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

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

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

Trends in estimates of maternal mortality ratio (1990-2015), including country breakdown

Definition: Maternal deaths per 100,000 live births

Background and objectivesThe acute respiratory infection caused by severe acute respiratory syndrome coronavirus disease (COVID-19) has resulted in increased mortality among pregnant, puerperal, and neonates. Brazil has the highest number of maternal deaths and a distressing fatality rate of 7.2%, more than double the country’s current mortality rate of 2.8%. This study investigates the impact of the COVID-19 pandemic on the Brazilian Maternal Mortality Ratio (BMMR) and forecasts the BMMR up to 2025.MethodsTo assess the impact of the COVID-19 pandemic on the BMMR, we employed Holt-Winters, Autoregressive Integrated Moving Average (ARIMA), and Neural Networks Autoregression (NNA). We utilized a retrospective time series spanning twenty-five years (1996–2021) to forecast the BMMR under both a COVID-19 pandemic scenario and a controlled COVID-19 scenario.ResultsBrazil consistently exhibited high maternal mortality values (mean BMMR [1996–2019] = 57.99 ±6.34/100,000 live births) according to World Health Organization criteria. The country experienced its highest mortality peak in the historical BMMR series in the second quarter of 2021 (197.75/100,000 live births), representing a more than 200% increase compared to the previous period. Holt-Winter and ARIMA models demonstrated better agreement with prediction results beyond the sample data, although NNA provided a better fit to previous data.ConclusionsOur study revealed an increase in BMMR and its temporal correlation with COVID-19 incidence. Additionally, it showed that Holt-Winter and ARIMA models can be employed for BMMR forecasting with lower errors. This information can assist governments and public health agencies in making timely and informed decisions.

This dataset presents the number of neonatal deaths per 1,000 live births, using data from the UNICEF Data Warehouse. Neonatal mortality refers to the death of a baby within the first 28 days of life and is a critical indicator of newborn health and health system performance. Monitoring this rate supports efforts to improve the quality of care around birth and during the early postnatal period, and to reduce preventable newborn deaths through timely, skilled interventions.Data Source:UNICEF Data Warehouse: https://data.unicef.org/resources/data_explorer/unicef_f/?ag=UNICEF&df=GLOBAL_DATAFLOW&ver=1.0&dq=.CME_MRM0.&startPeriod=1990&endPeriod=2024Data 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 statistic depicts the mean annual decline in maternal mortality worldwide between 1990 and 2010, sorted by region (WHO region). In the WHO region Eastern Mediterranean, maternal mortality declined 2.6 percent in the given period.

Abstract

The 2017 Ghana Maternal Health Survey (2017 GMHS) was designed to produce representative estimates for maternal mortality indicators for the country as a whole, and for each of the three geographical zones, namely Coastal (Western, Central, Greater Accra and Volta), Middle (Eastern, Ashanti and Brong Ahafo) and Northern (Northern, Upper East and Upper West). For other indicators such as maternal care, fertility and child mortality, the survey was designed to produce representative results for the country as whole, for the urban and rural areas, and for each of the country’s 10 administrative regions.

The primary objectives of the 2017 GMHS were as follows: • To collect data at the national level that will allow an assessment of the level of maternal mortality in Ghana for the country as a whole and for three zones: Coastal (Western, Central, Greater Accra, and Volta regions), Middle (Eastern, Ashanti, and Brong Ahafo regions), and Northern (Northern, Upper East, and Upper West regions) • To identify specific causes of maternal and non-maternal deaths, in particular deaths due to abortionrelated causes, among adult women • To collect data on women’s perceptions of and experiences with antenatal, maternity, and emergency obstetrical care, especially with regard to care received before, during, and following the termination or abortion of a pregnancy • To measure indicators of the utilisation of maternal health services, especially post-abortion care services • To allow follow-on studies and surveys that will be used to observe possible reductions in maternal mortality as well as abortion-related mortality

The information collected through the 2017 GMHS is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of the country’s population.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Woman age 15-49

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2017 GMHS was designed to provide estimates of key reproductive health indicators for the country as a whole, for urban and rural areas separately, for three zonal levels (Coastal, Middle, and Northern), and for each of the 10 administrative regions in Ghana (Western, Central, Greater Accra, Volta, Eastern, Ashanti, Brong Ahafo, Northern, Upper East, and Upper West).

The sampling frame used for the 2017 GMHS is the frame of the 2010 Population and Housing Census (PHC) conducted in Ghana. The 2010 PHC frame is maintained by GSS and updated periodically as new information is received from various surveys. The frame is a complete list of all census enumeration areas (EAs) created for the PHC.

The 2017 GMHS sample was stratified and selected from the sampling frame in two stages. Each region was separated into urban and rural areas; this yielded 20 sampling strata. Samples of EAs were selected independently in each stratum in two stages. Implicit stratification and proportional allocation were achieved at each of the lower administrative levels by sorting the sampling frame within each sampling stratum before the sample selection, according to administrative units at different levels, and by using a probability proportional to size selection at the first stage of sampling.

In the first stage, 900 EAs (466 EAs in urban areas and 434 EAs in rural areas) were selected with probability proportional to EA size and with independent selection in each sampling stratum. A household listing operation was implemented from 25 January to 9 April 2017 in all of the selected EAs. The resulting lists of households then served as a sampling frame for the selection of households in the second stage. The household listing operation included inquiring of each household if there had been any deaths in that household since January 2012 and, if so, the name, sex, and age at time of death of the deceased person(s).

Some of the selected EAs were very large. To minimise the task of household listing, each large EA selected for the 2017 GMHS was segmented. Only one segment was selected for the survey with probability proportional to segment size. Household listing was conducted only in the selected segment. Thus, in the GMHS, a cluster is either an EA or a segment of an EA. As part of the listing, the field teams updated the necessary maps and recorded the geographic coordinates of each cluster. The listing was conducted by 20 teams that included a supervisor, three listers/mappers, and a driver.

For further details on sample design, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the 2017 GMHS: the Household Questionnaire, the Woman’s Questionnaire, and the Verbal Autopsy Questionnaire.

Cleaning operations

All electronic data files for the 2017 GMHS were transferred via the IFSS to the GSS central office in Accra, where they were stored on a password-protected computer. The data processing operation included registering and checking for any inconsistencies and outliers. Data editing and cleaning included structure and consistency checks to ensure completeness of work in the field. The central office also conducted secondary editing, which required resolution of computer-identified inconsistencies and coding of openended questions. The data were processed by five GSS staff members. Data editing was accomplished using CSPro software. Secondary editing and data processing were initiated in June and completed in November 2017.

Response rate

A total of 27,001 households were selected for the sample, of which 26,500 were occupied at the time of fieldwork. Of the occupied households, 26,324 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 25,304 eligible women were identified for individual interviews; interviews were completed with 25,062 women, yielding a response rate of 99%.

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 2017 Ghana Maternal Health Survey (2017 GMHS) to minimize 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 2017 GMHS 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 in. For example, for any given statistic calculated from a sample survey, the true 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 2017 GMHS sample is the result of a multi-stage stratified sampling, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed by SAS programs developed by ICF International. These programs use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used 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 final report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months - Completeness of information on siblings - Sibship size and sex ratio of siblings - Pregnancy-related mortality trends

See details of the data quality tables in Appendix C of the survey final report.

Maternal health indicators (2022) and policy impacts by country group.

Time series data for the statistic Lifetime risk of maternal death (1 in: rate varies by country) and country Somalia. Indicator Definition:Life time risk of maternal death is the probability that a 15-year-old female will die eventually from a maternal cause assuming that current levels of fertility and mortality (including maternal mortality) do not change in the future, taking into account competing causes of death.The indicator "Lifetime risk of maternal death (1 in: rate varies by country)" stands at 30.00 as of 12/31/2023, the highest value at least since 12/31/1986, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 11.11 percent compared to the value the year prior.The 1 year change in percent is 11.11.The 3 year change in percent is 20.00.The 5 year change in percent is 36.36.The 10 year change in percent is 66.67.The Serie's long term average value is 15.49. It's latest available value, on 12/31/2023, is 93.71 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1988, to it's latest available value, on 12/31/2023, is +200.00%.The Serie's change in percent from it's maximum value, on 12/31/2023, to it's latest available value, on 12/31/2023, is 0.0%.

For current version see: https://www.sandiegocounty.gov/content/sdc/hhsa/programs/phs/maternal_child_family_health_services/MCFHSstatistics.html

Infant Mortality - Cohort Dataset Note: The Infant Mortality Rate is infant deaths (under one year of age) per 1,000 live births, by geography. Numerator represents infant's race/ethnicity. Denominator represents mother's race/ethnicity.

***API: Asian/Pacific Islander. ***AIAN: American Indian/Alaska Native. Blank Cells: Rates not calculated for fewer than 5 events. Rates not calculated in cases where infant's zip code of residence is unknown.

Sources: State of California, Department of Public Health, Death Statistical Master Files (before 2014), California Comprehensive Death Files (2014 and later), and Birth Statistical Master Files. Prepared by: County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics Unit, 2019.

Interpretation: "There were 5 infant deaths per 1,000 live births in Geography X".

Data Guide, Dictionary, and Codebook: https://www.sandiegocounty.gov/content/dam/sdc/hhsa/programs/phs/CHS/Community%20Profiles/Public%20Health%20Services%20Codebook_Data%20Guide_Metadata_10.2.19.xlsx

Health, medical care and caregiving include average life expectancy at birth, age of death - mean, age of death - median, cancer death age - mean, cancer death age - median, mortality rate (all causes of death), malignant tumor mortality rate, accidental injury mortality rate, deliberate self-harm (suicide) mortality rate, infant mortality, maternal mortality rate, number of maternal deaths, number of prenatal inspection declarations for pregnant women, breast cancer screening in women aged 45-69 within 2 years, number of HIV infections, smoking rate among population aged 18 and above, actual placement of service personnel in disability welfare service institutions, long-term care, nursing and residential care institutions, actual number of residents in day care services - elderly with dementia, number of day care service cases - elderly with disabilities.

This statistic shows the 20 countries* with the highest infant mortality rate in 2024. An estimated 101.3 infants per 1,000 live births died in the first year of life in Afghanistan in 2024. Infant and child mortality Infant mortality usually refers to the death of children younger than one year. Child mortality, which is often used synonymously with infant mortality, is the death of children younger than five. Among the main causes are pneumonia, diarrhea – which causes dehydration – and infections in newborns, with malnutrition also posing a severe problem. As can be seen above, most countries with a high infant mortality rate are developing countries or emerging countries, most of which are located in Africa. Good health care and hygiene are crucial in reducing child mortality; among the countries with the lowest infant mortality rate are exclusively developed countries, whose inhabitants usually have access to clean water and comprehensive health care. Access to vaccinations, antibiotics and a balanced nutrition also help reducing child mortality in these regions. In some countries, infants are killed if they turn out to be of a certain gender. India, for example, is known as a country where a lot of girls are aborted or killed right after birth, as they are considered to be too expensive for poorer families, who traditionally have to pay a costly dowry on the girl’s wedding day. Interestingly, the global mortality rate among boys is higher than that for girls, which could be due to the fact that more male infants are actually born than female ones. Other theories include a stronger immune system in girls, or more premature births among boys.

BackgroundSustainable Development Goal 3 (SDG 3), focusing on ensuring healthy lives and well-being for all, holds global significance and is particularly vital for Bangladesh. Neonatal Mortality Rate (NMR), Under-5 Mortality Rate (U5MR), Maternal Mortality Ratio (MMR) and Death Rate Due to Road Traffic Injuries (RTI) are considered responsible indicators of SDG 3 progress in Bangladesh. The objective of the study is to forecast these indicators of Bangladesh up to 2030 and compare these forecasts with predetermined 2030 targets. The data is obtained from the World Bank’s (WB) website.MethodFor forecasting, time series models were employed, specifically Autoregressive Integrated Moving Average- ARIMA (0,2,1) with Akaike Information Criterion (AIC) 94.6 for NMR and ARIMA (2,1,2) with AIC 423.2 for U5MR, selected based on their lowest AIC values. Additionally, Machine Learning (ML) models, including Bidirectional Recurrent Neural Networks (BRNN) and Elastic Neural Networks (ENET), were employed for all the indicators.ResultsENET demonstrates superior performance compared to both BRNN and ARIMA in the context of NMR, achieving a Root Mean Absolute Error (RMAE) of 0.603446 and a Root Mean Square Error (RMSE) of 0.451162. Furthermore, when considering U5MR, MMR, and Death Rate Due to RTI, ENET consistently exhibits lower error metrics compared to the alternative models. Following the time series and ML analyses, a consistent trend emerges in the forecasted values for NMR and U5MR, which consistently fall below their respective 2030 targets. This promising finding suggests that Bangladesh is making significant progress toward meeting its 2030 targets for NMR and U5MR. However, in the cases of MMR and Death Rate Due to RTI, the forecasted values exceeded 2030 targets. This indicates that Bangladesh faces challenges in meeting the 2030 targets for MMR and Death Rate Due to RTI.ConclusionThe analyses underscore the importance of SDG 3 in Bangladesh and its progress towards ensuring healthy lives and well-being for all. While there is optimism regarding NMR and U5MR, more focused efforts may be needed to address the challenges posed by MMR and Death Rate Due to RTI to align with the 2030 targets. This study contributes valuable insights into Bangladesh’s journey toward sustainable development in the realm of health and well-being.

As of 2023, the mortality rate of infants aged under one-year-old in Nigeria was measured at 55.17. This means that there were about 55 deaths of children under the age of one year per 1,000 live births. Child mortality rates in Africa are very high. Among the countries with the highest infant mortality rate in the world, almost all of them are African countries. Similarly, maternal mortality rates are high. In 2017, Nigeria recorded 917 deaths of mothers per 100,000 live births.

Data by medical encounter for the following conditions by age, race/ethnicity, and sex (gender): Congenital Anomalies Maternal Complications

Visit https://www.sandiegocounty.gov/content/sdc/hhsa/programs/phs/maternal_child_family_health_services/MCFHSstatistics.html to view MCFHS perinatal health indicators, including: Live Births Teen Births Early Prenatal Care Preterm Birth Low Birth Weight Fetal Mortality Infant Mortality Maternal Deaths

Rates per 100,000 population. Age-adjusted rates per 100,000 2000 US standard population. Blank Cells: Events less than 11 are suppressed. Starting with data year 2022, geographies with less than 20,000 population contain no age-adjusted rates and all rates based on events <20 are suppressed due to statistical instability. Rates not calculated in cases where zip code is unknown. SES: Is the median household income by Subregional Area (SRA) community. Data for SRA only.

Data sources: California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System (VRBIS), 2022. California Department of Health Care Access and Information (HCAI), Emergency Department Discharge Database and Patient Discharge Database, 2022. SANDAG Population Estimates, 2022 (v11/23). 2022 population estimates were derived from the 2020 decennial census. Comparison of rates to prior years may not be appropriate. Prepared by: County of San Diego, Health and Human Services Agency, Public Health Services, Community Health Statistics Unit, May 2024.

2022 Community Profile Data Guide and Data Dictionary Dashboard: https://public.tableau.com/app/profile/chsu/viz/2022COREDataGuideandDataDictionary/Home

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.

For current version see: https://www.sandiegocounty.gov/content/sdc/hhsa/programs/phs/maternal_child_family_health_services/MCFHSstatistics.html

Infant Mortality - VRBIS Dataset Note: This dataset is created from the mortality database. Cases are registered deaths only. The Infant Mortality Rate is infant deaths (under one year of age) per 1,000 live births, by geography. Rates not calculated in cases where infant's zip code of residence is unknown. Numerator represents infant's race/ethnicity. Denominator represents mother's race/ethnicity.

***API: Asian/Pacific Islander. ***AIAN: American Indian/Alaska Native. Blank Cells: Rates not calculated for fewer than 5 events.

Sources: California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System, 2016.
Prepared by: County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics Unit, 2019.

Interpretation: "There were 5 infant deaths per 1,000 live births in Geography X".

Data Guide, Dictionary, and Codebook: https://www.sandiegocounty.gov/content/dam/sdc/hhsa/programs/phs/CHS/Community%20Profiles/Public%20Health%20Services%20Codebook_Data%20Guide_Metadata_10.2.19.xlsx

Time series data for the statistic Number of maternal deaths and country Honduras. Indicator Definition:A maternal death refers to 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.The indicator "Number of maternal deaths" stands at 110.00 as of 12/31/2023. Regarding the One-Year-Change of the series, the current value is equal to the value the year prior.The 1 year change in percent is 0.0.The 3 year change in percent is -15.38.The 5 year change in percent is -8.33.The 10 year change in percent is -21.43.The Serie's long term average value is 196.15. It's latest available value, on 12/31/2023, is 43.92 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2022, to it's latest available value, on 12/31/2023, is +0.0%.The Serie's change in percent from it's maximum value, on 12/31/1985, to it's latest available value, on 12/31/2023, is -68.57%.

Abstract

During May to October 1995 in the Mwanza Region of northwestern Tanzania, an in-depth survey on adult and childhood mortality estimation was conducted. Entitled the "Sumve Survey on Adult and Childhood Mortality" (SACM), the study was implemented by the Tanzanian Bureau of Statistics (BOS) and the Demographic and Health Surveys (DHS) program with assistance provided by several local institutions.

The primary objective of the SACM was to establish whether data useful for the estimation of childhood mortality rates (birth histories) could be collected by proxy from the mothers' sisters. The proxy data on deceased sisters--that is, women not interviewed in a routine demographic survey could be used to adjust estimates of childhood mortality where adult mortality is on the rise due to the AIDS pandemic. This type of data collection had not been attempted in Africa. Aside from the methodological aims of the SACM, the study was also intended to provide descriptive information on the demographic situation and use of basic maternity service utilization in the study area for purposes of local program evaluation.

The SACM was a two-phase data collection exercise conducted in the Kwimba District of Mwanza Region which lies on the southern boundary of Lake Victoria. This is an area where approximately 100,000 persons, of predominantly Sukuma ethnic origins, reside. Very little modern sector development has occurred in the study area and the large majority of the population relies on subsistence agriculture and some cash cropping to make a living. Educational levels are very low: the SACM results show that about 40 percent of women age 15-49 had never been to school, and only 1 percent had reached secondary school. Most of the study population falls in the catchment area of the Sumve primary health care (PHC) program, which aims to provide health education and basic maternal and child health services through outreach and referral programs. The PHC program is (and the SACM study was) based in Sumve where a relatively large hospital serves much of the district's tertiary care needs as well.

In Phase I of the SACM, a representative sample of 1,488 households and 2,130 women age 15-50 were interviewed. In these interviews, full birth histories of the respondents ("own" reports) and full sibling histories were collected. Based on information in the latter, all sisters born 15-50 years ago were identified and full birth histories were collected on all of these sisters ("proxy" reports). In Phase II of the SACM, conducted a month after Phase I, all living sisters age 15-50 living in an expanded study area were "tracked" with 2,123 of 2,223 eligible sisters (96 percent) eventually interviewed. From Phase II respondents was elicited essentially the same information as was obtained from Phase 1 respondents. These data allow comparisons of own-reported and proxy-reported birth histories. One drawback of the design is that the SACM sister-pairs are not representative of all sister-pairs since they live closer to each other than the average sister-pair (i.e., by design, the Phase II sisters live in roughly the same area as Phase I respondents). The SACM found that nearly all women (99 percent) who gave birth in the five years before the survey had received some kind of antenatal care during their last pregnancy, with the majority of services provided by nurses, midwives, and maternal and child health (MCH) aides. Only 2 percent of the women received care by a doctor. Unfortunately, the data indicate that over 90 percent of these women did not initially receive services before the second trimester, and 15 percent did not before the third trimester, which indicates that the full benefits of antenatal care are not being realized for most women around Sumve. The SACM also found that 62 percent of deliveries still occur outside of health facilities. Nearly all of these home deliveries are assisted by relatives and friends. Thirty-nine percent of deliveries were assisted by a trained health professional; in 4 percent of deliveries, a doctor assisted. Previous use of antenatal services and advice by a health professional to deliver in a health facility is positively correlated with subsequent delivery in a hospital or clinic. Of women not delivering in a health facility, the most commonly reported reason for nonuse of a facility was transport- or distance-related; 61 percent said that it was "too far," and 44 percent said that no transport was available.

The Phase I SACM data provided an opportunity to establish representative estimates of fertility and mortality.Women living in the Sumve area bear, on average,7.4 children during their lifetime,and nearly 60 percent have begun their reproductive lives before reaching age 20.The under-five mortality rate was estimated to be 134 deaths per 1,000 live births, meaning that about 1 in 7 children in this area do not survive to their fifth birthday.Infant mortality stands at 83 deaths (under age 1) per 1,000 live births.The risk of dying in early childhood is closely linked to the length of the birth interval.Infant mortality is about twice as high among children with short intervals (less than 24 months) than among children born after long intervals (48 or more months).

Adult mortality is high in the study area.The mortality rate for adult females (age 15-49) is estimated to be 4 per1,000 person-years and male mortality (age 15-49) is 5 per 1,000 person-years. While high, these mortality levels indicate that AIDS has not yet impacted significantly on adult mortality during the 0-13 year period before the survey (circa 1982-1995). A measure of female mortality attributable to maternity-related causes, the maternal mortality ratio, was calculated using the SACM. The maternal mortality ratio for the Sumve area was found to be around 500 maternal deaths per 100,000 live births.

Phase II of the SACM provided for linkage of 2,711 own-reported birth histories with 3,719 proxy reported birth histories (1.37 proxy reports per own report). The analyses of proxy reports vis-a-vis own reports demonstrate that women are familiar with their sisters' experience regarding childbearing and child deaths. The quality of the proxy information is, in some respects, surprisingly good. Yet the study identified some important problems related to proxy reporting. The precision of dating of births was significantly worse in the proxy reports, and substantial birth date displacement was evident. Most importantly, a considerable 14 percent fewer non-surviving births were reported in the proxy birth histories than in the own reports.

These data quality problems had some impact on demographic estimates.The directly-estimated total fertility rate for ages 15-39 (TFR) in the five-year period before the survey was estimated to be 6.7 children per woman from the own data, but 5.9 children per woman from the proxy data.While the own and proxy data produce similar childhood mortality rates for the five years before the survey (due to offsetting underreports of surviving and nonsurviving births),the proxy effect resulted in a 23 percent underestimate of under-five mortality 5-9 years before the survey, and a 31 percent underestimate 10-14 years before the survey. Trend estimates from the proxy data thus produce a picture of rising mortality, whereas own data indicate falling or stable mortality. These results suggest that routine implementation of a methodology to correct for mother's survival bias involving use of proxy data is not realistic at this time. However, in settings where moderate to severe bias is expected (five-fold or greater increases in adult mortality), careful adjustment to mortality estimates based on proxy data, while difficult to support empirically, may be an improvement over no adjustment at all.The adjustment would need to involve estimation of a "proxy effect" as well as estimation of the substantive correction parameter that reflects the survival bias.

Evaluation and quantification of the biases influencing childhood mortality estimation in sub-Saharan Africa should be undertaken. In this study, the children of recently deceased women had significantly elevated mortality relative to children of survivors: under-five risk was more than doubled (340 versus 143 per 1,000 live births). Additional information on the fertility-inhibiting impact of HIV/AIDS and current levels and trend in adult HIV/AIDS-related mortality needs to be garnered. These data should be population based and refer to a recent time period in order to be useful for program and policy purposes.

Geographic coverage

Kwimba District of Northwestern Tanzania.

Analysis unit

Households Individuals

Universe

Sisters of deceased mothers (Women aged 15- 50)

Kind of data

Sample survey data [ssd]

Sampling procedure

The SACM was conducted in two phases. In the first phase, a random sample of women age 15-50 was interviewed in their households. A complete sibling history was collected from these respondents which included detailed locator information for all living sisters age 15-50. After Phase I was completed, the sibling histories and associated data were used to draw up a roster of all living sisters age 15-50 of the Phase I respondents. All sisters listed who lived in the expanded Phase II study area were eligible for Phase II interview.

Phase I of the SACM was conducted in the six wards of Kwimba District that surround the community of Sumve: Bungulwa, Mantare, Mwabomba, Mwaniko, Ngulla, and Wulla (Figure 2.1 - see Survey Report in external resources). The six wards comprise a total of 57 enumeration areas (EA) designated and mapped during the 1988 national census.A complete remapping and household listing of these 57 EAs