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

Maternal mortality ratio is defined as the number of female deaths due to obstetric causes (ICD-10 codes: A34, O00-O95, O98-O99) while pregnant or within 42 days of termination of pregnancy. The maternal mortality ratio indicates the likelihood of a pregnant person dying of obstetric causes. It is calculated by dividing the number of deaths among birthing people attributable to obstetric causes in a calendar year by the number of live births registered for the same period and is presented as a rate per 100,000 live births. The number of live births used in the denominator approximates the population of pregnant and birthing people who are at risk. Data are not presented for geographies with number of maternal deaths less than 11.Compared to other high-income countries, women in the US are more likely to die from childbirth or problems related to pregnancy. In addition, there are persistent disparities by race and ethnicity, with Black pregnant persons experiencing a much higher rate of maternal mortality compared to White pregnant persons. Improving the quality of medical care for pregnant individuals before, during, and after pregnancy can help reduce maternal deaths.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

Number of deaths caused by pregnancy, childbirth and the puerperium, by age group and sex, 2000 to most recent year.

Each year, there are audits to assess maternal & foetal outcomes across the UK. In 2016-18, 217 women died during or up to six weeks after pregnancy, from causes associated with their pregnancy, among 2,235,159 women giving birth in the UK. 9.7 women per 100k died during pregnancy or up to six weeks after childbirth or the end of pregnancy. There was an increase in the overall maternal death rate in the UK between 2013-15 & 2016-18. Assessors judged that 29% of women who died had good care. However, improvements in care which may have made a difference to the outcome were identified for 51% of women who died. Birmingham has a higher than average maternal & foetal death rate. This dataset includes detailed information about the reasons pregnant women seek acute care, & their care pathways & outcomes. PIONEER geography: The West Midlands (WM) has a population of 5.9m & includes a diverse ethnic, socio-economic mix. There is a higher than average % of minority ethnic groups. WM has the youngest population in the UK with a higher than average birth rate. There are particularly high rates of physical inactivity, obesity, smoking & diabetes. 51.2% of babies born in Birmingham have at least one parent born outside of the UK, this compares with 34.7% for England. Each day >100k people are treated in hospital, see their GP or are cared for by the NHS. EHR: University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. Scope: Pregnant or post-partum women from 2015 onwards who attended A&E in Queen Elizabeth hospital. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics (including gestation & postpartum period), co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care (admissions, wards, practitioner changes & discharge outcomes), presenting complaints, physiology readings (temperature, blood pressure, NEWS2, SEWS, AVPU), referrals, all prescribed & administered treatments & all outcomes. Available supplementary data: More extensive data including granular serial physiology, bloods, conditions, interventions, treatments. Ambulance, 111, 999 data, synthetic data. Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

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

Fetal mortality occurs after 20 weeks of gestation and before labor. Infant mortality occurs before the first year of age and is a sum of Neonatal (the first 28 days after birth) and Postneonatal (from 28 days up to 1 year) mortality. Rates are calculated per every 1000 births; rates are not available for disaggregated race/ethnicities. Fetal and infant mortality values are available for given race/ethnicities. Connecticut Department of Public Health collects and reports data annually. CTData.org carries 1-, 3- and 5-Year aggregations.

Historical chart and dataset showing Singapore maternal mortality rate by year from 1985 to 2023.

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

Country: Name of the country.

Density (P/Km2): Population density measured in persons per square kilometer.

Abbreviation: Abbreviation or code representing the country.

Agricultural Land (%): Percentage of land area used for agricultural purposes.

Land Area (Km2): Total land area of the country in square kilometers.

Armed Forces Size: Size of the armed forces in the country.

Birth Rate: Number of births per 1,000 population per year.

Calling Code: International calling code for the country.

Capital/Major City: Name of the capital or major city.

CO2 Emissions: Carbon dioxide emissions in tons.

CPI: Consumer Price Index, a measure of inflation and purchasing power.

CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.

Currency_Code: Currency code used in the country.

Fertility Rate: Average number of children born to a woman during her lifetime.

Forested Area (%): Percentage of land area covered by forests.

Gasoline_Price: Price of gasoline per liter in local currency.

GDP: Gross Domestic Product, the total value of goods and services produced in the country.

Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.

Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.

Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.

Largest City: Name of the country's largest city.

Life Expectancy: Average number of years a newborn is expected to live.

Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.

Minimum Wage: Minimum wage level in local currency.

Official Language: Official language(s) spoken in the country.

Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.

Physicians per Thousand: Number of physicians per thousand people.

Population: Total population of the country.

Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.

Tax Revenue (%): Tax revenue as a percentage of GDP.

Total Tax Rate: Overall tax burden as a percentage of commercial profits.

Unemployment Rate: Percentage of the labor force that is unemployed.

Urban Population: Percentage of the population living in urban areas.

Latitude: Latitude coordinate of the country's location.

Longitude: Longitude coordinate of the country's location.

Potential Use Cases

Analyze population density and land area to study spatial distribution patterns.

Investigate the relationship between agricultural land and food security.

Examine carbon dioxide emissions and their impact on climate change.

Explore correlations between economic indicators such as GDP and various socio-economic factors.

Investigate educational enrollment rates and their implications for human capital development.

Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.

Study labor market dynamics through indicators such as labor force participation and unemployment rates.

Investigate the role of taxation and its impact on economic development.

Explore urbanization trends and their social and environmental consequences.

The dataset contains year- and gender-wise number of medically certified deaths from the total registered deaths with Medical Certification of Cause of Death (MCCD). The MCCD is a system that medically certifies the registered deaths, under the Registration of Births and Deaths Act, 1969.

The major groups of diseases covered in the dataset include Pregnancy, Childbirth and the Puerperium, Certain Infectious and Parasitic Diseases, Injury, Poisoning and Certain Other Consequences of External Causes, Codes for Special Purposes, Neoplasms, Mental and Behavioural Disorders, Diseases of Blood and Blood Forming Organs and Certain Disorders Involving the Immune Mechanism, Endocrine, Nutritional and Metabolic Diseases, Symptoms, Signs and Abnormal Clinical and Laboratory Findings, Certain Conditions Originating in the Perinatal Period, Congenital Malformations, Deformations and Chromosomal Abnormalities, Diseases of the Digestive System, Skin and Subcutaneous Tissue, Musculoskeletal System and Connective Tissue, Genitourinary System, Nervous System, Eye and Adnexa, Ear and Mastoid, Circulatory System, Respiratory System

Maternal mortality ratio of Kenya declined by 3.87% from 155.0 deaths per 100,000 live births in 2022 to 149.0 deaths per 100,000 live births in 2023. Since the 4.55% growth in 2020, maternal mortality ratio plummeted by 19.02% in 2023. Maternal mortality ratio is the number of women who die during pregnancy and childbirth, per 100,000 live births. The data are estimated with a regression model using information on fertility, birth attendants, and HIV prevalence.

JP: Births Attended by Skilled Health Staff: % of Total data was reported at 99.900 % in 2015. This records an increase from the previous number of 99.800 % for 2014. JP: Births Attended by Skilled Health Staff: % of Total data is updated yearly, averaging 99.800 % from Dec 1990 (Median) to 2015, with 18 observations. The data reached an all-time high of 100.000 % in 1996 and a record low of 99.800 % in 2014. JP: Births Attended by Skilled Health Staff: % of Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Japan – Table JP.World Bank.WDI: Health Statistics. Births attended by skilled health staff are the percentage of deliveries attended by personnel trained to give the necessary supervision, care, and advice to women during pregnancy, labor, and the postpartum period; to conduct deliveries on their own; and to care for newborns.; ; UNICEF, State of the World's Children, Childinfo, and Demographic and Health Surveys.; Weighted average; Assistance by trained professionals during birth reduces the incidence of maternal deaths during childbirth. The share of births attended by skilled health staff is an indicator of a health system’s ability to provide adequate care for pregnant women.

🇬🇧 United Kingdom English Infant and Perinatal Deaths and Mortality. Perinatal deaths are deaths occuring after 22 weeks of pregnancy, during childbirth and up to 7 complicated days of life. Infant deaths under 1 year per 1000 live births. Relevant links: http://www.ons.gov.uk/ons/rel/vsob1/child-mortality-statistics--childhood--infant-and-perinatal/index.html https://indicators.ic.nhs.uk/webview/

Number of deaths caused by pregnancy, childbirth and the puerperium, by age group and sex, 2000 to most recent year.

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.

Abstract Objective To assess the possible impact of the COVID-19 pandemic on maternal mortality among admissions for childbirth in 2020 in relation of the last 10 years. Methods An ecological study with pregnant women who underwent hospital births at the Brazilian unified public health service (SUS, in the Portuguese acronym) in Brazil from 2010 to 2020. The mortality among admissions for childbirth was obtained based on the number of admissions for childbirth with reported death as outcome divided by the total number of admissions. The underlying gestational risk and route of delivery were considered based on the national surveillance system. The average mortality for the period between 2010 and 2019 (baseline) was compared with the rate of deaths in 2020 (1st pandemic year); the rate ratio was interpreted as the risk of death in 2020 in relation to the average of the previous period (RR), with 95% confidence intervals (CIs). Results In 2020, the 1st year of the COVID-19 pandemic, 1,821,775 pregnant women were hospitalized for childbirth and 651 deaths were reported, which represents 8.7% of the total hospitalizations and 11.3% of maternal deaths between 2010 and 2020. There was an increase in maternal mortality after births in 2020 compared with the average for the period between 2010 and 2019, specially in low-risk pregnancies, both in vaginal (RR = 1.60; 95%CI:1.39–1.85) and cesarean births (RR = 1.18; 95%CI:1.04–1.34). Conclusion Maternal mortality among admissions for childbirth according to SUS data increased in 2020 compared with the average between 2010 and 2019, with an increment of 40% in low-risk pregnancies. The increase was of 18% after cesarean section and of 60% after vaginal delivery.

TABLE 4.8: Perinatal Statistics Report 2014: Size of Maternity Unit (Number of Live Births and Stillbirths Annually) by Number of Units: Total Births, Live Births, Mortality Rates, and Maternities, 2014. Published by Health Service Executive. Available under the license cc-by (CC-BY-4.0).Presents the distribution of TOTAL births for 2014 by Size of Maternity Unit. This table outlines data for total births, live births, stillbirths, early neonatal deaths and perinatal mortality rates. The Perinatal Statistics Report 2014 is a report on national data on Perinatal events in 2014. Information on every birth in the Republic of Ireland is submitted to the National Perinatal Reporting System (NPRS). All births are notified and registered on a standard four part birth notification form (BNF01) which is completed where the birth takes place. Part 3 of this form is sent to the HPO for data entry and validation. The information collected includes data on pregnancy outcomes (with particular reference to perinatal mortality and important aspects of perinatal care), as well as descriptive social and biological characteristics of mothers giving birth. See the complete Perinatal Statistics Report 2014 at http://www.hpo.ie/latest_hipe_nprs_reports/NPRS_2014/Perinatal_Statistics_Report_2014.pdf...

Analysis of ‘Fetal Health Classification’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/andrewmvd/fetal-health-classification on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Abstract

Classify fetal health in order to prevent child and maternal mortality.

About this dataset

Context

Reduction of child mortality is reflected in several of the United Nations' Sustainable Development Goals and is a key indicator of human progress. The UN expects that by 2030, countries end preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce under‑5 mortality to at least as low as 25 per 1,000 live births.

Parallel to notion of child mortality is of course maternal mortality, which accounts for 295 000 deaths during and following pregnancy and childbirth (as of 2017). The vast majority of these deaths (94%) occurred in low-resource settings, and most could have been prevented.

In light of what was mentioned above, Cardiotocograms (CTGs) are a simple and cost accessible option to assess fetal health, allowing healthcare professionals to take action in order to prevent child and maternal mortality. The equipment itself works by sending ultrasound pulses and reading its response, thus shedding light on fetal heart rate (FHR), fetal movements, uterine contractions and more.

Data

This dataset contains 2126 records of features extracted from Cardiotocogram exams, which were then classified by three expert obstetritians into 3 classes: - Normal - Suspect - Pathological

How to use

• Create a multiclass model to classify CTG features into the three fetal health states.

Acknowledgements

If you use this dataset in your research, please credit the authors.

Citation

Ayres de Campos et al. (2000) SisPorto 2.0 A Program for Automated Analysis of Cardiotocograms. J Matern Fetal Med 5:311-318 (link)

License

License was not specified at the source, yet access to the data is public and a citation was requested.

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BackgroundPeripartum mistreatment of women contributes to maternal mortality across the globe and disproportionately affects vulnerable populations. While traditionally recognized in low/low-middle-income countries, the extent of research on respectful maternity care and the types of mistreatment occurring in high-income countries is not well understood. We conducted a scoping review to 1) map existing respectful maternity care research by location, country income level, and approach, 2) determine if high-income countries have been studied equally when compared to low/low-middle-income countries, and 3) analyze the types of disrespectful care found in high-income countries.MethodsA systematic search for published literature up to April 2021 using PubMed/MEDLINE, EMBASE, CINAHL Complete, and the Maternity & Infant Care Database was performed. Studies were included if they were full-length journal articles, published in any language, reporting original data on disrespectful maternal care received from healthcare providers during childbirth. Study location, country income level, types of mistreatment reported, and treatment interventions were extracted. This study was registered on PROSPERO, number CRD42021255337.ResultsA total of 346 included studies were categorized by research approach, including direct labor observation, surveys, interviews, and focus groups. Interviews and surveys were the most common research approaches utilized (47% and 29% of all articles, respectively). Only 61 (17.6%) of these studies were conducted in high-income countries. The most common forms of mistreatment reported in high-income countries were lack of informed consent, emotional mistreatment, and stigma/discrimination.ConclusionsMapping existing research on respectful maternity care by location and country income level reveals limited research in high-income countries and identifies a need for a more global approach. Furthermore, studies of respectful maternity care in high-income countries identify the occurrence of all forms of mistreatment, clashing with biases that suggest respectful maternity care is only an issue in low-income countries and calling for additional research to identify interventions that embrace an equitable, patient-centric empowerment model of maternity care.

No. of Deaths: Caused by: Pregnancy, Childbirth & Puerperium data was reported at 79.000 Person in Sep 2024. This records an increase from the previous number of 71.000 Person for Jun 2024. No. of Deaths: Caused by: Pregnancy, Childbirth & Puerperium data is updated quarterly, averaging 100.500 Person from Mar 2017 (Median) to Sep 2024, with 30 observations. The data reached an all-time high of 241.000 Person in Jun 2021 and a record low of 70.000 Person in Dec 2023. No. of Deaths: Caused by: Pregnancy, Childbirth & Puerperium data remains active status in CEIC and is reported by National Administrative Department of Statistics. The data is categorized under Global Database’s Colombia – Table CO.G012: Number of Deaths: Cause of Death.

Background: In 2020, 287,000 mothers died from complications of pregnancy or childbirth; one-third of these deaths (30%) occur during the first 6 weeks after birth. Precision public health approaches leverage risk prediction to identify the most vulnerable patients and inform decisions around use of scarce resources, including the frequency, intensity, and type of postnatal care follow-up visits. However, these approaches may not accurately or precisely predict risk for specific sub-groups of women who are statistically underrepresented in the total population, such as women who experience stillbirths. Methods: We leverage our existing dataset of sociodemographic and clinical variables and health outcomes for mother and baby dyads in Uganda to generate a synthetic dataset to enhance our risk prediction model for identifying women at a high-risk of death or readmission in the 6 weeks after a hospital delivery. Data Collection Methods: The original mom and baby project data were collected at the point of care using encrypted study tablets and these data were then uploaded to a Research Electronic Data Capture (REDCap) database hosted at the BC Children’s Hospital Research Institute (Vancouver, Canada). Following delivery and obtaining informed written consent, trained study nurses collected data grouped according to four periods of care; admission, delivery, discharge, and six-week post-discharge follow up. Data from admission and delivery were captured from the hospital medical record where possible and by direct observation, direct measurement or patient interview when not. Discharge and post-discharge data were collected by observation, measurement or interview. Six-weeks after delivery, field officers contacted every mother and/or caregivers of newborns who survived to discharge to determine vital status, readmission and care seeking for illnesses and routine postnatal care. In-person visits were completed in situations where participants could not be reached by phone. Mothers who had experienced a stillbirth were filtered from the overall dataset. The synthetic dataset was subsequently based off the stillbirth cohort and evaluated it to ensure its statistical properties were maintained. Data Processing Methods: Synthetic data and evaluation metrics were generated using the synthpop R package. The first variable (column) in the dataset is generated via random sampling with replacement with subsequent variables generated conditioned on all previously synthesized variables using a pre-specified algorithm. We used the classification and regression tree (CART) algorithm as it is non-parametric and compatible with all data types (continuous, categorical, ordinal). Additional setup for generating the synthetic dataset included identifying eligible and relevant variables for synthesis and outlining rules for variables that have branching logic (i.e., variables that are only entered if a previous variable has a specific response). For evaluation, we used the utility metric recommended by the authors of the synthpop package, the standardized propensity-score mean squared error (pMSE) ratio which measures how easy it is to tell whether a data point comes from the original data or the synthetic dataset. All the standardized pMSE ratios were below 10, which is the suggested cut-off for acceptable utility as proposed by the synthpop authors. Plots were also generated to visually compare the univariate distribution of each variable in the synthetic dataset against the original dataset. Ethics Declaration: Ethics approvals have been obtained from the Makerere University School of Public Health (MakSPH) Institutional Review Board (SPH-2021-177), the Uganda National Council of Science and Technology (UNCST) in Uganda (HS2174ES) and the University of British Columbia in Canada (H21-03709). This study has been registered at clinicaltrials.gov (NCT05730387). Abbreviations: JRRH: Jinja Regional Referral Hospital MRRH: Mbarara Regional Referral Hospital PNC: Post-natal care SES: Socio-economic index SpO2: Oxygen saturation Study Protocol & Supplementary Materials: Smart Discharges for Mom & Baby 2.0: A cohort study to develop prognostic algorithms for post-discharge readmission and mortality among mother-infant dyads NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at sepsiscolab@bcchr.ca or visit our website.