In 2021, it was estimated that the maternal mortality rate as a result of abortion in Argentina amounted to 0.25 deaths per 10,000 live births. Meanwhile, hypertension, edema, or proteinuria caused around 0.6 deaths per 10,000 live births in the South American country that year. As of that date, viral infections related to pregnancy were the leading cause of maternal death in Argentina, most of them related to COVID-19.

Maternal mortality rates can vary significantly around the world. For example, in 2022, Estonia had a maternal mortality rate of zero per 100,000 live births, while Mexico reported a rate of 38 deaths per 100,000 live births. However, the regions with the highest number of maternal deaths are Sub-Saharan Africa and Southern Asia, with differences between countries and regions often reflecting inequalities in health care services and access. Most causes of maternal mortality are preventable and treatable with the most common causes including severe bleeding, infections, complications during delivery, high blood pressure during pregnancy, and unsafe abortion. Maternal mortality in the United States In 2022, there were a total of 817 maternal deaths in the United States. Women aged 25 to 39 years accounted for 578 of these deaths, however, rates of maternal mortality are much higher among women aged 40 years and older. In 2022, the rate of maternal mortality among women aged 40 years and older in the U.S. was 87 per 100,000 live births, compared to a rate of 21 among women aged 25 to 39 years. The rate of maternal mortality in the U.S. has risen in recent years among all age groups. Differences in maternal mortality in the U.S. by race/ethnicity Sadly, there are great disparities in maternal mortality in the United States among different races and ethnicities. In 2022, the rate of maternal mortality among non-Hispanic white women was about 19 per 100,000 live births, while non-Hispanic Black women died from maternal causes at a rate of almost 50 per 100,000 live births. Rates of maternal mortality have risen for white and Hispanic women in recent years, but Black women have by far seen the largest increase in maternal mortality. In 2022, around 253 Black women died from maternal causes in the United States.

The number of maternal deaths and maternal mortality rates for selected causes, 2000 to most recent year.

In 2020, the leading causes of pregnancy-related deaths in the U.S. were different for different races and ethnicities. For example, mental health conditions were the leading cause of pregnancy-related deaths among ****************** women, while ****************** women mostly died from cardiovascular conditions, and******************* women from amniotic fluid embolism. This statistic shows the distribution of pregnancy-related deaths in 38 U.S. states in 2020, by underlying cause and ethnicity.

Estimates based on District hospital discharge data. Counts of and rates based on fewer than 10 births are suppressed for privacy reasons.

Source: Center for Policy Planning and Evaluation, DC Department of Health

Why This Matters

In recent decades, pregnancy-related deaths have risen in the United States. Although relatively rare and mostly preventable, the numbers are high relative to other high-income countries.

Leading underlying causes of pregnancy-related deaths include severe bleeding, cardiac and coronary conditions, and infections. Individual, social, and structural factors contribute to maternal death risk and trends, including maternal age, preexisting medical conditions, access to quality care, insurance, and longstanding racial and ethnic inequities.

Maternal mortality rates are disproportionately higher among birthing people who are Black, Indigenous, and people of color.

The District Response

Enhancements to District healthcare programs. Medicaid expansion provides greater access to prenatal care, extended postpartum Medicaid coverage for a full year, and reimbursement for doula services through all District programs. For a list of local and national resources on pregnancy and related topics, click here.

Paid family leave program providing 12 weeks to bond with a new child or care for a serious health condition, and 2 weeks specifically for prenatal care.

The District established the Maternal Mortality Review Committee, which investigates the causes of maternal deaths, and develops strategic frameworks to improve maternal health.

The dataset contains year and state wise Maternal Mortality Ratio

The World Health Organization (WHO) defines maternal death as the death of a woman while pregnant or within 42 days of termination of pregnancy, irrespective of the duration and site of the pregnancy, from any cause related to or aggravated by the pregnancy or its management but not from accidental or incidental causes.

Note: Maternal Mortality Ratio (MMR) is derived as the proportion of maternal deaths per 1,00,000 live births reported under the SRS.

In 2020, the leading causes of pregnancy-related deaths in the United States were mental health conditions, cardiovascular conditions, and infection. These three leading underlying causes were responsible for over **** of all pregnancy-related deaths in 2020. Mental health conditions alone accounted for *********** of all pregnancy-related deaths in the U.S. showing how important it is to screen for postpartum depression. This statistic shows the percentage of pregnancy-related deaths in 38 U.S. states in 2020, by underlying cause.

Sources:a National Institute for Population Research and Training, MEASURE Evaluation, International Centre for Diarrhoeal Disease Research (2012) Bangladesh Maternal Mortality and Health Care Survey 2010. Available: http://www.cpc.unc.edu/measure/publications/tr-12-87. Accessed October 15, 2012.b World Health Organization (ND) WHO Maternal Mortality Country Profiles. Available: www.who.int/gho/maternal_health/en/#M. Accessed 1 March 2015.c Lozano R, Wang H, Foreman KJ, Rajaratnam JK, Naghavi M, Marcus JR, et al. (2011) Progress towards Millennium Development Goals 4 and 5 on maternal and child mortality: an updated systematic analysis. Lancet 378(9797): 1139–65. 10.1016/S0140-6736(11)61337-8d UNFPA, UNICEF, WHO, World Bank (2012) Trends in maternal mortality: 1990–2010. Available: http://www.unfpa.org/public/home/publications/pid/10728. Accessed 7 October 2012.e Bangladesh Bureau of Statistics, Statistics Informatics Division, Ministry of Planning (December 2012) Population and Housing Census 2011, Socio-economic and Demographic Report, National Series–Volume 4. Available at: http://203.112.218.66/WebTestApplication/userfiles/Image/BBS/Socio_Economic.pdf. Accessed 15 February, 2015.f Mozambique National Institute of Statistics, U.S. Census Bureau, MEASURE Evaluation, U.S. Centers for Disease Control and Prevention (2012) Mortality in Mozambique: Results from a 2007–2008 Post-Census Mortality Survey. Available: http://www.cpc.unc.edu/measure/publications/tr-11-83. Accessed 6 October 2012.g Ministerio da Saude (MISAU), Instituto Nacional de Estatística (INE) e ICF International (ICFI). Moçambique Inquérito Demográfico e de Saúde 2011. Calverton, Maryland, USA: MISAU, INE e ICFI.h Mudenda SS, Kamocha S, Mswia R, Conkling M, Sikanyiti P, et al. (2011) Feasibility of using a World Health Organization-standard methodology for Sample Vital Registration with Verbal Autopsy (SAVVY) to report leading causes of death in Zambia: results of a pilot in four provinces, 2010. Popul Health Metr 9:40. 10.1186/1478-7954-9-40i Central Statistical Office (CSO), Ministry of Health (MOH), Tropical Diseases Research Centre (TDRC), University Teaching Hospital Virology Laboratory, University of Zambia, and ICF International Inc. 2014. Zambia Demographic and Health Survey 2013–14: Preliminary Report. Rockville, Maryland, USA. Available: http://dhsprogram.com/pubs/pdf/PR53/PR53.pdf. Accessed February 26, 2015.j Centers for Disease Control and Prevention (2014) Saving Mothers, Giving Life: Maternal Mortality.Phase 1 Monitoring and Evaluation Report. Atlanta, GA: Centers for Disease Control and Prevention, US Dept of Health and Human Services. Available at: http://www.savingmothersgivinglife.org/doc/Maternal%20Mortality%20(advance%20copy).pdf. Accessed 26 February 2015.k Central Statistical Office (CSO), Ministry of Health (MOH), Tropical Diseases Research Centre (TDRC), University of Zambia, and Macro International Inc. 2009. Zambia Demographic and Health Survey 2007. Calverton, Maryland, USA: CSO and Macro International Inc.Comparison of Maternal Mortality Estimates: Zambia, Bangladesh, Mozambique.

Death statistics (i) Number of Deaths for Different Sexes and Crude Death Rate for the Period from 1981 to 2023 (ii) Age-standardised Death Rate (Overall and by Sex) for the Period from 1981 to 2023 (iii) Age-specific Death Rate for Year 2013 and 2023 (iv) Death Rates by Leading Causes of Death for the Period from 2001 to 2023 (v) Number of Deaths by Leading Causes of Death for the Period from 2001 to 2023 (vi) Age-standardised Death Rates by Leading Causes of Death for the Period from 2001 to 2023 (vii) Late Foetal Mortality Rate for the Period from 1981 to 2023 (viii) Perinatal Mortality Rate for the Period from 1981 to 2023 (ix) Neonatal Mortality Rate for the Period from 1981 to 2023 (x) Infant Mortality Rate for the Period from 1981 to 2023 (xi) Number of Maternal Deaths for the Period from 1981 to 2023 (xii) Maternal Mortality Ratio for the Period from 1981 to 2023

Objectives: To identify and map all trials in maternal health conducted in low- and middle-income countries (LMIC) over the 10-year period 2010-2019, to identify geographical and thematic trends, as well as compare to global causes of maternal death and pre-identified priority areas. Design: Systematic scoping review. Primary and secondary outcome measures: Extracted data included location, study characteristics and whether trials corresponded to causes of mortality and identified research priority topics. Results: Our search identified 7,269 articles, 874 of which were included for analysis. Between 2010 and 2019, maternal health trials conducted in LMICs more than doubled (50 to 114). Trials were conducted in 61 countries – 231 trials (26.4%) were conducted in Iran. Only 225 trials (25.7%) were aligned with a cause of maternal mortality. Within these trials, pre-existing medical conditions, embolism, obstructed labour, and sepsis were all under-represented when compared with number of maternal deaths globally. Large numbers of studies were conducted on priority topics such as labour and delivery, obstetric haemorrhage, and antenatal care. Hypertensive disorders of pregnancy, diabetes, and health systems and policy – despite being high-priority topics – had relatively few trials. Conclusion: Despite trials conducted in LMICs increasing from 2010 to 2019, there were significant gaps in geographical distribution, alignment with causes of maternal mortality, and known research priority topics. The research gaps identified provide guidance and insight for future research conducted in low-resource settings. Methods With support from an information specialist, a search strategy was devised to capture eligible studies (Supplemental Table 1). Search terms for maternal and perinatal health were derived from search strategies used by Cochrane Pregnancy and Childbirth to maintain and update their specialised register. We consulted the search filters developed by Cochrane EPOC to identify search terms relating to LMICs. The search strategy was applied to the Cochrane Central Register of Controlled Trials (CENTRAL), which retrieves records from PubMed/MEDLINE, Embase, CINAHL, ClinicalTrials.gov, WHO’s International Clinical Trials Registry Platform (ICTRP), KoreaMed, Cochrane Review Group’s Specialised Registers, and hand-searched biomedical sources. Searching CENTRAL directly had the benefit of restricting search results to trials only, keeping the volume of citations to screen to a manageable level. Trial register records from ClinicalTrials.gov and WHO ICTRP were not included in the records retrieved from CENTRAL. The search was conducted on 1 May 2020. Citation management, identification of duplicates, and screening articles for eligibility were conducted using EndNote and Covidence. Two reviewers independently screened titles and abstracts of all retrieved citations to identify those that were potentially eligible. Full texts for these articles were accessed and assessed by two independent reviewers according to the eligibility criteria. At both steps, any disagreements were resolved through discussion or consulting a third author. Data collection and analysis For each included trial we extracted information on title, author, year of publication, location where the trial was conducted (country and SDG region), unit of randomisation (individual or cluster), category of intervention, intervention level (public health, community, primary care, hospital, and health system), and category of the primary outcome(s). The intervention and outcome categories were adapted from Cochrane’s list of ‘higher-level categories for interventions and outcomes’. For trials with more than one primary outcome, we identified a single, most appropriate outcome category through discussion and consensus amongst review authors. The level of intervention was determined based on the level of the healthcare system that the trial was primarily targeting – for example, trials recruiting women at an antenatal clinic were classified as primary care level. Public health and preventative care were defined as interventions for those in the community who were well, while home; and community care was defined as interventions for those in the community who were unwell. Based on the trial’s primary objective, we tagged each trial to one of 35 maternal health topics, as well as classified them by relevance to a cause of maternal death identified by Say et al in their global systematic analysis (Box 1). Included trials were additionally categorised into global research priority topics identified by Souza et al and Chapman et al. The research priorities identified by Souza et al were ranked based on the distribution of maternal health themes across the 190 priority research questions – i.e., the theme with the most research questions was considered the highest-ranked priority topic. This mirrored the process used by Chapman et al, where research topics with the greatest representation within the 100 research questions, based on percentage, were given the highest rank. For each trial identified in our review, we used the variables extracted to classify it according to priority topics identified in Souza et al or Chapman et al, where possible (Box 1). All data were extracted by two independent reviewers, with results compared to ensure consistency and any disputes resolved through discussion or consultation with a third author. As this was a scoping review, we did not perform quality assessments on individual trials. We conducted descriptive analyses using Excel to determine frequencies of extracted variables and used line graphs to explore trends. We assessed trends over time using proportions of each variable within studies available for a given year. While we initially planned to look at trends in individual countries and interventions, many had few or no data points.

Maternal Mortality Ratio per 100,000

The maternal mortality rate in Australia in 2016 was 8.5 deaths per 100,000 women giving birth. Between 2006 and 2016, 281 women were reported to have died during pregnancy or within 42 days of the end of pregnancy. The most common causes of maternal deaths in Australia are non-obstetric haemorrhage and heart disease.

Further information can be found here: https://www.aihw.gov.au/reports/mothers-babies/maternal-deaths-in-australia-2016/data

La statistique de mortalité maternelle est établie à partir de la base de données des causes de décès. Dans celle-ci, une sélection des « décès maternels » est faite, en suivant une longue procédure (décrite en détail dans les Métadonnées) qui respecte la définition de l’OMS. D’après la dixième révision de la Classification internationale des maladies (CIM-10), le décès maternel se définit comme « le décès d’une femme survenu au cours de la grossesse ou dans un délai de 42 jours après sa terminaison, quelles qu’en soient la durée ou la localisation, pour une cause quelconque déterminée ou aggravée par la grossesse ou les soins qu’elle a motivés, mais ni accidentelle, ni fortuite ». « Les décès maternels se subdivisent en deux groupes. Les décès par cause obstétricale directe sont ceux qui résultent de complications obstétricales (grossesse, travail et suites de couches), d’interventions, d’omissions, d’un traitement incorrect ou d’un enchaînement d’événements résultant de l’un quelconque des facteurs ci-dessus. Les décès par cause obstétricale indirecte sont ceux qui résultent d’une maladie préexistante ou d’une affection apparue au cours de la grossesse sans qu’elle soit due à des causes obstétricales directes, mais qui a été aggravée par les effets physiologiques de la grossesse ». La CIM-10 définit également le décès maternel tardif comme étant « le décès d’une femme résultant de causes obstétricales directes ou indirectes survenu plus de 42 jours, mais moins d’un an, après la terminaison de la grossesse ». Le ratio de mortalité maternelle est le rapport entre le nombre de décès maternels, directs et indirects, observés en une année, et le nombre de naissances vivantes de la même année, exprimé pour 100.000 naissances vivantes. Les décès maternels tardifs ne sont pas pris en compte pour le calcul de ce ratio. Etant donné le petit nombre de cas identifiés en Belgique chaque année et la grande variabilité de cet effectif, le choix a été fait de calculer le ratio en cumulant les décès maternels et les naissances vivantes de 5 années successives, en centrant le ratio sur l’année médiane. Lors de l’identification de ces décès maternels, le Groupe de travail ad hoc, qui rassemble autour de l’office belge de statistique toutes les entités fédérées productrices de données, n’a pas écarté le risque d’une sous-évaluation de ces décès, sur la base du seul bulletin statistique qui sert de source principale. Il demande donc de poursuivre les efforts afin d’améliorer davantage le suivi des décès liés à la maternité et soutient l’initiative prise récemment par le Collège de médecins pour la mère et le nouveau-né d’examiner la possibilité de création d’un Registre de la mortalité maternelle. Métadonnées La statistique de mortalité maternelle

Maternal mortality remains very high in Mozambique, with estimates from 2015 showing a maternal mortality ratio of 489 deaths per 100,000 live births, even though the rates tend to decrease since 1990. Pregnancy related hemorrhage, gestational hypertension and diseases such as malaria and HIV/AIDS are amongst the leading causes of maternal death in Mozambique, and a significant number of these deaths occur within health facilities. Often, the analysis of data on maternal mortality involves the use of counts of maternal deaths as outcome variable. Previously we showed that a class of hierarchical zero-inflated models were very successful in dealing with overdispersion and clustered counts when analyzing data on maternal deaths and related risk factors within health facilities in Mozambique. This paper aims at providing additional insights over previous analyses and presents an extension of such models to account for spatial variation in a disease mapping framework of facility-based maternal mortality in Mozambique.

BackgroundDespite global health efforts to reduce maternal mortality, rates continue to be unacceptably high in large parts of the world. Feasible, acceptable, and accurate postmortem sampling methods could provide the necessary evidence to improve the understanding of the real causes of maternal mortality, guiding the design of interventions to reduce this burden.Methods and findingsThe validity of a minimally invasive autopsy (MIA) method in determining the cause of death was assessed in an observational study in 57 maternal deaths by comparing the results of the MIA with those of the gold standard (complete diagnostic autopsy [CDA], which includes any available clinical information). Concordance between the MIA and the gold standard diagnostic categories was assessed by the kappa statistic, and the sensitivity, specificity, positive and negative predictive values and their 95% confidence intervals (95% CI) to identify the categories of diagnoses were estimated. The main limitation of the study is that both the MIA and the CDA include some degree of subjective interpretation in the attribution of cause of death.A cause of death was identified in the CDA in 98% (56/57) of cases, with indirect obstetric conditions accounting for 32 (56%) deaths and direct obstetric complications for 24 (42%) deaths. Nonobstetric infectious diseases (22/32, 69%) and obstetric hemorrhage (13/24, 54%) were the most common causes of death among indirect and direct obstetric conditions, respectively. Thirty-six (63%) women were HIV positive, and HIV-related conditions accounted for 16 (28%) of all deaths. Cerebral malaria caused 4 (7%) deaths. The MIA identified a cause of death in 86% of women. The overall concordance of the MIA with the CDA was moderate (kappa = 0.48, 95% CI: 0.31–0.66). Both methods agreed in 68% of the diagnostic categories and the agreement was higher for indirect (91%) than for direct obstetric causes (38%). All HIV infections and cerebral malaria cases were identified in the MIA. The main limitation of the technique is its relatively low performance for identifying obstetric causes of death in the absence of clinical information.ConclusionsThe MIA procedure could be a valuable tool to determine the causes of maternal death, especially for indirect obstetric conditions, most of which are infectious diseases.The information provided by the MIA could help to prioritize interventions to reduce maternal mortality and to monitor progress towards achieving global health targets.

In 2023, non-Hispanic Black women had the highest rates of maternal mortality among select races/ethnicities in the United States, with 50.3 deaths per 100,000 live births. The total maternal mortality rate in the U.S. at that time was 18.6 per 100,000 live births, a decrease from a rate of almost 33 in 2021. This statistic presents the maternal mortality rates in the United States from 2018 to 2023, by race and ethnicity.

Bangladesh maternal mortality rate for 2023 was 115.00, a 12.21% decline from 2022.

<ul style='margin-top:20px;'>

<li>Bangladesh maternal mortality rate for 2022 was <strong>131.00</strong>, a <strong>32.47% decline</strong> from 2021.</li>
<li>Bangladesh maternal mortality rate for 2021 was <strong>194.00</strong>, a <strong>27.63% increase</strong> from 2020.</li>
<li>Bangladesh maternal mortality rate for 2020 was <strong>152.00</strong>, a <strong>3.18% decline</strong> from 2019.</li>
</ul>Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data are estimated with a regression model using information on the proportion of maternal deaths among non-AIDS deaths in women ages 15-49, fertility, birth attendants, and GDP.

Morocco maternal mortality rate for 2023 was 70.00, a 1.41% decline from 2022.

<ul style='margin-top:20px;'>

<li>Morocco maternal mortality rate for 2022 was <strong>71.00</strong>, a <strong>22.83% decline</strong> from 2021.</li>
<li>Morocco maternal mortality rate for 2021 was <strong>92.00</strong>, a <strong>13.58% increase</strong> from 2020.</li>
<li>Morocco maternal mortality rate for 2020 was <strong>81.00</strong>, a <strong>2.53% increase</strong> from 2019.</li>
</ul>Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data are estimated with a regression model using information on the proportion of maternal deaths among non-AIDS deaths in women ages 15-49, fertility, birth attendants, and GDP.

Bangladesh BD: Cause of Death: by Communicable Diseases & Maternal, Prenatal & Nutrition Conditions: % of Total data was reported at 22.613 % in 2019. This records a decrease from the previous number of 28.808 % for 2015. Bangladesh BD: Cause of Death: by Communicable Diseases & Maternal, Prenatal & Nutrition Conditions: % of Total data is updated yearly, averaging 30.893 % from Dec 2000 (Median) to 2019, with 4 observations. The data reached an all-time high of 50.247 % in 2000 and a record low of 22.613 % in 2019. Bangladesh BD: Cause of Death: by Communicable Diseases & Maternal, Prenatal & Nutrition Conditions: % of Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bangladesh – Table BD.World Bank.WDI: Social: Health Statistics. Cause of death refers to the share of all deaths for all ages by underlying causes. Communicable diseases and maternal, prenatal and nutrition conditions include infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting.;Derived based on the data from Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019. Geneva, World Health Organization; 2020. Link: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death;Weighted average;

Abstract

The 2019 Pakistan Maternal Mortality Survey (2019 PMMS) was the first stand-alone maternal mortality survey conducted in Pakistan. A nationally representative sample of 1,396 primary sampling units were randomly selected. The survey was expected to result in about 14,000 interviews with ever-married women age 15-49.

The primary objective of the 2019 PMMS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the survey was designed and carried out with the purpose of assessing where Pakistan stands on maternal health indicators and how well the country is moving toward these targets. Overall aims of the 2019 PMMS were as follows: - To estimate national and regional levels of maternal mortality for the 3 years preceding the survey and determine whether the MMR has declined substantially since 2006-07 - To identify medical causes of maternal deaths and the biological and sociodemographic risk factors associated with maternal mortality - To assess the impact of maternal and newborn health services, including antenatal and postnatal care and skilled birth attendance, on prevention of maternal mortality and morbidity - To estimate the prevalence and determinants of common obstetric complications and morbidities among women of reproductive age during the 3 years preceding the survey

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Woman age 15-49
• Community

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2019 PMMS used a multistage and multiphase cluster sampling methodology based on updated sampling frames derived from the 6th Population and Housing Census, which was conducted in 2017 by the Pakistan Bureau of Statistics (PBS). The sampling universe consisted of urban and rural areas of the four provinces of Pakistan (Punjab, Sindh, Khyber Pakhtunkhwa, and Balochistan), Azad Jammu and Kashmir (AJK), Gilgit Baltistan (GB), Federally Administered Tribal Areas (FATA), and the Islamabad Capital Territory (ICT). A total of 153,560 households (81,400 rural and 72,160 urban) were selected using a two-stage and two-phase stratified systematic sampling approach. The survey was designed to provide representative results for most of the survey indicators in 11 domains: four provinces (by urban and rural areas with Islamabad combined with Punjab and FATA combined with Khyber Pakhtunkhwa), Azad Jammu and Kashmir (urban and rural), and Gilgit Baltistan. Restricted military and protected areas were excluded from the sample.

The sampled households were randomly selected from 1,396 primary sampling units (PSUs) (740 rural and 656 urban) after a complete household listing. In each PSU, 110 randomly selected households were administered the various questionnaires included in the survey. All 110 households in each PSU were asked about births and deaths during the previous 3 years, including deaths among women of reproductive age (15-49 years). Households that reported at least one death of a woman of reproductive age were then visited, and detailed verbal autopsies were conducted to determine the causes and circumstances of these deaths to help identify maternal deaths. In the second phase, 10 households in each PSU were randomly selected from the 110 households selected in the first phase to gather detailed information on women of reproductive age. All eligible ever-married women age 15-49 residing in these 10 households were interviewed to gather detailed information, including a complete pregnancy history.

Note: A detailed description of the sample design is provided in Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Six questionnaires were used in the 2019 PMMS: the Short Household Questionnaire, the Long Household Questionnaire, the Woman’s Questionnaire, the Verbal Autopsy Questionnaire, the Community Questionnaire, and the Fieldworker Questionnaire. A Technical Advisory Committee was established to solicit comments on the questionnaires from various stakeholders, including representatives of government ministries and agencies, nongovernmental organisations, and international donors. The survey protocol was reviewed and approved by the National Bioethics Committee, the Pakistan Health Research Council, and the ICF Institutional Review Board. After being finalised in English, the questionnaires were translated into Urdu and Sindhi. The 2019 PMMS used paper-based questionnaires for data collection, while computer-assisted field editing (CAFE) was used to edit questionnaires in the field.

Cleaning operations

The processing of the 2019 PMMS data began simultaneously with the fieldwork. As soon as data collection was completed in each cluster, all electronic data files were transferred via the Internet File Streaming System (IFSS) to the NIPS central office in Islamabad. These data files were registered and checked for inconsistencies, incompleteness, and outliers. A double entry procedure was adopted by NIPS to ensure data accuracy. The field teams were alerted about any inconsistencies and errors. Secondary editing of completed questionnaires, which involved resolving inconsistencies and coding open-ended questions, was carried out in the central office. The survey core team members assisted with secondary editing, and the NIPS data processing manager coordinated the work at the central office. Data entry and editing were carried out using the CSPro software package. The concurrent processing of the data offered a distinct advantage because it maximised the likelihood of the data being error-free and accurate.

Response rate

In the four provinces, the sample contained a total of 116,169 households. All households were visited by the field teams, and 110,483 households were found to be occupied. Of these households, 108,766 were successfully interviewed, yielding a household response rate of 98%. The subsample selected for the Long Household Questionnaire comprised 11,080 households, and interviews were carried out in 10,479 of these households. A total of 12,217 ever-married women age 15-49 were eligible to be interviewed based on the Long Household Questionnaire, and 11,859 of these women were successfully interviewed (a response rate of 97%).

In Azad Jammu and Kashmir, 16,755 households were occupied, and interviews were successfully carried out in 16,588 of these households (99%). A total of 1,707 ever-married women were eligible for individual interviews, of whom 1,666 were successfully interviewed (98%). In Gilgit Baltistan, 11,005 households were occupied, and interviews were conducted in 10,872 households (99%). A total of 1,219 ever-married women were eligible for interviews, of whom 1,178 were successfully interviewed (97%).

A total of 944 verbal autopsy interviews were conducted in Pakistan overall, 150 in Azad Jammu and Kashmir, and 88 in Gilgit Baltistan. The Verbal Autopsy Questionnaire was used in almost all of the interviews, and response rates were nearly 100%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2019 Pakistan Maternal Mortality Survey (2019 PMMS) to minimise this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2019 PMMS is only one of many samples that could have been selected from the same population, using the same design and sample size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected by simple random sampling, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2019 PMMS sample was the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed using SAS programmes developed by ICF. These programmes use the Taylor linearisation method to estimate variances for survey estimates that are means, proportions, or ratios and use the Jackknife repeated replication method for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in Appendix B of the survey report.

Data appraisal

Data Quality Tables

- Household age distribution

In Nigeria, Chad, South Sudan, and the Central African Republic, the maternal mortality rate was over 650 per 100,000 live births in 2023, respectively. Nigeria recorded the highest rate on the continent. That year, for every 100,000 children, 993 mothers died from any cause related to or aggravated by pregnancy or its management. The maternal death rate in Chad equaled 748. South Sudan and the Central African Republic followed with 692 deaths per 100,000 live births each.