The maternal mortality ratio in the Middle East and North Africa (MENA) region has drastically decreased, reaching 56 deaths per 100,000 live births in 2020 compared to 2000. However, improvement in the region's female mortality rate slowed down from 2014 to 2020.

This SAS macro generates childhood mortality estimates (neonatal, post-neonatal, infant (1q0), child (4q1) and under-five (5q0) mortality) and standard errors based on birth histories reported by women during a household survey. We have made the SAS macro flexible enough to accommodate a range of calculation specifications including multi-stage sampling frames, and simple random samples or censuses. Childhood mortality rates are the component death probabilities of dying before a specific age. This SAS macro is based on a macro built by Keith Purvis at MeasureDHS. His method is described in Estimating Sampling Errors of Means, Total Fertility, and Childhood Mortality Rates Using SAS (www.measuredhs.com/pubs/pdf/OD17/OD17.pdf, section 4). More information about Childhood Mortality Estimation can also be found in the Guide to DHS Statistics (www.measuredhs.com/pubs/pdf/DHSG1/Guide_DHS_Statistics.pdf, page 93). We allow the user to specify whether childhood mortality calculations should be based on 5 or 10 years of birth histories, when the birth history window ends, and how to handle age of death with it is reported in whole months (rather than days). The user can also calculate mortality rates within sub-populations, and take account of a complex survey design (unequal probability and cluster samples). Finally, this SAS program is designed to read data in a number of different formats.

United States US: Mortality Rate: Under-5: Male: per 1000 Live Births data was reported at 7.200 Ratio in 2017. This records a decrease from the previous number of 7.400 Ratio for 2015. United States US: Mortality Rate: Under-5: Male: per 1000 Live Births data is updated yearly, averaging 8.000 Ratio from Dec 1990 (Median) to 2017, with 5 observations. The data reached an all-time high of 12.500 Ratio in 1990 and a record low of 7.200 Ratio in 2017. United States US: Mortality Rate: Under-5: Male: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Under-five mortality rate, male is the probability per 1,000 that a newborn male baby will die before reaching age five, if subject to male age-specific mortality rates of the specified year.; ; Estimates Developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted average; Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.

Contains equation used to calculate death rates for farms. Data held within the Department of Agriculture

Norway maternal mortality rate for 2023 was 1.00, a 66.67% decline from 2022.

<ul style='margin-top:20px;'>

<li>Norway maternal mortality rate for 2022 was <strong>3.00</strong>, a <strong>50% increase</strong> from 2021.</li>
<li>Norway maternal mortality rate for 2021 was <strong>2.00</strong>, a <strong>0% increase</strong> from 2020.</li>
<li>Norway maternal mortality rate for 2020 was <strong>2.00</strong>, a <strong>0% increase</strong> from 2019.</li>
</ul>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.

Turkey maternal mortality rate for 2023 was 15.00, a 28.57% decline from 2022.

<ul style='margin-top:20px;'>

<li>Turkey maternal mortality rate for 2022 was <strong>21.00</strong>, a <strong>5% increase</strong> from 2021.</li>
<li>Turkey maternal mortality rate for 2021 was <strong>20.00</strong>, a <strong>5.26% increase</strong> from 2020.</li>
<li>Turkey maternal mortality rate for 2020 was <strong>19.00</strong>, a <strong>11.76% increase</strong> from 2019.</li>
</ul>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.

Avoidable mortality (AM) is a simple and practical population-based method of\r counting untimely and unnecessary deaths from diseases for which effective\r public health and medical interventions are available. An excess of deaths due\r to preventable causes should suggest shortcomings in the healthcare system\r that warrant further attention. Five years of data has been aggregated for all\r analyses to reduce year-to-year variability in deaths, and the width of\r confidence intervals for areas with small populations. Data are presented by\r calendar year (1 Jan to 31 Dec), consistent with the release of mortality data\r by the Australian Bureau of Statistics.\r \r

Survey variables needed to calculate fertility and childhood mortality rates.

Colombia maternal mortality rate for 2023 was 59.00, a 20.27% decline from 2022.

<ul style='margin-top:20px;'>

<li>Colombia maternal mortality rate for 2022 was <strong>74.00</strong>, a <strong>49.66% decline</strong> from 2021.</li>
<li>Colombia maternal mortality rate for 2021 was <strong>147.00</strong>, a <strong>56.38% increase</strong> from 2020.</li>
<li>Colombia maternal mortality rate for 2020 was <strong>94.00</strong>, a <strong>42.42% increase</strong> from 2019.</li>
</ul>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.

In 2021, the maternal mortality rate amounted to **** to ** women per *** hundred thousands live births in South Korea. Overall, there was a decrease in the maternal mortality ratio over the considered timespan.

This dataset of U.S. mortality trends since 1900 highlights childhood mortality rates by age group for age at death. Age-adjusted death rates (deaths per 100,000) after 1998 are calculated based on the 2000 U.S. standard population. Populations used for computing death rates for 2011–2017 are postcensal estimates based on the 2010 census, estimated as of July 1, 2010. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years between 2000 and 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Data on age-adjusted death rates prior to 1999 are taken from historical data (see References below). Age groups for childhood death rates are based on age at death. SOURCES CDC/NCHS, National Vital Statistics System, historical data, 1900-1998 (see https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm); CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov). REFERENCES National Center for Health Statistics, Data Warehouse. Comparability of cause-of-death between ICD revisions. 2008. Available from: http://www.cdc.gov/nchs/nvss/mortality/comparability_icd.htm. National Center for Health Statistics. Vital statistics data available. Mortality multiple cause files. Hyattsville, MD: National Center for Health Statistics. Available from: https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm. Kochanek KD, Murphy SL, Xu JQ, Arias E. Deaths: Final data for 2017. National Vital Statistics Reports; vol 68 no 9. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_09-508.pdf. Arias E, Xu JQ. United States life tables, 2017. National Vital Statistics Reports; vol 68 no 7. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_07-508.pdf. National Center for Health Statistics. Historical Data, 1900-1998. 2009. Available from: https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm.

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

Life expectancy at a given age is a summary measure of mortality rates present in a population (estimated as the area under the survival curve), and represents the average number of years an individual at that age is expected to live if current age-specific mortality rates apply now and in the future. A complementary metric is the number of Life Years Lost, which is used to measure the reduction in life expectancy for a specific group of persons, for example those diagnosed with a specific disease or condition (e.g. smoking). However, calculation of life expectancy among those with a specific disease is not straightforward for diseases that are not present at birth, and previous studies have considered a fixed age at onset of the disease, e.g. at age 15 or 20 years. In this paper, we present the R package lillies (freely available through the Comprehensive R Archive Network; CRAN) to guide the reader on how to implement a recently-introduced method to estimate excess Life Years Lost associated with a disease or condition that overcomes these limitations. In addition, we show how to decompose the total number of Life Years Lost into specific causes of death through a competing risks model, and how to calculate confidence intervals for the estimates using non-parametric bootstrap. We provide a description on how to use the method when the researcher has access to individual-level data (e.g. electronic healthcare and mortality records) and when only aggregated-level data are available.

These indicators are designed to accompany the SHMI publication. Information on the main condition the patient is in hospital for (the primary diagnosis) is used to calculate the expected number of deaths used in the calculation of the SHMI. A high percentage of records with an invalid primary diagnosis may indicate a data quality problem. A high percentage of records with a primary diagnosis which is a symptom or sign may indicate problems with data quality or timely diagnosis of patients, but may also reflect the case-mix of patients or the service model of the trust (e.g. a high level of admissions to acute admissions wards for assessment and stabilisation). Contextual indicators on the percentage of provider spells with an invalid primary diagnosis and the percentage of provider spells with a primary diagnosis which is a symptom or sign are produced to support the interpretation of the SHMI. Notes: 1. There is a shortfall in the number of records for East Lancashire Hospitals NHS Trust (trust code RXR), Guy’s and St Thomas’ NHS Foundation Trust (trust code RJ1), and King’s College Hospital NHS Foundation Trust (trust code RJZ). Values for these trusts are based on incomplete data and should therefore be interpreted with caution. 2. Frimley Health NHS Foundation Trust (trust code RDU) stopped submitting data to the Secondary Uses Service (SUS) during June 2022 and did not start submitting data again until April 2023 due to an issue with their patient records system. This is causing a large shortfall in records and values for this trust should be viewed in the context of this issue. 3. A number of trusts are now submitting Same Day Emergency Care (SDEC) data to the Emergency Care Data Set (ECDS) rather than the Admitted Patient Care (APC) dataset. The SHMI is calculated using APC data. Removal of SDEC activity from the APC data may impact a trust’s SHMI value and may increase it. More information about this is available in the Background Quality Report. 4. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of this page.

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 …Show full descriptionMaternal 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

Chad TD: Mortality Rate: Adult: Female: per 1000 Female Adults data was reported at 309.354 Ratio in 2023. This records a decrease from the previous number of 315.145 Ratio for 2022. Chad TD: Mortality Rate: Adult: Female: per 1000 Female Adults data is updated yearly, averaging 360.496 Ratio from Dec 1960 (Median) to 2023, with 64 observations. The data reached an all-time high of 406.629 Ratio in 1960 and a record low of 309.354 Ratio in 2023. Chad TD: Mortality Rate: Adult: Female: per 1000 Female Adults data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Chad – Table TD.World Bank.WDI: Social: Health Statistics. Adult mortality rate, female, is the probability of dying between the ages of 15 and 60--that is, the probability of a 15-year-old female dying before reaching age 60, if subject to age-specific mortality rates of the specified year between those ages.;(1) United Nations Population Division. World Population Prospects: 2024 Revision. (2) HMD. Human Mortality Database. Max Planck Institute for Demographic Research (Germany), University of California, Berkeley (USA), and French Institute for Demographic Studies (France). Available at www.mortality.org.;Weighted average;

Sources:a National Institute for Population Research and Training, MEASURE Evaluation, International Centre for Diarrhoeal Disease Research (2012) Bangladesh Maternal Mortality and Health Care Survey 2010. Available: http://www.cpc.unc.edu/measure/publications/tr-12-87. Accessed October 15, 2012.b World Health Organization (ND) WHO Maternal Mortality Country Profiles. Available: www.who.int/gho/maternal_health/en/#M. Accessed 1 March 2015.c Lozano R, Wang H, Foreman KJ, Rajaratnam JK, Naghavi M, Marcus JR, et al. (2011) Progress towards Millennium Development Goals 4 and 5 on maternal and child mortality: an updated systematic analysis. Lancet 378(9797): 1139–65. 10.1016/S0140-6736(11)61337-8d UNFPA, UNICEF, WHO, World Bank (2012) Trends in maternal mortality: 1990–2010. Available: http://www.unfpa.org/public/home/publications/pid/10728. Accessed 7 October 2012.e Bangladesh Bureau of Statistics, Statistics Informatics Division, Ministry of Planning (December 2012) Population and Housing Census 2011, Socio-economic and Demographic Report, National Series–Volume 4. Available at: http://203.112.218.66/WebTestApplication/userfiles/Image/BBS/Socio_Economic.pdf. Accessed 15 February, 2015.f Mozambique National Institute of Statistics, U.S. Census Bureau, MEASURE Evaluation, U.S. Centers for Disease Control and Prevention (2012) Mortality in Mozambique: Results from a 2007–2008 Post-Census Mortality Survey. Available: http://www.cpc.unc.edu/measure/publications/tr-11-83. Accessed 6 October 2012.g Ministerio da Saude (MISAU), Instituto Nacional de Estatística (INE) e ICF International (ICFI). Moçambique Inquérito Demográfico e de Saúde 2011. Calverton, Maryland, USA: MISAU, INE e ICFI.h Mudenda SS, Kamocha S, Mswia R, Conkling M, Sikanyiti P, et al. (2011) Feasibility of using a World Health Organization-standard methodology for Sample Vital Registration with Verbal Autopsy (SAVVY) to report leading causes of death in Zambia: results of a pilot in four provinces, 2010. Popul Health Metr 9:40. 10.1186/1478-7954-9-40i Central Statistical Office (CSO), Ministry of Health (MOH), Tropical Diseases Research Centre (TDRC), University Teaching Hospital Virology Laboratory, University of Zambia, and ICF International Inc. 2014. Zambia Demographic and Health Survey 2013–14: Preliminary Report. Rockville, Maryland, USA. Available: http://dhsprogram.com/pubs/pdf/PR53/PR53.pdf. Accessed February 26, 2015.j Centers for Disease Control and Prevention (2014) Saving Mothers, Giving Life: Maternal Mortality.Phase 1 Monitoring and Evaluation Report. Atlanta, GA: Centers for Disease Control and Prevention, US Dept of Health and Human Services. Available at: http://www.savingmothersgivinglife.org/doc/Maternal%20Mortality%20(advance%20copy).pdf. Accessed 26 February 2015.k Central Statistical Office (CSO), Ministry of Health (MOH), Tropical Diseases Research Centre (TDRC), University of Zambia, and Macro International Inc. 2009. Zambia Demographic and Health Survey 2007. Calverton, Maryland, USA: CSO and Macro International Inc.Comparison of Maternal Mortality Estimates: Zambia, Bangladesh, Mozambique.

Standardised mortality ratios for deaths from all causes and from eight causes in Glasgow from 2000 to 2012 . The Glasgow ratios are a percentage of the numbers dead in Glasgow from that cause that would be expected for Glasgow City if it had the same age/sex-specific death rates as Scotland as a whole. The eight causes are: all cancers; Stomach Cancer; Large Intestine cancer; Trachea, Bronchus, Lung cancer; Female breast cancer; Ischaemic Heart Disease; CerebroVascular and Pneumonia. They were calculated using the 'rebased' mid-year population estimates for 2002 to 2011- see Births and Deaths Rates: breaks in series circa 2011 Data extracted 2014-04-09 from the General Register Office for Scotland Licence: None

In 2023, ******* had the highest maternal mortality rate in the world, with around *** maternal deaths per 100,000 live births. ******* was followed by **** with a rate of *** maternal deaths per 100,000 live births. This statistic shows the 20 countries with the highest maternal mortality rate per 100,000 live births in 2023.

Deaths by local authority of usual residence, numbers and standardised mortality ratios (SMRs) by sex. SMR measures whether the population of an area has a higher or lower number of deaths than expected based on the age profile of the population (more deaths are expected in older populations). The SMR is defined as follows: SMR = (Observed no. of deaths per year)/(Expected no. of deaths per year). SMRs are calculated using the previous year's mid-year population estimates. Live birth figures are used for calculations involving deaths under 1 year. The age-standardised mortality rates in this release are directly age-standardised to the European Standard Population, which cover all ages and allows comparisons between populations with different age structures, including between males and females and over time. Note: SMR and deaths by sex data only available since 2001. Download from ONS website