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.

Maternal Mortality Ratio per 100,000

The maternal mortality rate in Australia in 2018 was 5 deaths per 100,000 women giving birth.

From 2009 to 2018, there were 251 women reported to have died during pregnancy or within 42 days of the end of pregnancy and a maternal mortality rate of 6.7 deaths per 100,000 women giving birth.

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

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

NCHS released maternal mortality statistics for 2018, an extensive review of data quality, and new coding procedures for death certificates based on this review.

Maternal mortality ratio shown by province from 2001 to 2018*Number of maternal deaths per 10,000 live births.The maternal mortality rate for every one hundred thousand live births is 38 according to 2016 data. Its fluctuations during the last decades demonstrate the need to focus on access and care by qualified professionals to reduce the causes of preventable deaths.Source: https://www.ossyr.org.ar/indicadores.php#Raz%C3%B3n-de-mortalidad-materna

Time series data for the statistic Maternal mortality ratio (modeled estimate, per 100,000 live births) and country Malta. Indicator Definition:Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data are estimated with a regression model using information on the proportion of maternal deaths among non-AIDS deaths in women ages 15-49, fertility, birth attendants, and GDP measured using purchasing power parities (PPPs).The indicator "Maternal mortality ratio (modeled estimate, per 100,000 live births)" stands at 8.00 as of 12/31/2023, the lowest value since 12/31/2018. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -20.00 percent compared to the value the year prior.The 1 year change in percent is -20.00.The 3 year change in percent is -20.00.The 5 year change in percent is -11.11.The 10 year change in percent is 14.29.The Serie's long term average value is 12.56. It's latest available value, on 12/31/2023, is 36.33 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2013, to it's latest available value, on 12/31/2023, is +14.29%.The Serie's change in percent from it's maximum value, on 12/31/1985, to it's latest available value, on 12/31/2023, is -61.90%.

ObjectiveTo evaluate the validity of WHO’s near-miss approach in a low-resource, high maternal mortality setting.DesignProspective cohort study.SettingMnazi Mmoja Hospital, the main referral hospital of Zanzibar, Tanzania, from 1 April 2017 until 31 December 2018.PopulationAll women, pregnant or until 42 days after the end of pregnancy, admitted at Mnazi Mmoja Hospital, the tertiary referral hospital in Zanzibar.MethodsCases of maternal morbidity and mortality were evaluated according to WHO’s near-miss approach. The approach’s performance was determined by calculating its accuracy through sensitivity, specificity and positive and negative likelihood ratios. The approach’s validity was assessed with Pearson’s correlation coefficient between the number of organ dysfunction markers and risk of mortality.Main outcomes measuresCorrelation between number of organ dysfunction markers and risk of mortality, sensitivity and specificity.Results26,842 women were included. There were 335 with a severe maternal outcome: 256 maternal near-miss cases and 79 maternal deaths. No signs of organ dysfunction were documented in only 4 of the 79 cases of maternal death. The number of organ dysfunction markers was highly correlated to the risk of mortality with Pearson’s correlation coefficient of 0.89.ConclusionsWHO’s near-miss approach adequately identifies women at high risk of maternal mortality in Zanzibar’s referral hospital. There is a strong correlation between the number of markers of organ dysfunction and mortality risk.

Costa Rica CR: Maternal Mortality Ratio: National Estimate: per 100,000 Live Births data was reported at 24.000 Ratio in 2018. This records a decrease from the previous number of 29.000 Ratio for 2014. Costa Rica CR: Maternal Mortality Ratio: National Estimate: per 100,000 Live Births data is updated yearly, averaging 28.500 Ratio from Dec 1985 (Median) to 2018, with 30 observations. The data reached an all-time high of 39.000 Ratio in 2007 and a record low of 14.000 Ratio in 2013. Costa Rica CR: Maternal Mortality Ratio: National Estimate: per 100,000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Costa Rica – Table CR.World Bank.WDI: Social: Health Statistics. Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births.;The country data compiled, adjusted and used in the estimation model by the Maternal Mortality Estimation Inter-Agency Group (MMEIG). The country data were compiled from the following sources: civil registration and vital statistics; specialized studies on maternal mortality; population based surveys and censuses; other available data sources including data from surveillance sites.;;

This layer was developed by the Research & Analytics Division of the Atlanta Regional Commission using data from the Georgia Department of Public Health to show births classified by baby health and mother type by census tract across the state of Georgia. Attributes can be found from the maternal health data 2018 manifest.Date: 2008-2018

Time series data for the statistic Maternal mortality ratio (modeled estimate, per 100,000 live births) and country Albania. Indicator Definition:Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data are estimated with a regression model using information on the proportion of maternal deaths among non-AIDS deaths in women ages 15-49, fertility, birth attendants, and GDP measured using purchasing power parities (PPPs).The indicator "Maternal mortality ratio (modeled estimate, per 100,000 live births)" stands at 7.00 as of 12/31/2023, the lowest value at least since 12/31/1986, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -12.50 percent compared to the value the year prior.The 1 year change in percent is -12.50.The 3 year change in percent is 0.0.The 5 year change in percent is 0.0.The 10 year change in percent is -30.00.The Serie's long term average value is 16.41. It's latest available value, on 12/31/2023, is 57.34 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2018, to it's latest available value, on 12/31/2023, is +0.0%.The Serie's change in percent from it's maximum value, on 12/31/1985, to it's latest available value, on 12/31/2023, is -81.58%.

Time series data for the statistic Lifetime risk of maternal death (1 in: rate varies by country) and country Malta. Indicator Definition:Life time risk of maternal death is the probability that a 15-year-old female will die eventually from a maternal cause assuming that current levels of fertility and mortality (including maternal mortality) do not change in the future, taking into account competing causes of death.The indicator "Lifetime risk of maternal death (1 in: rate varies by country)" stands at 11.06 Thousand as of 12/31/2023, the highest value since 12/31/2018. Regarding the One-Year-Change of the series, the current value constitutes an increase of 19.38 percent compared to the value the year prior.The 1 year change in percent is 19.38.The 3 year change in percent is 25.52.The 5 year change in percent is 17.05.The 10 year change in percent is 3.24.The Serie's long term average value is 6.62 Thousand. It's latest available value, on 12/31/2023, is 67.18 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1985, to it's latest available value, on 12/31/2023, is +346.95%.The Serie's change in percent from it's maximum value, on 12/31/2017, to it's latest available value, on 12/31/2023, is -1.88%.

Listening to Mothers™ surveys (2002 - to present) investigate women's childbearing experiences from pregnancy (and sometimes earlier) through the postpartum period, and their views about these matters. National Listening to Mothers surveys to date were led by Childbirth Connection, which became a core program of the National Partnership for Women & Families in 2014. Listening to Mothers in California, led by the National Partnership for Women & Families, is the first state-level Listening to Mothers survey. This population-based survey of women who gave birth in California hospitals in 2016 was carried out by core Listening to Mothers investigators at the National Partnership for Women & Families and at Boston University School of Public Health, joined by investigators at what is now known as the University of California San Francisco Center for Health Equity, in collaboration with the survey research firm Quantum Market Research. California Health Care Foundation and Yellow Chair Foundation funded the survey. As investigators had access to selected birth certificate items for sampling, contacting sampled women, data weighting and data analyses, the methodology of this survey differs from the methodology used in national Listening to Mothers surveys. Other differences between past national surveys and this state-level survey include the ability to participate in the state survey in either English or Spanish and to participate on any device, as well as with a trained interviewer. Eligible women could participate in past national surveys in English only and either on their own with a laptop or desktop computer or by telephone with a trained interviewer. The Listening to Mothers in California survey questionnaire retained some core items that had been included in previous surveys, adapted others (including for mobile-first display), and included new items developed to explore the evolving U.S. health and maternity care environment and topics relevant to the California context. Topics included care arrangements, maternity care (and especially care during the hospital stay for giving birth), mode of birth, respectful and disrespectful treatment, postpartum experiences, and perinatal mental health (especially anxiety and depression). The public dataset is limited to items provided by survey participants while completing the survey, exclusive of personally identifiable information. For their analyses, the survey investigators have access to two additional sources of information about survey participants that cannot be made public: selected items on participants’ birth certificates and selected items abstracted from the California Department of Health Care Services Management Information System/Decision Support System Warehouse. Much information about the California survey is available at either www.nationalpartnership.org/LTMCA or www.chcf.org/listening-to-mothers-CA. Information about national surveys (including a bibliography of analyses carried out to date and other reports) is available at www.nationalpartnership.org/listeningtomothers/.

Abstract

The primary objective of the 2018 ZDHS was to provide up-to-date estimates of basic demographic and health indicators. Specifically, the ZDHS collected information on: - Fertility levels and preferences; contraceptive use; maternal and child health; infant, child, and neonatal mortality levels; maternal mortality; and gender, nutrition, and awareness regarding HIV/AIDS and other health issues relevant to the achievement of the Sustainable Development Goals (SDGs) - Ownership and use of mosquito nets as part of the national malaria eradication programmes - Health-related matters such as breastfeeding, maternal and childcare (antenatal, delivery, and postnatal), children’s immunisations, and childhood diseases - Anaemia prevalence among women age 15-49 and children age 6-59 months - Nutritional status of children under age 5 (via weight and height measurements) - HIV prevalence among men age 15-59 and women age 15-49 and behavioural risk factors related to HIV - Assessment of situation regarding violence against women

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-59

Universe

The survey covered all de jure household members (usual residents), all women age 15-49, all men age 15-59, and all children age 0-5 years who are usual members of the selected households or who spent the night before the survey in the selected households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2018 ZDHS is the Census of Population and Housing (CPH) of the Republic of Zambia, conducted in 2010 by ZamStats. Zambia is divided into 10 provinces. Each province is subdivided into districts, each district into constituencies, and each constituency into wards. In addition to these administrative units, during the 2010 CPH each ward was divided into convenient areas called census supervisory areas (CSAs), and in turn each CSA was divided into enumeration areas (EAs). An enumeration area is a geographical area assigned to an enumerator for the purpose of conducting a census count; according to the Zambian census frame, each EA consists of an average of 110 households.

The current version of the EA frame for the 2010 CPH was updated to accommodate some changes in districts and constituencies that occurred between 2010 and 2017. The list of EAs incorporates census information on households and population counts. Each EA has a cartographic map delineating its boundaries, with identification information and a measure of size, which is the number of residential households enumerated in the 2010 CPH. This list of EAs was used as the sampling frame for the 2018 ZDHS.

The 2018 ZDHS followed a stratified two-stage sample design. The first stage involved selecting sample points (clusters) consisting of EAs. EAs were selected with a probability proportional to their size within each sampling stratum. A total of 545 clusters were selected.

The second stage involved systematic sampling of households. A household listing operation was undertaken in all of the selected clusters. During the listing, an average of 133 households were found in each cluster, from which a fixed number of 25 households were selected through an equal probability systematic selection process, to obtain a total sample size of 13,625 households. Results from this sample are representative at the national, urban and rural, and provincial levels.

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

Mode of data collection

Face-to-face [f2f]

Research instrument

Four questionnaires were used in the 2018 ZDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s Model Questionnaires, were adapted to reflect the population and health issues relevant to Zambia. Input on questionnaire content was solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international cooperating partners. After all questionnaires were finalised in English, they were translated into seven local languages: Bemba, Kaonde, Lozi, Lunda, Luvale, Nyanja, and Tonga. In addition, information about the fieldworkers for the survey was collected through a self-administered Fieldworker Questionnaire.

Cleaning operations

All electronic data files were transferred via a secure internet file streaming system to the ZamStats central office in Lusaka, where they were stored on a password-protected computer. The data processing operation included secondary editing, which required resolution of computer-identified inconsistencies and coding of open-ended questions. The data were processed by two IT specialists and one secondary editor who took part in the main fieldwork training; they were supervised remotely by staff from The DHS Program. Data editing was accomplished using CSPro software. During the fieldwork, field-check tables were generated to check various data quality parameters, and specific feedback was given to the teams to improve performance. Secondary editing and data processing were initiated in July 2018 and completed in March 2019.

Response rate

Of the 13,595 households in the sample, 12,943 were occupied. Of these occupied households, 12,831 were successfully interviewed, yielding a response rate of 99%.

In the interviewed households, 14,189 women age 15-49 were identified as eligible for individual interviews; 13,683 women were interviewed, yielding a response rate of 96% (the same rate achieved in the 2013-14 survey). A total of 13,251 men were eligible for individual interviews; 12,132 of these men were interviewed, producing a response rate of 92% (a 1 percentage point increase from the previous survey).

Of the households successfully interviewed, 12,505 were interviewed in 2018 and 326 in 2019. As the large majority of households were interviewed in 2018 and the year for reference indicators is 2018.

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 2018 Zambia Demographic and Health Survey (ZDHS) 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 2018 ZDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would 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 as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2018 ZDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearisation method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Note: 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 - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months - Completeness of information on siblings - Sibship size and sex ratio of siblings - Height and weight data completeness and quality for children - Number of enumeration areas completed by month, according to province, Zambia DHS 2018

Note: Data quality tables are presented in APPENDIX C of the report.

This dataset shows births classified by baby health and mother type by various geographies across the state of Georgia 2018-2022.This dataset was developed by the Research & Analytics group of the Atlanta Regional Commission using data from the Georgia Department of Public Health to show births classified by baby health and mother type by various geographies across the state of Georgia 2018-2022.See the data manifest for detail on ARC defined naming conventions, field names and aliases; source tables; notes and so forth for all metrics.

This is a report on NHS-funded maternity services in England for June 2018, using data submitted to the Maternity Services Data Set (MSDS). The MSDS has been developed to help achieve better outcomes of care for mothers, babies and children. The MSDS is a patient-level 'secondary uses' data set that re-uses clinical and operational data for purposes other than direct patient care, such as commissioning and clinical audit. It captures key information at each stage of the maternity service care pathway in NHS-funded maternity services, such as those provided by GP practices and hospitals. The data collected include mother's demographics, booking appointments, admissions and re-admissions, screening tests, labour and delivery along with baby's demographics, diagnoses and screening tests. The Quality Improvement Metrics file includes rates for 4 of the 14 Clinical Quality Improvement Metrics that form part of NHS England's Maternity Transformation Programme. The following analysis files are published within the zip file 'Additional experimental analysis using MSDS data' for the relevant month due to low data quality and completeness: • Delivery method by previous births • Delivery method by Robson group • Smoking status at delivery (for births one month earlier) • Postpartum haemorrhage and other maternal critical incidents (for births one month earlier) • Antenatal pathway level • Births without intervention. These statistics are classified as experimental and should be used with caution. Experimental statistics are new official statistics undergoing evaluation. More information about experimental statistics can be found on the UK Statistics Authority website.

This dataset contains infant, neonatal, perinatal and maternal mortality estimates for countries members of OECD (The Organization for Economic Co-operation and Development), for OECD key partners and countries in accession negotiations with OECD. The estimated values of the four birth and related indicators, cover periods from 1960 to 2018.

State of Palestine (West Bank and Gaza) PS: Maternal Mortality Ratio: National Estimate: per 100,000 Live Births data was reported at 14.000 Ratio in 2018. This records a decrease from the previous number of 34.000 Ratio for 2017. State of Palestine (West Bank and Gaza) PS: Maternal Mortality Ratio: National Estimate: per 100,000 Live Births data is updated yearly, averaging 41.000 Ratio from Dec 2010 (Median) to 2018, with 9 observations. The data reached an all-time high of 56.000 Ratio in 2010 and a record low of 14.000 Ratio in 2018. State of Palestine (West Bank and Gaza) PS: Maternal Mortality Ratio: National Estimate: per 100,000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s State of Palestine (West Bank and Gaza) – Table PS.World Bank.WDI: Social: Health Statistics. Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births.;The country data compiled, adjusted and used in the estimation model by the Maternal Mortality Estimation Inter-Agency Group (MMEIG). The country data were compiled from the following sources: civil registration and vital statistics; specialized studies on maternal mortality; population based surveys and censuses; other available data sources including data from surveillance sites.;;

Number of infant deaths and infant mortality rates, by age group (neonatal and post-neonatal), 1991 to most recent year.

This dataset shows births classified by baby health and mother type by various geographies across the state of Georgia 2018-2022.This dataset was developed by the Research & Analytics group of the Atlanta Regional Commission using data from the Georgia Department of Public Health to show births classified by baby health and mother type by various geographies across the state of Georgia 2018-2022.See the data manifest for detail on ARC defined naming conventions, field names and aliases; source tables; notes and so forth for all metrics.

The Pakistan Demographic and Health Survey PDHS 2017-18 was the fourth of its kind in Pakistan, following the 1990-91, 2006-07, and 2012-13 PDHS surveys. The primary objective of the 2017-18 PDHS is to provide up-to-date estimates of basic demographic and health indicators. The PDHS provides a comprehensive overview of population, maternal, and child health issues in Pakistan. Specifically, the 2017-18 PDHS collected information on: Key demographic indicators, particularly fertility and under-5 mortality rates, at the national level, for urban and rural areas, and within the country’s eight regions Direct and indirect factors that determine levels and trends of fertility and child mortality Contraceptive knowledge and practice Maternal health and care including antenatal, perinatal, and postnatal care Child feeding practices, including breastfeeding, and anthropometric measures to assess the nutritional status of children under age 5 and women age 15-49 Key aspects of family health, including vaccination coverage and prevalence of diseases among infants and children under age 5 Knowledge and attitudes of women and men about sexually transmitted infections (STIs), including HIV/AIDS, and potential exposure to risk Women's empowerment and its relationship to reproductive health and family planning Disability level Extent of gender-based violence Migration patterns The information collected through the 2017-18 PDHS is intended to assist policymakers and program managers at the federal and provincial government levels, in the private sector, and at international organisations in evaluating and designing programs and strategies for improving the health of the country’s population. The data also provides information on indicators relevant to the Sustainable Development Goals.