This dataset presents the footprint of statistics of potentially preventable hospitalisations (PPH). PPH does not mean that a patient admitted for that condition did not need to be hospitalised at the time of admission. Rather the hospitalisation could have potentially been prevented through the provision of appropriate preventative health interventions and early disease management in primary care and community-based care settings. PPH rates are indicators of the effectiveness of non-hospital care. The data spans the financial years of 2013-2017 and is aggregated to Statistical Area Level 3 (SA3) geographic areas from the 2016 Australian Statistical Geography Standard (ASGS). The data is sourced from the Australian Institute of Health and Welfare (AIHW) - National Hospital Morbidity Database (NHMD), which is a compilation of episode-level records from admitted patient morbidity data collection systems in Australian public and private hospitals. For further information about this dataset visit the data source:Australian Institute of Health and Welfare - Potentially Preventable Hospitalisations Data Tables.

Through its data linkage program, NCHS has been able to expand the analytic utility of the data collected from National Hospital Care Survey (NHCS) by augmenting it with mortality data from the National Death Index (NDI). Linkage of NHCS data with the NDI mortality data provides the opportunity to conduct a vast array of outcome studies designed to investigate the association of a wide variety of health factors with mortality.

Extracted in machine readable form from the AIHW General Record of Incidence of Mortality (GRIM) books.

GRIM books are Excel workbooks that contain national level, historical and recent deaths data for specific causes of death. The tables present age- and sex-specific counts and rates by cause of death, along with other summary measures.

GRIM books are available for all causes of death combined and 55 other cause of death groupings. They span different years for different causes of death, depending on the data available. GRIM books for some causes of death start at 1907 and they are the only national electronic tabulations of deaths data by cause registered before 1964. Data from 1964 onwards are sourced from the AIHW National Mortality Database. They include mortality data up to 2023.

For more information, please see Deaths data at AIHW or contact us at deaths@aihw.gov.au.

Also available on data.gov.au are the AIHW Mortality Over Regions and Time (MORT) books.

Through its data linkage program, NCHS has been able to expand the analytic utility of the data collected from National Hospital Care Survey (NHCS) by augmenting it with mortality data from the National Death Index (NDI). Linkage of NHCS data with the NDI mortality data provides the opportunity to conduct a vast array of outcome studies designed to investigate the association of a wide variety of health factors with mortality.

Extracted in machine readable form from the AIHW Mortality Over Regions and Time (MORT) books.

MORT books are Excel workbooks that contain recent deaths data for specific geographical areas, sourced from the AIHW National Mortality Database. They present summary deaths statistics by sex for each geographic area, including counts, rates, median age at death, premature deaths, potential years of life lost and potentially avoidable deaths. The workbooks also present leading causes of death by sex for each geographic area.

The MORT books present data for 2015–2019. Due to changes in geographic classifications over time, long-term trends are not available.

For more information, please see Deaths data at AIHW or contact us at deaths@aihw.gov.au..

Also available on data.gov.au are the AIHW General Record of Incidence of Mortality (GRIM) data.

This data collection supplies date and cause of death data for sample persons included in the National Health Interview Surveys (NHIS) for the years 1986 through 1994. Beginning with survey year 1986, linkage information was collected on NHIS respondents aged 18 and older to allow for matching with other data systems such as the National Death Index (NDI). The Multiple Cause of Death (MCD) data files contain information on those persons with scores high enough to be considered deceased or scores high enough that they may be included in an analysis as deceased. The Ineligible Cases data files contain person IDs of those NHIS participants under the age of 18 as well as those with insufficient information to permit linkage with the NDI. These cases should be excluded from the NHIS survey data files prior to analysis. Linkage of the NHIS respondents with the NDI provides a longitudinal component to the NHIS that allows for the ascertainment of vital status. The addition of vital status permits the use of NHIS data to estimate survival, mortality, and life expectancy while using the richness of the NHIS questionnaires, both core and supplements, as covariates. These data files must be used in conjunction with the basic NHIS data files (1986 [ICPSR 8976], 1987 [ICPSR 9195], 1988 [ICPSR 9412], 1989 [ICPSR 9583], 1990 [ICPSR 9839], 1991 [ICPSR 6049], 1992 [ICPSR 6343], 1993 [ICPSR 6534], 1994 [ICPSR 6724]). Variables included in the MCD files cover year of interview, quarter, household number, person number, month and year of death, vital status, and causes of death. The Ineligible Cases files contain a person ID that matches columns 3-16 on the NHIS public use data files.

A database based on a random sample of the noninstitutionalized population of the United States, developed for the purpose of studying the effects of demographic and socio-economic characteristics on differentials in mortality rates. It consists of data from 26 U.S. Current Population Surveys (CPS) cohorts, annual Social and Economic Supplements, and the 1980 Census cohort, combined with death certificate information to identify mortality status and cause of death covering the time interval, 1979 to 1998. The Current Population Surveys are March Supplements selected from the time period from March 1973 to March 1998. The NLMS routinely links geographical and demographic information from Census Bureau surveys and censuses to the NLMS database, and other available sources upon request. The Census Bureau and CMS have approved the linkage protocol and data acquisition is currently underway. The plan for the NLMS is to link information on mortality to the NLMS every two years from 1998 through 2006 with research on the resulting database to continue, at least, through 2009. The NLMS will continue to incorporate data from the yearly Annual Social and Economic Supplement into the study as the data become available. Based on the expected size of the Annual Social and Economic Supplements to be conducted, the expected number of deaths to be added to the NLMS through the updating process will increase the mortality content of the study to nearly 500,000 cases out of a total number of approximately 3.3 million records. This effort would also include expanding the NLMS population base by incorporating new March Supplement Current Population Survey data into the study as they become available. Linkages to the SEER and CMS datasets are also available. Data Availability: Due to the confidential nature of the data used in the NLMS, the public use dataset consists of a reduced number of CPS cohorts with a fixed follow-up period of five years. NIA does not make the data available directly. Research access to the entire NLMS database can be obtained through the NIA program contact listed. Interested investigators should email the NIA contact and send in a one page prospectus of the proposed project. NIA will approve projects based on their relevance to NIA/BSR''s areas of emphasis. Approved projects are then assigned to NLMS statisticians at the Census Bureau who work directly with the researcher to interface with the database. A modified version of the public use data files is available also through the Census restricted Data Centers. However, since the database is quite complex, many investigators have found that the most efficient way to access it is through the Census programmers. * Dates of Study: 1973-2009 * Study Features: Longitudinal * Sample Size: ~3.3 Million Link: *ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/00134

Number of new cases and incidence calculated based on reporting by outpatient specialists for cancer, diabetes and mental health problems.

This survey was designed primarily to obtain information on the smoking habits of decedents by examining death certificates and questionnaires mailed to death record informants. Smoking variables in this data collection include number of cigarettes smoked when the decedent smoked most, number smoked the year before death, number smoked three years before death, and cigar and pipe smoking occurrence three years before death. Demographic variables include marital status, family type, number of children, living arrangements, size of family, birth and death of the decedent, family income and family debt, and cause of death.

Through its data linkage program, NCHS has been able to expand the analytic utility of the data collected from the 2021 National Ambulatory Medical Care Survey (NAMCS) by augmenting it with mortality data from the National Death Index (NDI). Linkage of NAMCS data with the NDI mortality data provides the opportunity to conduct a vast array of outcome studies designed to investigate the association of a wide variety of health factors with mortality.

NCHS has linked data from various surveys with death certificate records from the National Death Index (NDI). Linkage of the NCHS survey participant data with the NDI mortality data provides the opportunity to conduct a vast array of outcome studies designed to investigate the association of a wide variety of health factors with mortality. The Linked Mortality Files (LMF) have been updated with mortality follow-up data through December 31, 2019. Public-use Linked Mortality Files (LMF) are available for 1986-2018 NHIS, 1999-2018 NHANES, and NHANES III. The files include a limited set of mortality variables for adult participants only. The public-use versions of the NCHS Linked Mortality Files were subjected to data perturbation techniques to reduce the risk of participant re-identification. For select records, synthetic data were substituted for follow-up time or underlying cause of death. Information regarding vital status was not perturbed.

The National Health and Nutrition Examination Survey I Epidemiologic Follow-Up Study (NHEFS) is a longitudinal study which uses as its baseline those adult persons aged 25 to 74 years who were examined in the first National Health and Nutrition Examination Survey (NHANES I). The NHEFS surveys were designed to investigate the association between factors measured at the baseline and the development of specific health conditions. The NHEFS is comprised of a series of follow-up surveys, three of which have been completed. The first wave of data collection, the 1982-1984 NHEFS (ICPSR 8900), included all persons who were between 25 and 74 years of age at their NHANES I examination. The second wave of data collection, the 1986 NHEFS (ICPSR 9466), included the NHEFS cohort who were 55-74 years at their baseline examination and not known to be deceased at the time of the 1982-1984 NHEFS. The third wave, the 1987 NHEFS, was conducted for the entire nondeceased NHEFS cohort. The 1982-1984 NHEFS consisted of five steps. The first step focused on tracing and locating all subjects in the cohort or their proxies and determining their vital status. The second step involved obtaining death certificates for subjects who were deceased. Interviews with the participants or their proxies constituted the third phase of the follow-up. The fourth phase of the follow-up included measurements of pulse, blood pressure, and weight for interviewed respondents, and the fifth step was the acquisition of relevant hospital and nursing home records, including pathology reports and electrocardiograms. The 1986 NHEFS assessed changes to the health and functional status of the oldest members of the NHEFS cohort since the last contact period. The 1987 NHEFS also collected information on changes in the health and functional status of the NHEFS cohort since the last contact period. The Vital and Tracing Status file contains summary information about the status of the entire NHEFS cohort. The Health Care Facility Record file contains information on reports of stays in hospitals and nursing homes as well as information abstracted from facility medical records. The Mortality Data file contains data abstracted from the death certificates from all three NHEFS surveys. The Interview Data file contains information on selected aspects of the subject's health history since the time of the NHANES I exam.

The Global Subnational Infant Mortality Rates, Version 2.01 consist of Infant Mortality Rate (IMR) estimates for 234 countries and territories, 143 of which include subnational Units. The data are benchmarked to the year 2015 (Version 1 was benchmarked to the year 2000), and are drawn from national offices, Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and other sources from 2006 to 2014. In addition to Infant Mortality Rates, Version 2.01 includes crude estimates of births and infant deaths, which could be aggregated or disaggregated to different geographies to calculate infant mortality rates at different scales or resolutions, where births are the rate denominator and infant deaths are the rate numerator. Boundary inputs are derived primarily from the Gridded Population of the World, Version 4 (GPWv4) data collection. National and subnational data are mapped to grid cells at a spatial resolution of 30 arc-seconds (~1 km) (Version 1 has a spatial resolution of 1/4 degree, ~28 km at the equator), allowing for easy integration with demographic, environmental, and other spatial data.

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

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

1. 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.

2. 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.

3. 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.

4. 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.

5. National Center for Health Statistics. Historical Data, 1900-1998. 2009. Available from: https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm.

Abstract

The principal objective of the Republic of the Marshall Islands 2007 Demographic and Health Survey (2007 RMIDHS) is to provide current and reliable data on fertility and family planning behavior, child mortality, adult and maternal mortality, children’s nutritional status, the utilization of maternal and child health services, and knowledge of HIV and AIDS. The specific objectives of the survey are to: • collect data at the national level that will allow the calculation of key demographic rates; • analyze the direct and indirect factors that determine the level and trends of fertility; • measure the level of contraceptive knowledge and practice among women and men by method, urban/rural residence, and region; • collect high-quality data on family health, including immunization coverage among children, prevalence and treatment of diarrhea and other diseases among children under five, and maternity care indicators (including antenatal visits, assistance at delivery, and postnatal care); • collect data on infant and child mortality; • obtain data on child feeding practices, including breastfeeding, and collect ‘observation’ information to use in assessing the nutritional status of women and children; • collect data on knowledge and attitudes of women and men about sexually transmitted infections (STIs), HIV and AIDS and evaluate patterns of recent behavior regarding condom use; and • collect data on support to mentally ill persons and information on the incidence of suicide.

This information is essential for informed policy decisions, planning, monitoring, and evaluation of programs on health in general and reproductive health in particular at both national level and in urban and rural areas. A long-term objective of the survey is to strengthen the technical capacity of government organizations to plan, conduct, process, and analyze data from complex national population and health surveys. Moreover, the 2007 RMIDHS provides national, rural, and urban estimates on population and health that are comparable to data collected in similar surveys in other Pacific DHS pilot countries and other developing countries.

Geographic coverage

National

Analysis unit

• Household
• Women age 15-49
• Men age 15-59

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary focus of the 2007 RMIDHS was to provide estimates of key population and health indicators, including fertility and mortality rates, for the country as a whole and for urban and rural areas separately. The survey used the sampling frame provided by the list of census enumeration areas, with population and household information from the 1999 RMI Census and the 2006 Community Survey.

The survey was designed to obtain completed interviews of 1,070 women aged 15-49. In addition, males aged 15-59 in every second household were interviewed. To take non-response into account, a total of 608 households countrywide were selected: 295 in urban areas and 313 in rural areas.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were administered for the 2007 RMIDHS: a household questionnaire, a women’s questionnaire, and a men’s questionnaire. These were adapted to reflect population and health issues relevant to the Marshall Islands at a series of meetings with various stakeholders from government ministries and agencies, non-governmental organizations (NGOs) and international donors. The final draft of the questionnaires was discussed at a questionnaire design workshop organized by EPPSO in September 2006 in Majuro. The survey questionnaires were then translated into the local language (Marshallese) and pretested from November 16 to December 13, 2006.

The household questionnaire was used to list all the usual members and visitors in the selected households and to identify women and men who were eligible for the individual interview. Some basic information was collected on the characteristics of each person listed, including age, sex, education, and relationship to the head of the household. For children under age 18, the survival status of their parents was determined. The household questionnaire also collected information on characteristics of the household’s dwelling unit, such as the source of water, type of toilet facilities, materials used for the floor of the house, and ownership of various durable goods. Additionally, it was used to record information on mental illness and suicide experiences of members of the household.

The women’s questionnaire was used to collect information from all women aged 15–49. The women were asked questions on: • characteristics such as education, residential history, and media exposure; • pregnancy history and childhood mortality; • knowledge and use of family planning methods; • fertility preferences; • antenatal, delivery, and postnatal care; • breastfeeding and infant feeding practices; • immunization and childhood illnesses; • marriage and sexual activity; • their own work and their husband’s background characteristics; and • awareness and behavior regarding HIV and other STIs.

The men’s questionnaire was administered to all men aged 15–59 living in every second household in the 2007 RMIDHS sample. It collected much of the same information found in the women’s questionnaire, but was shorter because it did not contain a detailed reproductive history or questions on maternal and child health or nutrition.

Cleaning operations

The processing of the 2007 RMIDHS results began soon after the start of fieldwork. Completed questionnaires were returned periodically from the field to the EPPSO data processing center in Majuro, where they were entered and edited by four data processing personnel specially trained for this task. The data processing personnel were supervised by EPPSO staff. The concurrent processing of the data was an advantage since field check tables were generated early on to monitor various data quality parameters. As a result, specific and ongoing feedback was given to the field teams to improve performance. The data entry and editing of the questionnaires was completed by June 30, 2007. Data processing was done using CSPro.

Response rate

A total of 1,141 households were selected for the sample, of which 1,131 were found to be occupied during data collection. Of these existing households, 1,106 were successfully interviewed, giving a household response rate of 98 percent.

In the households, 1,742 women were identified as eligible for the individual interview. Interviews were completed with 1,625 women, yielding a response rate of 93 percent. Of the 1,218 eligible men identified in the selected sub-sample of households, 87 percent were successfully interviewed. Response rates were higher in rural than urban areas, with the rural–urban difference in response rates most marked among eligible men.

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

Note: See detailed tables in APPENDIX D of the final survey report.

Abstract

This cumulative dataset contains statistics on mortality and causes of death in South Africa covering the period 1997-2015. The mortality and causes of death dataset are part of a regular series published by Stats SA, based on data collected through the civil registration system. The first dataset in the series is the separately available dataset Recorded Deaths 1996.

The main objective of this dataset is to outline emerging trends and differentials in mortality by selected socio-demographic and geographic characteristics for deaths that occurred in the registered year and over time. Reliable mortality statistics, are the cornerstone of national health information systems, and are necessary for population health assessment, health policy and service planning; and programme evaluation. They are essential for studying the occurrence and distribution of health-related events, their determinants and management of related health problems. These data are particularly critical for monitoring the Sustainable Development Goals (SDGs) and Agenda 2063 which share the same goal for a high standard of living and quality of life, sound health and well-being for all and at all ages. Mortality statistics are also required for assessing the impact of non-communicable diseases (NCD's), emerging infectious diseases, injuries and natural disasters.

Geographic coverage

National coverage

Analysis unit

Individuals

Universe

This dataset is based on information on mortality and causes of death from the South African civil registration system. It covers all death notification forms from the Department of Home Affairs for deaths that occurred in 1997-2015, that reached Stats SA during the 2016/2017 processing phase.

Kind of data

Administrative records data [adm]

Mode of data collection

Other [oth]

Research instrument

The registration of deaths is captured using two instruments: form BI-1663 and form DHA-1663 (Notification/Register of death/stillbirth).

Data appraisal

This cumulative dataset is part of a regular series published by Stats SA and includes all previous rounds in the series (excluding Recorded Deaths 1996). Stats SA only includes one variable to classify the occupation group of the deceased (OccupationGrp) in the current round (1997-2017). Prior to 2016, Stats SA included both occupation group (OccupationGrp) and industry classification (Industry) in all previous rounds. Therefore, DataFirst has made the 1997-2015 cumulative round available as a separately downloadable dataset which includes both occupation group and industry classification of the deceased spanning the years 1997-2015.

A longitudinal study which follows the cohort of current residents and discharged residents sampled from the 1985 National Nursing Home Survey (NNHS), thus permitting study of nursing home and hospital utilization over time. The study was conducted in three waves. To supplement the current and discharged resident components, the 1985 NNHS included a new component - the Next-of-Kin (NOK). The NOK, using a Computer Assisted Telephone Interviewing (CATI) system, was designed to collect information about current and former nursing home residents that is not generally available from patient records or other sources in the nursing home. The NNHSF obtains additional information on a portion of the residents for whom a Current Resident Questionnaire (CRQ) or a Discharged Resident Questionnaire (DRQ) was completed. In September 1994, the NNHSF Mortality Public Use Data Tape was released, covering the years 1984-1990. It contains the multiple cause-of-death information for 6,507 subjects from the NNHSF found to be deceased after linking and matching of files with the National Death Index. Information on the mortality tape includes the date of death, region of occurrence and residence, etc. All NNHSF tapes include a patient identification number common across files to allow linkage among them. Data Availability: Public Use data tapes for each wave and the mortality tape are available through the National Technical Information Office (NTIS), NACDA and the ICPSCR at the University of Michigan. The 1985 survey tape includes eight files: the facility questionnaire, nursing staff questionnaire, current resident questionnaire, discharged resident questionnaire, expense questionnaire, nursing staff sampling list, current resident sampling list, discharged resident sampling list. The next-of-kin questionnaire is available on a separate tape. * Dates of Study: 1987-1990 * Study Features: Longitudinal * Sample Size: ** 1987: 6,001 (Wave I) ** 1988: 3,868 (Wave II) ** 1990: 3,041 (Wave III) Links: * Wave I (ICPSR): http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/09813 * Wave II (ICPSR): http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/09838 * Wave III (ICPSR): http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06142

The Mortality - Multiple Cause of Death data on CDC WONDER are county-level national mortality and population data spanning the yehttps://healthdata.gov/d/2sz9-6c59ars 1999-2006. These data are available in two separate data sets: one data set for years 1999-2004 with 3 race groups, and another data set for years 2005-2006 with 4 race groups and 3 Hispanic origin categories. Data are based on death certificates for U.S. residents. Each death certificate contains a single underlying cause of death, up to twenty additional multiple causes, and demographic data. The number of deaths, crude death rates, age-adjusted death rates, standard errors and 95% confidence intervals for death rates can be obtained by place of residence (total U.S., state, and county), age group (including infants), race, Hispanic ethnicity (years 2005-2006 only), sex, year of death, and cause-of-death (4-digit ICD-10 code or group of codes). The data are produced by the National Center for Health Statistics.

This dataset describes drug poisoning deaths at the U.S. and state level by selected demographic characteristics, and includes age-adjusted death rates for drug poisoning. Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent). Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011–2017 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Death rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Drug poisoning death rates may be underestimated in those instances. REFERENCES 1. National Center for Health Statistics. National Vital Statistics System: Mortality data. Available from: http://www.cdc.gov/nchs/deaths.htm. CDC. CDC Wonder: Underlying cause of death 1999–2016. Available from: http://wonder.cdc.gov/wonder/help/ucd.html.