Statistics on maternal mortality are produced based on the database of causes of death. "Maternal deaths" are selected from the database via a complex procedure, which takes into account the definition given by the WHO and is described in detail in the metadata. The tenth revision of the International Classification of Diseases (ICD-10) defines maternal death as "the death of a woman while pregnant or within 42 days of termination of pregnancy, irrespective of the duration and the site of the pregnancy, from any cause related to or aggravated by the pregnancy or its management, but not from accidental or incidental causes." "Maternal deaths should be subdivided into two groups. Direct obstetric deaths: those resulting from obstetric complications of the pregnant state (pregnancy, labour and puerperium), from interventions, omissions, incorrect treatment, or from a chain of events resulting from any of the above. Indirect obstetric deaths: those resulting from previous existing disease or disease that developed during pregnancy and which was not due to direct obstetric causes, but which was aggravated by physiologic effects of pregnancy." Furthermore, the ICD-10 also defines late maternal death as "the death of a woman from direct or indirect obstetric causes more than 42 days but less than one year after termination of pregnancy." The "maternal mortality rate" is the ratio between the number of recorded direct and indirect maternal deaths over one year and the number of live birth in the same year, expressed per 100,000 live births. Late maternal deaths are not taken into account in the calculation of this ratio. Given the small and markedly variable number of cases recorded each year in Belgium, it has been decided to calculate this ratio based on the cumulated maternal deaths and live births of five consecutive years, with the ratio calculated being recorded in the middle year. When identifying these maternal deaths, the ad hoc working group, bringing together the Belgian statistical office and all data producing federated entities, did not exclude the risk of an underestimation of these deaths, based on the only statistical bulletin used as main source. It therefore asks for continued efforts to further improve the follow-up of maternal deaths, and supports the recent initiative of the College of physicians for Mother and Newborn to consider the creation of a maternal mortality register.

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.

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

*Significantly different (95% level) estimates (confidence intervals do not overlap).

Statbel, the Belgian statistical office, publishes an overview of the provisional mortality figures for each year, for all causes of death. The publication contains provisional mortality figures for all Belgian municipalities, and an analysis of the mortality by age category, gender and by month.

General mortality statistics are compiled on the basis of data from the National Register of Natural Persons (RNPP). They make it possible to consolidate the statistics on causes of death, the source of which is the civil status forms. This statistic breaks down the deaths of people residing in Belgium according to sex, municipality of residence (district, province and region), month of death, civil status and nationality (Belgian or foreign). They also make it possible to calculate the gross mortality rate, i.e. the ratio between the number of deaths during the year and the population in the middle of that year.

a All numbers are weighted unless otherwise specified.b The INCAM report provides an estimate of the MMR among women age 15–49 of 489.3 per 100,000 live births (Table 32) but this estimate is based on the 2007 census data not on the INCAM data [16].Maternal mortality statistics by country and survey platform.a

This dataset presents the estimated number of live births in each country for the most recent reference year, based on the 2024 revision of the UN Population Division’s World Population Prospects. Live birth estimates are a key demographic indicator, used for planning health services, calculating health coverage indicators, and understanding population growth trends. These figures support maternal and newborn health monitoring and workforce planning at national and global levels.Data Source:UN Population Division World Population Prospects: https://population.un.org/wpp/Download/StandardData Dictionary: The data is collated with the following columns:Column headingContent of this columnPossible valuesRefNumerical counter for each row of data, for ease of identification1+CountryShort name for the country195 countries in total – all 194 WHO member states plus PalestineISO3Three-digit alphabetical codes International Standard ISO 3166-1 assigned by the International Organization for Standardization (ISO). e.g. AFG (Afghanistan)ISO22 letter identifier code for the countrye.g. AF (Afghanistan)ICM_regionICM Region for countryAFR (Africa), AMR (Americas), EMR (Eastern Mediterranean), EUR (Europe), SEAR (South east Asia) or WPR (Western Pacific)CodeUnique project code for each indicator:GGTXXnnnGG=data group e.g. OU for outcomeT = N for novice or E for ExpertXX = identifier number 00 to 30nnn = identifier name eg mmre.g. OUN01sbafor Outcome Novice Indicator 01 skilled birth attendance Short_nameIndicator namee.g. maternal mortality ratioDescriptionText description of the indicator to be used on websitee.g. Maternal mortality ratio (maternal deaths per 100,000 live births)Value_typeDescribes the indicator typeNumeric: decimal numberPercentage: value between 0 & 100Text: value from list of text optionsY/N: yes or noValue_categoryExpect this to be ‘total’ for all indicators for Phase 1, but this could allow future disaggregation, e.g. male/female; urban/ruraltotalYearThe year that the indicator value was reported. For most indicators, we will only report if 2014 or more recente.g. 2020Latest_Value‘LATEST’ if this is the most recent reported value for the indicator since 2014, otherwise ‘No’. Useful for indicators with time trend data.LATEST or NOValueIndicator valuee.g. 99.8. NB Some indicators are calculated to several decimal places. We present the value to the number of decimal places that should be displayed on the Hub.SourceFor Caesarean birth rate [OUN13cbr] ONLY, this column indicates the source of the data, either OECD when reported, or UNICEF otherwise.OECD or UNICEFTargetHow does the latest value compare with Global guidelines / targets?meets targetdoes not meet targetmeets global standarddoes not meet global standardRankGlobal rank for indicator, i.e. the country with the best global score for this indicator will have rank = 1, next = 2, etc. This ranking is only appropriate for a few indicators, others will show ‘na’1-195Rank out ofThe total number of countries who have reported a value for this indicator. Ranking scores will only go as high as this number.Up to 195TrendIf historic data is available, an indication of the change over time. If there is a global target, then the trend is either getting better, static or getting worse. For mmr [OUN04mmr] and nmr [OUN05nmr] the average annual rate of reduction (arr) between 2016 and latest value is used to determine the trend:arr <-1.0 = getting worsearr >=-1.0 AND <=1.0 = staticarr >1.0 = getting betterFor other indicators, the trend is estimated by comparing the average of the last three years with the average ten years ago:decreasing if now < 95% 10 yrs agoincreasing if now > 105% 10 yrs agostatic otherwiseincreasingdecreasing Or, if there is a global target: getting better,static,getting worseNotesClarification comments, when necessary LongitudeFor use with mapping LatitudeFor use with mapping DateDate data uploaded to the Hubthe following codes are also possible values:not reported does not apply don’t knowThis is one of many datasets featured on the Midwives’ Data Hub, a digital platform designed to strengthen midwifery and advocate for better maternal and newborn health services.

BackgroundFor countries to contribute to Sustainable Development Goal 3.1 of reducing the global maternal mortality ratio (MMR) to less than 70 per 100,000 live births by 2030, identifying the drivers of maternal mortality is critically important. The ability of countries to identify the key drivers is however hampered by the lack of data sources with sufficient observations of maternal death to allow a rigorous analysis of its determinants. This paper overcomes this problem by utilising census data. In the context of Indonesia, we merge individual-level data on pregnancy-related deaths and households’ socio-economic status from the 2010 Indonesian population census with detailed data on the availability and quality of local health services from the Village Census. We use these data to test the hypothesis that health service access and quality are important determinants of maternal death and explain the differences between high maternal mortality and low maternal mortality provinces.MethodsThe 2010 Indonesian Population Census identifies 8075 pregnancy-related deaths and 5,866,791 live births. Multilevel logistic regression is used to analyse the impacts of demographic characteristics and the existence of, distance to and quality of health services on the likelihood of maternal death. Decomposition analysis quantifies the extent to which the difference in maternal mortality ratios between high and low performing provinces can be explained by demographic and health service characteristics.FindingsHealth service access and characteristics account for 23% (CI: 17.2% to 28.5%) of the difference in maternal mortality ratios between high and low-performing provinces. The most important contributors are the number of doctors working at the community health centre (8.6%), the number of doctors in the village (6.9%) and distance to the nearest hospital (5.9%). Distance to health clinics and the number of midwives at community health centres and village health posts are not significant contributors, nor is socio-economic status. If the same level of access to doctors and hospitals in lower maternal mortality Java-Bali was provided to the higher maternal mortality Outer Islands of Indonesia, our model predicts 44 deaths would be averted per 100,000 pregnancies.ConclusionIndonesia has employed a strategy over the past several decades of increasing the supply of midwives as a way of decreasing maternal mortality. While there is evidence of reductions in maternal mortality continuing to accrue from the provision of midwife services at village health posts, our findings suggest that further reductions in maternal mortality in Indonesia may require a change of focus to increasing the supply of doctors and access to hospitals. If data on maternal death is collected in a subsequent census, future research using two waves of census data would prove a useful validation of the results found here. Similar research using census data from other countries is also likely to be fruitful.

Maternal deaths as a share of all female deaths by age group (15–49), weighted.

Mortality ratios and the proportion of abortion-related deaths by place of residence in states with or without a constitutional amendment to protect the unborn starting at conception. Left panels illustrate trends for MMR (top panel), MMRAO (middle top panel), iAMR (middle bottom panel), and the proportion of abortion-related deaths (bottom panel) by place of residence between 2008 and 2011 in Mexican states, grouped as with amendment (in dark green) or without amendment (in cyan) in terms of abortion legislation in their criminal code (see Material and Methods). Right panels show average ratios and proportion of abortion-related deaths for each group (dark green and cyan bars, respectively). * p<0.05 using Z-test. Abbreviations: iAMR, induced abortion mortality ratio; MMR, maternal mortality ratio; MMRAO, maternal mortality ratio with abortive outcome.

Objectives: To explore whether the rule of law is a foundational determinant of health that underlies other socioeconomic, political and cultural factors that have been associated with health outcomes. Setting: Global project. Participants: Data set of 96 countries, comprising 91% of the global population. Primary and secondary outcome measures: The following health indicators, infant mortality rate, maternal mortality rate, life expectancy, and cardiovascular disease and diabetes mortality rate, were included to explore their association with the rule of law. We used a novel Rule of Law Index, gathered from survey sources, in a cross-sectional and ecological design. The Index is based on eight subindices: (1) Constraints on Government Powers; (2) Absence of Corruption; (3) Order and Security; (4) Fundamental Rights; (5) Open Government; (6) Regulatory Enforcement, (7) Civil Justice; and (8) Criminal Justice. Results: The rule of law showed an independent association with infant mortality rate, maternal mortality rate, life expectancy, and cardiovascular disease and diabetes mortality rate, after adjusting for the countries’ level of per capita income, their expenditures in health, their level of political and civil freedom, their Gini measure of inequality and women's status (p<0.05). Rule of law remained significant in all the multivariate models, and the following adjustment for potential confounders remained robust for at least one or more of the health outcomes across all eight subindices of the rule of law. Findings show that the higher the country's level of adherence to the rule of law, the better the health of the population. Conclusions: It is necessary to start considering the country's adherence to the rule of law as a foundational determinant of health. Health advocates should consider the improvement of rule of law as a tool to improve population health. Conversely, lack of progress in rule of law may constitute a structural barrier to health improvement.

This dataset provides the estimated number of women aged 15–49 years in each country, based on the 2024 revision of the UN Population Division’s World Population Prospects. This age group is commonly defined as women of reproductive age and is used as the denominator in calculating key sexual and reproductive health indicators. These estimates support health system planning, resource allocation, and monitoring of service coverage for women across the reproductive life course.Data Source:UN Population Division’s World Population Prospects: https://population.un.org/wpp/ Data Dictionary: The data is collated with the following columns:Column headingContent of this columnPossible valuesRefNumerical counter for each row of data, for ease of identification1+CountryShort name for the country195 countries in total – all 194 WHO member states plus PalestineISO3Three-digit alphabetical codes International Standard ISO 3166-1 assigned by the International Organization for Standardization (ISO). e.g. AFG (Afghanistan)ISO22 letter identifier code for the countrye.g. AF (Afghanistan)ICM_regionICM Region for countryAFR (Africa), AMR (Americas), EMR (Eastern Mediterranean), EUR (Europe), SEAR (South east Asia) or WPR (Western Pacific)CodeUnique project code for each indicator:GGTXXnnnGG=data group e.g. OU for outcomeT = N for novice or E for ExpertXX = identifier number 00 to 30nnn = identifier name eg mmre.g. OUN01sbafor Outcome Novice Indicator 01 skilled birth attendance Short_nameIndicator namee.g. maternal mortality ratioDescriptionText description of the indicator to be used on websitee.g. Maternal mortality ratio (maternal deaths per 100,000 live births)Value_typeDescribes the indicator typeNumeric: decimal numberPercentage: value between 0 & 100Text: value from list of text optionsY/N: yes or noValue_categoryExpect this to be ‘total’ for all indicators for Phase 1, but this could allow future disaggregation, e.g. male/female; urban/ruraltotalYearThe year that the indicator value was reported. For most indicators, we will only report if 2014 or more recente.g. 2020Latest_Value‘LATEST’ if this is the most recent reported value for the indicator since 2014, otherwise ‘No’. Useful for indicators with time trend data.LATEST or NOValueIndicator valuee.g. 99.8. NB Some indicators are calculated to several decimal places. We present the value to the number of decimal places that should be displayed on the Hub.SourceFor Caesarean birth rate [OUN13cbr] ONLY, this column indicates the source of the data, either OECD when reported, or UNICEF otherwise.OECD or UNICEFTargetHow does the latest value compare with Global guidelines / targets?meets targetdoes not meet targetmeets global standarddoes not meet global standardRankGlobal rank for indicator, i.e. the country with the best global score for this indicator will have rank = 1, next = 2, etc. This ranking is only appropriate for a few indicators, others will show ‘na’1-195Rank out ofThe total number of countries who have reported a value for this indicator. Ranking scores will only go as high as this number.Up to 195TrendIf historic data is available, an indication of the change over time. If there is a global target, then the trend is either getting better, static or getting worse. For mmr [OUN04mmr] and nmr [OUN05nmr] the average annual rate of reduction (arr) between 2016 and latest value is used to determine the trend:arr <-1.0 = getting worsearr >=-1.0 AND <=1.0 = staticarr >1.0 = getting betterFor other indicators, the trend is estimated by comparing the average of the last three years with the average ten years ago:decreasing if now < 95% 10 yrs agoincreasing if now > 105% 10 yrs agostatic otherwiseincreasingdecreasing Or, if there is a global target: getting better,static,getting worseNotesClarification comments, when necessary LongitudeFor use with mapping LatitudeFor use with mapping DateDate data uploaded to the Hubthe following codes are also possible values:not reported does not apply don’t knowThis is one of many datasets featured on the Midwives’ Data Hub, a digital platform designed to strengthen midwifery and advocate for better maternal and newborn health services.

This thematic report presents the status of maternal mortality in Myanmar. The analysis shows that maternal mortality in the country is high at 282 deaths per 100,000 live births, and that there is a need for concerted efforts to ensure that women have access to services that provide high quality health care before, during and after childbirth. Furthermore, about 10 per cent of female deaths of women of reproductive age (15-49 years) are attributed to maternal deaths. By State and Region, maternal mortality ratios are very high in Chin, Ayeyawady and Magway, while they are lowest in Tanintharyi, Nay Pyi Taw and Yangon. Although steps are being taken to provide health services in these areas with high maternal mortality, there is also a need to carry out more specialized surveys to determine why mortality rates remain high in the country and in specific States and Regions.

Abstract

The 1997 Yemen Demographic Maternal and Child Health Survey (YDMCHS) is part of the worldwide Demographic and Health Surveys (DHS) program. The DHS program is designed to collect data on fertility, family planning and maternal and child health.

The YDMCHS-97 has the following objectives: 1. Provide policymakers and decisionmakers with a reliable database and analyses useful for policy choices and population programs, and provide researchers, other interested persons, and scholars with such data. 2. Update and expand the national population and health data base through collection of data which will allow the calculation of demographic rates, especially fertility rates, and infant and child mortality rates; 3. Analyse the direct and indirect factors which determine levels and trends of fertility. Indicators related to fertility will serve to elaborate plans for social and economic development; 4. Measure the level of contraceptive knowledge and practice by method, by rural and urban residence including some homogeneous governorates (Sana’a, Aden, Hadhramaut, Hodeidah, Hajjah and Lahj). 5. Collect quality data on family health: immunizations, prevalence and treatment of diarrhea and other diseases among children under five, prenatal visits, assistance at delivery and breastfeeding; 6. Measure the nutritional status of mothers and their children under five years (anthropometric measurements: weight and height); 7. Measure the level of maternal mortality at the national level. 8. Develop skills and resources necessary to conduct high-quality demographic and health surveys.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLE DESIGN

The 1997 YDMCHS was based on a national sample in order to provide estimates for general indicators for the following domains: Yemen as a whole, urban and rural areas (each as a separate domain), three ecological zones identified as Coastal, Mountainous, and Plateau and Desert, as well as governorates with a sample size of at least 500 completed cases. The survey sample was designed as a two-stage cluster sample of 475 enumeration areas (EA), 135 in urban areas and 340 in rural areas. The master sample, based on the 1994 census frame, was used as the frame for the 1997 YDMCHS. The population covered by the Yemen survey was the universe of all ever-married women age 15-49. The initial target sample was 10,000 completed interviews among eligible women, and the final sample was 10,414. In order to get this number of completed interviews, and using the response rate found in the 1991-92 YDMCHS survey, a total of 10,701 of the 11,435 potential households selected for the household sample were completed.

In each selected EA, a complete household listing operation took place between July and September 1997, and was undertaken by nineteen (19) field teams, taking into consideration the geographical closeness of the areas assigned to each team.

Note: See detailed description of sample design in APPENDIX B of the final survey report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two Questionnaires were used to collect survey data:

Household Questionnaire: The household questionnaire consists of two parts: a household schedule and a series of questions relating to the health and socioeconomic status of the household. The household schedule was used to list all usual household members. For each of the individuals included in the schedule, information was collected on the relationship to the household head, age, sex, marital status (for those 10 years and older), educational level (for those 6 years and older) and work status (for those 10 years and older). It also collects information on fertility, general mortality and child survival. The second part of the household questionnaire included questions on housing characteristics including the type of dwelling, location, materials used in construction, number of rooms, kitchen in use, main source of drinking water and health related aspects, lighting and toilet facilities, disposal of garbage, durable commodities, and assets, type of salt the household uses for cooking, and other related residential information.

Individual Questionnaire: The individual questionnaire was administered to all ever-married women age 15-49 years who were usual residents. It contained 10 sections on the followings topics: - Respondent's background - Reproduction - Family planning - Pregnancy and breastfeeding - Immunization and health - Birth preferences - Marriage and husband's background - Maternal mortality - Female circumcision - Height and weight

Response rate

10,701 households, distributed between urban (3,008 households) and rural areas (7,693), households which were successfully interviewed in the 1997 YDMCHS. This represents a country-wide response rate of 98.2 percent (98.7 and 98.0 percent, respectively, for urban and rural areas).

A total of 11,158 women were identified as eligible to be interviewed. Questionnaires were completed for 10,414 women, which represents a response rate of 93.3 percent. The response rate in urban areas was 93 percent; and in rural areas it was 93.5 percent.

Note: See summarized response rates by place of residence in Table 1.1 of the final survey report.

Sampling error estimates

The estimates from a sample surveys are affected by two types of errors: (1) non-sampling error, and (2) sampling error. Non-sampling 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 YDMCHS-97 to minimize this type of error, non-sampling 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 YDMCHS-97 is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would have yielded results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of standard error of 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 statistics in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the YDMCHS-97 sample is the result of a two-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the YDMCHS-97 is the ISSA Sampling Error Module (SAMPERR). This module used the Taylor linearization method of variance estimate for survey estimates that are means or proportions. The Jackknife repeated replication method is used for variance estimate of more complex statistics such as fertility and mortality rates.

Note: See detailed estimate of sampling error calculation in APPENDIX C of the final survey report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women and men - Completeness of reporting - Births by calendar year - Reporting of age at death in days - Reporting of age at death in months

Note: See detailed tables in APPENDIX D of the final survey report.

This feature layer represents Sustainable Development Goal indicator 3.1.1 'Maternal Mortality Ratio' for Ireland. The layer was created using data produced by the Central Statistics Office (CSO) as part of the CSO's Vital Statistics Annual Report 2014 and NUTS 3 boundary data produced by Tailte Éireann. Ratios for each NUTS 3 region reflect a national average due to low instances of maternal mortality.Note that the NUTS 3 boundary refers to the former Regional Authorities established under the NUTS Regulation (Regulation (EU) 1059/2003). These boundaries were subsequently revised in 2016 through Commission Regulation (EU) 2016/2066 amending annexes to Regulation 1059/2003 (more info).

In 2015 UN countries adopted a set of 17 goals to end poverty, protect the planet and ensure prosperity for all as part of a new sustainable development agenda. Each goal has specific targets to help achieve the goals set out in the agenda by 2030. Governments are committed to establishing national frameworks for the achievement of the 17 Goals and to review progress using accessible quality data. With these goals in mind the CSO and Tailte Éireann are working together to link geography and statistics to produce indicators that help communicate and monitor Ireland’s performance in relation to achieving the 17 sustainable development goals.The indicator displayed supports the efforts to achieve goal number 3 which aims to ensure healthy lives and promote well-being for all at all ages.

Background: The threat of maternal mortality can be reduced by increasing use of maternal health services. Maternal death and access to maternal health care services are inequitable in low and middle income countries.The aim of this study is to assess associated paternal factors and degree of inequity in access to maternal health care service utilization. Methods: Analysis illustrates on a cross-sectional household survey that followed multistage-cluster sampling. Concentration curve and indices were calculated. Binary logistic regression analysis was executed to account paternal factors associated with the utilization of maternal health services. Path model with structural equation modeling (SEM) examined the predictors of antenatal care (ANC) and institutional delivery. Results: The finding of this study revealed that 39.9% and 45.5% of the respondents’ wives made ANC visits and utilized institutional delivery services respectively. Men with graduate and higher level of education were more likely (AOR: 5.91, 95% CI; 4.02, 8.70) to have ANC of their wives than men with no education or primary level of education. Men with higher household income (Q5) were more likely (1.99, 95% CI; 1.39, 2.86) to have ANC for their wives. Similarly, higher household income (Q5) also determined (2.74, 95% CI; 1.81, 4.15) for institutional delivery of their wives. Concentration curve and indices also favored rich than the poor. SEM revealed that ANC visit was directly associated to institutional delivery. Conclusions: Paternal factors like age, household wealth, number of children, ethnicity, education, knowledge of danger sign during pregnancy, and husband’s decision making for seeking maternal and child health care are crucial factors associated to maternal health service utilization. Higher ANC coverage predicts higher utilization of the institutional delivery. Wealthier population is more concentrated to maternal health services. The inequities between the poor and the rich are necessary to be addressed through effective policy and programs.

Maternal Mortality Ratios and Rates, and Percent Distribution of Deaths, by Age.

The primary objective of the 2016 Nepal Demographic and Health Survey (NDHS) is to provide up-to-date estimates of basic demographic and health indicators. The NDHS provides a comprehensive overview of population, maternal, and child health issues in Nepal. Specifically, the 2016 NDHS: Collected data that allowed calculation of key demographic indicators, particularly fertility and under-5 mortality rates, at the national level, for urban and rural areas, and for the country’s seven provinces Collected data that allowed for calculation of adult and maternal mortality rates at the national level Explored the direct and indirect factors that determine levels and trends of fertility and child mortality Measured levels of contraceptive knowledge and practice Collected data on key aspects of family health, including immunization coverage among children, prevalence and treatment of diarrhea and other diseases among children under age 5, maternity care indicators such as antenatal visits and assistance at delivery, and newborn care Obtained data on child feeding practices, including breastfeeding Collected anthropometric measures to assess the nutritional status of children under age 5 and women and men age 15-49 Conducted hemoglobin testing on eligible children age 6-59 months and women age 15-49 to provide information on the prevalence of anemia in these groups Collected data on knowledge and attitudes of women and men about sexually transmitted diseases and HIV/AIDS and evaluated potential exposure to the risk of HIV infection by exploring high-risk behaviors and condom use Measured blood pressure among women and men age 15 and above Obtained data on women’s experience of emotional, physical, and sexual violence The information collected through the 2016 NDHS is intended to assist policymakers and program managers in the Ministry of Health and other organizations in designing and evaluating programs and strategies for improving the health of the country’s population. The 2016 NDHS also provides data on indicators relevant to the Nepal Health Sector Strategy (NHSS) 2016-2021 and the Sustainable Development Goals (SDGs).

description:

All birth data by race before 1980 are based on race of the child; starting in 1980, birth data by race are based on race of the mother. Birth data are used to calculate infant mortality rate.

https://www.cdc.gov/nchs/data-visualization/mortality-trends/

All birth data by race before 1980 are based on race of the child; starting in 1980, birth data by race are based on race of the mother. Birth data are used to calculate infant mortality rate.

https://www.cdc.gov/nchs/data-visualization/mortality-trends/

Abstract

The 1992 Namibia Demographic and Health Survey (NDHS) is a nationally representative survey conducted by the Ministry of Health and Social Services, assisted by the Central Statistical Office, with the aim of gathering reliable information on fertility, family planning, infant and child mortality, maternal mortality, maternal and child health and nutrition. Interviewers collected information on the reproductive histories of 5,421 women 15-49 years and on the health of 3,562 children under the age of five years.

The Namibia Demographic and Health Survey (NDHS) is a national sample survey of women of reproductive age designed to collect data on mortality and fertility, socioeconomic characteristics, marriage patterns, breastfeeding, use of contraception, immunisation of children, accessibility to health and family planning services, treatment of children during episodes of illness, and the nutritional status of women and children. More specifically, the objectives of NDHS are: - To collect data at the national level which will allow the calculation of demographic rates, particularly fertility rates and child mortality rates, and maternal mortality rates; To analyse the direct and indirect factors which determine levels and trends in fertility and childhood mortality, Indicators of fertility and mortality are important in planning for social and economic development; - To measure the level of contraceptive knowledge and practice by method, region, and urban/rural residence; - To collect reliable data on family health: immunisations, prevalence and treatment of diarrhoea and other diseases among children under five, antenatal visits, assistance at delivery and breastfeeding; - To measure the nutritional status of children under five and of their mothers using anthropometric measurements (principally height and weight).

MAIN RESULTS

According to the NDHS, fertility is high in Namibia; at current fertility levels, Namibian women will have an average of 5.4 children by the end of their reproductive years. This is lower than most countries in sub-Saharan Africa, but similar to results from DHS surveys in Botswana (4.9 children per woman) and Zimbabwe (5.4 children per woman). Fertility in the South and Central regions is considerably lower (4.1 children per woman) than in the Northeast (6.0) and Northwest regions (6.7).

About one in four women uses a contraceptive method: 29 percent of married women currently use a method (26 percent use a modem method), and 23 percent of all women are current users. The pill, injection and female sterilisation are the most popular methods among married couples: each is used by about 7 to 8 percent of currently married women. Knowledge of contraception is high, with almost 90 percent of all women age 15-49 knowing of any modem method.

Certain groups of women are much more likely to use contraception than others. For example, urban women are almost four times more likely to be using a modem contraceptive method (47 percent) than rural women (13 percent). Women in the South and Central regions, those with more education, and those living closer to family planning services are also more likely to be using contraception.

Levels of fertility and contraceptive use are not likely to change until there is a drop in desired family size and until the idea of reproductive choice is more widely accepted. At present, the average ideal family size (5.0 children) is only slightly lower than the total fertility rate (5.4 children). Thus, the vast majority of births are wanted.

On average, Namibian women have their first child when they are about 21 years of age. The median age at first marriage is, however, 25 years. This indicates that many women give birth before marriage. In fact, married women are a minority in Namibia: 51 percent of women 15-49 were not married, 27 percent were currently married, 15 percent were currently living with a man (informal union), and 7 percent were widowed, divorced or separated. Therefore, a large proportion of children in Namibia are born out of wedlock.

The NDHS also provides inlbrmation about maternal and child health. The data indicate that 1 in 12 children dies before the fifth birthday. However, infant and child mortality have been declining over the past decade. Infant mortality has fallen from 67 deaths per 1,000 live births for the period 1983-87 to 57 per 1,000 live births for the period 1988-92, a decline of about 15 percent. Mortality is higher in the Northeast region than elsewhere in Namibia.

The leading causes of death are diarrhoea, undemutrition, acute respiratory infection (pneumonia) and malaria: each of these conditions was associated with about one-fifth of under-five deaths. Among neonatal deaths low birth weight and birth problems were the leading causes of death. Neonatal tetanus and measles were not lbund to be major causes of death.

Maternal mortality was estimated from reports on the survival status of sisters of the respondent. Maternal mortality was 225 per 100,000 live births for the decade prior to the survey. NDHS data also show considerable excess male mortality at ages 15-49, which may in part be related to the war of independence during the 1980s.

Utilisation of maternal and child health services is high. Almost 90 percent of mothers received antenatal care, and two-thirds of children were bom in health facilities. Traditional birth attendants assisted only 6 percent of births in the five years preceding the survey. Child vaccination coverage has increased rapidly since independence. Ninety-five percent of children age 12-23 months have received at least one vaccination, while 76 percent have received a measles vaccination, and 70 percent three doses of DPT and polio vaccines.

Children with symptoms of possible acute respiratory infection (cough and rapid breathing) may have pneumonia and need to be seen by a health worker. Among children with such symptoms in the two weeks preceding the survey two-thirds were taken to a health facility. Only children of mothers who lived more than 30 km from a health facility were less likely to be taken to a facility.

About one in five children had diarrhoea in the two weeks prior to the survey. Diarrhoea prevalence was very high in the Northeast region, where almost half of children reportedly had diarrhoea. The dysentery epidemic contributed to this high figure: diarrhoea with blood was reported for 17 percent of children under five in the Northeast region. Among children with diarrhoea in the last two weeks 68 percent were taken to a health facility, and 64 percent received a solution prepared from ORS packets. NDHS data indicate that more emphasis needs to put on increasing fluids during diarrhoea, since only I 1 percent mothers of children with diarrhoea said they increased the amount of fluids given during the episode.

Nearly all babies are breastfed (95 percent), but only 52 percent are put on the breast immediately. Exclusive breastfeeding is practiced for a short period, but not for the recommended 4-6 months. Most babies are given water, formula, or other supplements within the first four months of life, which both jeopardises their nutritional status and increases the risk of infection. On average, children are breastfed for about 17 months, but large differences exist by region. In the South region children are breastfed lor less than a year, in the Northwest region for about one and a half years and in the Northeast region for almost two years.

Most babies are weighed at birth, but the actual birth weight could be recalled for only 44 percent of births. Using these data and data on reported size of the newborn, for all births in the last five years, it was estimated that the mean birth weight in Namibia is 3048 grams, and that 16 percent of babies were low birth weight (less than 2500 grams).

Stunting, an indication of chronic undemutrition, was observed for 28 percent of children under five. Stunting was more common in the Northeast region (42 percent) than elsewhere in Namibia. Almost 9 percent of children were wasted, which is an indication of acute undemutrition. Wasting is higher than expected for Namibia and may have been caused by the drought conditions during 1992.

Matemal height is an indicator of nutritional status over generations. Women in Namibia have an average height of 160 cm and there is little variation by region. The Body Mass Index (BM1), defined as weight divided by squared height, is a measure of current nutritional status and was lower among women in the Northwest and the Northeast regions than among women in the South and Central regions.

On average, women had a health facility available within 40 minutes travel time. Women in the Northwest region, however, had to travel more than one hour to reach the nearest health facility. At a distance of less than 10 km, 56 percent of women had access to antenatal services, 48 percent to maternity services, 72 percent to immunisation services, and 49 percent to family planning services. Within one hour of travel time, fifty-two percent of women had antenatal services, 48 percent delivery services, 64 percent immunisation services and 49 percent family planning services. Distance and travel time were greatest in the Northwest region.

Geographic coverage

The sample for the NDHS was designed to be nationally representative. The design involved a two- stage stratified sample which is self-weighting within each of the three health regions for which estimates of fertility and mortality were required--Northwest, Northeast, and the combined Central/South region. In order to have a sufficient number of cases for analysis, oversampling was necessary for the Northeast region, which has only 14.8 percent of the population. Therefore, the sample was not allocated proportionally across regions and is not completely