Maternal Deaths Account for a Small Proportion of the Total Deaths Among Reproductive Age Women

Abstract

Background:

For more than 30 years, the focus for women's health in low- and middle-income countries has been on reductions in maternal mortality. This perception was reinforced by the choice of the maternal mortality ratio as the primary indicator for women's health in the Millennium Development Goals. This analysis provides a more objective view by comparing the relative magnitudes of mortality among reproductive age women during pregnancy and the 6-week postpartum period versus other periods during this age range.

Materials and Methods:

Data were aggregated from 38 countries in three regions with Demographic and Health Surveys that contained a maternal mortality module and were conducted in the prior 10 years to derive the proportion of total mortality ascribed to maternal mortality (proportion maternal [PM]) among women 15–49 years of age in 5-year age groups by country, region, and human immunodeficiency virus (HIV) prevalence. Estimates of maternal and nonmaternal deaths were based on the sisterhood method. Age-adjusted PM ranged from 5.7% in Swaziland to 41.7% in Timor-Leste. Regional averages were 14.3% in Latin America and the Caribbean, 24.2% in Asia, and 19.8% in sub-Saharan Africa (SSA). The age-specific pattern of PM showed an increasing trend into the mid-30s followed by a decline. The age-adjusted PM for each country in SSA stratified by HIV prevalence showed an inverse relationship between HIV prevalence and PM with countries with high and low HIV at the lower and upper ends of the PM distribution, respectively.

Conclusions:

Maternal deaths account for only 6%–40% of all deaths occurring among reproductive age women in a selection of low- and middle-income countries. Although a continued focus and push to reduce maternal mortality is warranted, attention to other causes of death and health issues for women of reproductive age is clearly needed. Research on the causes of death among women and prevention and treatment policies that provide health, education, and nutrition services to women need to be a priority.

Introduction

Over the past 30 years, the international health community has focused on maternal mortality as the primary indicator of women's health, largely due, in part, to the launch of the Safe Motherhood Initiative in 1987. This is demonstrated by the fact that the only Millennium Development Goal (MDG) to address directly women's health is MDG5 with the objective of improving maternal health. The original and only indicator used to measure this goal was the maternal mortality ratio (MMR). Subsequently, subgoals were added, including access to skilled health personnel at delivery and several reproductive health indicators such as prevalence of contraceptive use, adolescent birth rate, antenatal care coverage, and unmet needs for family planning. Therefore, while the MDG5 includes indicators beyond maternal deaths, women's health is still largely considered synonymous with maternal health. Yet recent global burden of disease models estimated that there are 3.5 million deaths among women of reproductive age (15–49 years) from all causes.¹ Among these, the proportion that are maternal deaths from country to country and region to region varies along with the distribution of causes and contributing risk factors for disease and threats to physical well-being among women of reproductive age.

There have been recent suggestions to extend the challenge of improving maternal health to include the broader issue of women's health. Studies from rural Northwest Bangladesh, Northern Ethiopia, and rural Nepal found that only 22%, 4.1%, and 7%, respectively, of deaths of reproductive age women were due to pregnancy or child-bearing causes.^2
–4 While maternal mortality remains an important issue throughout the world, continuing to improve the lives and health of women requires a commitment to address the most common causes of death and morbidity. This study aims to provide the justification for broadening the research and policy focus on maternal health to include all women's health. The primary objective was to determine and compare maternal mortality patterns to total mortality patterns among women of reproductive age (15–49 years) in select countries and regions with Demographic and Health Surveys (DHS) conducted since 2004.

Methods

The data to estimate the MMR, maternal mortality rates (MM Rate), and proportion maternal (PM) deaths were collected from the maternal mortality module of the DHS conducted in 38 countries since 2004. The DHS is a retrospective, nationally representative household survey that has utilized the direct sisterhood method developed in 1991 for estimating maternal and nonmaternal mortality.⁵ A detailed sibling history is provided from eligible female household members. The sibling history includes data on the number of siblings born to the same mother, the current age of living siblings and for siblings reported deceased, and the age at death and years since death. Maternal mortality is determined based on responses to additional questions asked about deceased female siblings aged 12 years or older to determine timing of death in relation to pregnancy. MMRs and PMs were calculated based upon 7 years of recall to avoid heaping that can occur with 5- or 10-year recalls. Furthermore, the majority of countries included in this analysis (26/37; 70%) used this recall period and allowed for comparison to published estimates (Table 1).

Table 1.

Maternal Mortality Statistics, Number of Deaths, and Exposure Times Among Women of Reproductive Age (15–49 Years) by Region

Region/country	MM rate	MMR category	HIV prevalence in adult population category	Total number of maternal deaths	Total number of adult female deaths (15–49 years)	Exposure (person-years)	PM	Year of DHS	Reporting period (years)
LAC
Dominican Republic	0.152	Low	Low	54	618	350,133	0.087	2007	10
Peru	0.126	Low	Low	36	258	272,943	0.138	2011	7
Bolivia	0.309	Mid	Low	56	321	184,328	0.175	2008	5
Haiti	0.776	High	Low	103	591	120,296	0.174	2005–2006	7
Regional average							0.143
SSA
Sao Tome and Principe	0.177	Low	Low	6	95	38,177	0.066	2008–2009	10
Benin	0.774	Mid	Low	159	674	206,846	0.236	2006
Burkina Faso	0.682	Mid	Low	136	730	200,082	0.186	2010	7
Congo	0.687	Mid	Mid	90	686	128,148	0.132	2011/12	7
Gabon	0.412	Mid	Mid	43	404	104,841	0.106	2011	7
Madagascar	0.812	Mid	Low	190	923	230,724	0.206	2008/2009	7
Mali	0.79	Mid	Low	76	239	97,578	0.317	2012/13	7
Mozambique	0.763	Mid	High	103	737	130,964	0.139	2011	7
Rwanda	0.751	Mid	Mid	129	539	165,352	0.240	2010	5
Senegal	0.79	Mid	Low	149	543	180,103	0.274	2010–2011	13
Tanzania	0.88	Mid	Mid	115	646	125,086	0.179	2010	10
Uganda	0.913	Mid	Mid	104	565	109,274	0.183	2011	7
Burundi	0.972	High	Low	101	406	106,697	0.249	2010	7
Cameroon	1.33	High	Mid	252	1151	182,804	0.219	2011	7
Congo Democratic Republic	1.09	High	Low	127	715	114,534	0.178	2007	5
Cote d'Ivoire	1.004	High	Mid	120	711	122,569	0.169	2011/12	7
Ethiopia	1.14	High	Low	217	732	182,034	0.296	2011	7
Guinea	1.25	High	Low	114	418	88,957	0.274	2012	7
Kenya	0.818	High	Mid	99	677	117,126	0.146	2008–2009	10
Lesotho	1.343	High	High	107	1017	73,526	0.105	2009	10
Liberia	1.737	High	Low	188	500	105,273	0.375	2013	7
Malawi	1.331	High	High	331	2134	252,653	0.155	2010	7
Namibia	0.579	High	High	70	1133	120,922	0.062	2006–2007	10
Niger	1.411	High	Low	194	503	135,032	0.387	2012	7
Nigeria	1.054	High	Mid	480	1514	443,102	0.317	2013	7
Sierra Leone	1.483	High	Low	97	358	62,082	0.271	2008	7
Swaziland	0.771	High	High	48	841	56,458	0.057	2006–2007	7
Zambia	1.171	High	High	106	1153	87,290	0.092	2007	7
Zimbabwe	1.262	High	High	136	1174	104,114	0.116	2010–2011	7
Regional average							0.198
HIV prevalence						Low	0.255
						Mid	0.188
						High	0.091
S/SEA
Cambodia	0.197	Low	Low	40	474	213,143	0.085	2010	7
Nepal	0.334	Low	Low	39	217	111,382	0.180	2006	7
Indonesia	0.235	Mid	Low	112	978	457,801	0.115	2012	5
Afghanistan	0.590	Mid	Low	256	621	434,546	0.413	2010	7
Timor-Leste	0.959	High	Low	120	288	121,927	0.417	2009–2010	7
Regional Average							0.242

DHS, Demographic and Health Surveys; HIV, human immunodeficiency virus; LAC, Latin American and Caribbean Countries; MMR, maternal mortality ratio; PM, proportion maternal mortality; SSA, Sub-Saharan Africa; SEA, Southeast Asia.

Country-specific maternal mortality data were downloaded directly from the DHS website and used to construct individual datasets with each observation corresponding to a sister of the woman who completed the DHS questionnaire. We tabulated the total number of deaths of sisters as well as the number of deaths of sisters that occurred during pregnancy, at childbirth, or within 2 months of the delivery. In the DHS, “maternal death” is defined solely based on timing of death in relation to a sibling's pregnancy. The cause of death is not ascertained through the direct sisterhood method. Siblings with missing data on survival were excluded from the analysis. MM Rate among women 15–49 years of age were calculated using standard DHS methods and person-years of exposure was defined as the number of years a sister was 15–49 years of age within the 7-year recall period and stratified into 5-year age groups (i.e., 15–19, 20–24 years, etc.).⁶ Age-specific MM Rate and PM were adjusted to the population age distribution of women of reproductive age within each country and aggregated to obtain an overall estimate for women 15–49 years of age.

The country-specific MM Rate, defined as the number of pregnancy-related deaths per 1000 person-years of exposure, was calculated by counting the number of deaths in a given age group multiplied by 1000 and divided by person-years of exposure. The MMR was calculated by dividing the age-adjusted MM Rate for women 15–49 years of age by the age-specific fertility rate among respondents derived from the birth history section of the woman's DHS questionnaire. Age-specific PM was calculated by dividing the number of pregnancy-related deaths within an age group by the total number of female deaths within an age group. An age-adjusted overall PM can be calculated based on the age distribution of the survey: \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} \begin{align*}PM = \frac { \sum \nolimits_ { x = 15 } ^ { 49 } P_x M_x^ { mat } } { \sum \nolimits_ { x = 15 } ^ { 49 } P_x M_x^ { all } } \end{align*} \end{document}

where:

\documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document}$$M_x^{preg}$$ \end{document} = pregnancy-related mortality at age x

\documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document}$$M_x^{all}$$ \end{document} = all-cause mortality at age x

\documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document}$$P_x$$ \end{document} = proportion of the female population at age x

All calculations were weighted by the survey weight assigned to each woman. Comparisons of PM were also stratified by each country's MMR (high: ≥500; mid: ≥250–500; low: <250) and human immunodeficiency virus (HIV) prevalence (high: ≥10%; mid: 2%–10%; low: <2%) based on the prevalence of HIV in adults (15–49 years) from data compiled by the UN Joint Programme on HIV/AIDS (UNAIDS) for the year of the DHS survey in the analysis for that country.⁷ Stratification by urban/rural status is not appropriate since these data are based on the residence of the sister respondent and not necessarily reflective of where the female sibling resided. Regional comparisons were made for Latin American and Caribbean Countries (LAC), sub-Saharan Africa (SSA), and Asia. The overall mean PM was calculated within region as well as by HIV status within SSA.

Results

A summary of the countries and the calculated mortality statistics are presented in Table 1. There were 38 countries that had completed a DHS in the prior 10 years and included the maternal mortality module sufficient to estimate MMR and PM with over 6 million person-years of exposure time accounted for in these estimates. There were 4, 29, and 5 countries that represented regions of LAC, SSA, and Asia, respectively. Notably absent from Asia are China and India; China does not have a DHS, while India did not include a maternal mortality module for calculation of PM. The year the DHS study was conducted ranged from 2005 through 2013 and reporting periods for the maternal mortality module ranging from 5 to 13 years with the majority having a 7-year recall for sibling deaths. The MM Rate (deaths per year per 1000 women aged 15–49 years) ranged from 0.126 in Peru to 1.72 in Liberia and all regions showing a range from low to high MMR in the countries included in this analysis. HIV prevalence was classified as low (<2%) in all countries within LAC and Asia but 7 out of the 29 countries in SSA were classified as high HIV prevalence and 9 as medium HIV prevalence.

Among all countries included in the analysis, the PM ranged from 5.7% in Swaziland to 41.7% in Timor-Leste, respectively. In LAC, the average PM was 14.3%, while Asia was almost two times higher at 24.2%, mainly due to the small number of countries that represent that large region and the presence of both Afghanistan and Timor-Leste with high maternal mortality estimates. If those two countries are excluded from Asia, the average PM was estimated to be 12.7%, lower than both LAC (14.3%) and SSA (19.8%). Age-specific PM estimates are presented in Table 2 by country and region with indication of HIV prevalence within each country at the time of the DHS survey. All three regions showed a trend of increasing PM up to 30–35 years of age, which supports the concept that PM will be higher when more women are giving birth, and start to decline in the later reproductive years (Fig. 1). The PM among 15–19 year olds was higher compared to women 40 years of age and older, which was a combination of the fact that the overall mortality burden in this younger age group is lower in addition to a smaller number of births compared to the next two age groups.

FIG. 1.

Proportion maternal (PM) deaths among women of reproductive age (15–49 years) by region.

Table 2.

Proportion Maternal Mortality (PM) by Age Group

Region/country	HIV prevalence in adult population category	15–19 Years	20–24 Years	25–29 Years	30–34 Years	35–39 Years	40–44 Years	45–49 Years
LAC
Dominican Republic	Low	0.24	0.10	0.12	0.11	0.06	0.03	0.01
Peru	Low	0.20	0.10	0.22	0.22	0.18	0.08	0
Bolivia	Low	0.13	0.15	0.28	0.13	0.32	0.10	0.09
Haiti	Low	0.03	0.25	0.18	0.23	0.23	0.14	0.03
Regional average		0.15	0.15	0.20	0.17	0.20	0.09	0.03
SSA
Benin	Low	0.16	0.22	0.30	0.31	0.29	0.12	0.05
Burkina Faso	Low	0.14	0.29	0.19	0.21	0.18	0.11	0.09
Burundi	Low	0.19	0.24	0.33	0.23	0.41	0.14	0.25
Congo Democratic Republic	Low	0.15	0.18	0.24	0.22	0.08	0.23	0.09
Ethiopia	Low	0.22	0.36	0.29	0.37	0.37	0.19	0.10
Guinea	Low	0.30	0.35	0.31	0.30	0.25	0.15	0.07
Liberia	Low	0.32	0.39	0.45	0.48	0.28	0.37	0.20
Madagascar	Low	0.26	0.22	0.27	0.21	0.26	0.11	0.07
Mali	Low	0.29	0.23	0.28	0.65	0.32	0.20	0.07
Niger	Low	0.34	0.42	0.45	0.43	0.47	0.23	0.15
Sao Tome and Principe	Low	0	0	0.07	0.13	0.03	0.21	0
Senegal	Low	0.15	0.30	0.41	0.41	0.24	0.13	0.05
Sierra Leone	Low	0.29	0.27	0.28	0.41	0.20	0.11	0.10
Average among countries with Low HIV prevalence		0.22	0.27	0.30	0.34	0.26	0.18	0.10
Cameroon	Mid	0.25	0.32	0.24	0.23	0.16	0.10	0.12
Congo	Mid	0.20	0.18	0.12	0.16	0.20	0.03	0.08
Cote d'Ivoire	Mid	0.15	0.22	0.23	0.13	0.22	0.10	0.12
Gabon	Mid	0.34	0.10	0.09	0.04	0.12	0.07	0.02
Kenya	Mid	0.23	0.20	0.16	0.12	0.19	0.06	0.11
Nigeria	Mid	0.31	0.44	0.31	0.28	0.37	0.23	0.12
Rwanda	Mid	0.11	0.29	0.33	0.29	0.26	0.15	0.12
Tanzania	Mid	0.14	0.36	0.17	0.23	0.13	0.19	0.03
Uganda	Mid	0.17	0.32	0.14	0.27	0.13	0.13	0.10
Average among countries with Mid HIV prevalence		0.21	0.27	0.20	0.19	0.20	0.12	0.09
Lesotho	High	0.15	0.17	0.16	0.10	0.07	0.05	0.03
Malawi	High	0.14	0.18	0.18	0.13	0.18	0.14	0.11
Mozambique	High	0.24	0.21	0.16	0.09	0.09	0.09	0.08
Namibia	High	0.09	0.09	0.06	0.07	0.03	0.04	0.08
Swaziland	High	0.03	0.06	0.04	0.07	0.08	0.07	0.00
Zambia	High	0.06	0.11	0.10	0.11	0.09	0.05	0.06
Zimbabwe	High	0.26	0.23	0.19	0.09	0.08	0.05	0.04
Average among countries with High HIV prevalence		0.14	0.15	0.13	0.09	0.09	0.07	0.06
Regional average		0.20	0.24	0.23	0.23	0.20	0.13	0.09
S/SEA
Cambodia	Low	0.07	0.08	0.16	0.14	0.06	0.11	0.02
Nepal	Low	0.27	0.26	0.13	0.29	0.13	0.14	0.00
Indonesia	Low	0.09	0.24	0.18	0.17	0.12	0.09	0.03
Afghanistan	Low	0.32	0.47	0.52	0.42	0.46	0.38	0.28
Timor-Leste	Low	0.20	0.43	0.48	0.46	0.45	0.46	0.34
Regional average		0.19	0.30	0.29	0.30	0.24	0.24	0.13

Within SSA, we examined the PM distribution stratified by prevalence of HIV among adults at the time of the DHS survey. Among countries in SSA with low HIV prevalence, the expected PM distribution of increasing through the peak reproduction ages and then declining was observed (Fig. 2). This group of countries also has the highest PM compared to medium and high HIV-prevalent countries indicating that, while the burden of deaths among women of reproductive age in SSA may be similar, women in countries with higher HIV burden are dying of other causes that are not maternal related. The pattern of PM among high HIV-prevalent countries showed a steady decline from 15 to 19 years of age through 45–49 years of age. This could be due, in part, to women not able to become pregnant and therefore at increasing risk of death due to chronic illnesses and comorbidity related to HIV later in life. The overall age-adjusted PM for each country in SSA by HIV prevalence showed an inverse relationship between HIV prevalence and PM with countries with high HIV prevalence at the lower end of the PM distribution (Fig. 3). For example, Swaziland had the lowest estimated PM among women 15–49 years of age and the highest adult HIV prevalence at 26.3% (95%CI: 25.6–27.0), while on the other end of the PM distribution, Niger had the highest estimated PM in SSA and the lowest HIV prevalence of 0.4% (95%CI:0.4%–0.6%).⁷

FIG. 2.

PM deaths among women of reproductive age (15–49 years) in sub-Saharan Africa (SSA) by HIV prevalence.

FIG. 3.

PM deaths among women of reproductive age (15–49 years) in SSA by country and HIV prevalence.

Discussion

The results of this aggregated analysis of 38 surveys from countries in three regions demonstrated that the proportion of deaths among women of reproductive age due to maternal causes ranged sevenfold from 5.7% to 41.7% with large variations observed within regions. The trend throughout the reproductive period of 15–49 years reflected a generally increasing PM from 15 to 34 years with a relatively sharp decline after 40 years with PMs reduced by approximately half from 35–39 to 45–49 years. A previous analysis of aggregated DHS data to investigate age patterns for maternal mortality found the greatest number of maternal deaths occurs from 25 to 29, the highest MM Rate occurs from 30 to 34, and the greatest risk (MMR) occurs above 40.⁸ This previous DHS analysis combined with the current analysis on PM suggests that to reduce the absolute number of maternal deaths, maternal health should be a primary, although not sole, focus for women in their mid to late 20s. However, for the older age groups, a focus on broader women's health issues is warranted and should be a part of the policy and research agenda.

Regardless of age, this analysis indicates that more than half of the deaths among women of reproductive age in these countries are not classified as maternal deaths. In countries with high HIV prevalence, maternal mortality is a smaller piece of the distribution of causes of death among women of reproductive age. Although a continued push to reduce maternal mortality and achieve MDG5 in every region of the developing world is warranted, attention to causes of death that are not related to pregnancy and childbirth is clearly needed. Research on the distribution of causes of death among women combined with comprehensive preventive and treatment policies that protect and provide health, education, and nutrition services to women needs to be a priority. Several recent studies have demonstrated the expansion of the dialogue surrounding women's health by investigating the causes of death among women. A recent study in Bangladesh reported that 48% of deaths among women of reproductive age were due to noncommunicable diseases, 17% were due to infections, and 9% to injuries. This compared to 22% that were classified as maternal deaths.³ In Ethiopia, parasitic and infectious diseases alongside noncommunicable diseases were the top causes of death among women.² And finally, in rural Nepal, chronic diseases followed by poisonings, snake bites, and suicide and accidents were the top causes of death among women of reproductive age.⁴ This distribution of causes of death illustrates the broad scope of women's health issues throughout all life stages that should be included in research and policy agendas if the goal is to reduce the burden of death and disease among this population.

There were several limitations to this analysis. First, we were limited by the availability of DHS data and the inclusion of the maternal mortality module in the countries' surveys. Notably absent from our analysis is China and India in the Asian region. Furthermore, there were only four countries in LAC that had conducted the module and therefore represented in this analysis. As a result, conclusions as to the representativeness of the regional averages as well as comparisons between regions should be made with caution. Second, the definition of maternal mortality in the DHS module is solely based on timing of the sister's death with relation to pregnancy and includes deaths up through 2 months postpartum (instead of the traditional definition of maternal death as pregnancy and within 6 weeks postpartum) with no data available on cause. The amount of overestimation of maternal mortality this noncause-specific death variable may create is unclear and many experts believe that the overestimation is minimal. A study in Bangladesh reported that 63 out of 304 maternal deaths were misreported as nonmaternal deaths using the sisterhood method when compared to surveillance records.⁹

The overestimation that may occur by including potentially nonmaternal deaths may be compensated by the fact that in the DHS surveys, only those deaths that are reported to have occurred during pregnancy, delivery, or within 2 months of the postpartum period are considered maternal deaths. Deaths with unknown status about the pregnancy state are classified as nonmaternal. It is possible that the respondents may be truly unware of the pregnancy status of the sisters who died, especially in the early trimester period, or they deliberately underreport, especially if a sister died from abortion-related complications since many induced abortions are not reported by women in areas where they are restricted or illegal. In these settings, it is possible that the sibling would not know that the sister was ever pregnant and therefore, any death that may have occurred as a result of that pregnancy would not be included in the count of maternal deaths.¹⁰ Misclassification and underreporting of maternal deaths due to abortion was demonstrated by Walker et al. who showed that second trimester deaths due to abortions were misclassified both as nonabortion-related maternal deaths as well as nonmaternal deaths.¹¹ Therefore, the DHS may underestimate maternal mortality estimates in areas with high nonresponse rates to the timing of death variable. Nonresponse rates for this variable in the DHS can range from 0.5% to ∼40%, with many of the countries substantially high (≥10%).¹²

The direct method also requires and assumes that the respondent can report accurately on the age of their living siblings and the age at death and years since death for their dead siblings. This is a large assumption especially for those siblings that are the youngest of the family and may not remember or know of siblings that were born and died either before they were born or when they were too young to remember. A potential source of bias in the sisterhood method results from the assumption that the risk of mortality among siblings is independent. In other words, the risk of a sibling dying is not related to the risk of another sibling in that family dying. If mortality risk within family is correlated, then siblings with a higher risk of dying will have fewer available siblings to respond and thus bias the estimates downward.¹³

The results of this analysis will help to provide the evidence and justification to support the suggestions that the international health community's focus on maternal mortality needs to be broadened to include all women's health starting in their early reproductive years. This analysis is particularly relevant given the current work to frame the sustainable development goals (SDGs), which will build on the MDGs. It is important that these SDGs include women's health throughout all life stages as a specific focus. Therefore, the upcoming SDGs provide a suitable framework to address a broader range of health issues for women; it will be up to the policy makers and health officials in many of these low-income countries to enact and support changes to better protect all women. For several countries included in the current analysis, health surveys were only recently initiated; providing invaluable insight into women's health status that before the DHS was largely unknown or only estimated using complex modeling. The results of this analysis should stimulate discussion and thought regarding the scope of women's health and the priorities for future research and policy action.

Footnotes

Acknowledgment

We acknowledge Dr. Saifuddin Ahmed for his expertise and knowledge on working with the DHS datasets.

Author Contributions

C.G.S. designed and conducted the analysis and wrote the first draft of the article. J.M.T. conceptualized and designed the analysis and reviewed and edited the article.

Author Disclosure Statement

No competing financial interests exist.

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