This statistic depicts the maternal mortality rate (per 100,000 live births) for developed nations in Europe, Australia and North America in 2015. According to the data, the United States had a maternal mortality rate of 26.4, compared to Finland with a maternal mortality rate of just 3.8. The U.S. has by far the highest maternal mortality rate among developed countries.

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

For most of human history, pregnancy and childbirth were very risky; mothers would die in at least 1 in 100 pregnancies.1

Since the average woman would have at least four or five children, the lifetime risk of dying from maternal causes would be at least 1 in 25.2 This was true everywhere.

Thankfully, that’s no longer the case. We’ve made huge strides in not only protecting infants in childbirth and the early stages of their lives, but we’ve also made it much safer for women.

But we’re not done yet. There are still huge inequalities in the risks of pregnancy across the world. Pregnant women in countries like Sierra Leone and Kenya are around 100 times more likely to die during pregnancy or childbirth than those in countries like Norway, Sweden, or Germany.3 But it doesn’t have to be this way. We could save hundreds of thousands of lives a year by closing these gaps.

I’ve compared three scenarios in the chart below to clarify these points.

First, we can see that the situation today is awful. 286,000 women died from maternal causes in 2020.4 That’s 784 deaths per day on average, or one mother dying every two minutes.5

Second, we can consider the very high maternal mortality rates of the past. Particularly good long-term data is available for Finland or Sweden, which shows that in 1750, around 900 women died per 100,000 live births.6 Since there were 135 million births in 2020, I calculate that 1.2 million women would have died from maternal causes that year if these rates hadn’t improved.7 Things are much, much better than they used to be.

Finally, things can still be much better. We know this because some countries have maternal mortality rates that are far lower than the global average. And they all used to be in a similar position to the worst-off countries today. In Europe, the maternal mortality rate was 8 deaths per 100,000 live births in 2020. That’s around 25 times lower than the global average.8 If all countries could achieve the same outcomes as Europe, 11,000 women would have died from maternal causes in 2020 — a small fraction of the 286,000 deaths that occurred.9

Providing the best conditions for women everywhere would reduce the global death toll by 275,000 maternal deaths a year.

This statistic shows the 20 countries * with the lowest infant mortality rate in 2024. An estimated 1.5 out of 1,000 live births died in the first year of life in Slovenia and Singapore in 2024. Infant mortality Infant mortality rates are often used as an indicator of the health and well-being of a nation. Monaco, Iceland, and Japan are among the top three countries with the lowest infant mortality rates with around 2 infant deaths per 1,000 infants within their first year of life. Generally, the countries with the lowest infant mortality also have some of the highest average life expectancy figures. Additionally, the countries with the highest density of physicians and doctors also generally report low infant mortality. Yet, many different factors contribute to differing rates, including the overall income of a country, health spending per capita, a mother’s level of education, environmental conditions, and medical infrastructure, to name a few. This creates a lot of variation concerning the level of childbirth and infant care around the world. The countries with the highest rates of infant mortality include Afghanistan, Mali, and Somalia. These countries experience around 100 infant deaths per 1,000 infants in their first year of life. While the reasons for high rates of infant mortality are numerous, the leading causes of death for children under the year five around the world are Pneumonia, Diarrhea, and Prematurity.

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.

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.

English

Context

Maternal mortality reflects the level of development in a society and involves various social and familial factors. In 2000, the UN established the goal of Improving Maternal Health within the Millennium Development Goals (MDGs), aiming to reduce maternal mortality rate (MMR) by 75% between 1990 and 2015. However, this goal was not achieved.

Currently, the Good Health and Well-Being goal within the Sustainable Development Goals (SDGs) seeks to reduce MMR to less than 70 per 100,000 live births by 2030. This issue disproportionately affects developing countries, with Latin America and the Caribbean experiencing stagnation at 67.2 maternal deaths per 100,000 live births.

To contribute to the analysis and solution of this problem through data science and artificial intelligence, we developed this synthetic dataset of pregnant patients as part of the project "Computational Phenotypes of Maternal Morbidity Using AI".

Development

This dataset was built using open data sources and patient simulation tools. The following resources were used:

• DANE - 2018 Population Census
• DIVIPOLA - Municipality Codes
• List of Healthcare Providers in Colombia

A new pregnancy module was developed for Synthea, based on evidence and domain knowledge from the research team. The generated data was transformed into the OMOP CDM data model using the ETL-Synthea tool.

Scope and Considerations

• Cost-related tables were removed due to differences in Synthea modeling compared to the local context.
• For the medical vocabulary, OMOP tables need to be created (See Instructions), and required vocabularies must be downloaded, some of which may require a license.

Acknowledgments

This dataset was developed with funding from the Latin American Center for Artificial Intelligence (CLIAS), as part of the project "Computational Phenotypes of Maternal Morbidity Using Artificial Intelligence" (ID 82).

Spanish

Contexto

La muerte materna refleja el nivel de desarrollo de una sociedad e implica diversos factores sociales y familiares. En el año 2000, la ONU estableció el objetivo de Mejorar la salud materna dentro de los Objetivos de Desarrollo del Milenio, buscando reducir en un 75% la tasa de mortalidad materna (MMR) entre 1990 y 2015. Sin embargo, la meta no se alcanzó.

Actualmente, el objetivo Salud y Bienestar dentro de los Objetivos de Desarrollo Sostenible (ODS) busca reducir la MMR a menos de 70 para 2030. Este problema afecta especialmente a países en desarrollo, y en América Latina y el Caribe, la MMR se ha estancado en 67,2 muertes por cada 100.000 nacidos vivos.

Para contribuir al análisis y solución de este problema desde la ciencia de datos e inteligencia artificial, desarrollamos este conjunto de datos sintéticos de pacientes gestantes como parte del proyecto "Fenotipos Computacionales de Morbilidad Materna mediante IA".

Desarrollo

Este dataset se construyó utilizando fuentes de datos abiertos y herramientas de simulación de pacientes. Se utilizaron los siguientes recursos:

• DANE - Censo de población 2018
• DIVIPOLA - Códigos de municipios
• Listado de IPS en Colombia

Se desarrolló un nuevo módulo de embarazo en Synthea, basado en evidencia y conocimientos del equipo de investigación. Los datos generados fueron transformados al modelo de datos OMOP CDM utilizando la herramienta ETL-Synthea.

Alcance y Consideraciones

• Las tablas relacionadas con los costos fueron eliminadas debido a las diferencias en la modelación de Synthea frente al contexto local.
• Para el vocabulario médico, se requiere la creación de las tablas en OMOP (Ver **[Instr...

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

In 2022, the infant mortality rate in the United States was 5.4 out of every 1,000 live births. This is a significant decrease from 1960, when infant mortality was at around 26 deaths out of every 1,000 live births. What is infant mortality? The infant mortality rate is the number of deaths of babies under the age of one per 1,000 live births. There are many causes for infant mortality, which include birth defects, low birth weight, pregnancy complications, and sudden infant death syndrome. In order to decrease the high rates of infant mortality, there needs to be an increase in education and medicine so babies and mothers can receive the proper treatment needed. Maternal mortality is also related to infant mortality. If mothers can attend more prenatal visits and have more access to healthcare facilities, maternal mortality can decrease, and babies have a better chance of surviving in their first year. Worldwide infant mortality rates Infant mortality rates vary worldwide; however, some areas are more affected than others. Afghanistan suffered from the highest infant mortality rate in 2024, and the following 19 countries all came from Africa, with the exception of Pakistan. On the other hand, Slovenia had the lowest infant mortality rate that year. High infant mortality rates can be attributed to lack of sanitation, technological advancements, and proper natal care. In the United States, Massachusetts had the lowest infant mortality rate, while Mississippi had the highest in 2022. Overall, the number of neonatal and post neonatal deaths in the United States has been steadily decreasing since 1995.

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

Dataset Description

This dataset serves as a comprehensive repository of global development metrics, consolidating data from multiple international organizations into a single, unified structure. It provides a granular view of the state of health, economy, and nutrition across 193 countries over a 30-year period (1990–2019).

The data is organized by Country, Year, and Gender (Male, Female, and Both Sexes), making it a valuable resource for longitudinal studies, demographic analysis, and socio-economic research. It combines high-level economic indicators (like GDP) with granular health metrics (specific mortality rates) and detailed nutritional breakdowns (diet composition by food group).

Content Overview

The dataset covers a wide spectrum of categories:

• Demographics & Economy: Population stats, GNI, GDP, and poverty rates.
• Mortality & Life Expectancy: Survival rates at various ages, maternal mortality, and life expectancy.
• Public Health: Incidence of infectious diseases (Malaria, Tuberculosis, Hepatitis B) and prevalence of health risks (Tobacco, road traffic accidents).
• Environmental Health: Mortality attributed to air pollution, sanitation access, and clean fuel availability.
• Nutrition: Detailed caloric and quantity breakdown of food consumption (fruits, vegetables, cereals, meats, etc.).
• Healthcare Infrastructure: Coverage of essential health services and density of medical professionals.

Sources

The data was extracted and unified via an ETL process from the following organizations:

• World Bank: World Development Indicators
• WHO: Global Health Observatory Data
• UNICEF: Data Warehouse
• Our World In Data: Statistics and Research
• FAOSTAT: Food and Agriculture Data
• Kaggle: WHO World Health Statistics 2020 Complete

Data Dictionary

Index Columns

• Country: Name of the country.
• Year: The calendar year of the recorded data.
• Gender: The gender category for the data (Female, Male, or Both sexes).

Demographics & Health Metrics

• Life Expectancy: The average number of years a newborn is expected to live.
• Infant Mortality Rate: Number of infants dying before reaching one year of age, per 1,000 live births.
  • Includes Low/High Confidence Interval (CI) columns.
• Under 5 Mortality Rate: Probability of a child dying before reaching age 5, per 1,000 live births.
  • Includes Low/High CI columns.
• Neonatal Mortality Rate: Number of deaths during the first 28 days of life per 1,000 live births.
  • Includes Low/High CI columns.
• Maternal Mortality Ratio: Number of maternal deaths due to childbirth per 100,000 live births.
  • Includes Low/High CI columns.
• Birth Rate: Number of births per 1,000 inhabitants.
• Death Rate: Number of deaths per 1,000 inhabitants.
• Adolescent Birth Rate: Number of births by women aged 15 to 19 per 1,000 women in that age range.
• % Population Aged 0-14 / 15-64 / 65+: Percentage of the total population falling into these specific age brackets.
• % Population Aged 65-69 / 70-74 / 75-79 / 80+: Granular breakdown of the elderly population percentages.
• Total Population: Total number of inhabitants.

Causes of Death & Disease

• % Death Cardiovascular: Probability of dying from cardiovascular diseases, cancer, diabetes, or chronic respiratory diseases between ages 30 and 70.
  • Includes Low/High CI columns.
• Incidence of Malaria: Number of malaria cases per 1,000 inhabitants at risk per year.
• Incidence of Tuberculosis: Estimated cases of tuberculosis per 100,000 inhabitants.
  • Includes Low/High CI columns.
• Hepatitis B Surface Antigen: Prevalence of hepatitis B surface antigen.
  • Includes Low/High CI columns.
• Road Traffic Deaths: Number of deaths due to traffic accidents per 100,000 people.
• Poisoning Mortality Rate: Deaths attributed to unintentional poisoning per 100,000 people.
• Conflict and Terrorism Deaths: Number of deaths due to armed conflicts and terrorism.
• Battle Related Deaths: Number of deaths related to battles in an armed conflict.
• % Injury Deaths: Percentage of deaths caused by injuries.
• Suicides Rate: Number of deliberate deaths per 100,000 inhabitants.
• Homicide Rate: Number of homicides per 100,000 inhabitants.

Air Pollution Mortality

• Air Pollution Death Rate Total: Probability of dying fr...

BackgroundSepsis was the main cause of maternal mortality in Suriname, a middle-income country. Objective of this study was to perform a qualitative analysis of the clinical and management aspects of sepsis-related maternal deaths with a focus on the ‘golden hour’ principle of antibiotic therapy.MethodsA nationwide reproductive age mortality survey was performed from 2010 to 2014 to identify and audit all maternal deaths in Suriname. All sepsis-related deaths were reviewed by a local expert committee to assess socio-demographic characteristics, clinical aspects and substandard care.ResultsOf all 65 maternal deaths in Suriname 29 (45%) were sepsis-related. These women were mostly of low socio-economic class (n = 23, 82%), of Maroon ethnicity (n = 14, 48%) and most deaths occurred postpartum (n = 21, 72%). Underlying causes were pneumonia (n = 14, 48%), wound infections (n = 3, 10%) and endometritis (n = 3, 10%). Bacterial growth was detected in 10 (50%) of the 20 available blood cultures. None of the women with sepsis as underlying cause of death received antibiotic treatment within the first hour, although most women fulfilled the diagnostic criteria of sepsis upon admission. In 27 (93%) of the 29 women from which sufficient information was available, substandard care factors were identified: delay in monitoring in 16 (59%) women, in diagnosis in 17 (63%) and in treatment in 21 (78%).ConclusionIn Suriname, a middle-income country, maternal mortality could be reduced by improving early recognition and timely diagnosis of sepsis, vital signs monitoring and immediate antibiotic infusion (within the golden hour).

Percentage of excess maternal deaths by age group and region, Brazil, 2020–2021.

This dataset presents the percentage of mothers who received postnatal care from a trained health provider within two days of delivery, using data from UNICEF’s 'Delivery Care' dataset. The immediate postnatal period is critical for detecting complications such as haemorrhage and infection. Monitoring postnatal care coverage helps assess health system responsiveness and supports advocacy for timely, respectful care to protect maternal health during the first days after childbirth.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.

About 295,000 women died globally during and following pregnancy and childbirth in 2017. Two-thirds of these deaths occurred in Sub-Saharan Africa. By linking individual and regional data from 135 regions in 17 Sub-Saharan African countries over the period 2002–2018 this study explores how bribery affects maternal mortality in Sub-Saharan Africa. Our results show that the percentage of people who had first-hand experience in bribery is significantly and positively associated with pregnancy related deaths. We find that a 10 p.p. increase in the prevalence of bribery is associated with up to 41 [95% CI: 10–73] additional deaths for every 1,000 pregnancy-related deaths. However, the healthcare system quality appears to be an important moderator. To reduce maternal mortality, policy makers should not only increase investments in healthcare, they need also to implement measures to combat corruption.

Abstract

The principal objective of the 2007 Ghana Maternal Health Survey (GMHS) is intended to serve as a source of data on maternal health and maternal death for policymakers and the research community involved in the Reducing Maternal Morbidity and Mortality (R3M) program. Specifically, the data collected in the GMHS is intended to help the Government of Ghana and the consortium of organizations participating in the R3M program to launch a series of collaborative efforts to significantly expand women's access to modern family planning services and comprehensive abortion care (CAC), reduce unwanted fertility, and reduce severe complications and deaths resulting from unsafe abortion. The GMHS collected data from a nationally representative sample of households and women of reproductive age (15-49). The data were collected in two phases. The primary objectives of the 2007 GMHS were: • 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, for the R3M program regions (Greater Accra, Ashanti and Eastern Regions), and for the non-program regions; • To identify specific causes of maternal and non-maternal deaths, and specifically to be able to identify deaths due to abortion-related causes, among adult women; •To collect data on women’s perceptions and experience with antenatal, maternity, and emergency obstetrical care, especially with regard to care received before, during, and after the termination or abortion of a pregnancy; • To measure indicators of the utilization of maternal health services and especially post-abortion care services in Ghana; and • To provide baseline data for the R3M program and for follow-on studies and surveys that will be used to observe possible reductions in maternal mortality as well as reductions in abortion-related mortality.It also contributes to the ever-growing international database on maternal health-related information.

The pregnancy-related mortality ratio (PRMR) for the 7-year period preceding the survey, calculated from the sibling history data, is 451 deaths per 100,000 live births and for the 5-year period preceding the survey is 378 deaths per 100,000 live births.Induced abortion accounts for more than one in ten maternal deaths and the obstetric risk from induced abortion is highest among young women age 15-24. Although almost all women seek antenatal care from a health professional, only one in two women deliver in a health facility, and three in four women seek postnatal care. Despite the emphasis on continuity of care, less than one in two women receive all three maternity care components (antenatal care, delivery care, and postnatal care) from a skilled provider. Clearly, Ghana has a long way to go towards achieving the MDG-5 target.

Geographic coverage

National

Analysis unit

Individual

Universe

1. All women age 12-49 years in households and residents in Ghana

Kind of data

Sample survey data [ssd]

Sampling procedure

To achieve the above-mentioned objectives and to obtain an accurate measure of the causes of maternal mortality at the national level, and for the Reducing Maternal Morbidity and Mortality( R3M) regions (Greater Accra, Ashanti and Eastern regions) and other regions (Western, Central, Volta, Brong Ahafo, Northern, Upper East and Upper West), 1600 primary sampling units were selected (half from the R3M regions and half from the other regions) within the 10 administrative regions of the country, across urban and rural areas. The primary sampling units consisted of wards or subwards drawn from the 2000 Population Census. This sample size was estimated from information in the 2003 Ghana DHS survey; it was expected that each primary sampling unit would yield, on average, 150 households. GSS and GHS enumerators carried out a complete mapping and listing of the 1600 selected clusters. This first phase of data collection yielded a total of 227,715 households.

A short household questionnaire was administered to identify deaths that occurred in the five years preceding the survey to women age 12-49 in each household listed in the selected cluster. In the second phase of data collection a verbal autopsy questionnaire was administered in all households identified in the first phase as having experienced the death of a woman age 12-49. This yielded a total of 4,203 completed verbal autopsy questionnaires.

In the second phase of fieldwork, 400 clusters were randomly selected from the 1600 clusters identified in the first phase. Households with women age 15-49 were selected from these 400 clusters (half from the R3M regions and half from the other regions) and were stratified by region and urban-rural residence to yield 10,858 completed household interviews and 10,370 individual women's interviews. These households were selected randomly and independently from the households identified in the first phase as having experienced a female death.

Institutional populations (those in hospitals, army barracks, etc.) and households residing in refugee camps were excluded from the GMHS sample.

Sampling deviation

No deviation of the original sample design was made

Mode of data collection

Face-to-face [f2f]

Research instrument

The GMHS used four questionnaires: (1) a Phase I short household questionnaire administered at the time of listing; (2) a Phase II verbal autopsy questionnaire administered in households identified at listing as having experienced the death of a female household member age 12-49; (3) a Phase II long-form household questionnaire administered in independently selected households chosen for the individual woman’s interview, and (4) a Phase II questionnaire for individual women age 15-49 in the same phase two selected households. The primary purpose of the short household questionnaire administered at the time of listing during Phase I was to identify deaths to women age 12-49, for administering the verbal autopsy questionnaire on the causes of female deaths, particularly maternal deaths and abortion-related deaths. Unique identifiers for households in phase one and households in phase two were not maintained; therefore households cannot be matched across both phases of the survey. During the first phase of the survey, all households in each selected cluster were listed and administered the short household questionnaire. This questionnaire was administered to identify households that experienced the death of a female [regular] household member in the five years preceding the survey. The verbal autopsy questionnaire (VAQ) was administered during the second phase of fieldwork in those households in which thefemale who died was age 12-49. The VAQ was designed to collect as much information as possible on the causes of all female deaths, to inform the subsequent categorization of maternal deaths, and facilitate specific identification of abortion-related deaths. During the second phase of fieldwork, a longer household questionnaire was administered in the independent subsample of households, to identify eligible women age 15- 49 for the individual woman’s questionnaire and to obtain some background information on the socioeconomic status of these women. The individual questionnaire included the maternal mortality module, which allows for the calculation of direct estimates of pregnancy-related mortality rates and ratios based on the sibling history. The individual questionnaire also gathered information on abortions and miscarriages, the utilization of maternal health services and post-abortion care, women’s knowledge of the legality of abortion in Ghana, the services they have utilized for abortion and if not, the reasons they have not been able to access professional health care for abortions, the places that offer abortion-related care, the persons offering such services, and other related questions. During the design of these questionnaires, input was sought from a variety of organizations that are expected to use the resulting data. After preparation of the questionnaires in English, they were translated into three languages: Akan, Ga, and Ewe. Back translations into English were carried out by people other than the initial translators to verify the accuracy of the translations in the three languages to be used. All problems arising during the translations were resolved before the pretest. The translated questionnaires were pretested to detect any problems in the translations or the flow of the questionnaire, as well as to gauge the length of time required for interviews. GSS and GHS engaged 20 interviewers for approximately two weeks for the pretest (with proficiency in each of the local languages used in the survey). All the pretest interviewers were trained for two weeks. The pretest interviewing took about one week to complete, during which approximately 30 women were interviewed in each of the local languages. The pretest results were used to modify the survey instruments as necessary. All changes in the questionnaire after the pretest were agreed to by GSS, GHS, and Macro. GSS and GHS were responsible for producing a sufficient number of the various questionnaires for the main fieldwork. During the pretest and main survey training, experts in the areas of health and family planning were identified by GSS and GHS to provide guidance in the presentation of topics in their fields, as they relate to the GMHS questionnaires. Other technical documents that were finalized include: • Household listing manual, listing forms and cartographic materials; • Interviewer’s manual; • Supervisor’s manual; • Interviewer and Supervisor’s

The "motherhood and childhood health index" symbolizes the health condition on birth related issues of a certain area in 2010. The quality of health system is an important factor determining the adaptive capacity. Beside the lack of medical services we should consider also the lack of access to these services. The index results from the third cluster of the Principal Component Analysis preformed among 16 potential variables. The analysis identify three dominant variables, namely "maternal mortality", "infant mortality" and "percentage of delivery in a healthcare facility", assigning respectively the weights of 0.39, 0.38 and 0.23. Before to perform the analysis all the variables were log transformed (except "infant mortality") to shorten the extreme variation and then were score-standardized (converted to distribution with average of 0 and standard deviation of 1; with inverse method for "maternal mortality" and "infant mortality") in order to be comparable. Country-based data of maternal mortality rate were collected from World Bank in particular the modeled mortality per 100,000 live births average of the period 2008-2012 was computed. Tabular data were linked by country to the national boundaries shapefile (FAO/GAUL) and then converted into raster format (resolution 0.5 arc-minute). The first administrative level data for "infant mortality" (deaths per 1,000 live births before 12 months of life) was derived by the Center for International Earth Science Information Network (CIESIN) at Columbia University using survey data (collected between 1998 and 2012) from DHS, UNDP National Human Development Reports, UNICEF statistics, and in some cases national survey data. Tabular data were linked by first administrative unit to the first administrative boundaries shapefile (FAO/GAUL) and then converted into raster format (resolution 0.5 arc-minute). The first administrative level data for the "percentage of delivery in a healthcare facility" was derived using survey data collected between 1998 and 2012 from DHS, UNDP National Human Development Reports, UNICEF statistics, and in some cases national survey data. Maternal and infant mortality are proxy to measure the quality of the health system. Moreover, the "percentage of delivery in a healthcare facility" is traditionally used to assess the capacity to access to healthcare by local population. This dataset has been produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

Abstract

The Sudan Demographic and Health Survey (SDHS) was conducted in two phases between November 15, 1989 and May 21, 1990 by the Department of Statistics of the Ministry of Economic and National Planning. The survey collected information on fertility levels, marriage patterns, reproductive intentions, knowledge and use of contraception, maternal and child health, maternal mortality, and female circumcision. The survey findings provide the National Population Committee and the Ministry of Health with valuable information for use in evaluating population policy and planning public health programmes.

A total of 5860 ever-married women age 15-49 were interviewed in six regions in northern Sudan; three regions in southern Sudan could not be included in the survey because of civil unrest in that part of the country. The SDHS provides data on fertility and mortality comparable to the 1978-79 Sudan Fertility Survey (SFS) and complements the information collected in the 1983 census.

The primary objective of the SDHS was to provide data on fertility, nuptiality, family planning, fertility preferences, childhood mortality, indicators of maternal health care, and utilization of child health services. Additional information was coUected on educational level, literacy, source of household water, and other housing conditions.

The SDHS is intended to serve as a source of demographic data for comparison with the 1983 census and the Sudan Fertility Survey (SFS) 1978-79, and to provide population and health data for policymakers and researchers. The objectives of the survey are to: - assess the overall demographic situation in Sudan, - assist in the evaluation of population and health programmes, - assist the Department of Statistics in strengthening and improving its technical skills for conducting demographic and health surveys, - enable the National Population Committee (NPC) to develop a population policy for the country, and - measure changes in fertility and contraceptive prevalence, and study the factors which affect these changes, and - examine the basic indicators of maternal and child health in Sudan.

MAIN RESULTS

Fertility levels and trends

Fertility has declined sharply in Sudan, from an average of six children per women in the Sudan Fertility Survey (TFR 6.0) to five children in the Sudan DHS survey flTR 5.0). Women living in urban areas have lower fertility (TFR 4.1) than those in rural areas (5.6), and fertility is lower in the Khartoum and Northern regions than in other regions. The difference in fertility by education is particularly striking; at current rates, women who have attained secondary school education will have an average of 3.3 children compared with 5.9 children for women with no education, a difference of almost three children.

Although fertility in Sudan is low compared with most sub-Saharan countries, the desire for children is strong. One in three currently married women wants to have another child within two years and the same proportion want another child in two or more years; only one in four married women wants to stop childbearing. The proportion of women who want no more children increases with family size and age. The average ideal family size, 5.9 children, exceeds the total fertility rate (5.0) by approximately one child. Older women are more likely to want large families than younger women, and women just beginning their families say they want to have about five children.

Marriage

Almost all Sudanese women marry during their lifetime. At the time of the survey, 55 percent of women 15-49 were currently married and 5 percent were widowed or divorced. Nearly one in five currently married women lives in a polygynous union (i.e., is married to a man who has more than one wife). The prevalence of polygyny is about the same in the SDHS as it was in the Sudan Fertility Survey.

Marriage occurs at a fairly young age, although there is a trend toward later marriage among younger women (especially those with junior secondary or higher level of schooling). The proportion of women 15-49 who have never married is 12 percentage points higher in the SDHS than in the Sudan Fertiliy Survey.

There has been a substantial increase in the average age at first marriage in Sudan. Among SDHS. Since age at first marriage is closely associated with fertility, it is likely that fertility will decrease in the future. With marriages occurring later, women am having their first birth at a later age. While one in three women age 45-49 had her first birth before age 18, only one in six women age 20-24 began childbearing prior to age 18. The women most likely to postpone marriage and childbearing are those who live in urban areas ur in the Khartoum and Northern regions, and women with pest-primary education.

Breastfeeding and postpartum abstinence

Breastfeeding and postpartum abstinence provide substantial protection from pregnancy after the birth uf a child. In addition to the health benefits to the child, breastfeeding prolongs the length of postpartum amenorrhea. In Sudan, almost all women breastfeed their children; 93 percent of children are still being breastfed 10-11 months after birth, and 41 percent continue breastfeeding for 20-21 months. Postpartum abstinence is traditional in Sudan and in the first two months following the birth of a child 90 percent of women were abstaining; this decreases to 32 percent after two months, and to 5 percent at~er one year. The survey results indicate that the combined effects of breastfeeding and postpartum abstinence protect women from pregnancy for an average of 15 months after the birth of a child.

Knowledge and use of contraception

Most currently married women (71 percent) know at least one method of family planning, and 59 percent know a source for a method. The pill (70 percent) is the most widely known method, followed by injection, female sterilisation, and the IUD. Only 39 percent of women knew a traditional method of family planning.

Despite widespread knowledge of family planning, only about one-fourth of ever-married women have ever used a contraceptive method, and among currently married women, only 9 percent were using a method at the time of the survey (6 percent modem methods and 3 percent traditional methods). The level of contraceptive use while still low, has increased from less than 5 percent reported in the Sudan Fertility Survey.

Use of family planning varies by age, residence, and level of education. Current use is less than 4 percent among women 15-19, increases to 10 percent for women 30-44, then decreases to 6 percent for women 45-49. Seventeen percent of urban women practice family planning compared with only 4 percent of rural women; and women with senior secondary education are more likely to practice family planning (26 percent) than women with no education (3 percent).

There is widespread approval of family planning in Sudan. Almost two-thirds of currently married women who know a family planning method approve of the use of contraception. Husbands generally share their wives's views on family planning. Three-fourths of married women who were not using a contraceptive method at the time of the survey said they did not intend to use a method in the future.

Communication between husbands and wives is important for successful family planning. Less than half of currently married women who know a contraceptive method said they had talked about family planning with their husbands in the year before the survey; one in four women discussed it once or twice; and one in five discussed it more than twice. Younger women and older women were less likely to discuss family planning than those age 20 to 39.

Mortality among children

The neonatal mortality rate in Sudan remained virtually unchanged in the decade between the SDHS and the SFS (44 deaths per 1000 births), but under-five mortality decreased by 14 percent (from 143 deaths per 1000 births to 123 per thousand). Under-five mortality is 19 percent lower in urban areas (117 per 1000 births) than in rural areas (144 per 10(30 births).

The level of mother's education and the length of the preceding birth interval play important roles in child survival. Children of mothers with no education experience nearly twice the level of under-five mortality as children whose mother had attained senior secondary or nigher education. Mortality among children under five is 2.7 times higher among children born after an interval of less than 24 months than among children born after interval of 48 months or more.

Maternal mortality

The maternal mortality rate (maternal deaths per 1000 women years of exposure) has remained nearly constant over the twenty years preceding the survey, while the maternal mortality ratio (number of maternal deaths per 100,000 births), has increased (despite declining fertility). Using the direct method of estimation, the maternal mortality ratio is 352 maternal deaths per 100,000 births for the period 1976-82, and 552 per 100,000 births for the period 1983-89. The indirect estimate for the maternal mortality ratio is 537. The latter estimate is an average of women's experience over an extended period before the survey centred on 1977.

Maternal health care

The health care mothers receive during pregnancy and delivery is important to the survival and well-being of both children and mothers. The SDHS results indicate that most women in Sudan made at least one antenatal visit to a doctor or trained health worker/midwife. Eighty-seven percent of births benefitted from professional antenatal care in urban areas compared with 62 percent in rural areas. Although the proportion of pregnant mothers seen by trained health workers/midwives are similar in urban and rural areas, doctors provided antenatal care for 42 percent and 19 percent of births in urban and rural areas, respectively.

Neonatal tetanus, a major

INTRO

This dataset covers the most recent and updated health statistics of the world (countries recognized by WHO- all), BUT the data could not be directly used as the major indicator of various subtopics in the dataset was mixed so I have filtered based on various indicators and hence, divided into subcategories. I know so many datasets seem overwhelming, but I will be giving the various categories they belong to and what they represent so do not worry!)

The dataset was filtered to increase user readability and create amazing and beautiful visualizations and EDA’s. Listed below will be the various datasets (named csv’s) and what they represent under their categories.

Also, before starting I will soon be uploading a viz for the same and this data cleaning and filtering has along with compiling has been a great task so...

Let us get started.

DATASET (CONTENTS)

Life expectancy and Healthy life expectancy

lifeExpectancyAtBirth.csv -> Life expectancy at birth, country wise mentioned in age (years). HALElifeExpectancyAtBirth.csv -> Healthy life expectancy (HALE) at birth, country wise mentioned in age(years).csv WHOregionLifeExpectancAtBirth.csv -> Life expectancy at birth, Region wise mentioned in age (years). HAleWHOregionLifeExpectancy.csv -> Healthy life expectancy at birth, region wise mentioned in age(years). %HaleInLifeExpectancy.csv -> Healthy life and life expectancy at birth with the % of HALE in life expectancy.

Maternal mortality

Data from 2014 to 2019 indicate that approximately 81% of all births globally took place in the presence of skilled health personnel, an increase from 64% in the 2000–2006 period

maternalMortalityRatio.csv-> Maternal mortality ratio per 100,000 births birthAttendedBySkilledPersonal.csv-> Births attended by skilled personals (percentile)

Newborn and child mortality

infantMortalityRate.csv-> Probability of dying between birth and age 1 per 1000 live births. neonatalMortalityRate.csv -> Probability of children dying in the first 28 days of life. under5MortalityRate.csv- > Probability of children dying below the age of 5 per 1000 live births.

Communicable Diseases

incedenceOfMalaria.csv-> Malaria incidence per 1000 population at risk incedenceOfTuberculosis.csv-> Incidence of TB per 100,000 population per year. hepatitusBsurfaceAntigen.csv -> Hepatitis B surface antigen (HBsAg) prevalence among children under 5 years) interventionAgianstNTD's.csv -> Reported number of people requiring interventions against NTDs. newHivInfections.csv ->New HIV infections per 1000 uninfected population

Noncommunicable diseases and mental health

30-70cancerChdEtc.csv -> Probability of dying between the age of 30 and exact age of 70 from any of the cardiovascular disease, cancer, diabetes, or chronic respiratory disease. crudeSuicideRates.csv -> Crude suicide rates per 100,000 population

Substance abuse

AlcoholSubstanceAbuse.csv -> Total (recorded + unrecorded) alcohol per capita (15 +) consumption’s

Road Traffic Injuries

roadTrafficDeaths.csv -> Estimated road traffic death rate per 100,000 population

Sexual and reproductive health

reproductiveAgeWomen.csv -> Married or in-union women of reproductive age who have their need for family planning satisfied with modern methods (%) adolescentBirthRate.csv -> Adolescent birth rate per 1000 women aged 15-19 years

Achieve universal health coverage (UHC) including financial risk protection

uhcCoverage.csv ->UHC index of service coverage (SCI) dataAvailibilityForUhc.csv ->Data availability of UHC index of essential service coverage (%) population10%SDG3.8.2.csv ->Population with household expenditures on health greater than 10% of total household expenditure or income (SDG indicator 3.8.2) (%) population25%SDG3.8.2.csv -> Population with household expenditures on health greater than 25% of total household expenditure or income (SDG indicator 3.8.2) (%)

Mortality from environment pollution

airPollutionDeathRate.csv -> Ambient and household air pollution attributable death rate per 100,00 population and the same data with age-standardized. mortalityRateUnsafeWash.csv -> Mortality rate attributed to exposure to unsafe WASH services per 100,000 population SDG3.9.2 mortalityRatePoisoning.csv -> Mortality rate attributed to unintentional poisoning per 100,000 population

Tobacco control

tobaccoAge15.csv ->Prevalence of current tobacco use among persons aged 15 years and older (age- standardized rate)

Health Workforce

medicalDoctors.csv -> Medical doctors per 10,000 population. nursingAndMidwife.csv -> Nursing and midwifery personnel per 10,000 ...

Eurostat’s annual data collections on demographic and migration statistics are structured as follows:

• NOWCAST: Annual data collection on provisional monthly data on live births and deaths covering at least six months of the reference year (Article 4.3 of the Commission implementing regulation (EU) No 205/2014).
• DEMOBAL (Demographic balance): Annual data collection on provisional data on population, total live births and total deaths at national level (Article 4.1 of the Commission implementing regulation (EU) No 205/2014).
• POPSTAT (Population statistics): The most in-depth annual national and regional demographic and migration data collection. The data relate to populations, births, deaths, immigrants, emigrants, marriages and divorces, and is broken down into several categories (Article 3 of Regulation (EU) No 1260/2013 and Article 3 of Regulation (EC) No 862/2007).

The aim is to collect annual mandatory and voluntary demographic data from the national statistical institutes. Mandatory data are those defined by the legislation listed under ‘6.1. Institutional mandate - legal acts and other agreements’.

The completeness of the demographic data collected on a voluntary basis depends on the availability and completeness of information provided by the national statistical institutes. For more information on mandatory/voluntary data collection, see 6.1. Institutional mandate - legal acts and other agreements’.

The following statistics on live births are collected from the National Statistical Institutes:

• Live births by month of occurrence;
• Live births by mother's age, year of birth and by:
  • region (NUTS 2) of residence
  • region (NUTS 3) of residence
  • mother's country of birth
  • mother's country of citizenship
  • live-birth order
  • sex of the new-born
  • mother's legal marital status
  • employment status of the mother
  • mother's educational attainment (ISCED 2011);
• Live births by birth weight and duration of gestation;
• Legally induced abortions by mother's age and parity;
• Late fœtal deaths by mother's age.

Statistics on fertility: based on the different breakdowns of data on live births and on legally induced abortions received, Eurostat produces the following:

• Statistics available in the online table Population change - Demographic balance and crude rates at national level (demo_gind):
  • natural change of the population, crude birth rate;
• Statistics available in the online table Fertility indicators (demo_find):
  • the proportion of live births outside marriage
  • total fertility rate
  • the mean age of women at childbirth
  • the mean age of women at the birth of first / second / third / fourth and higher child
  • the median age of women at childbirth
  • the percentage of first / second / third / fourth and higher live births Fertility rates by age (demo_frate);
• Fertility rates by age and NUTS 2 region (demo_r_frate2);
• Total fertility rate by NUTS 3 region (demo_r_frate3);
• Statistics available in the online table Abortion indicators (demo_fabortind):
  • abortion rate
  • abortion ratio