). PM is less than 1% in 24 countries. Democratic Republic of the Congo (16 000; UI 12 000 to 24 000), Ethiopia (14 000; Regarding the estimated lifetime risk of UI 10 000 to 20 000) and the United Republic maternal mortality for a 15-year-old girl in of Tanzania (11 000; UI 8100 to 14 000). Ten 2017, the two countries with the highest other countries had between 5000 and 9999 estimated risk are Chad (1 in 15) and South maternal deaths in 2017 (in order from higher Sudan (1 in 18), followed by Sierra Leone and to lower numbers of deaths): Indonesia, Somalia, both at 1 in 20. The countries with the Pakistan, Afghanistan, Chad, Uganda, Côte lowest risk are Italy (1 in 51 300), Poland (1 in d’Ivoire, Bangladesh, Niger, Somalia, Kenya. 30 300) and Greece (1 in 26 900). Sixty-one countries were estimated to have had just 10 or fewer maternal deaths in 2017. Annex 5 also presents the percentage of HIV-related indirect maternal deaths by country PM is estimated to be highest in Afghanistan in 2017, for those countries where there was and Mauritania (37% in both), Chad (34%), at least 5% prevalence of HIV-related indirect and Niger and Somalia (31% in both). Eleven maternal deaths among all maternal deaths in other countries have high PMs, in the range of 2017 (1). Although at a regional level the overall 20–30%: Gambia, South Sudan and Liberia (all proportions of HIV-related indirect maternal Figure 4.1. Maternal mortality ratio (MMR, maternal deaths per 100 000 live births), 2017 1−19 20−99 100−299 0 875 1,750 3,500 Kilometres 300−499 © World Health Organization 2019 The designations employed and the presentation of the material in this map do not imply the 500−999 Data not available expression of any opinion whatsoever on the part of WHO concerning the legal status of any Some rights reserved. This work is country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers ≥ 1000 Not applicable available under the CC BY-NC-SA 3.0 IGO licence. or boundaries. Dotted and dashed lines on maps represent approximate border lines for which there may not yet be full agreement. 38 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 deaths out of all maternal deaths are relatively before dropping to just over a third of that small, for countries with high HIV prevalence number (3600) in 2017. The effect of HIV on they are substantial. In six countries, 15% or maternal mortality in 2017 appears to be less more of maternal deaths were estimated to be pronounced than in earlier years; HIV-related HIV-related indirect maternal deaths in 2017: indirect maternal deaths now account for South Africa (21%), Turkmenistan, (17%), approximately 1% of all maternal deaths Belize (17%), Bahamas (16%), and the Russian compared with approximately 2.5% in 2005, Federation and Italy (both 15%). at the peak of the epidemic. This likely reflects improved care and management of HIV disease 4.2 Trends in maternal mortality: in general, and during pregnancy in particular. 2000 to 2017 Continued attention to reducing new infections and providing optimal care to people living with Little time has passed between the start of HIV will ensure that these health gains are not the SDG reporting period on 1 January 2016 eroded. and the date of the estimates presented in this report, which are for the year 2017. Therefore, Table 4.3 presents the estimated MMRs and for the purposes of understanding meaningful numbers of maternal deaths for 2000 and 2017 trends in maternal mortality, we report on along with percentage changes over time for progress from 2000 to 2017. This interval also SDG regions, subregions and other groupings, reflects the time period since reporting of and Annexes 7, 9, 11, 13, 15, 16 and 17 also health progress on global goals was initiated, present maternal mortality trend data for with the launch of Millennium Declaration and different regional groupings and per country. the MDGs in 2000 (2). When interpreting changes in MMRs over time, Global MMR in 2017 had declined 38% since one should take into consideration that it is 2000, when it was estimated at 342 maternal easier to reduce the MMR when the level is deaths per 100 000 live births. The average high than when the MMR level is already low. annual rate of reduction in global MMR between 2000 and 2017 was 2.9%; this means 4.2.1 Regional-level trends that, on average, the global MMR declined by 2.9% every year between 2000 and 2017. The Between 2000 and 2017, the subregion of global number of maternal deaths in 2017 was Southern Asia achieved the greatest overall estimated to be 35% lower than in 2000 when percentage reduction in MMR, with a reduction there were an estimated 451 000 (UI 431 000 of 59% (from 384 [UI 347 to 432] to 157 [UI 136 to 485 000) maternal deaths. The overall to 189] maternal deaths per 100 000 live proportion of deaths to women of reproductive births), as shown in Table 4.3. This equates to age that are due to maternal causes (PM) was an average annual rate of reduction of 5.3% estimated to be 26.3% lower in 2017 than in (UI 4.2 to 6.3). Four other subregions roughly 2000. The lifetime risk for a 15-year-old girl halved their MMRs during this period: Central of dying of a maternal cause nearly halved Asia (52%), Eastern Asia (50%), Europe (53%) between 2000 and 2017, globally, from 1 in and Northern Africa (54%); all of these except 100, to 1 in 190. Northern Africa already had low MMR (< 100) in 2000. Land-locked developing countries and Globally, following the trend of the HIV the least developed countries also reduced epidemic, the number of HIV-related indirect their MMRs by almost half: 48% and 46%, maternal deaths increased until 2005 when respectively. this number peaked at an estimated 10 000, 39 Assessing progress and setting a trajectory towards ending preventable maternal mortality Despite its very high MMR in 2017, Africa (65% decline) and Latin American and sub-Saharan Africa also achieved a substantial the Caribbean (59%) and in the subregion of reduction in overall regional MMR of roughly Oceania (excluding Australia and New Zealand) 38% since 2000. In regions where MMR was (56%). Lower levels of decline were observed already very low, less reduction was observed, in Eastern and South-Eastern Asia (13%). such as the 11% reduction in Australia and Notably, numbers of HIV-related indirect New Zealand (from 8 to 7). However, notably, maternal deaths nearly doubled in Northern one subregion with very low MMR in 2000 Africa and Western Asia and increased by one (12) – Northern America – had an increase in third in Europe and Northern America, but the MMR of almost 52% during this period, rising numbers are still relatively low. to 18 in 2017. This is likely due to already low levels of MMR, as well as improvements in Annexes 7, 9, 11, 13, 15 and 16 present the data collection, changes in life expectancy MMR trends and percentage changes in MMR and/or changes in disparities between between 2000 and 2017 for WHO, UNICEF, subpopulations. UNFPA, World Bank Group, UNPD and SDG regions, respectively. The greatest declines in the proportion of maternal deaths among women of reproductive 4.2.2 Country-level trends age (PM) occurred in Central and Southern Asia (decline of 56.4%) and Northern Africa Annex 17 presents the MMR trends (point and Western Asia (decline of 42.6%). Oceania estimates for five different years) and the (excluding Australia and New Zealand), Latin average annual rates of reduction (ARR) in America and the Caribbean, and Eastern and MMR between 2000 and 2017, as well as South-Eastern Asia all had declines higher the range of the uncertainty intervals on the than the world average reduction of 26.3%, average ARRs, for each country. Assessment with declines of 35.6%, 30.9% and 30.3%, of country-level progress contributing to respectively. Almost no change was seen in the achieving the SDG target of global MMR less PM in Europe and Northern America. than 70 per 100 000 live births by 2030 (SDG target 3.1) is somewhat premature given the Declines in lifetime risk of maternal death for short reporting period since the start of the a 15-year-old girl were greater than the global SDG reporting period (1 January 2016). average decline, between 2000 and 2017, in the regions of Central and Southern Asia (cut The 10 countries with the highest MMRs in to less than a third of the risk) and Northern 2017 (in order of highest to lowest: South Africa and Western Asia (cut to less than half), Sudan, Chad, Sierra Leone, Nigeria, Central and in the subregion of Oceania (excluding African Republic, Somalia, Mauritania, Guinea- Australia and New Zealand) (cut to less than Bissau, Liberia and Afghanistan) all have half). Little change was observed in lifetime average ARRs between 2000 and 2017 of risk in the region of Europe and Northern less than 5%. When comparing the average America and in the subregion of Australia and ARRs between the year ranges of 2000–2010 New Zealand. and 2010–2017, these 10 countries have also had stagnant or slowing levels of ARR and With regard to HIV, the greatest declines in therefore remain at greatest risk. The impact of numbers of HIV-related indirect maternal interruptions or loss of quality health services deaths, after peaking globally in 2005, must be considered in crisis and other unstable were observed in the regions of Central and situations. For countries with low MMR, Southern Asia (72% decline), sub-Saharan attention to potential disparities between 40 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 Table 4.3. Comparison of maternal mortality ratio (MMR, maternal deaths per 100 000 live births) and number of maternal deaths, by United Nations Sustainable Development Goal (SDG) region, subregion and other grouping, 2000 and 2017 2000 2017 Overall Average percentage annual rate change of reduction in MMR in MMR SDG region Number of Number of between between MMR point MMR point maternal maternal 2000 and 2000 and estimatea estimate deathsb deaths 2017c,d 2017d (%) (%) World 342 451 000 211 295 000 38.4  2.9 Sub-Saharan Africa e 878 234 000 542 196 000 38.3 2.8 Northern Africa and Western Asia 158 15 000 84 9 700 46.6 3.7 Northern Africa f 244 11 000 112 6 700 54.1 4.6 Western Asiag 81 4 000 55 3 000 32.4 2.3 Central and Southern Asia 375 153 000 151 58 000 59.7 5.3 Central Asia h 49 590 24 390 52.0 4.3 Southern Asia i 384 152 000 157 58 000 59.2 5.3 Eastern and South-Eastern Asia 114 36 000 69 21 000 39.3 2.9 Eastern Asia j 56 11 000 28 5 300 49.9 4.1 South-Eastern Asia k 214 25 000 137 16 000 36.0 2.6 Latin America and the Caribbean l 96 11 000 74 7 800 22.6 1.5 Oceania 106 590 60 400 43.0 3.3 Australia and New Zealand 8 23 7 26 11.0 0.7 Oceania (excl. Australia and 223 560 129 380 42.0 3.2 New Zealand)m Europe and Northern America 17 2 000 12 1 500 27.5 1.9 Europen 20 1 500 10 740 53.4 4.5 Northern Americao 12 500 18 760 -52.2 -2.5 Landlocked developing countries p 788 98 000 408 65 000 48.2 3.9 Least developed countries q 763 194 000 415 130 000 45.6 3.6 Small island developing Statesr 249 3 100 210 2 600 15.7 1.0 a MMR point estimates have been rounded according to the following scheme: < 100 rounded to nearest 1; 100–999 rounded to nearest 1; and ≥ 1000 rounded to nearest 10. b Numbers of maternal deaths have been rounded according to the following scheme: < 100 rounded to nearest 1; 100–999 rounded to nearest 10; 1000– 9999 rounded to nearest 100; and ≥ 10 000 rounded to nearest 1000. c Overall change for the whole period since the first year of the millennium (data from 1 January 2000). d Percentage changes and annual rates of reduction were calculated on rounded numbers. e–r See footnotes for Table 4.1. 41 Assessing progress and setting a trajectory towards ending preventable maternal mortality subpopuulations and consideration of reducing overall PM will be important. Countries with the highest rates of reduction between 2000 and 2017 (average ARR of 7% or above), starting with the highest, were Belarus, Kazakhstan, Timor-Leste, Rwanda, Turkmenistan, Mongolia, Angola and Estonia (see Annex 17). In considering the uncertainty Care should be taken to use only these estimates for the interpretation of trends in maternal mortality from 2000 to 2017, rather than extrapolating trends based on comparison with previously published estimates. Please refer to Chapter 3 for full information about the methods used to develop the current estimates for 2000–2017. 0 around these average ARRs, we can only be References very sure about this high level of acceleration 1. Life tables. In: Global Health Observatory (where the lower bound of uncertainty in the (GHO) data [website]. Geneva: World Health ARR is greater than or equal to 7%) in Belarus Organization; 2019 (https://www.who.int/ gho/mortality_burden_disease/life_tables/ (13.0%; UI 9.6% to 16.7%), Kazakhstan life_tables/en/, accessed 18 June 2019). (10.9%; UI 9.2% to 12.6%), Timor-Leste (9.8%; 2. Millennium Development Goals and Beyond UI 7.7% to 11.9%) and Rwanda (9.1%; UI 7% to 2015: Background. In: United Nations [website]. 10.7%). In 13 countries, MMR increased in the United Nations; undated (https://www.un.org/ same period. In considering the uncertainty millenniumgoals/bkgd.shtml, accessed 30 August 2019). around the rate and direction of change, we believe there have been true MMR increases between 2000 and 2017 in the United States of America (ARR –2.6%; UI –3.3% to –1.9%) and the Dominican Republic (ARR –1%; UI –1.6% to –0.5%). Seventy-one countries had MMR greater than or equal to 100 in 2015, and of these only five countries had an overall MMR reduction of at least 66% (i.e. two thirds reduction) between 2000 and 2017: Angola, Cambodia, Nepal, Rwanda and Timor-Leste. 4.3 Comparison with previous maternal mortality estimates The results described in this report include the first available estimates for maternal mortality for years that fall within the SDG reporting period; but since two years (2016 and 2017) is not sufficient to show trends, estimates have been developed and presented covering the period 2000 to 2017. In 2023, halfway through the SDG reporting period, a full review of SDG progress is planned, and at that time it will become possible to present trends from the start of the SDG reporting period (2016 onwards). 42 05 CONTENTS 44 Transition from MDG to SDG reporting 46 Strategies for improving maternal health: 2016 to 2030 TRENDS IN MATERNAL MORTALITY ASSESSING PROGRESS AND SETTING A TRAJECTORY TOWARDS ENDING PREVENTABLE MATERNAL MORTALITY AND ACHIEVING SDG TARGET 3.1 43 Box 5.1. GLOBAL TARGETS FOR REDUCING MATERNAL MORTALITY SDG target 3.1: By 2030, reduce the global maternal mortality ratio to less than 70 per 100 000 live births (1). Ending preventable maternal mortality (EPMM): By 2030, every country should reduce its maternal mortality ratio (MMR) by at least two thirds from the 2010 baseline, and the average global target is an MMR of less than 70 maternal deaths per 100 000 live births. • EPMM supplementary national target: By 2030, no country should have an MMR higher than 140 deaths per 100 000 live births (twice the global target). Country targets for 2030 depend on baseline levels of MMR, to increase equity in maternal mortality (2). Development Agenda also moves beyond 5.1 Transition from MDG to SDG individual countries with the poorest health and reporting development outcomes to the contributions of all countries to the global targets of all SDGs, During the MDG era, which kicked off in 2000 with a view to improved equity. As the SDG with the United Nations Millennium Declaration, reporting period – 2016 to 2030 – progresses there were just eight MDGs, including MDG 5: and data become consistently available for Improve maternal health. MDG 5 had two analysis (i.e. when countries provide more targets: 5.A: Reduce by three quarters, data, disaggregated data and more data between 1990 and 2015, the maternal points), reporting should also focus on the mortality ratio (MMR), and 5.B: Achieve effect of inequities and how to address them, by 2015 universal access to reproductive as articulated within the SDGs. health (3). The baseline year against which all MDG-era progress was assessed was fixed In the era of the SDGs, an acceleration of at 1990, and notable progress was made in current progress is required in order to achieve reducing maternal mortality by 2015, but it was SDG target 3.1, working towards a vision of insufficient to meet the MDG target (4). In the ending all preventable maternal mortality transition from MDGs to SDGs, 17 new goals (see Box 5.1). By the current projection, were set, with 13 health-related targets placed achieving this global goal will require countries under the umbrella of one of those goals: to reduce their MMRs by at least 6.1% each SDG 3: Ensure healthy lives and promote year between 2016 and 2030. Based on the wellbeing for all at all ages. One of those new point estimates for MMR in 2000 and health-related targets is SDG target 3.1, which 2017, only 16 countries (Angola, Belarus, is the focus of this report: By 2030, reduce the Cambodia, Estonia, Iran, Kazakhstan, Lao global MMR to less than 70 per 100 000 live People’s Democratic Republic, Mongolia, births. The focus of attention in the Sustainable Nepal, Poland, Romania, Russian Federation, 44 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 Rwanda, Tajikistan, Timor-Leste and the estimated number in 2015, and will save Turkmenistan) have demonstrated this rate (or approximately 1.4 million women’s lives higher) of average annual reduction of MMR. between 2016 and 2030, as compared with Highlighting the strategies employed by these a situation in which the rate of reduction of and other countries with overall improvements MMR since 2015 remains the same as the rate in maternal health can illuminate routes to observed in the 2010–2017 period. progress that other countries may find useful. For the countries with the highest MMRs in Under the scenario where the current pace (i.e. 2017, substantially higher annual rates of the pace seen during the period 2010–2017) reduction will be required to attain levels below continues during the first half of the SDG 140 maternal deaths per 100 000 live births reporting period, the global MMR is projected in 2030, which is the EPMM supplementary to be approximately 189 in 2023 (at the halfway national target (see Box 5.1). point), a significant gap from the MMR of 118 which we need to reach by that year in order to Projections indicate that accomplishing the be on track to achieve the final SDG target of target of global MMR less than 70 will result below 70 by 2030. in nearly 70% fewer deaths in 2030 than Box 5.2. STRATEGIC FRAMEWORK FOR ENDING PREVENTABLE MATERNAL MORTALITY (EPMM) Guiding principles for EPMM • Empower women, girls and communities. • Protect and support the mother–baby dyad. • Ensure country ownership, leadership and supportive legal, technical and financial frameworks. • Apply a human rights framework to ensure that high-quality reproductive, maternal and newborn health care is available, accessible and acceptable to all who need it. Cross-cutting actions for EPMM • Improve metrics, measurement systems and data quality to ensure that all maternal and newborn deaths are counted. • Allocate adequate resources and effective health care financing. Five strategic objectives for EPMM • Address inequities in access to and quality of sexual, reproductive, maternal and newborn health care. • Ensure universal health coverage for comprehensive sexual, reproductive, maternal and newborn health care. • Address all causes of maternal mortality, reproductive and maternal morbidities, and related disabilities. • Strengthen health systems to respond to the needs and priorities of women and girls. • Ensure accountability to improve quality of care and equity. Source: WHO 2015 45 Assessing progress and setting a trajectory towards ending preventable maternal mortality 5.2. Strategies for improving Republic of the Congo, Chad, Afghanistan, maternal health: 2016 to 2030 Iraq, Haiti, Guinea, Nigeria, Zimbabwe, Ethiopia) (7).28 These 15 countries had MMRs in The Global Strategy for Women’s, Children’s 2017 ranging from 31 (Syrian Arab Republic) to and Adolescents’ Health describes the vision 1150 (South Sudan); this is in contrast to MMR for improving the health of every woman and of 3 in the single “very sustainable” country every child, everywhere, between 2016 and (Finland), and MMRs ranging from 2 (Norway) 2030 (6). Some of the drivers of success in to 10 (Canada) in the 14 countries labelled reducing maternal mortality range from making as “sustainable”(7).29 In crisis and disaster improvements at the provider and health settings, the breakdown of health systems system level, to implementing interventions can cause a dramatic rise in deaths due to aimed at reducing social and structural complications that would be easily treatable barriers. These strategies are part of the EPMM under stable conditions (see Annex 3). strategic framework for policy and programme planning, which is informed by a set of four Many of the most vulnerable populations are guiding principles (see Box 5.2) (2). not represented in the current global data, as there are simply no systems in place for many 5.2.1 Specialized population groups: such populations. Even for countries with humanitarian and crisis settings, good overall progress indicators, the national- vulnerable populations and late level data often mask extreme disparities that maternal deaths exist between population groups within these countries. For example, new data on maternal Examining countries that have experienced deaths in Australia suggest that Aboriginal little to no reduction in maternal mortality since and Torres Strait Islander women have a 2000 reveals a number of factors that impede higher incidence of maternal death than other progress, both for those with high levels of non-Indigenous women. Data suggest that maternal mortality, and those where national the MMR was 4.6 times higher for Indigenous levels are already low, but where levels in women compared with non-Indigenous women certain subpopulations are high. in 2016: 31.6 versus 6.9 maternal deaths per 100 000 live births (8). Another study, from the Emergent humanitarian settings and situations USA, found that during 2007–2016, black and of conflict, post-conflict and disaster American Indian/Alaska Native women had significantly hinder progress. The Fragile States significantly more maternal deaths (including Index assesses and ranks 178 countries, late maternal deaths) per 100 000 births than based on 12 cohesion, economic, social and did white, Hispanic and Asian/Pacific Islander political indicators, resulting in a score that women. These differences persisted over indicates their susceptibility to instability.27 In time and across age groups and education 2017, the 178 countries ranged in rank from levels (9). Marginalized subpopulations often South Sudan (1st, most fragile, score = 113.9) lack representation in the data, and disparities to Finland (178th, least fragile, score = 18.7). may not be evident without disaggregating the Six countries were considered to be “very high alert” (from highest to lowest: South Sudan, At the top of the range (most fragile), the scores are 28 Somalia, Central African Republic, Yemen, categorized as follows: > 110 = very high alert; 100–110 = high alert. These two categories, in 2017, include the 15 Syrian Arab Republic, Sudan) while nine most fragile countries, as mentioned here. There are 10 other categories ranging from “very sustainable” to “alert”, were categorized as “high alert” (Democratic which include the remaining 163 countries (7). 29 Analysis using 2017 data from this current report against the countries/categories presented in the 2017 Fragile 27 Further information about indicators and methodology is States Index (7). available at: https://fragilestatesindex.org/. 46 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 data. This lack of accurate and representative 5.2.2 Challenges remain: need for information makes it nearly impossible to improved civil registration and vital determine how to best address the maternal statistics (CRVS) systems and other health needs among the most vulnerable. data sources An emerging challenge is increasing late Impressive efforts to establish and improve maternal mortality, a phenomenon referred CRVS systems or implement alternative to as part of the “obstetric transition” (10). methods of rigorously recording maternal A late maternal death refers to a death deaths have been made in recent years, from direct or indirect obstetric causes that including the expansion of the use of occurs more than 42 days but less than one confidential enquiries into maternal death year after termination of pregnancy (see (CEMD) and maternal death surveillance and Chapter 2 for this and other definitions). As response (MDSR) in an increasing number of health systems improve and are better able countries (see Annex 2 for further information to manage the immediate complications of on these and other methods of gathering labour and childbirth, more deaths within the accurate data on maternal mortality). The first 48 hours of delivery and within the first efforts of countries to produce high-quality 42 days postpartum may be averted, but the data and correct for errors in maternal death proportion of mortality (and also morbidity) classification have prompted the development caused by late maternal sequelae or late of refined estimation methods that fully utilize maternal complications will tend to increase. country-level data to produce a more accurate With the understanding that further analysis and realistic picture of global maternal of this subset of deaths is warranted, the mortality trends. definitions related to deaths occurring during pregnancy, childbirth and the puerperium Given the high percentage of births and were expanded in the ICD-11 to include a maternal deaths that occur outside of health- new group called “comprehensive maternal care facilities, there is a critical need to obtain deaths”, which includes late maternal deaths and communicate vital events data from the along with other maternal deaths. The intention community level. Digital solutions delivered is to facilitate further analysis of the timing of via mobile devices (mHealth tools) that maternal deaths (including disaggregation of connect front-line health workers to national data). Monitoring overall maternal health is health systems can simultaneously improve increasingly important for ensuring accurate health-care service delivery, strengthen documentation to detect shifting dynamics accountability and generate real-time data (11). in maternal morbidity and mortality, up to a A growing proportion of these digital tools year after termination of pregnancy. More and focus on registration of pregnancies and more countries are collecting and reporting notification of births and deaths, linking on this information; as of October 2018, 61 out information directly to facility-, district- and of 142 (43%) countries included in the global national-level routine reporting systems and maternal mortality database had data on late 30 vital events registers (12). Pilot tests of digital maternal deaths (ICD codes O96 and O97). tools integrated with national routine reporting However, this report does not present data on systems are under way across many countries late maternal deaths; analyses of these data in Asia and Africa. are planned for future reports on maternal mortality. Yet, while the estimates presented in this report provide a valuable basis for policy WHO Mortality Database: https://www.who.int/healthinfo/ 30 and programme planning guidance, still the mortality_data/en/ (select indicator for “pregnancy, childbirth and the puerperium”). 47 Assessing progress and setting a trajectory towards ending preventable maternal mortality fact remains that many women who die from and girls outside the standard 15–49 year age maternal causes go uncounted, such that interval, documenting the disturbing fact that even more efforts are needed to improve maternal deaths are occurring among girls data collection/recording systems. The broad even younger than 15. uncertainty intervals associated with the estimates presented throughout this report Ultimately, respect for human rights and directly reflect the critical need for better data human life necessitates improved record- on maternal mortality. Of the various sources keeping – so that all births, deaths and causes of data that can be used for producing MMR of death are officially accounted for – as well estimates (i.e. CRVS, population-based as improved data analysis and disaggregation. household surveys, reproductive-age mortality For these reasons, improving metrics, studies [RAMOS], CEMD, verbal autopsies, measurement systems and data quality are censuses and other specialized maternal crucial cross-cutting actions for all strategies mortality studies), complete, accurate and aimed at ensuring maternal survival (2). validated CRVS systems are the best sources, where available. Governments are called upon to establish well functioning CRVS systems with accurate attribution of cause of References death. Improvements in measurement must 1. Sustainable Development Goal 3. In: be driven by action at the country level, with Sustainable Development Goals Knowledge governments creating systems to capture data Platform [website]. New York (NY): United Nations; 2019 (https://sustainabledevelopment. specific to their information needs; systems un.org/SDG3, accessed 10 June 2019). that must also meet the standards required 2. Strategies towards ending preventable for international comparability. Globally, maternal mortality (EPMM). Geneva: World standardized methods for preventing errors in Health Organization; 2015 (http://www. CRVS reporting (i.e. incomplete CRVS systems everywomaneverychild.gghbgorg/images/ EPMM_final_report_2015.pdf, accessed 5 [unregistered deaths] and misclassification November 2015). of cause of death) should be established to 3. Goal 5: Improve maternal health. In: Millennium enhance international comparability. Development Goals and Beyond 2015 [website]. United Nations; 2015 (https://www.un.org/ Finally, data that can be disaggregated to millenniumgoals/maternal.shtml, accessed 12 September 2019). examine trends and measure the mortality burden within the most vulnerable and most 4. World Health Organization (WHO), United Nations Children’s Fund (UNICEF), United frequently overlooked populations (see section Nations Population Fund (UNFPA), World Bank 5.2.1) are critical for implementing strategies Group, United Nations Population Division. Trends in maternal mortality: 1990 to 2015: to address inequities and accelerate progress estimates by WHO, UNICEF, UNFPA, World towards maternal mortality reduction. Better Bank Group and the United Nations Population data are needed on the maternal mortality Division. Geneva: World Health Organization; 2015 (https://www.who.int/reproductivehealth/ burden among sub-populations. For example, publications/monitoring/maternal- among adolescent girls aged 15-19 years,, mortality-2015/en/, accessed 4 September 2019). pregnancy and childbirth complications are the leading cause of death globally (13)31. Several 5. Alkema L, Chou D, Hogan D, Zhang S, Moller A, Gemmill A, et al. Global, regional, and national countries, particularly those in Latin America levels and trends in maternal mortality between and the Caribbean, and in South-East Asia, 1990 and 2015, with scenario-based projections have already begun reporting data for women to 2030: a systematic analysis by the UN Maternal Mortality Estimation Inter-Agency Group. Lancet. 2016;387(10017):462–74. doi:10.1016/S0140-6736(15)00838-7. Special tabulations were done, as source does not 31 provide information for ages 15–19 years. 48 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 6. Global strategy for women’s, children’s and adolescents’ health (2016–2030). New York (NY): Every Woman Every Child; 2015 (http:// globalstrategy.everywomaneverychild.org/, accessed 10 June 2019). 7. Messner JJ, Haken N, Taft P, Blyth H, Maglo M, Murp C, et al. 2017 Fragile States Index. Washington (DC): The Fund for Peace; 2017 (https://fragilestatesindex.org/wp-content/ uploads/2017/05/951171705-Fragile-States- Index-Annual-Report-2017.pdf, accessed 4 September 2019). 8. Maternal deaths in Australia 2016. Canberra: Australian Institute of Health and Welfare; 2018 (https://www.aihw.gov.au/reports/mothers- babies/maternal-deaths-in-australia-2016, accessed 3 September 2019). 9. Petersen EE, Davis NL, Goodman D, Cox S, Syverson C, Seedet K, al. Racial/ethnic disparities in pregnancy-related deaths – United States, 2007–2016. MMWR Morb Mortal Wkly Rep. 2019;68:762–5. doi:10.15585/mmwr. mm6835a3. 10. Souza J, Tunçalp Ö, Vogel J, Bohren M, Widmer M, Oladapo O, et al. Obstetric transition: the pathway towards ending preventable maternal deaths. BJOG. 2014;121(s1):1–4. 11. Mehl G, Labrique A. Prioritizing integrated mHealth strategies for universal health coverage. Science. 2014;345(6202):1284–7. 12. Labrique AB, Pereira S, Christian P, Murthy N, Bartlett L, Mehl G. Pregnancy registration systems can enhance health systems, increase accountability and reduce mortality. Reprod Health Matters. 2012;20(39):113–7. 13. Global health estimates 2015: deaths by cause, age, sex, by country and by region, 2000–2015. Geneva: World Health Organization; 2016. 49 Assessing progress and setting a trajectory towards ending preventable maternal mortality 50 © UNICEF/Paul 0 06 TRENDS IN MATERNAL MORTALITY CONCLUSIONS The Sustainable Development Goals (SDGs) include a direct emphasis on reducing maternal mortality (SDG target 3.1) while also highlighting the importance of moving beyond the focus on survival, as expressed by SDG 3: Ensure healthy lives and promote wellbeing for all at all ages (1). Despite the ambition to end preventable maternal deaths by 2030, the world will fall short of this target by more than 1 million lives with the current pace of progress. There is a continued urgent need for maternal health and survival to remain high on the global health and development agenda; the state of maternal health interacts with and reflects efforts to improve on the accessibility and quality of health care. The 2018 Declaration of Astana (2) repositioned primary health care as the most (cost) effective and inclusive means of delivering health services to achieve the SDGs (3). When effectively linked with higher levels of care, primary health care is thereby considered the cornerstone for achieving universal health coverage (UHC), which only exists when all people receive the quality health services they need without suffering financial hardship (4,5). Unfortunately, the theory of this approach is not necessarily reflected in the daily reality of much of the world’s population. During the MDG reporting era, hundreds of health financing schemes and programmes were initiated throughout low- and middle-income 51 countries to serve the public health needs of migration and humanitarian crises (3) – the population (6). However, gaps still exist not only because of the environmental in coverage of maternal health, especially in risks presented, but also because of their the availability of comprehensive maternal contribution to health complications. health services, including emergency obstetric care, and adequate numbers of competent In consideration of the above, it must be noted health-care providers, such as midwives that this report on the levels and trends of (6,7). Scratching below the surface of the maternal mortality provides but one critical admirable efforts to facilitate uptake of care facet of information, which synthesizes and and improve health outcomes shows that draws from the available data, to assess only about half of the financial schemes that one aspect of global progress towards emerged between 1990 and 2014 covered achieving global goals for improved health hospital services and maternal care (6). From and sustainable development. In the context a behavioural and economics perspective, of efforts to achieve UHC, improving maternal it is difficult for individuals and households health is critical to fulfilling the aspiration to to plan for low-probability events, such as a reach SDG 3. One can only hope that the maternal health emergency. Furthermore, global community will not be indifferent to the failing to prepare for such health emergencies shortfalls that are expected if we can’t improve will have greater consequences for the poor the current rate of reduction in maternal (8,9). Financial implications aside, the ability to mortality. Ultimately, we need to expand achieve UHC is also predicated on identifying horizons beyond a sole focus on mortality, the population in need of care. Countries are to look at the broader aspects – country and striving to register all births within their CRVS regional situations and trends including health systems, but there remains a need to be systems, UHC, quality of care, morbidity levels able to (uniquely) identify individuals within a and socioeconomic determinants of women’s population. empowerment and education – and ensure that appropriate action is taken to support Taking effective action to tackle the causes of family planning, healthy pregnancy and safe maternal death is also critical to developing childbirth. programmes that will be able to address health needs across the life course. This will require attention to shifting population dynamics and the increasing burden and impact of References noncommunicable diseases in women of 1. Sustainable Development Goal 3. In: reproductive age. The need for states to Sustainable Development Goals Knowledge establish mechanisms to provide health care Platform [website]. New York (NY): United Nations; 2019 (https://sustainabledevelopment. must be qualified, in that health services that un.org/SDG3, accessed 4 September 2019). are unavailable, inaccessible or of poor quality 2. Declaration of Astana. Geneva and New York will not support the achievement of UHC, as (NY): World Health Organization and the United envisioned. Clearly, complex intricacies exist Nations Children’s Fund (UNICEF), 2018 and the relevant stakeholders in this discourse (https://www.who.int/docs/default-source/ primary-health/declaration/gcphc-declaration. include those within and beyond the health pdf, accessed 4 September 2019). sector. Efforts to increase the provision of 3. Binagwaho A, Ghebreyesus TA. Primary skilled and competent care to more women, healthcare is cornerstone of universal health before, during and after childbirth, must also coverage. BMJ. 2019;365. doi:10.1136/bmj. be seen in the context of external forces l2391. including but not limited to climate change, 52 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 4. Arguing for universal health coverage. Geneva: World Health Organization; 2013 (https://www. who.int/health_financing/UHC_ENvs_BD.PDF, accessed 4 September 2019). 5. Xu K, Soucat A, Kutzin J, Brindley C, Dale E, Van de Maeleet N, et al. New perspectives on global health spending for universal health coverage. Geneva: World Health Organization; 2018 (WHO/ HIS/HGF/HFWorkingPaper/18.2; https://apps. who.int/iris/bitstream/handle/10665/259632/ WHO-HIS-HGF-HFWorkingPaper-17.10-eng. pdf, accessed 12 September 2019). 6. Vargas V, Ahmed S, Adams AM. Factors enabling comprehensive maternal health services in the benefits package of emerging financing schemes: a cross-sectional analysis from 1990 to 2014. PLoS One. 2018;13(9):e0201398. doi:10.1371/journal. pone.0201398. 7. The state of the world’s midwifery 2014: a universal pathway: a woman’s right to health. New York (NY): United Nations Population Fund; 2014 (https://www.unfpa.org/sowmy, accessed 13 September 2019). 8. Wagstaff A. Measuring financial protection in health. Policy Research Working Paper 4554. Washington (DC): The World Bank Development Research Group; 2008 (http://documents.worldbank.org/curated/ en/157391468140940134/pdf/wps4554.pdf, accessed 4 September 2019). 9. Mullainathan S. Development economics through the lens of psychology. In: Annual World Bank Conference on Development Economics 2005: Lessons of Experience. Washington (DC) and New York (NY): World Bank and Oxford University Press 2005;45–70 (http://www. bjstrew.com/be/Mullainathan.pdf, accessed 4 September 2019). 53 Conclusions 54 © UNFPA A Annexes TRENDS IN MATERNAL MORTALITY 55 Annexes 56 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 ANNEX 1 SUMMARY DESCRIPTION OF THE 2019 COUNTRY CONSULTATIONS The development of global, regional and the West Bank and Gaza Strip.34 It is carried country-level estimates and trends in morbidity out after the development of preliminary and mortality is one of the core functions of the estimates and prior to the publication of final World Health Organization (WHO). WHO is the estimates for the period of interest. During custodian agency within the United Nations the consultation period, WHO invites technical system that leads the development of updated focal person(s)/offices – who have been maternal mortality estimates together with nominated to speak on behalf of their country the United Nations Children’s Fund (UNICEF), about maternal mortality data – to review the the United Nations Population Fund (UNFPA), UN MMEIG’s input data sources, methods the World Bank Group and the United Nations for estimation and the preliminary estimates. Population Division (UNPD), as members of the The focal person(s)/offices are encouraged to United Nations Maternal Mortality Estimation submit additional data that may not have been Inter-Agency Group (UN MMEIG). taken into account in the preliminary estimates. In 2001, the WHO Executive Board endorsed The country consultation process for the 2019 a resolution (EB.107.R8) which included the round of maternal mortality estimates was proposal to “establish a technical consultation initiated with an official communication from process bringing together personnel and WHO to the countries on 9 May 2018. This letter perspectives from Member States in different informed them of the forthcoming exercise WHO regions”.32 A key objective of this country to estimate maternal mortality for the years consultation process is “to ensure that each 2000–2017 and requested the designation Member State is consulted on the best data of an official technical focal person (typically to be used” for international estimation and within the national ministry of health and/or reporting purposes. Since the process is an the central statistics office) to participate in integral step in the overall maternal mortality the consultation. These designated officials estimation strategy, as well as an SDG and also the existing SDG national focal requirement to consult with national focal points subsequently, in May 2019, received points33, it is described here in brief. the following items by email: (1) a copy of the official communication from WHO (CL.15.2018, The WHO country consultation process entails dated 9 May 2018); (2) draft estimates and an exchange between WHO and technical data sources; and (3) a summary of the focal person(s)/offices in each Member State, methodology used. WHO headquarters in addition to the territories Puerto Rico and and regional offices actively collaborated in identifying technical focal persons through their networks. Resolution of the Executive Board of the WHO: Health 32 systems performance assessment (EB.107.R8: http://apps. who.int/gb/archive/pdf_files/EB107/eer8.pdf). National focal points for the SDGs are contact 33 persons within national statistics offices who facilitate 34 Puerto Rico is an Associate Member, and the West Bank discussions with countries in relation to the reporting for and Gaza Strip is a member in the regional committee for SDGs. Report of the Inter-Agency and Expert Group on the WHO Eastern Mediterranean Region (EM/RC40/R.2: Sustainable Development Goal Indicators (E/CN.3/2018/2: https://apps.who.int/iris/bitstream/handle/10665/121332/ https://unstats.un.org/unsd/statcom/49th-session/ em_rc40_r2_en.pdf). The WHO governing bodies use the documents/2018-2-SDG-IAEG-E.pdf). name “West Bank and Gaza Strip”. 57 Annexes The formal consultation period ran from 15 May 2019 for four weeks, and the process was officially completed on 12 June 2019. The table below provides a summary of the nominations of designated country WHO officials (technical focal persons for maternal mortality) and country SDG officials (SDG focal points), and numbers of countries providing feedback during the 2019 country consultations, by WHO region. WHO technical focal Number of countries SDG focal points providing feedback WHO region persons (number of countries) during the country (number of countries) consultation African Region 22 23 12 Region of the Americas 25 16 19 South-East Asia Region 10 6 8 European Region 31 45 28 Eastern Mediterranean 20 11 11 Region Western Pacific Region 11 13 12 Total 119 114 90 During the consultation period, new data However, an increase in the number of other submitted by countries were reviewed by the new observations/data points, from various UN MMEIG Secretariat and statisticians to sources of data, shows that countries lacking determine whether they met the inclusion functioning CRVS systems are increasingly criteria of this global estimation exercise. Data investing in monitoring maternal mortality with were considered acceptable to use as new empirical data from alternative sources, such input if they were representative of the national as surveillance systems. population and referred to a specific time interval within the period from 1990 to 2017. The inputs received during the 2019 country consultations were added to the input databases. The current estimates are based on 2975 records corresponding to 4123 country- years of information. As in the previous country consultation, the new observations were from CRVS systems, specialized studies and household surveys. 58 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 ANNEX 2 MEASURING MATERNAL MORTALITY Definitions and measures of maternal and for the calculation of maternal mortality mortality as used in this report have already ratios and rates (i.e. excluding late maternal been presented and described in Chapter 2. deaths).35,36 This  annex provides further details on ICD coding and approaches to measuring maternal In 2012, WHO published Application of ICD-10 mortality. to deaths during pregnancy, childbirth and the puerperium: ICD maternal mortality (ICD-MM) Despite the standard definitions noted in to guide countries to reduce errors in coding Chapter 2, accurate identification of the maternal deaths and to improve the attribution causes of maternal deaths by differentiating of cause of maternal death (2). The ICD-MM the extent to which they are due to direct or is to be used together with the three ICD-10 indirect obstetric causes, or due to accidental volumes. For example, the ICD-MM clarifies or incidental events, is not always possible – that deaths among HIV-positive women who particularly in settings where deliveries occur were pregnant, in labour or postpartum may be mostly at home, and/or where civil registration due to one of the following. and vital statistics (CRVS) systems do not reliably include correct attribution of cause of • Obstetric/maternal causes, such as death. haemorrhage or hypertensive disorders in pregnancy: These should be identified as direct maternal deaths. Coding of maternal deaths • The interaction between HIV and pregnancy With the publication of ICD-10, WHO (i.e. aggravating effects of pregnancy recommended adding a checkbox on death on HIV): These should be identified as certificates for recording a woman’s pregnancy indirect maternal deaths, and they are status at the time of death or within 42 days referred to in this report as “HIV-related or up to a year before death (1). This helps indirect maternal deaths”. These deaths to identify indirect maternal deaths and are coded in the ICD-10 to O98.737 (“HIV pregnancy-related deaths, but unfortunately it disease complicating pregnancy, childbirth has not been implemented in many countries and the puerperium”), and categorized to date. Historically, for countries using ICD-10 coding for registered deaths, the United Nations Maternal Mortality Estimation Inter- 35 ICD-11, Part 2, section 2.28.5.7: “International reporting of maternal mortality: For the purpose of the international Agency Group (UN MMEIG) counted all deaths reporting of maternal mortality, only those maternal deaths occurring before the end of the 42-day reference period coded to the maternal chapter (O codes) and should be included in the calculation of the various ratios A34 (maternal tetanus) as maternal deaths. As and rates, although the recording of later deaths is useful for national analytical purposes” (3). indicated in the ICD-11 (and previously in the 36 Late maternal deaths coded to O96 (late maternal ICD-10), only maternal deaths occurring up to deaths) and O97 (late maternal deaths due to sequalae of complications) are also of interest for national- and 42 days postpartum are considered relevant international-level analysis, but are not reported in this publication. for the purposes of international reporting 37 Search for O98.7 in the current (2016) version of ICD-10: https://icd.who.int/browse10/2016/en. 59 Annexes in the ICD-MM as Group 7: non-obstetric not have been reported as maternal deaths. complications. Before 2010, these should Third, in most low- and middle-income country have been coded to Chapter 1 (Certain settings where medical certification of cause Infectious and Parasitic Disease) according of death is not systematically implemented, to ICD-10 rule 5.8.3: “Note that when accurate attribution of a female death as a calculating maternal mortality rates, maternal death remains difficult. cases not coded to Chapter XV (O codes) should be included. These include those Even in countries where routine registration categories presented in the ‘Exclusion of deaths is in place, maternal deaths may Note’ at the beginning of Chapter XV, be underreported due to misclassification provided that they meet the specifications of cause of death using ICD-10 coding, and outlined in Section 4.3.16 a) for indirect identification of the true numbers of maternal obstetric causes” (4). deaths may require additional special investigations into the causes of death. A • AIDS: In these cases, the woman’s specific example of such an investigation is pregnancy status is incidental to the course the confidential enquiry into maternal death of her HIV infection and her death is a (CEMD), a system first established in England result of an HIV complication, as described and Wales in 1928 (5,6,7). The United Kingdom by ICD-10 codes B20–24. These are not and Ireland CEMD report for 2009–2012 considered maternal deaths. Thus, proper identified 79% more maternal deaths than reporting of the mutual influence of HIV or were reported in the routine CRVS system (8). AIDS and pregnancy in Part 1 of the death Other studies on the accuracy of the number certificate38 will facilitate the identification of maternal deaths reported in CRVS systems and correct coding of these deaths. have shown that the true number of maternal deaths could be twice as high as indicated by routine reports, or even more (9,10). A recent paper by Peterson et al. describes a Bayesian Approaches for measuring bivariate random walk model developed by the maternal mortality authors to estimate sensitivity and specificity of the reporting on maternal mortality in CRVS Ideally, a country’s data collection system data and the fitting of the model to a global for maternal mortality provides accurate data set of CRVS and specialized (validation) data on mortality and the causes of death. study data (the searches included publications However, in countries with poor quality data from 1990 to 2016) (11). (e.g. incomplete CRVS systems or high rates of misclassification of cause of death), These studies into the causes of death are it is difficult to accurately measure levels of diverse in terms of the definition of maternal maternal mortality. First, it is challenging to mortality used, the sources considered (death identify maternal deaths precisely, as the certificates, other vital event certificates, deaths of women of reproductive age might medical records, questionnaires or autopsy not be recorded at all. Second, even if such reports) and the way maternal deaths are deaths were recorded, the pregnancy status identified (record linkage or assessment from or cause of death may not have been known experts). In addition, the system of reporting or recorded, and the deaths would therefore causes of death to a civil registry differs from one country to another, depending on the death certificate forms, the type of certifiers 38 Available at: https://icd.who.int/icd11refguide/en/index. and the coding practice. These studies have html#2.23.00AnnexesForMortalityCoding|international- form-of-medical-death-certificate|c2-23-1 60 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 estimated underreporting of maternal mortality studies (RAMOS) and verbal autopsies. Each due to misclassification in death registration of these methods has limitations in estimating data, ranging from 0.85 to 5.0, with a median the true levels of maternal mortality. Brief value of 1.5 (i.e. a misclassification rate of descriptions of these methods together with 50%). Reporting errors in the registration of their limitations are provided below. maternal deaths (i.e. incompleteness and/or misclassification of cause of death) were more Methods, systems and common among (12): tools for identifying and • early pregnancy deaths, including those not measuring maternal deaths linked to a reportable birth outcome; • deaths in the later postpartum period (i.e. a. Routine or regular data collection after the first 7 days and up to 42 days efforts postpartum; these were less likely to be reported as maternal deaths than early Civil registration and vital statistics (CRVS) postpartum deaths); system • deaths at the extremes of maternal age A national CRVS system involves the routine (youngest/teenage [i.e. under age 20] and registration of births and deaths (input), and oldest/advanced maternal age [i.e. age 35 the compilation of vital statistics (output). and over]); The record of each death should include • miscoding (in terms of ICD codes), most the age and sex of the deceased, as well often seen in cases of deaths caused by: as the cause of death, based on a medical certificate completed by a physician. Ideally, – cerebrovascular diseases maternal mortality data should be among the – cardiovascular diseases. vital statistics that can be obtained through the CRVS system. However, even where Potential reasons cited for incompleteness CRVS coverage is complete nationally (i.e. (unregistered maternal deaths) and/or full geographic coverage) and the causes misclassification of cause of death include: of all registered deaths have been identified and reported based on standard medical • inadequate understanding of the ICD rules certificates, in the absence of active case • death certificates completed without finding and review, maternal deaths may still be mention of pregnancy status unregistered or misclassified (9). • desire to avoid litigation In some countries and territories with • desire to suppress information (especially incomplete CRVS systems, specific effort is information about abortion-related deaths). made to identify unregistered deaths. These efforts may be published under various labels The definitions of misclassification and or may exist as administrative processes incompleteness of maternal death reporting to “clean” data. See subsection below: are provided in Box 3.1 in Chapter 3. Specialized studies to identify maternal deaths. Sampled vital registration systems also In the absence of complete and accurate CRVS exist in countries, such as India.39 The basic systems, MMR estimates are based on data from a variety of sources, including censuses, 39 Available at: http://censusindia.gov.in/vital_statistics/ household surveys, reproductive-age mortality SRS/Sample_Registration_System.aspx 61 Annexes structure of these sample registration systems of reproductive age, could support estimates include a baseline survey and continuous of maternal mortality. This approach eliminates enumeration of vital events with verification by sampling errors (because all women are verbal autopsy. covered) and hence allows a more detailed breakdown of the results, including trend Household surveys (13,14,15) analysis, geographic subdivisions and social strata. Demographic and Health Surveys (DHS) and 40 Multiple Indicator Cluster Surveys (MICS)41 use • This approach allows identification the direct “sisterhood” method to collection of deaths in the household in a relatively maternal mortality data using household short reference period (1–2 years prior surveys. This method obtains information to the census), thereby providing recent by interviewing a representative sample of maternal mortality estimates, but censuses respondents about the survival of all their are conducted at 10-year intervals, siblings (to determine the age of all siblings, therefore limiting the monitoring of how many are alive, how many are dead, age maternal mortality. at death and year of death of those dead, and • It identifies pregnancy-related deaths (not among sisters who reached reproductive age, maternal deaths); however, if combined how many died during pregnancy, delivery with verbal autopsy (see below), maternal or within two months of pregnancy). This deaths could be identified. approach has the following limitations. • Training of census enumerators is crucial, • It identifies pregnancy-related deaths, since census activities collect information rather than maternal deaths (same as the on a wide range of topics. original indirect sisterhood method). • Results must be adjusted for • It produces estimates with wide confidence characteristics such as completeness of intervals, thereby diminishing opportunities death and birth statistics and population for trend analysis (same as the indirect structures, in order to arrive at reliable method). estimates. • It provides a retrospective rather than b. Specialized studies to identify maternal a current maternal mortality estimate deaths (referring to a period three to four years prior to the survey (15), which is better than Reproductive-age mortality studies 10–12 years in the past using the indirect (RAMOS) (14,18) sisterhood method). • It requires a larger sample size and more This approach involves first identifying and then questions than the original indirect variant investigating and establishing the causes of of the method and the collection and all deaths of women of reproductive age in a analysis of the data are more complicated. defined area or population, by using multiple sources of data, such as CRVS systems, Census (16,17) health-care facility records, burial records, and interviews with family members, community A national census, with the addition of a limited leaders, health-care providers (including number of questions about deaths to females physicians) and traditional birth attendants. The RAMOS approach has the following 40 https://dhsprogram.com/ characteristics. http://mics.unicef.org/ 41 62 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 • Multiple and diverse sources of information • Misclassification of causes of deaths must be used to identify deaths of women in women of reproductive age is not of reproductive age; no single source uncommon with this technique. identifies all the deaths. • It may fail to identify correctly a group • Interviews with household members, of maternal deaths, particularly those health-care providers and reviews of facility occurring early in pregnancy (e.g. ectopic, records are used to classify the deaths as abortion-related) and indirect causes of maternal or otherwise. maternal death (e.g. malaria and HIV). • If properly conducted, this approach • The accuracy of the estimates depends on provides a fairly complete estimation of the extent of family members’ knowledge maternal mortality in the absence of reliable of the events leading to the death, the skill CRVS systems with national coverage, and of the interviewers, and the competence could provide subnational MMRs. However, of physicians who do the diagnosis and inadequate identification of all deaths of coding. The latter two factors are largely women of reproductive age at the start of overcome by the use of software. the process results in underestimation of • Detailed verbal autopsy for research maternal mortality levels. purposes that aims to identify the cause of • This approach can be complicated, time- death of an individual requires physician consuming and expensive to undertake – assessment and long interviews. Such particularly on a large scale. systems are expensive to maintain, and the findings cannot be extrapolated to • The number of live births used in the obtain national MMRs. This limitation does computation of MMR may not be accurate, not exist where simplified verbal autopsy especially in settings where most women is aiming to identify causes at a population deliver at home. level and where software helps to formulate the diagnoses. Verbal autopsy (19–22) This approach is used to assign cause of death Confidential enquiries into maternal deaths through interviews with family or community (CEMD) members, where medical certification of cause of death is not available (e.g. as part CEMD is “a systematic multidisciplinary of the RAMOS method). Verbal autopsies anonymous investigation of all or a may be conducted as part of a demographic representative sample of maternal deaths surveillance system maintained by research occurring at an area, regional (state) or institutions that collect records of births and national level which identifies the numbers, deaths periodically among small populations causes and avoidable or remediable factors (typically in a district). This approach may associated with them” (23). This approach can also be combined with household surveys or also involve efforts to ensure that suspected censuses (see above). In special versions, maternal deaths are reported from a defined and in combination with software that helps catchment area, such as a health-care facility to identify the diagnosis, verbal autopsy is or a district. Case records from suspected suitable for routine use as an inexpensive maternal deaths are then reviewed by method in populations where no other method committee to examine the circumstances of assessing the cause of death is in place. of the death and then the assigned cause The following limitations characterize this of death is either confirmed or revised. In approach. 63 Annexes some contexts, CEMD is intended to assess maternal deaths be made a notifiable event. the response of the health system in each Notifications (of maternal deaths in health-care maternal death to inform programmatic facilities and communities) would be followed changes. In other contexts, there may be an by a review to assess contributing factors effort to search beyond deaths labelled as and avoidability – the results of these district- “suspected maternal deaths” and review the level reviews feed into national-level analysis, causes of death for all women of reproductive leading to recommendations for further age, including deaths that have not yet been action, and finally response (implementation registered; in this way, CEMD can address of recommendations) (29). In countries lacking both incompleteness and misclassification in national CRVS systems, MDSR can serve the CRVS system. CEMD was developed in as “a building block for a comprehensive, England and Wales (24), where it is still used, national-level data collection system” (12). and studies of CEMD have been conducted in The uptake and implementation of MDSR are a number of other countries, such as Australia, being studied, with surveys every two years and France, Ireland, Mexico and New Zealand (12). there is optimism it will contribute to eliminating Kazakhstan and South Africa both conducted preventable maternal mortality (30). Although CEMD studies, identifying 29% and 40% MDSRs thus far fall short of being nationally more maternal deaths, respectively, than were representative, there are ongoing analyses to initially recorded in the CRVS system (25,26). assess whether data collected at subnational level might eventually become usable as input Surveillance of maternal deaths for the UN MMEIG database, which is used to derive the estimates (using the estimation Active surveillance for maternal mortality has model) as described in this report. been initiated in some settings where CRVS is incomplete or not fully functional. Surveillance may include active review of hospital registers, morgue records and police reports, as well References as community outreach, with the intention of 1. 2.17.2 Data source: the international death finding cases of unregistered deaths to women certificate. In: ICD-11 Reference guide, Part of reproductive age and then to classify their 2. Geneva: World Health Organization; 2019 (https://icd.who.int/icd11refguide/en/index. cause of death (27). html#2.17.2DataSourceIntlDeathCertifica te|data-source-the-international-death- Maternal death surveillance and response certificate|c2-17-2, accessed 12 July 2019). (MDSR) offers a method for obtaining more 2. Application of ICD-10 to deaths during complete information on maternal deaths in pregnancy, childbirth and the puerperium: ICD maternal mortality (ICD-MM). Geneva: “real time” and could thus contribute to better World Health Organization; 2012 (https://www. data on maternal mortality and stimulate who.int/reproductivehealth/publications/ monitoring/9789241548458/en/, accessed 20 more timely response and action to prevent June 2019). future deaths (28). In 2013, WHO and partners issued technical guidance on MDSR (29) – “a 3. 2.28.5 Standards and reporting requirements related for maternal mortality. In: ICD-11 continuous action cycle” that builds on the Reference guide, Part 2. Geneva: World maternal death review (MDR) approach. Health Organization; 2019 (https://icd.who. int/icd11refguide/en/index.html#2.28.5Sta MDR, both community- and facility-based, ndardsMarternalMortaltiy|standards-and- was described by WHO in 2004 in Beyond reporting-requirements-related-for-maternal- the numbers: reviewing maternal deaths mortality|c2-28-5, accessed 12 July 2019). and complications to make pregnancy safer (23). An effective MDSR system requires that 64 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 4. International statistical classification of diseases 13. Hill K, Arifeen SE, Koenig M, Al-Sabir A, and related health problems, 10th revision. Jamil K, Raggers H. How should we measure Volume 2: Instruction manual. Geneva; World maternal mortality in the developing world? A Health Organization; 2010 (https://www.who. comparison of household deaths and sibling int/classifications/icd/ICD10Volume2_en_2010. history approaches. Bull World Health Organ. pdf, accessed 10 June 2019). 2006;84(3):173–80. 5. Lewis G, editor. Why mothers die 2000–2002: 14. Ahmed S, Li Q, Scrafford C, Pullum TW. An the confidential enquiries into maternal deaths assessment of DHS maternal mortality data and in the United Kingdom. London: RCOG Press; estimates. DHS Methodological Reports No. 13. 2004. Rockville, MD: ICF International; 2014 (https:// dhsprogram.com/pubs/pdf/MR13/MR13.pdf, 6. Lewis G, editor. Saving mothers’ lives: reviewing accessed 25 July 2019). maternal deaths to make motherhood safer 2003–2005. The seventh report on confidential 15. The sisterhood method for estimating maternal enquiries into maternal deaths in the United mortality: guidance notes for potential users. Kingdom. London: Confidential Enquiry into Geneva: World Health Organization; 1997 Maternal and Child Health (CEMACH); 2007. (https://www.who.int/reproductivehealth/ publications/monitoring/RHT_97_28/en/, 7. Centre for Maternal and Child Enquiries accessed 25 July 2019). (CMACE). Saving mothers’ lives: reviewing maternal deaths to make motherhood safer: 16. Stanton C, Hobcraft J, Hill K, Kodjogbé N, 2006–2008. The eighth report on confidential Mapeta WT, Munene F Stanton C, et al. Every enquiries into maternal deaths in the United death counts: measurement of maternal Kingdom. BJOG. 2011;118(Suppl.1):1–203. mortality via a census. Bull World Health Organ. doi:10.1111/j.1471-0528.2010.02847.x. 2001;79:657–64. 8. Knight M, Kenyon S, Brocklehurst P, Neilson J, 17. WHO guidance for measuring maternal Shakespeare J, Kurinczuk JJ, editors (on behalf mortality from a census. Geneva: World Health of MBRRACE-UK). Saving lives, improving Organization; 2013. mothers’ care – lessons learned to inform future maternity care from the UK and Ireland 18. Stanton C, Abderrahim N, Hill K. DHS maternal Confidential Enquiries into Maternal Deaths and mortality indicators: an assessment of data Morbidity 2009–2012. Oxford: National Perinatal quality and implications for data use (DHS Epidemiology Unit, University of Oxford; 2014. Analytical Report No. 4). Calverton (MD): Macro International; 1997 (https://dhsprogram.com/ 9. Deneux-Tharaux C, Berg C, Bouvier-Colle pubs/pdf/AR4/AR4.pdf, accessed 25 July MH, Gissler M, Harper M, Nannini A, et al. 2019). Underreporting of pregnancy-related mortality in the United States and Europe. Obstet 19. Chandramohan D, Rodrigues LC, Maude GH, Gynecol. 2005;106:684–92. Hayes RJ. The validity of verbal autopsies for assessing the causes of institutional maternal 10. Atrash HK, Alexander S, Berg CJ. Maternal death. Stud Fam Plann. 1998;29:414–22. mortality in developed countries: not just a concern of the past. Obstet Gynecol. 1995;86(4 20. Chandramohan D, Stetal P, Quigley M. pt 2):700–5. Misclassification error in verbal autopsy: can it be adjusted? Int J Epidemiol. 2001;30:509–14. 11. Peterson E, Chou D, Gemmill A, Moller AB, Say L, Alkema L. Estimating maternal mortality 21. Leitao J, Chandramohan D, Byass P, Jakob R, using vital registration data: a Bayesian Bundhamcharoen K, Choprapawon C, et al. hierarchical bivariate random walk model to Revising the WHO verbal autopsy instrument to estimate sensitivity and specificity of reporting facilitate routine cause-of-death monitoring. for population-periods without validation data. Global Health Action. 2013;6:21518. 2019. https://arxiv.org/abs/1909.08578 22. Riley ID, Hazard RH, Joshi R, Chowdhury HR, 12. World Health Organization (WHO), United Lopez A. Monitoring progress in reducing Nations Children’s Fund (UNICEF), United maternal mortality using verbal autopsy Nations Population Fund (UNFPA), World Bank methods in vital registration systems: what Group, United Nations Population Division. can we conclude about specific causes of Trends in maternal mortality: 1990 to 2015: maternal death? BMC Medicine. 2019;17:104. estimates by WHO, UNICEF, UNFPA, World doi:10.1186/s12916-019-1343-4. Bank Group and the United Nations Population Division. Geneva: World Health Organization; 23. Beyond the numbers: reviewing maternal 2015 (https://www.who.int/reproductivehealth/ deaths and complications to make pregnancy publications/monitoring/maternal- safer. Geneva: World Health Organization; mortality-2015/en/, accessed 4 September 2004 (https://www.who.int/maternal_child_ 2019). adolescent/documents/9241591838/en/, accessed 4 September 2019). 65 Annexes 24. Knight M, Kenyon S, Brocklehurst P, Neilson J, Shakespeare J, Kurinczuk JJ, editors (on behalf of MBRRACE-UK). Saving lives, improving mothers’ care: lessons learned to inform future maternity care from the UK and Ireland Confidential Enquiries into Maternal Deaths and Morbidity 2009–2012. Oxford: National Perinatal Epidemiology Unit, University of Oxford; 2014 (https://www.npeu.ox.ac.uk/downloads/files/ mbrrace-uk/reports/Saving%20Lives%20 Improving%20Mothers%20Care%20report%20 2014%20Full.pdf, accessed 5 November 2015). 25. Findings of a confidential audit of maternal mortality rates in the Republic of Kazakhstan 2011–2013. Kazakhstan: Central Commission on Confidential Audit CCAC; 2014.42. 26. Dorrington RE, Bradshaw D, Laubscher R, Nannan N. Rapid Mortality Surveillance Report 2013. Cape Town: South African Medical Research Council; 2014. 27. McCaw-Binns A, Lindo JLM, Lewis-Bell, KN, Ashley DEC. Maternal mortality surveillance in Jamaica. Int J Gynecol Obstet. 2008;100(1):31–6. doi:10.1016/j. ijgo.2007.06.054. 28. Danel I, Graham WJ, Boerma T. Maternal death surveillance and response. Bull World Health Organ. 2011;89:779-a. 29. Maternal death surveillance and response: technical guidance. Information for action to prevent maternal death. Geneva: World Health Organization; 2013. 30. Time to respond: a report on the global implementation of maternal death surveillance and response (MDSR). Geneva: World Health Organization; 2016 (https://www.who.int/ maternal_child_adolescent/documents/ maternal_death_surveillance_implementation/ en/, accessed 25 July 2019).   66 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 ANNEX 3 CALCULATION OF MATERNAL MORTALITY DURING CRISIS YEARS Crisis years and mortality for the estimation of child mortality (3) (this includes crises in potentially longer shocks periods, i.e. for recent ongoing crises). The 1990–2016 life tables published by WHO in 2018 (1) account for “crises” due to natural disasters and conflict (as defined by Maternal mortality the International statistical classification of estimation diseases and related health problems [ICD], 10th revision [2]), because of the potential for The approach taken in this round of maternal substantial increases in death rates during mortality estimation was to estimate the the crisis-affected years, a phenomenon “crisis-free” proportion maternal (PM)42 to described as “mortality shocks”. According maintain consistency across all countries. to the life tables, “mortality shocks” include deaths whose underlying cause was due to The method for estimation of maternal deaths a natural disaster or – in the case of war and for countries with one or more crisis years is conflict deaths – “an injury due to war, civil described below. insurrection or organized conflict, whether or not that injury occurred during the time of war Any data points that overlap with the or after cessation of hostilities” (1). crisis period are recalculated to refer to the proportion of crisis-free maternal or A crisis year for the purpose of estimated pregnancy-related deaths among the total maternal mortality is defined in the following number of crisis-free deaths to women of two ways (all years that meet either definition reproductive age, referred to as “crisis-free are included as crisis years): observed PM”43. Pregnancy-related PMs are adjusted based on the assumption that the • a year in which (a) there are at least 10 proportion of pregnancy-related deaths among deaths attributable to mortality shocks the deaths attributable to mortality shocks among women of reproductive age is equal to the proportion of women in the (i.e. 15–49 years) and (b) these deaths population who are pregnant or postpartum constitute at least 10% of the total number at the time of the crisis. The proportion of of deaths to women aged 15–49 in that pregnant women in the population is set respective country-year (1) and in addition equal to the general fertility rate, based on the (c) in the five-year period surrounding the year, there are at most two additional crisis years; and PM = proportion of deaths among women of reproductive 42 age that are due to maternal causes • a year identified by the United Nations 43 Although by definition PM refers strictly to maternal deaths (and the model is based on this definition), some observed Inter-agency Group for Child Mortality PMs are based on the definition of pregnancy-related deaths Estimation (UN IGME) as a crisis year (which includes but is not limited to maternal deaths; see definitions in Chapter 2). 67 Annexes assumption of a one-year period associated References with a live birth (4). 7. WHO methods and data sources for life tables 1990–2016. Global Health Estimates Technical For each year, the MMEIG Bayesian maternal Paper. Geneva: World Health Organization; 2018 (WHO/HIS/IER/GHE/2018.2; https://www. mortality estimation (BMat) model provides who.int/healthinfo/statistics/LT_method.pdf, posterior samples of maternal deaths: accessed 5 July 2019). 8. International Statistical Classification of • The median of this sample constitutes the Diseases and Related Health Problems 10th point estimate for the number of maternal Revision. Geneva: World Health Organization; 2016 (current version) (https://icd.who.int/ deaths. The reported estimates of PM are browse10/2016/en#/, accessed 26 August the crisis-free PMs, the point estimate for 2019). the number of maternal deaths divided 9. United Nations Inter-agency Group for Child by the total number of crisis-free deaths Mortality Estimation (UN IGME). Levels & among women of reproductive age. trends in child mortality: report 2018: estimates developed by the UN Inter-agency Group for Child Mortality Estimation. New York (NY): • For non-crisis years, the 10th and 90th United Nations Children’s Fund; 2018 (https:// percentiles of the BMat samples for www.unicef.org/publications/index_103264. maternal deaths constitute the 80% html, accessed 5 July 2019). uncertainty interval (UI). For crisis years, 10. Wilmoth JR, Mizoguchi N, Oestergaard MZ, we include additional uncertainty by Say L, Mathers CD, Zureick-Brown S, et al. A new method for deriving global estimates multiplying the samples of maternal deaths of maternal mortality. Stat Politics Policy. by values between 0.9 and 1.2. 2012;3(2):2151-7509.1038. This approach results in estimates of maternal mortality that are considered crisis-free within the larger envelope of all deaths among women of reproductive age, because deaths among pregnant women that are attributable to mortality shocks would be considered pregnancy-related deaths but not maternal deaths, according to the ICD definition. It is possible that crisis-related factors may contribute to maternal mortality but empirical evidence to distinguish maternal deaths from among pregnancy-related deaths in the context of mortality shocks is limited. To reflect the paucity of evidence on the effect of crisis on maternal mortality, UIs were widened. Future estimation exercises will continue to review the methods developed to account for natural disasters, conflict and other types of mortality shocks (e.g. disease pandemics). 68 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 ANNEX 4 METHODS USED TO DERIVE A COMPLETE SERIES OF ANNUAL ESTIMATES FOR EACH PREDICTOR VARIABLE A complete series of annual estimates for each goes into this database. Using this database of the three predictor variables was obtained or as an input, annual series were estimated for constructed. countries with any value of SBA coverage less than 95% and with four or more observations, Gross domestic product (GDP) per capita by fitting a regression model with time as the measured in purchasing power parity (PPP) sole predictor for the logit (or log-odds) of equivalent US dollars using 2011 as the SBA; such a model was estimated separately baseline year were taken from the World Bank for each country. For all other countries, Group (1). A five-year moving average was including those with no available SBA data, applied to this GDP series to smooth year-to- the SBA annual series were estimated using a year GDP fluctuations (1). multilevel model. In the multilevel model, logit (or log-odds) of observed SBA proportions for General fertility rate (GFR) estimates were all countries were regressed against time. The computed from data on live births and the model included region- and country-specific population size (number of women aged 15–49 intercepts and slopes. years), from the UNPD’s 2019 revision of World population prospects (2). References Skilled birth attendant (SBA)44 coverage data 1. DataBank: World Development Indicators consist of time series derived using all available [website]. Washington (DC): The World Bank data from health surveys and countries’ routine Group; 2019 (https://databank.worldbank. org/source/world-development-indicators, reporting mechanisms, which are compiled accessed 31 July 2019). in a database jointly maintained by WHO 2. World population prospects: the 2019 revision. and UNICEF (3). This database is primarily New York (NY): United Nations Population compiled for SDG reporting purposes. Jointly, Division, Department of Economic and Social UNICEF and WHO are co-custodians of “SDG Affairs; 2019 (https://population.un.org/wpp/, accessed 18 June 2019). indicator 3.1.2: Skilled birth attendant” and collaborate actively in the compilation and 3. World Health Organization (WHO), United Nations Children’s Fund (UNICEF). WHO and harmonization of this database. As part of the UNICEF Joint Skilled Birth Attendant (SBA) regular consultations with countries by the database. Geneva: WHO; 2019. custodians of the SDG indicator, UNICEF leads 4. Definition of skilled health personnel providing an annual process during which countries are care during childbirth: the 2018 joint statement consulted on each value and data source that by WHO, UNFPA, UNICEF, ICM, ICN, FIGO, IPA. Geneva: World Health Organization; 2018 (https://www.who.int/reproductivehealth/ publications/statement-competent-mnh- 44 The definition of this SBA coverage indicator was updated professionals/en/, accessed 18 June 2019). in a new joint statement (and full background document) in 2018 (4), but the data used for the estimates presented in this present publication are based on application of the previous (2004) definition/joint statement (5), which was still in effect through 2017. 69 Annexes 5. Making pregnancy safer: the critical role of the skilled attendant: a joint statement by WHO, ICM and FIGO. Geneva: World Health Organization; 2004 (https://www.who.int/ reproductivehealth/publications/maternal_ perinatal_health/9241591692/en/, accessed 18 June 2019). 70 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 ANNEX 5 ESTIMATES OF MATERNAL MORTALITY RATIO (MMR, MATERNAL DEATHS PER 100 000 LIVE BIRTHS), NUMBER OF MATERNAL DEATHS, LIFETIME RISK, PERCENTAGE OF HIV-RELATED INDIRECT MATERNAL DEATHS AND PROPORTION OF DEATHS AMONG WOMEN OF REPRODUCTIVE AGE THAT ARE DUE TO MATERNAL CAUSES (PM), BY COUNTRY AND TERRITORY, 2017a PM point estimate and range MMRb point estimate and range Lifetime % of HIV- Number of uncertainty interval of uncertainty interval (UI: 80%) risk of related of (UI: 80%) Country and territory maternal indirect maternal MMR deathd maternal deathsc Lower PM point Upper Lower UI point Upper UI 1 in deathse UI estimate UI estimate Afghanistan 427 638 1 010 7 700 33 24 37 58 Albania 8 15 26 5 3 800 0 1 1 Algeria 64 112 206 1 200 270 6 10 18 Angola 167 241 346 3 000 69 3 10 14 20 Antigua and Barbuda 24 42 69 1 1 200 1 2 3 Argentina 35 39 43 290 1 100 1 3 3 3 Armenia 21 26 32 11 2 000 2 2 3 Australia 5 6 8 20 8 200 1 1 1 Austria 4 5 7 4 13 500 0 0 1 Azerbaijan 21 26 32 44 1 700 2 2 3 Bahamas 48 70 110 4 820 25 1 2 3 Bahrain 10 14 21 3 3 000 1 2 3 Bangladesh 131 173 234 5 100 250 7 9 12 Barbados 17 27 39 1 2 400 1 1 2 Belarus 1 2 4 3 23 800 0 0 0 Belgium 4 5 7 6 11 200 0 0 1 Belize 26 36 48 3 1 100 1 2 2 Benin 291 397 570 1 600 49 1 13 17 25 Bhutan 127 183 292 24 250 3 4 6 Bolivia (Plurinational 113 155 213 380 220 5 6 9 State of) Bosnia and 5 10 16 3 8 200 0 0 1 Herzegovina Botswana 124 144 170 81 220 15 3 4 4 Brazil 58 60 61 1 700 940 1 3 3 3 71 Annexes PM point estimate and range MMRb point estimate and range Lifetime % of HIV- Number of uncertainty interval of uncertainty interval (UI: 80%) risk of related of (UI: 80%) Country and territory maternal indirect maternal MMR deathd maternal deathsc Lower PM point Upper Lower UI point Upper UI 1 in deathse UI estimate UI estimate Brunei Darussalam 21 31 45 2 1 700 1 2 3 Bulgaria 6 10 14 6 7 000 0 0 1 Burkina Faso 220 320 454 2 400 57 1 9 13 19 Burundi 413 548 728 2 400 33 16 21 27 Cabo Verde 45 58 75 6 670 3 4 5 Cambodia 116 160 221 590 220  1 5 7 10 Cameroon 376 529 790 4 700 40  4 10 14 21 Canada 8 10 14 40 6 100 1 1 1 Central African 463 829 1 470 1 400 25  3 10 18 32 Republic Chad 847 1 140 1 590 7 300 15 25 34 48 Chile 11 13 14 29 4 600 7 1 1 1 China 22 29 35 4 900 2 100 1 2 2 Colombia 71 83 98 610 630 3 4 4 Comoros 167 273 435 72 83 8 13 20 Congo 271 378 523 650 58  3 9 13 18 Costa Rica 24 27 31 19 1 900 1 2 2 Côte d’Ivoire 426 617 896 5 400 34  2 9 13 19 Croatia 6 8 11 3 9 100 0 1 1 Cuba 33 36 40 42 1 800 2 2 2 Cyprus 4 6 10 1 11 000 0 1 1 Czechia 2 3 5 4 17 900 0 0 0 Democratic People’s 38 89 203 310 620 1 3 7 Republic of Korea Democratic Republic 341 473 693 16 000 34 1 17 23 34 of the Congo Denmark 3 4 5 2 16 200 0 0 1 Djibouti 116 248 527 51 140 4 2 5 11 Dominican Republic 88 95 102 200 410 3 4 4 5 Ecuador 53 59 65 200 640 1 4 4 5 Egypt 27 37 47 960 730 3 4 5 El Salvador 36 46 57 54 960 2 2 2 3 Equatorial Guinea 181 301 504 130 67 2 6 10 17 Eritrea 327 480 718 510 46 1 11 16 24 Estonia 5 9 13 1 6 900 0 1 1 Eswatini 255 437 792 130 72 10 3 6 10 Ethiopia 298 401 573 14 000 55 1 14 19 27 Fiji 27 34 43 6 1 000 1 2 2 Finland 2 3 4 2 20 900 0 0 0 France 6 8 9 56 7 200 2 1 1 1 72 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 PM point estimate and range MMRb point estimate and range Lifetime % of HIV- Number of uncertainty interval of uncertainty interval (UI: 80%) risk of related of (UI: 80%) Country and territory maternal indirect maternal MMR deathd maternal deathsc Lower PM point Upper Lower UI point Upper UI 1 in deathse UI estimate UI estimate Gabon 165 252 407 170 93 4 7 11 17 Gambia 440 597 808 520 31 1 19 26 35 Georgia 21 25 29 14 1 900 1 2 2 Germany 5 7 9 53 9 400 0 0 1 Ghana 223 308 420 2 700 82 2 7 10 13 Greece 2 3 4 2 26 900 0 0 0 Grenada 15 25 39 0 1700 1 2 2 Guatemala 86 95 104 400 330 1 6 7 8 Guinea 437 576 779 2 600 35 1 15 20 27 Guinea-Bissau 457 667 995 440 32 2 16 24 35 Guyana 132 169 215 26 220 4 5 6 Haiti 346 480 680 1 300 67 1 9 13 18 Honduras 55 65 76 130 560 1 3 3 3 Hungary 9 12 16 11 6 200 0 1 1 Iceland 2 4 6 0 14 400 0 1 1 India 117 145 177 35 000 290 4 5 7 Indonesia 127 177 254 8 600 240 1 4 6 9 Iran (Islamic Republic 13 16 20 250 2 600 2 1 2 2 of) Iraq 53 79 113 870 320 3 5 7 Ireland 3 5 7 3 11 300 0 1 1 Israel 2 3 4 5 10 800 20 1 1 1 Italy 1 2 2 7 51 300 14 0 0 0 Jamaica 67 80 98 38 600 3 3 3 4 Japan 3 5 6 44 16 700 0 0 0 Jordan 31 46 65 100 730 3 4 6 Kazakhstan 8 10 12 37 3 500 3 0 1 1 Kenya 253 342 476 5 000 76 3 10 14 19 Kiribati 49 92 158 3 290 3 5 8 Kuwait 8 12 17 7 4 200 1 1 1 Kyrgyzstan 50 60 76 95 480 4 5 7 Lao People’s 139 185 253 310 180 6 7 10 Democratic Republic Latvia 15 19 26 4 3 100 1 1 1 Lebanon 22 29 40 34 1 600 2 2 3 Lesotho 391 544 788 310 58 11 4 6 8 Liberia 481 661 943 1 000 32 2 19 26 38 Lithuania 5 8 12 2 7 500 0 0 1 Luxembourg 3 5 8 0 14 300 0 1 1 73 Annexes PM point estimate and range MMRb point estimate and range Lifetime % of HIV- Number of uncertainty interval of uncertainty interval (UI: 80%) risk of related of (UI: 80%) Country and territory maternal indirect maternal MMR deathd maternal deathsc Lower PM point Upper Lower UI point Upper UI 1 in deathse UI estimate UI estimate Madagascar 229 335 484 2 800 66 11 16 23 Malawi 244 349 507 2 100 60 5 11 15 22 Malaysia 24 29 36 150 1 600 1 2 2 3 Maldives 35 53 84 4 840 6 9 14 Mali 419 562 784 4 400 29 1 18 24 33 Malta 4 6 11 0 10 200 0 1 1 Mauritania 528 766 1 140 1 100 28 25 37 55 Mauritius 46 61 85 8 1 200 2 2 3 Mexico 32 33 35 740 1 300 1 2 2 2 Micronesia (Federated 40 88 193 2 370 2 5 11 States of) Mongolia 36 45 56 35 710 3 3 4 Montenegro 3 6 10 0 9 900 0 0 1 Morocco 54 70 91 480 560 5 6 8 Mozambique 206 289 418 3 100 67 9 7 9 13 Myanmar 182 250 351 2 400 190 5 7 10 Namibia 144 195 281 140 140 11 4 5 7 Nepal 135 186 267 1 100 230 6 9 12 Netherlands 4 5 7 9 11 900 0 0 1 New Zealand 7 9 11 5 6 100 1 1 1 Nicaragua 77 98 127 130 380 1 5 6 8 Niger 368 509 724 5 100 27 23 31 44 Nigeria 658 917 1 320 67 000 21 1 17 23 33 Norway 2 2 3 1 25 700 0 0 0 Oman 16 19 22 17 1 600 3 3 4 Pakistan 85 140 229 8 300 180 6 10 17 Panama 45 52 59 41 750 2 3 4 4 Papua New Guinea 67 145 318 340 190 1 3 7 15 Paraguay 72 84 97 120 440 1 3 4 5 Peru 69 88 110 500 480 4 5 6 Philippines 91 121 168 2 700 300 4 6 8 Poland 2 2 3 8 30 300 13 0 0 0 Portugal 6 8 11 6 10 700 0 0 1 Puerto Rico 16 21 29 5 4 000 1 1 1 Qatar 6 9 14 2 5 000 1 2 2 Republic of Korea 9 11 13 43 8 300 1 1 1 Republic of Moldova 15 19 24 8 3 900 1 1 1 Republic of North 5 7 10 2 9 000 0 0 1 Macedonia 74 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 PM point estimate and range MMRb point estimate and range Lifetime % of HIV- Number of uncertainty interval of uncertainty interval (UI: 80%) risk of related of (UI: 80%) Country and territory maternal indirect maternal MMR deathd maternal deathsc Lower PM point Upper Lower UI point Upper UI 1 in deathse UI estimate UI estimate Romania 14 19 25 36 3 600 1 1 1 Russian Federation 13 17 23 320 3 100 15 0 1 1 Rwanda 184 248 347 960 94 2 9 12 17 Saint Lucia 71 117 197 3 580 2 3 5 Saint Vincent and the 44 68 100 1 750 1 2 3 Grenadines Samoa 20 43 97 2 590 3 6 14 Sao Tome and Principe 73 130 217 9 170 4 7 13 Saudi Arabia 10 17 30 100 2 300 1 1 2 Senegal 237 315 434 1 700 65 1 16 21 29 Serbia 9 12 17 10 5 800 1 1 1 Seychelles 26 53 109 1 790 1 3 6 Sierra Leone 808 1 120 1 620 2 900 20 15 21 30 Singapore 5 8 13 4 9 900 0 1 1 Slovakia 4 5 7 3 12 600 0 0 0 Slovenia 5 7 9 1 9 300 0 1 1 Solomon Islands 70 104 157 22 200 7 10 15 Somalia 385 829 1 590 5 100 20 15 31 60 South Africa 96 119 153 1 400 330 21 2 2 3 South Sudan 789 1 150 1 710 4 500 18 2 18 26 38 Spain 3 4 5 14 21 500 0 0 0 Sri Lanka 31 36 41 120 1 300 2 2 3 State of Libya 30 72 164 92 590 2 4 8 Sudan 207 295 408 3 900 75 9 13 19 Suriname 96 120 144 13 330 4 5 6 Sweden 3 4 6 5 12 600 0 0 1 Switzerland 3 5 7 4 13 900 0 1 1 Syrian Arab Republic 20 31 50 130 1 000 2 3 4 Tajikistan 10 17 26 46 1 400 2 1 2 4 Thailand 32 37 44 270 1 900 6 1 1 1 Timor-Leste 102 142 192 52 170 10 14 20 Togo 270 396 557 1 000 56 2 8 12 17 Tonga 24 52 116 1 540 2 5 11 Trinidad and Tobago 50 67 90 12 840 2 2 3 Tunisia 33 43 54 90 970 3 4 4 Turkey 14 17 20 220 2 800 1 1 1 Turkmenistan 5 7 10 10 4 400 20 0 0 1 Uganda 278 375 523 6 000 49 2 11 15 21 Ukraine 14 19 26 83 3 700 12 0 1 1 75 Annexes PM point estimate and range MMRb point estimate and range Lifetime % of HIV- Number of uncertainty interval of uncertainty interval (UI: 80%) risk of related of (UI: 80%) Country and territory maternal indirect maternal MMR deathd maternal deathsc Lower PM point Upper Lower UI point Upper UI 1 in deathse UI estimate UI estimate United Arab Emirates 2 3 5 3 17 900 0 0 1 United Kingdom of Great Britain and 6 7 8 52 8 400 2 0 1 1 Northern Ireland United Republic of 399 524 712 11 000 36 1 17 22 30 Tanzania United States of 17 19 21 720 3 000 1 1 1 1 America Uruguay 14 17 21 8 2 900 1 1 1 Uzbekistan 23 29 37 200 1 200 1 2 2 3 Vanuatu 33 72 161 6 330 4 8 17 Venezuela (Bolivarian 97 125 170 670 330 1 5 7 9 Republic of) Viet Nam 32 43 61 700 1 100 1 2 3 5 West Bank and Gaza 23 27 32 39 880 3 3 4 Stripf Yemen 109 164 235 1 400 150 5 7 10 Zambia 159 213 289 1 300 93 7 6 8 10 Zimbabwe 360 458 577 2 100 55 6 7 9 12 PM: proportion of deaths among women of reproductive age (15–49 years) that are due to maternal causes; UI: uncertainty interval. a Estimates have been computed to ensure comparability across countries, thus they are not necessarily the same as official statistics of the countries, which may use alternative rigorous methods. Countries included in all tables presented in this report (185 countries) are limited to WHO Member States with populations over 100 000, excluding those for which life tables were unavailable (Andorra, Cook Islands, Dominica, Marshall Islands, Monaco, Nauru, Niue, Palau, Saint Kitts and Nevis, San Marino, Tuvalu), plus two territories (Puerto Rico [an Associate Member], and the West Bank and Gaza Strip [a member in the regional committee for the WHO Eastern Mediterranean Region]). b MMR estimates have been rounded according to the following scheme: < 100 rounded to nearest 1; 100–999 rounded to nearest 1; and ≥ 1000 rounded to nearest 10. c Numbers of maternal deaths have been rounded according to the following scheme: < 100 rounded to nearest 1; 100–999 rounded to nearest 10; 1000–9999 rounded to nearest 100; and ≥ 10 000 rounded to nearest 1000. d Life time risk has been rounded according to the following scheme: < 100 rounded to nearest 1; 100–999 rounded to nearest 10; and ≥ 1000 rounded to nearest 100. e Percentage of HIV-related indirect maternal deaths are presented only for countries with an HIV prevalence ≥ 5% in 2017 (1). f UNICEF, UNPFA, World Bank Group and UNPD refer to this territory as the State of Palestine. Reference zation; 2019 (https://www.who.int/gho/morta­lity_burden_disease/ 1. Life tables. In: Global Health Observatory (GHO) data [website]. Geneva: World Health Organi­ life_tables/life_tables/en/, accessed 18 June 2019). 76 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 ANNEX 61 ESTIMATES OF MATERNAL MORTALITY RATIO (MMR, MATERNAL DEATHS PER 100 000 LIVE BIRTHS), NUMBER OF MATERNAL DEATHS, AND LIFETIME RISK, BY WORLD HEALTH ORGANIZATION (WHO) REGION, 2017 MMR point estimate and range of uncertainty interval (UI: 80%) Number of Lifetime risk WHO region maternal of maternal MMR Lower Upper deaths death: 1 in UI point UI estimate Africa 480 525 629 19 2000 39 Americas 55 58 63 8 600 850 South-East Asia 132 152 180 53 000 280 Europe 12 13 14 1 400 4 300 Eastern Mediterranean 138 164 217 30 000 170 Western Pacific 36 41 49 9 800 1 400 World 199 211 243 295 000 190 WHO Member States in each WHO region can be found at: African Region: https://www.afro.who.int/countries Region of the Americas: https://www.paho.org/hq/index.php?option=com_wrapper&view=wrapper&Itemid=2005 South-East Asia Region: http://www.searo.who.int/en/ European Region: http://www.euro.who.int/en/countries Eastern Mediterranean Region: http://www.emro.who.int/countries.html Western Pacific Region: https://www.who.int/westernpacific/about/where-we-work 1 For countries included, refer to note “a” for Annex 5, and web links for lists of WHO Member States provided in Annex 6. 77 Annexes 78 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 ANNEX 72 TRENDS IN ESTIMATES OF MATERNAL MORTALITY RATIO (MMR, MATERNAL DEATHS PER 100 000 LIVE BIRTHS), BY WHO REGION, 2000–2017 MMR point estimates Average annual rate Overall of reduction reduction in in MMR WHO region MMR between between 2000 and 2017 2000 2005 2010 2015 2017 2000 and (%) 2017 (%) Africa 857 735 615 548 525 38.7 2.9 Americas 73 68 64 60 58 20.9 1.4 South-East Asia 355 280 214 165 152 57.3 5.0 Europe 27 22 17 14 13 52.8 4.4 Eastern 330 275 220 175 164 50.3 4.1 Mediterranean Western Pacific 75 61 51 43 41 45.8 3.6 World 342 296 248 219 211 38.4 2.9 2 For countries included, refer to note “a” for Annex 5, and web links for lists of WHO Member States provided in Annex 6. 79 Annexes 80 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 ANNEX 83 ESTIMATES OF MATERNAL MORTALITY RATIO (MMR, MATERNAL DEATHS PER 100 000 LIVE BIRTHS), NUMBER OF MATERNAL DEATHS, AND LIFETIME RISK, BY UNITED NATIONS CHILDREN’S FUND (UNICEF) REGION, 2017 MMR point estimate and range of uncertainty interval (UI: 80%) Number of Lifetime risk UNICEF region and subregion maternal of maternal MMR deaths death: 1 in Lower UI point Upper UI estimate East Asia and the Pacific 61 69 85 21 000 790 Europe and Central Asia 12 13 14 1 400 4 300 Eastern Europe and Central 17 19 21 1 200 2 600 Asia Western Europe 5 5 6 260 11 700 Latin America and the 70 74 81 7 800 630 Caribbean Middle East and North Africa 50 57 71 5 800 570 North America 16 18 20 760 3 100 South Asia 141 163 196 57 000 240 Sub-Saharan Africa 490 533 636 200 000 38 Eastern and Southern 356 384 450 70 000 58 Africa West and Central Africa 582 674 850 131 000 28 Least developed countries a 396 415 477 130 000 56 World 199 211 243 295 000 190 UI: uncertainty interval Countries in each UNICEF region are listed at: https://www.unicef.org/where-we-work a Afghanistan, Angola, Bangladesh, Benin, Bhutan, Burkina Faso, Burundi, Cambodia, Central African Republic, Chad, Comoros, Democratic Republic of the Congo, Djibouti, Equatorial Guinea, Eritrea, Ethiopia, Gambia, Guinea, Guinea-Bissau, Haiti, Kiribati, Lao People’s Democratic Republic, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mozambique, Myanmar, Nepal, Niger, Rwanda, Sao Tome and Principe, Senegal, Sierra Leone, Solomon Islands, Somalia, South Sudan, Sudan, Timor-Leste, Togo, Tuvalu, Uganda, United Republic of Tanzania, Vanuatu, Yemen, Zambia. 3 For countries included, refer to note “a” for Annex 5, and web links for lists of WHO Member States provided in Annex 6. 81 Annexes 82 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 ANNEX 94 TRENDS IN ESTIMATES OF MATERNAL MORTALITY RATIO (MMR, MATERNAL DEATHS PER 100 000 LIVE BIRTHS), BY UNICEF REGION, 2000–2017 MMR point estimates Average annual Overall rate of reduction reductiona in UNICEF region and in MMR MMR between subregion between 2000 2000 and 2017 2000 2005 2010 2015 2017 and 2017 (%) (%) East Asia and the Pacific 114 100 86 73 69 39.4 2.9 Europe and Central Asia 27 22 17 14 13 10.0 4.4 Eastern Europe and 45 36 26 20 19 58.2 5.1 Central Asia Western Europe 8 7 6 6 5 33.4 2.4 Latin America and the 96 91 85 77 74 22.8 1.5 Caribbean Middle East and North 95 81 63 59 57 40.0 3.0 Africa North America 12 13 14 17 18 -52.2 -2.5 South Asia 395 309 235 179 163 58.7 5.2 Sub-Saharan Africa 870 746 626 557 533 38.7 2.9 Eastern and 780 645 494 406 384 50.8 4.2 Southern Africa West and Central 962 847 755 699 674 30.0 2.1 Africa Least developed 763 635 520 442 415 45.6 3.6 countriesb World 342 296 248 219 211 38.4 2.9 Countries in each UNICEF region are listed at: https://www.unicef.org/where-we-work aNegative number indicates percentage increase in MMR. b Afghanistan, Angola, Bangladesh, Benin, Bhutan, Burkina Faso, Burundi, Cambodia, Central African Republic, Chad, Comoros, Democratic Republic of the Congo, Djibouti, Equatorial Guinea, Eritrea, Ethiopia, Gambia, Guinea, Guinea-Bissau, Haiti, Kiribati, Lao People’s Democratic Republic, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mozambique, Myanmar, Nepal, Niger, Rwanda, Sao Tome and Principe, Senegal, Sierra Leone, Solomon Islands, Somalia, South Sudan, Sudan, Timor-Leste, Togo, Tuvalu, Uganda, United Republic of Tanzania, Vanuatu, Yemen, Zambia. 4 For countries included, refer to note “a” for Annex 5, and web links for lists of WHO Member States provided in Annex 6. 83 Annexes 84 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 ANNEX 105 ESTIMATES OF MATERNAL MORTALITY RATIO (MMR, MATERNAL DEATHS PER 100 000 LIVE BIRTHS), NUMBER OF MATERNAL DEATHS, AND LIFETIME RISK, BY UNITED NATIONS POPULATION FUND (UNFPA) REGION, 2017 MMR point estimate and range of uncertainty interval (UI: 80%) Lifetime risk Number of of maternal UNFPA region maternal death: MMR deaths Lower UI point Upper UI 1 in estimate Arab States 121 151 208 14 000 180 Asia and the Pacific 108 120 140 79 000 380 East and Southern Africa 361 391 463 77 000 54 Eastern Europe and Central Asia 18 20 22 800 2 300 Latin America and the Caribbean 70 74 81 7 800 630 West and Central Africa 606 717 917 114 000 27 Non-UNFPA lista 11 12 13 1 600 5 200 World 199 211 243 295 000 190 UI: uncertainty interval Countries in each UNFPA region are listed at: https://www.unfpa.org/worldwide a The countries in this category are not included among the countries listed in UNFPA regions (i.e. they do not have UNFPA country offices/programmes): Australia, Austria, Bahrain, Belgium, Brunei Darussalam, Bulgaria, Canada, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Kuwait, Latvia, Lithuania, Luxembourg, Malta, Montenegro, Netherlands, New Zealand, Norway, Poland, Portugal, Puerto Rico, Qatar, Republic of Korea, Romania, Russian Federation, Saudi Arabia, Seychelles, Singapore, Slovakia, Slovenia, Spain, Sweden, Switzerland, United Arab Emirates, United Kingdom of Great Britain and Northern Ireland, United States of America. 5 For countries included, refer to note “a” for Annex 5, and web links for lists of WHO Member States provided in Annex 6. 85 Annexes 86 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 ANNEX 116 TRENDS IN ESTIMATES OF MATERNAL MORTALITY RATIO (MMR, MATERNAL DEATHS PER 100 000 LIVE BIRTHS), BY UNFPA REGION, 2000–2017 MMR point estimates Average Overall annual reduction rate of in MMR reduction UNFPA region between in MMR 2000 and between 2000 2005 2010 2015 2017 2017 2000 and 2017 (%) (%) Arab States 262 223 178 155 151 42.5 3.3 Asia and the Pacific 272 218 167 130 120 56.0 4.8 East and Southern Africa 773 639 494 413 391 49.4 4.0 Eastern Europe and 42 34 26 21 20 52.0 4.3 Central Asia Latin America and the 96 91 85 77 74 22.8 1.5 Caribbean West and Central Africa 1000 890 798 744 717 28.4 2.0 Non-UNFPA lista 16 14 12 12 12 26.4 1.8 World 342 296 248 219 211 38.4 2.9 a See note for previous table. 6 For countries included, refer to note “a” for Annex 5, and web links for lists of WHO Member States provided in Annex 6. 87 Annexes 88 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 ANNEX 127 ESTIMATES OF MATERNAL MORTALITY RATIO (MMR, MATERNAL DEATHS PER 100 000 LIVE BIRTHS), NUMBER OF MATERNAL DEATHS, AND LIFETIME RISK, BY WORLD BANK GROUP REGION AND INCOME GROUP, 2017 MMR point estimate and range of uncertainty interval (UI: 80%) Lifetime risk Number of of maternal World Bank Group region and maternal death: income group MMR point deaths Lower UI Upper UI 1 in estimate Region East Asia and Pacific 61 69 85 21 000 790 Europe and Central Asia 12 13 14 1 400 4 300 Latin America and the 70 74 81 7 800 630 Caribbean Middle East and North Africa 50 57 71 5 800 570 North America 16 18 20 760 3 100 South Asia 141 163 196 57 000 240 Sub-Saharan Africa 490 534 636 200 000 38 World 199 211 243 295 000 190 Income group Low income 437 462 540 111 000 45 Lower middle income 226 254 307 166 000 140 Upper middle income 40 43 48 16 000 1 200 High income 10 11 12 1 400 5 400 UI: uncertainty interval. Countries in each World Bank Group region are listed at: https://www.worldbank.org/en/where-we-work Countries in each World Bank Group region and income group are listed at: https://datahelpdesk.worldbank.org/knowledgebase/ articles/906519-world-bank-country-and-lending-groups 7 For countries included, refer to note “a” for Annex 5, and web links for lists of WHO Member States provided in Annex 6. 89 Annexes 90 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 ANNEX 138 TRENDS IN ESTIMATES OF MATERNAL MORTALITY RATIO (MMR, MATERNAL DEATHS PER 100 000 LIVE BIRTHS), BY WORLD BANK GROUP REGION AND INCOME GROUP, 2000–2017 Average MMR point estimates Overall annual reductiona rate of in MMR reduction World Bank Group region between in MMR and income group 2000 and between 2000 2005 2010 2015 2017 2017 2000 and 2017 (%) (%) Region East Asia and Pacific 114 100 86 73 69 42.0 2.9 Europe and Central Asia 27 22 17 14 13 52.7 4.4 Latin America and the 96 90 85 77 74 19.0 1.5 Caribbean Middle East and North 96 82 63 59 57 40.3 3.0 Africa North America 12 13 14 17 18 -52.2 -2.5 South Asia 395 309 235 179 163 58.7 5.2 Sub-Saharan Africa 870 746 626 557 534 38.7 2.9 World 342 296 248 219 211 38.4 2.9 Income group Low income 833 696 573 491 462 45.0 3.5 Lower middle income 428 363 302 265 254 40.7 3.1 Upper middle income 69 61 51 45 43 37.6 2.8 High income 12 11 11 11 11 4.1 0.3 a Negative number indicates percentage increase in MMR. 8 For countries included, refer to note “a” for Annex 5, and web links for lists of WHO Member States provided in Annex 6. 91 Annexes 92 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 ANNEX 149 ESTIMATES OF MATERNAL MORTALITY RATIO (MMR, MATERNAL DEATHS PER 100 000 LIVE BIRTHS), NUMBER OF MATERNAL DEATHS, AND LIFETIME RISK, BY UNITED NATIONS POPULATION DIVISION (UNPD) REGION AND SUBREGION, 2017 MMR point estimate and range of uncertainty interval (UI: 80%) Number of Lifetime risk UNPD region and subregiona maternal of maternal MMR point deaths death: 1 in Lower UI Upper UI estimate Africa 443 481 572 203 000 45 Northern Africa 91 112 145 6 700 260 Sub-Saharan Africa 498 542 649 196 000 37 Asia 100 110 129 82 000 410 Central Asia 21 24 28 390 1 400 Eastern Asia 22 28 35 5 300 2 200 South-Eastern Asia 115 137 173 16 000 320 Southern Asia 136 157 189 58 000 250 Western Asia 45 55 69 3 000 650 Europe 9 10 11 740 6 500 Eastern Europe 12 14 18 480 4 200 Northern Europe 5 6 7 72 9 300 Southern Europe 4 4 5 55 17 700 Western Europe 6 7 8 130 9 000 Americas 55 58 63 8 600 850 Latin America and the 70 74 81 7 800 630 Caribbean Northern America 16 18 20 760 3 100 Oceania 34 60 120 400 690 Australia and New Zealand 6 7 8 26 7 800 Melanesia 68 132 276 370 200 Micronesia 59 90 157 5 330 Polynesia 28 46 94 3 570 Small island developing States 178 210 277 2 600 190 Least developed countries 396 415 477 130 000 56 Landlocked developing countries 378 408 484 65 000 57 Less developed regions b 219 232 268 293 000 160 More developed regions c 11 12 13 1 600 5 200 World 199 211 243 295 000 190 9 For countries included, refer to note “a” for Annex 5, and web links for lists of WHO Member States provided in Annex 6. 93 Annexes a The data are from the United Nations Statistics Division, a different division of the United Nations Department of Economic and Social Affairs (UN DESA). Countries in each UNPD region are listed at: https://unstats.un.org/unsd/methodology/m49/ (select ‘Geographic regions' or ‘Other regions’) b Afghanistan, Algeria, Angola, Antigua and Barbuda, Argentina, Armenia, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belize, Benin, Bhutan, Bolivia (Plurinational State of), Botswana, Brazil, Brunei Darussalam, Burkina Faso, Burundi, Cabo Verde, Cambodia, Cameroon, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo, Costa Rica, Côte d'Ivoire, Cuba, Cyprus, Democratic People's Republic of Korea, Democratic Republic of the Congo, Djibouti, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Ethiopia, Eswatini, Fiji, Gabon, Gambia, Georgia, Ghana, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, India, Indonesia, Iran (Islamic Republic of), Iraq, Israel, Jamaica, Jordan, Kazakhstan, Kenya, Kiribati, Kuwait, Kyrgyzstan, Lao People's Democratic Republic, Lebanon, Lesotho, Liberia, State of Libya, Madagascar, Malawi, Malaysia, Maldives, Mali, Mauritania, Mauritius, Mexico, Micronesia (Federated States of), Mongolia, Morocco, Mozambique, Myanmar, Namibia, Nepal, Nicaragua, Niger, Nigeria, Oman, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Puerto Rico, Qatar, Republic of Korea, Rwanda, Saint Lucia, Saint Vincent and the Grenadines, Samoa, Sao Tome and Principe, Saudi Arabia, Senegal, Seychelles, Sierra Leone, Singapore, Solomon Islands, Somalia, South Africa, South Sudan, Sri Lanka, Sudan, Suriname, Syrian Arab Republic, Tajikistan, Thailand, Timor-Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, United Arab Emirates, United Republic of Tanzania, Uruguay, Uzbekistan, Vanuatu, Venezuela (Bolivarian Republic of), Viet Nam, West Bank and Gaza Strip, Yemen, Zambia, Zimbabwe. c Albania, Australia, Austria, Belarus, Belgium, Bosnia and Herzegovina, Bulgaria, Canada, Croatia, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Latvia, Lithuania, Luxembourg, Malta, Montenegro, Netherlands, New Zealand, Norway, Poland, Portugal, Republic of Moldova, Republic of North Macedonia, Romania, Russian Federation, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Ukraine, United Kingdom of Great Britain and Northern Ireland. 94 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 ANNEX 1510 TRENDS IN ESTIMATES OF MATERNAL MORTALITY RATIO (MMR, MATERNAL DEATHS PER 100 000 LIVE BIRTHS), BY UNPD REGION AND SUBREGION, 2000–2017 MMR point estimate Average Overall annual rate of reductiona in UNPD region and reduction in MMR between subregion MMR between 2000 2005 2010 2015 2017 2000 and 2017 2000 and 2017 (%) (%) Africa 788 678 565 501 481 39.0 2.9 Northern Africa 244 193 145 118 112 54.1 4.6 Sub-Saharan Africa 878 754 635 566 542 38.3 2.8 Asia 250 201 154 120 110 55.9 4.8 Central Asia 49 40 30 25 24 52.0 4.3 Eastern Asia 56 43 35 29 28 49.9 4.1 South-Eastern Asia 214 194 171 145 137 36.0 2.6 Southern Asia 384 301 228 172 157 59.2 5.3 Western Asia 81 78 58 56 55 32.4 2.3 Europe 20 17 13 10 10 53.4 4.5 Eastern Europe 40 31 20 16 14 64.0 6.0 Northern Europe 10 10 9 7 6 39.8 3.0 Southern Europe 7 6 5 5 4 36.8 2.7 Western Europe 9 8 7 6 7 23.5 1.6 Americas 73 68 64 60 58 20.8 1.4 Latin America and the 96 90 85 77 74 22.6 1.5 Caribbean Northern America 12 13 14 17 18 -52.2 -2.5 Oceania 106 84 69 62 60 43.0 3.3 Australia and New 8 6 6 7 7 11.0 0.7 Zealand Melanesia 230 185 155 138 132 42.4 3.2 Micronesia 146 126 111 96 90 38.1 2.8 Polynesia 84 70 58 48 46 45.0 3.5 Small island developing 249 233 226 214 210 15.7 1.0 States Least developed countries 763 635 520 442 415 45.6 3.6 Landlocked developing 788 666 525 435 408 48.2 3.9 countries Less developed regions 378 328 275 241 232 37.0 2.9 More developed regions 16 15 12 12 12 88.0 1.9 World 342 296 248 219 211 38.4 2.9 a Negative number indicates percentage increase in MMR. 10 For countries included, refer to note “a” for Annex 5, and web links for lists of WHO Member States provided in Annex 6 95 Annexes 96 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 ANNEX 1611 TRENDS IN ESTIMATES OF MATERNAL MORTALITY RATIO (MMR, MATERNAL DEATHS PER 100 000 LIVE BIRTHS), BY UNITED NATIONS SUSTAINABLE DEVELOPMENT GOAL (SDG) REGION, SUBREGION AND OTHER GROUPING, 2000–2017 Overall reduction in Average annual rate MMR point estimates SDG region, subregion and MMR between 2000 of reduction in MMR other grouping and 2017 between 2000 and 2017 2000 2005 2010 2015 2017 (%) (%) World 342 296 248 219 211 38.4 2.9 Sub-Saharan Africa 878 754 635 566 542 38.3 2.8 Eastern Africaa 851 710 548 453 428 49.8 4.1 Middle Africab 866 701 586 524 505 41.7 3.2 Southern Africa c 205 239 199 155 148 27.9 1.9 Western Africa d 993 885 796 743 716 27.9 1.9 Northern Africa and Western 158 133 101 88 84 46.6 3.7 Asia Northern Africae 244 193 145 118 112 54.1 4.6 Western Asia f 81 78 58 56 55 32.4 2.3 Central and Southern Asia 375 293 220 166 151 59.7 5.3 Central Asiag 49 40 30 25 24 52.0 4.3 Southern Asia h 384 301 228 172 157 59.2 5.3 Eastern and South-Eastern 114 100 86 73 69 39.3 2.9 Asia Eastern Asiai 56 43 35 29 28 49.9 4.1 South-Eastern Asia j 214 194 171 145 137 36.0 2.6 Latin America and the 96 90 85 77 74 22.6 1.5 Caribbean Caribbeank 194 208 233 229 229 -18.1 -1.0 Central America l 75 70 62 50 47 38.1 2.8 South America m 95 88 80 74 71 25.1 1.7 Oceania 106 84 69 62 60 43.0 3.3 Australia and New Zealand 8 6 6 7 7 11.0 0.7 Oceania (excl. Australia and 223 180 151 135 129 42.0 3.2 New Zealand)n Europe and Northern America 17 16 13 12 12 27.5 1.9 Europe 20 17 13 10 10 53.4 4.5 Eastern Europe o 40 31 20 16 14 64.0 6.0 11 For countries included, refer to note “a” for Annex 5, and web links for lists of WHO Member States provided in Annex 6. 97 Annexes Annex 16 (continued) Overall change in Average annual rate MMR point estimates SDG region, subregion and MMR between 2000 of reduction in MMR other grouping and 2017 between 2000 and 2017 2000 2005 2010 2015 2017 (%) (%) Northern Europe p 10 10 9 7 6 39.8 3.0 Southern Europe q 7 6 5 5 4 36.8 2.7 Western Europe r 9 8 7 6 7 23.5 1.6 Northern Americas 12 13 14 17 18 -52.2 -2.5 Landlocked developing 788 666 525 435 408 48.2 3.9 countriest Least developed countriesu 763 635 520 442 415 45.6 3.6 Small island developing Statesv 249 233 226 214 210 15.7 1.0 Note: In the last two columns, negative numbers indicate increases instead of reductions. a Burundi, Comoros, Djibouti, Eritrea, Ethiopia, Kenya, Madagascar, Malawi, Mauritius, Mozambique, Rwanda, Seychelles, Somalia, South Sudan, Uganda, United Republic of Tanzania, Zambia, Zimbabwe. b Angola, Cameroon, Central African Republic, Chad, Congo, Democratic Republic of the Congo, Equatorial Guinea, Gabon, Sao Tome and Principe. c Botswana, Eswatini, Lesotho, Namibia, South Africa. d Benin, Burkina Faso, Cabo Verde, Côte d’Ivoire, Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone, Togo. e Algeria, Egypt, Morocco, State of Libya, Sudan, Tunisia. f Armenia, Azerbaijan, Bahrain, Cyprus, Georgia, Iraq, Israel, Jordan, Kuwait, Lebanon, Oman, Qatar, Saudi Arabia, Syrian Arab Republic, Turkey, United Arab Emirates, West Bank and Gaza Strip, Yemen. g Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, Uzbekistan. h Afghanistan, Bangladesh, Bhutan, India, Iran (Islamic Republic of), Maldives, Nepal, Pakistan, Sri Lanka. i China, Democratic People’s Republic of Korea, Japan, Mongolia, Republic of Korea. j Brunei Darussalam, Cambodia, Indonesia, Lao People’s Democratic Republic, Malaysia, Myanmar, Philippines, Singapore, Thailand, Timor-Leste, Viet Nam. k Antigua and Barbuda, Bahamas, Barbados, Cuba, Dominican Republic, Grenada, Haiti, Jamaica, Puerto Rico, Saint Lucia, Saint Vincent and the Grenadines, Trinidad and Tobago l Belize, Costa Rica, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama. m Argentina, Bolivia (Plurinational State of), Brazil, Chile, Colombia, Ecuador, Guyana, Paraguay, Peru, Suriname, Uruguay, Venezuela (Bolivarian Republic of) ⁿ Fiji, Kiribati, Micronesia (Federated States of), Papua New Guinea, Samoa, Solomon Islands, Tonga, Vanuatu. o Belarus, Bulgaria, Czechia, Hungary, Poland, Republic of Moldova, Romania, Russian Federation, Slovakia, Ukraine p Denmark, Estonia, Finland, Iceland, Ireland, Latvia, Lithuania, Norway, Sweden, United Kingdom of Great Britain and Northern Ireland q Albania, Bosnia and Herzegovina, Croatia, Greece, Italy, Malta, Montenegro, Portugal, Republic of North Macedonia, Serbia, Slovenia, Spain r Austria, Belgium, France, Germany, Luxembourg, Netherlands, Switzerland s Canada, United States of America. t Afghanistan, Armenia, Azerbaijan, Bhutan, Bolivia (Plurinational State of), Botswana, Burkina Faso, Burundi, Central African Republic, Eswatini, Ethiopia, Kazakhstan, Kyrgyzstan, Lao People’s Democratic Republic, Lesotho, Malawi, Mali, Mongolia, Nepal, Niger, Paraguay, Republic of Moldova, Republic of North Macedonia, Rwanda, South Sudan, Tajikistan, Turkmenistan, Uganda, Uzbekistan, Zambia, Zimbabwe. u Afghanistan, Angola, Bangladesh, Benin, Bhutan, Burkina Faso, Burundi, Cambodia, Central African Republic, Chad, Comoros, Democratic Republic of the Congo, Djibouti, Eritrea, Ethiopia, Gambia, Guinea, Guinea-Bissau, Haiti, Kiribati, Lao People’s Democratic Republic, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mozambique, Myanmar, Nepal, Niger, Rwanda, Sao Tome and Principe, Senegal, Sierra Leone, Solomon Islands, Somalia, South Sudan, Sudan, Timor-Leste, Togo, Uganda, United Republic of Tanzania, Vanuatu, Yemen, Zambia. v Antigua and Barbuda, Bahamas, Barbados, Belize, Cabo Verde, Comoros, Cuba, Dominican Republic, Fiji, Grenada, Guinea-Bissau, Guyana, Haiti, Jamaica, Kiribati, Maldives, Mauritius, Micronesia (Federated States of), Papua New Guinea, Puerto Rico, Saint Lucia, Saint Vincent and the Grenadines, Samoa, Sao Tome and Principe, Seychelles, Singapore, Solomon Islands, Suriname, Timor-Leste, Tonga, Trinidad and Tobago, Vanuatu. 98 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 ANNEX 17 12 TRENDS IN ESTIMATES OF MATERNAL MORTALITY RATIO (MMR, MATERNAL DEATHS PER 100 000 LIVE BIRTHS), BY COUNTRY AND TERRITORY, 2000–2017a Average annual rate of Overall reduction (ARR) point estimate reduction and range of uncertainty MMR point estimatesb in MMR interval on ARR between 2000 Country and territory between and 2017 (UI: 80%) 2000 and (%) 2017c Average (%) Lower Upper 2000 2005 2010 2015 2017 ARR point UI UI estimated Afghanistan 1450 1140 954 701 638 56 1.4 4.8 7.3 Albania 23 22 21 15 15 35 -0.1 2.5 5.7 Algeria 161 127 115 114 112 30 -0.5 2.1 4.4 Angola 827 519 326 251 241 71 5.4 7.2 9.3 Antigua and Barbuda 44 40 44 43 42 5 -1.8 0.2 2.4 Argentina 66 59 51 41 39 41 2.1 3.1 4.2 Armenia 43 35 32 28 26 40 1.5 3.0 4.3 Australia 7 5 5 6 6 14 -1.4 0.2 1.7 Austria 6 6 5 5 5 17 -0.5 1.6 3.1 Azerbaijan 47 42 31 27 26 45 2.2 3.5 4.9 Bahamas 75 77 78 74 70 7 -2.4 0.4 2.6 Bahrain 27 19 18 15 14 48 1.6 3.6 5.4 Bangladesh 434 343 258 200 173 60 3.4 5.4 7.1 Barbados 50 42 36 31 27 46 1.9 3.7 6.0 Belarus 22 11 5 3 2 91 9.6 13.0 16.7 Belgium 8 7 6 5 5 38 1.0 2.5 4.1 Belize 89 70 54 43 36 60 3.7 5.3 7.5 Benin 520 500 464 421 397 24 -0.4 1.6 3.2 Bhutan 423 310 247 203 183 57 2.1 4.9 7.0 Bolivia (Plurinational State of) 331 271 212 168 155 53 2.7 4.5 6.2 Bosnia and Herzegovina 17 13 11 10 10 41 1.3 3.3 6.3 Botswana 262 239 179 156 144 45 2.1 3.5 4.7 Brazil 69 71 65 63 60 13 0.7 0.9 1.1 Brunei Darussalam 28 29 28 30 31 -11 -2.5 -0.7 1.6 Bulgaria 19 15 12 10 10 47 1.9 4.0 6.5 12 For countries included, refer to note “a” for Annex 5, and web links for lists of WHO Member States provided in Annex 6 99 Annexes Annex 17 (continued) Average annual rate of Overall reduction (ARR) point estimate reduction and range of uncertainty MMR point estimatesa.b in MMR interval on ARR between 2000 Country and territory between and 2017 (UI: 80%) 2000 and (%) 2017c Average (%) Lower Upper 2000 2005 2010 2015 2017 ARR point UI UI estimated Burkina Faso 516 437 385 343 320 38 0.9 2.8 4.9 Burundi 1010 814 665 568 548 46 1.7 3.6 5.5 Cabo Verde 118 86 70 61 58 51 2.5 4.2 5.7 Cambodia 488 351 248 178 160 67 4.6 6.6 8.4 Cameroon 886 692 597 554 529 40 0.8 3.0 4.8 Canada 9 11 11 11 10 -11 -2.5 -0.6 1.2 Central African Republic 1280 1200 1000 912 829 35 0.3 2.6 4.9 Chad 1420 1330 1240 1160 1140 20 -0.7 1.3 2.9 Chile 31 25 20 14 13 58 4.3 5.4 6.7 China 59 44 36 30 29 51 2.9 4.2 6.0 Colombia 94 83 85 85 83 12 -0.4 0.8 1.7 Comoros 444 404 341 285 273 39 0.8 2.9 4.9 Congo 739 677 506 416 378 49 2.0 3.9 5.7 Costa Rica 40 33 32 28 27 33 1.2 2.2 3.4 Côte d’Ivoire 704 704 701 658 617 12 -1.2 0.8 2.7 Croatia 11 10 9 8 8 27 0.0 2.0 3.7 Cuba 46 41 41 38 36 22 0.6 1.4 2.2 Cyprus 14 12 8 7 6 57 2.9 4.9 7.0 Czechia 7 5 4 4 3 57 2.0 4.0 6.3 Democratic People’s Republic of 139 120 106 91 89 36 0.2 2.6 4.9 Korea Democratic Republic of the Congo 760 627 542 490 473 38 0.1 2.8 4.7 Denmark 8 6 5 4 4 50 2.8 4.3 6.2 Djibouti 507 393 283 247 248 51 2.0 4.2 6.5 Dominican Republic 80 83 96 94 95 -19 -1.6 -1.0 -0.5 Ecuador 122 94 78 63 59 52 3.4 4.3 5.2 Egypt 64 52 45 39 37 42 1.7 3.2 5.4 El Salvador 73 62 54 48 46 37 1.3 2.7 4.3 Equatorial Guinea 454 344 308 296 301 34 0.3 2.4 4.5 Eritrea 1280 804 567 518 480 63 3.6 5.8 7.9 Estonia 29 18 11 10 9 69 5.0 7.1 9.6 Eswatini 521 532 450 435 437 16 -1.6 1.0 3.0 Ethiopia 1030 865 597 446 401 61 3.0 5.5 7.4 Fiji 51 46 39 35 34 33 0.8 2.4 4.0 Finland 6 5 4 3 3 50 1.7 3.6 5.2 100 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 Annex 17 (continued) Average annual rate of Overall reduction (ARR) point estimate reduction and range of uncertainty MMR point estimatesa.b in MMR interval on ARR between 2000 Country and territory between and 2017 (UI: 80%) 2000 and (%) 2017c Average (%) Lower Upper 2000 2005 2010 2015 2017 ARR point UI UI estimated France 10 9 9 8 8 20 0.2 1.4 2.6 Gabon 380 348 314 261 252 34 0.1 2.4 4.3 Gambia 932 756 661 625 597 36 0.6 2.6 4.5 Georgia 31 39 32 27 25 19 0.1 1.3 2.5 Germany 7 6 6 5 7 0 -1.3 0.2 1.8 Ghana 484 371 339 320 308 36 0.9 2.7 4.5 Greece 3 3 3 3 3 0 -1.3 0.6 2.7 Grenada 38 33 29 25 25 34 0.4 2.4 4.5 Guatemala 161 142 129 103 95 41 2.5 3.1 3.7 Guinea 1020 920 747 699 576 44 1.6 3.4 4.9 Guinea-Bissau 1210 979 779 694 667 45 1.0 3.5 5.4 Guyana 231 223 179 172 169 27 0.4 1.8 3.3 Haiti 437 459 506 488 480 -10 -2.7 -0.6 1.3 Honduras 85 77 74 67 65 24 0.4 1.6 2.7 Hungary 16 15 13 12 12 25 -0.6 2.0 4.2 Iceland 6 5 5 4 4 33 0.7 2.7 4.9 India 370 286 210 158 145 61 4.2 5.5 7.0 Indonesia 272 252 228 192 177 35 0.5 2.5 4.3 Iran (Islamic Republic of) 48 34 22 17 16 67 5.0 6.3 8.0 Iraq 79 127 70 83 79 0 -1.9 0.0 2.5 Ireland 7 7 6 6 5 29 0.0 2.5 4.3 Israel 7 5 4 3 3 57 3.4 4.9 6.5 Italy 4 3 2 2 2 50 3.3 5.1 6.9 Jamaica 77 80 79 78 80 -4 -1.5 -0.2 0.9 Japan 9 7 6 5 5 44 2.1 3.8 5.7 Jordan 70 62 53 48 46 34 0.6 2.4 4.7 Kazakhstan 61 43 22 12 10 84 9.2 10.9 12.6 Kenya 708 618 432 353 342 52 2.4 4.3 5.9 Kiribati 136 119 112 97 92 32 0.1 2.3 4.7 Kuwait 10 10 10 11 12 -20 -2.8 -0.7 1.2 Kyrgyzstan 79 82 79 66 60 24 0.0 1.6 2.8 Lao People’s Democratic Republic 544 410 292 209 185 66 4.4 6.3 8.0 Latvia 34 30 26 23 19 44 1.6 3.5 5.0 Lebanon 28 24 23 29 29 -4 -2.9 -0.4 1.6 Lesotho 614 679 594 574 544 11 -1.6 0.7 2.5 101 Annexes Annex 17 (continued) Average annual rate of Overall reduction (ARR) point estimate reduction and range of uncertainty MMR point estimatesa.b in MMR interval on ARR between 2000 Country and territory between and 2017 (UI: 80%) 2000 and (%) 2017c Average (%) Lower Upper 2000 2005 2010 2015 2017 ARR point UI UI estimated Liberia 894 816 708 691 661 26 -0.4 1.8 3.5 Lithuania 17 14 10 9 8 53 2.1 4.2 6.5 Luxembourg 10 9 8 5 5 50 2.4 4.5 6.3 Madagascar 559 526 453 363 335 40 1.0 3.0 5.0 Malawi 749 610 444 370 349 53 2.3 4.5 6.5 Malaysia 38 31 30 30 29 24 0.2 1.5 2.7 Maldives 125 75 67 54 53 58 2.1 5.1 7.3 Mali 836 691 660 620 562 33 0.3 2.3 3.9 Malta 9 8 8 7 6 33 0.1 2.3 4.4 Mauritania 834 826 824 785 766 8 -2.0 0.5 2.6 Mauritius 59 53 66 73 61 -3 -2.8 -0.2 1.9 Mexico 55 54 46 36 33 40 2.6 3.0 3.3 Micronesia (Federated States of) 154 133 110 95 88 43 1.0 3.3 5.6 Mongolia 155 98 66 47 45 71 5.8 7.3 8.8 Montenegro 12 9 7 6 6 50 2.1 4.3 6.9 Morocco 188 131 92 74 70 63 4.2 5.8 7.5 Mozambique 798 577 412 318 289 64 3.9 6.0 7.7 Myanmar 340 299 265 246 250 26 -0.7 1.8 4.1 Namibia 348 346 266 217 195 44 1.4 3.4 4.9 Nepal 553 415 305 236 186 66 4.0 6.4 8.4 Netherlands 13 11 7 6 5 62 3.8 5.6 7.5 New Zealand 12 11 11 10 9 25 0.5 1.8 3.3 Nicaragua 162 131 112 101 98 40 1.2 3.0 4.5 Niger 813 755 663 555 509 37 0.8 2.7 4.5 Nigeria 1200 1080 978 931 917 24 -0.8 1.6 3.5 Norway 6 5 4 3 2 67 3.4 5.3 7.8 Oman 20 19 18 19 19 5 -1.0 0.3 1.6 Pakistan 286 237 191 154 140 51 2.0 4.2 6.4 Panama 91 88 79 58 52 43 2.1 3.3 4.7 Papua New Guinea 249 200 168 151 145 42 0.9 3.2 5.5 Paraguay 162 136 107 89 84 48 2.5 3.9 5.5 Peru 144 118 104 94 88 39 1.5 2.9 4.6 Philippines 160 156 144 127 121 24 -0.3 1.7 3.3 Poland 7 4 3 2 2 71 4.5 6.6 8.9 Portugal 10 9 9 9 8 20 -0.6 1.6 3.3 102 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017 Annex 17 (continued) Average annual rate of Overall reduction (ARR) point estimate reduction and range of uncertainty MMR point estimatesa.b in MMR interval on ARR between 2000 Country and territory between and 2017 (UI: 80%) 2000 and (%) 2017c Average (%) Lower Upper 2000 2005 2010 2015 2017 ARR point UI UI estimated Puerto Rico 26 23 21 20 21 19 -0.6 1.3 2.7 Qatar 14 12 10 9 9 36 0.5 2.6 4.5 Republic of Korea 17 15 15 12 11 35 1.4 2.4 3.6 Republic of Moldova 44 34 29 22 19 57 3.3 4.9 6.6 Republic of North Macedonia 13 10 8 8 7 46 1.7 3.5 5.8 Romania 54 35 27 21 19 65 4.3 6.3 8.3 Russian Federation 56 42 25 18 17 70 5.0 6.9 8.9 Rwanda 1160 643 373 275 248 79 7.0 9.1 10.7 Saint Lucia 86 83 109 115 117 -36 -4.7 -1.8 0.8 Saint Vincent and the Grenadines 80 59 63 64 68 15 -0.9 0.9 3.1 Samoa 88 72 58 45 43 51 1.7 4.2 6.6 Sao Tome and Principe 179 163 140 130 130 27 -0.1 1.9 4.3 Saudi Arabia 24 22 19 17 17 29 -0.2 2.1 4.5 Senegal 553 519 447 346 315 43 1.4 3.3 4.8 Serbia 13 12 12 13 12 8 -2.0 0.6 2.9 Seychelles 53 55 55 54 53 0 -2.4 0.0 2.4 Sierra Leone 2480 1760 1360 1180 1120 55 2.2 4.7 6.6 Singapore 13 13 10 9 8 38 0.4 2.9 5.3 Slovakia 8 7 6 6 5 38 0.6 2.3 4.0 Slovenia 12 10 8 7 7 42 1.6 3.3 5.0 Solomon Islands 245 188 141 112 104 58 3.0 5.0 7.0 Somalia 1210 1040 985 855 829 31 0.3 2.2 4.6 South Africa 160 201 171 125 119 26 0.1 1.7 3.0 South Sudan 1730 1480 1100 1110 1150 34 0.1 2.4 4.5 Spain 5 5 4 4 4 20 0.0 1.7 3.0 Sri Lanka 56 45 38 36 36 36 1.7 2.7 3.5 State of Libya 70 57 53 70 72 -3 -2.6 -0.2 2.3 Sudan 667 529 408 320 295 56 2.7 4.8 7.1 Suriname 221 164 148 122 120 46 2.3 3.6 5.4 Sweden 5 5 4 4 4 20 -0.2 1.5 2.9 Switzerland 7 7 6 5 5 29 0.4 2.6 4.2 Syrian Arab Republic 26 25 27 30 31 -19 -4.0 -1.1 1.3 Tajikistan 53 32 23 18 17 68 4.3 6.8 9.5 Thailand 43 43 42 38 37 14 -0.5 0.8 2.1 Timor-Leste 745 415 219 160 142 81 7.7 9.8 11.9 103 Annexes Annex 17 (continued) Average annual rate of Overall reduction (ARR) point estimate reduction and range of uncertainty MMR point estimatesa.b in MMR interval on ARR between 2000 Country and territory between and 2017 (UI: 80%) 2000 and (%) 2017c Average (%) Lower Upper 2000 2005 2010 2015 2017 ARR point UI UI estimated Togo 489 492 440 398 396 19 -0.5 1.3 3.1 Tonga 77 66 57 54 52 32 0.0 2.3 4.6 Trinidad and Tobago 81 76 71 68 67 17 -0.6 1.1 2.7 Tunisia 66 51 46 46 43 35 0.7 2.4 4.8 Turkey 42 33 24 19 17 60 3.6 5.3 7.5 Turkmenistan 29 18 10 8 7 76 5.9 8.2 10.5 Uganda 578 491 430 387 375 35 0.5 2.5 4.2 Ukraine 35 33 25 21 19 46 1.6 3.6 5.5 United Arab Emirates 6 5 4 3 3 50 1.9 4.0 6.9 United Kingdom of Great Britain 10 11 10 8 7 30 1.9 2.7 3.6 and Northern Ireland United Republic of Tanzania 854 721 644 556 524 39 0.9 2.9 4.4 United States of America 12 13 15 18 19 -58 -3.3 -2.6 -1.9 Uruguay 26 22 17 18 17 35 1.2 2.4 3.6 Uzbekistan 41 38 31 30 29 29 0.1 2.0 3.6 Vanuatu 140 113 92 76 72 49 1.6 4.0 6.1 Venezuela (Bolivarian Republic of) 119 113 117 115 125 -5 -2.2 -0.3 1.3 Viet Nam 68 54 47 45 43 37 0.5 2.6 4.6 West Bank and Gaza Stripe 70 59 45 32 27 61 3.4 5.6 8.1 Yemen 301 242 192 169 164 46 1.7 3.6 6.1 Zambia 528 421 305 232 213 60 3.7 5.3 6.8 Zimbabwe 579 685 598 480 458 21 0.1 1.4 2.9 a Estimates have been computed to ensure comparability across countries, thus they are not necessarily the same as official statistics of the countries, which may use alternative rigorous methods. b MMR estimates have been rounded according to the following scheme: < 100 rounded to nearest 1; 100–999 rounded to nearest 1; and ≥ 1000 rounded to nearest 10; and all calculations are based on rounded numbers. c Overall percentage change (reduction) for the whole period since the first year of the millennium (from 1 January 2000). Negative numbers indicate percentage increases in MMR. d Average annual rate of reduction, for the whole period from the first year of the millennium (1 January 2000). e UNICEF, UNPFA, World Bank Group and UNPD refer to this territory as the State of Palestine. 104 TRENDS IN MATERNAL MORTALITY: 2000 TO 2017