This data collection resulted from the project "Linked Homicide File for 1990-1999," which was conducted by the California Department of Health Services (CDHS), Epidemiology and Prevention for Injury Control Branch, for the purpose of studying homicide and providing evidence for the development of strategies to reduce homicide in California. The researchers combined the strengths of law enforcement reporting and medical reporting in one dataset. The homicide data contain information on victims and circumstances of the 34,542 homicides investigated by law enforcement agencies in California for the period 1990 to 1999. The data are Supplementary Homicide Reports (SHR), which are received monthly by the Department of Justice from all local California law enforcement agencies as part of the national Uniform Crime Reporting program (UNIFORM CRIME REPORTS [UNITED STATES]: SUPPLEMENTARY HOMICIDE REPORTS, 1976-1999 [ICPSR 3180]). The researchers linked the SHRs to the CDHS vital statistics mortality data, which contain the death records provided by the medical examiner or coroner of each county after investigation of the death. Variables include total number of offenders involved, weapon used in the homicide, county of the victim's residence, location and date of the incident, date of death, cause of death, date of arrest for the suspect, and whether supplemental homicide report matched the death record. Demographic data include age, sex, and race of the victim and the suspect, relationships between the suspect and the victim, and the victim's marital status.

There has been little research on United States homicide rates from a long-term perspective, primarily because there has been no consistent data series on a particular place preceding the Uniform Crime Reports (UCR), which began its first full year in 1931. To fill this research gap, this project created a data series on homicides per capita for New York City that spans two centuries. The goal was to create a site-specific, individual-based data series that could be used to examine major social shifts related to homicide, such as mass immigration, urban growth, war, demographic changes, and changes in laws. Data were also gathered on various other sites, particularly in England, to allow for comparisons on important issues, such as the post-World War II wave of violence. The basic approach to the data collection was to obtain the best possible estimate of annual counts and the most complete information on individual homicides. The annual count data (Parts 1 and 3) were derived from multiple sources, including the Federal Bureau of Investigation's Uniform Crime Reports and Supplementary Homicide Reports, as well as other official counts from the New York City Police Department and the City Inspector in the early 19th century. The data include a combined count of murder and manslaughter because charge bargaining often blurs this legal distinction. The individual-level data (Part 2) were drawn from coroners' indictments held by the New York City Municipal Archives, and from daily newspapers. Duplication was avoided by keeping a record for each victim. The estimation technique known as "capture-recapture" was used to estimate homicides not listed in either source. Part 1 variables include counts of New York City homicides, arrests, and convictions, as well as the homicide rate, race or ethnicity and gender of victims, type of weapon used, and source of data. Part 2 includes the date of the murder, the age, sex, and race of the offender and victim, and whether the case led to an arrest, trial, conviction, execution, or pardon. Part 3 contains annual homicide counts and rates for various comparison sites including Liverpool, London, Kent, Canada, Baltimore, Los Angeles, Seattle, and San Francisco.

This data set depicts unintentional overdose deaths by county for Tennessee from 1999-2017.Data was compiled from the CDC Wonder database for each year and combined into a single spreadsheet. Each year has both a death field and a rate of fatalities per 100,000 people. The CDC does not publish the number of fatalities by county if the total is less than 10 in a given year. The CDC does not post a rate of fatalities if the total number of deaths per county is less than 20. The population field contains estimates from 2018 and is NOT the data used to generate the rates over time.The following details are copied directly from the CDC Wonder database text file. Note that the year is different for each data download from the original database."Dataset: Underlying Cause of Death, 1999-2017""Query Parameters:""Drug/Alcohol Induced Causes: Drug poisonings (overdose) Unintentional (X40-X44)""States: Tennessee (47)""Year/Month: 1999""Group By: County""Show Totals: True""Show Zero Values: False""Show Suppressed: False""Calculate Rates Per: 100,000""Rate Options: Default intercensal populations for years 2001-2009 (except Infant Age Groups)""---""Help: See http://wonder.cdc.gov/wonder/help/ucd.html for more information.""---""Query Date: Aug 19, 2019 10:22:15 PM""1. Rows with suppressed Deaths are hidden, but the Deaths and Population values in those rows are included in the totals. Use""Quick Options above to show suppressed rows.""---"Caveats:"1. Data are Suppressed when the data meet the criteria for confidentiality constraints. More information:""http://wonder.cdc.gov/wonder/help/ucd.html#Assurance of Confidentiality.""2. Death rates are flagged as Unreliable when the rate is calculated with a numerator of 20 or less. More information:""http://wonder.cdc.gov/wonder/help/ucd.html#Unreliable.""3. The population figures for year 2017 are bridged-race estimates of the July 1 resident population, from the Vintage 2017""postcensal series released by NCHS on June 27, 2018. The population figures for year 2016 are bridged-race estimates of the July""1 resident population, from the Vintage 2016 postcensal series released by NCHS on June 26, 2017. The population figures for""year 2015 are bridged-race estimates of the July 1 resident population, from the Vintage 2015 postcensal series released by NCHS""on June 28, 2016. The population figures for year 2014 are bridged-race estimates of the July 1 resident population, from the""Vintage 2014 postcensal series released by NCHS on June 30, 2015. The population figures for year 2013 are bridged-race""estimates of the July 1 resident population, from the Vintage 2013 postcensal series released by NCHS on June 26, 2014. The""population figures for year 2012 are bridged-race estimates of the July 1 resident population, from the Vintage 2012 postcensal""series released by NCHS on June 13, 2013. The population figures for year 2011 are bridged-race estimates of the July 1 resident""population, from the Vintage 2011 postcensal series released by NCHS on July 18, 2012. Population figures for 2010 are April 1""Census counts. The population figures for years 2001 - 2009 are bridged-race estimates of the July 1 resident population, from""the revised intercensal county-level 2000 - 2009 series released by NCHS on October 26, 2012. Population figures for 2000 are""April 1 Census counts. Population figures for 1999 are from the 1990-1999 intercensal series of July 1 estimates. Population""figures for the infant age groups are the number of live births.
Note: Rates and population figures for years 2001 -""2009 differ slightly from previously published reports, due to use of the population estimates which were available at the time""of release.""4. The population figures used in the calculation of death rates for the age group 'under 1 year' are the estimates of the""resident population that is under one year of age. More information: http://wonder.cdc.gov/wonder/help/ucd.html#Age Group."

STATE DATA REPORTING SYSTEMS. Annual reports on the Fatality Analysis Reporting System (FARS) which is data regarding fatalities that is collected by a FARS analyst from each State and provided to NHTSA.

The National Crime Victimization Survey: Unbounded Data (also referred to as the All Rotations Data) are now being made available to the public for analytic use. These data differ from the \regular\ National Crime Victimization Survey (NCVS) data in that

The National Crime Victimization Survey (NCVS) Series, previously called the National Crime Surveys (NCS), has been collecting data on personal and household victimization through an ongoing survey of a nationally-representative sample of residential addr

Abstract

Round 1 of the Afrobarometer survey was conducted from July 1999 through June 2001 in 12 African countries, to solicit public opinion on democracy, governance, markets, and national identity. The full 12 country dataset released was pieced together out of different projects, Round 1 of the Afrobarometer survey,the old Southern African Democracy Barometer, and similar surveys done in West and East Africa.

The 7 country dataset is a subset of the Round 1 survey dataset, and consists of a combined dataset for the 7 Southern African countries surveyed with other African countries in Round 1, 1999-2000 (Botswana, Lesotho, Malawi, Namibia, South Africa, Zambia and Zimbabwe). It is a useful dataset because, in contrast to the full 12 country Round 1 dataset, all countries in this dataset were surveyed with the identical questionnaire

Geographic coverage

Botswana Lesotho Malawi Namibia South Africa Zambia Zimbabwe

Analysis unit

Basic units of analysis that the study investigates include: individuals and groups

Kind of data

Sample survey data [ssd]

Sampling procedure

A new sample has to be drawn for each round of Afrobarometer surveys. Whereas the standard sample size for Round 3 surveys will be 1200 cases, a larger sample size will be required in societies that are extremely heterogeneous (such as South Africa and Nigeria), where the sample size will be increased to 2400. Other adaptations may be necessary within some countries to account for the varying quality of the census data or the availability of census maps.

The sample is designed as a representative cross-section of all citizens of voting age in a given country. The goal is to give every adult citizen an equal and known chance of selection for interview. We strive to reach this objective by (a) strictly applying random selection methods at every stage of sampling and by (b) applying sampling with probability proportionate to population size wherever possible. A randomly selected sample of 1200 cases allows inferences to national adult populations with a margin of sampling error of no more than plus or minus 2.5 percent with a confidence level of 95 percent. If the sample size is increased to 2400, the confidence interval shrinks to plus or minus 2 percent.

Sample Universe

The sample universe for Afrobarometer surveys includes all citizens of voting age within the country. In other words, we exclude anyone who is not a citizen and anyone who has not attained this age (usually 18 years) on the day of the survey. Also excluded are areas determined to be either inaccessible or not relevant to the study, such as those experiencing armed conflict or natural disasters, as well as national parks and game reserves. As a matter of practice, we have also excluded people living in institutionalized settings, such as students in dormitories and persons in prisons or nursing homes.

What to do about areas experiencing political unrest? On the one hand we want to include them because they are politically important. On the other hand, we want to avoid stretching out the fieldwork over many months while we wait for the situation to settle down. It was agreed at the 2002 Cape Town Planning Workshop that it is difficult to come up with a general rule that will fit all imaginable circumstances. We will therefore make judgments on a case-by-case basis on whether or not to proceed with fieldwork or to exclude or substitute areas of conflict. National Partners are requested to consult Core Partners on any major delays, exclusions or substitutions of this sort.

Sample Design

The sample design is a clustered, stratified, multi-stage, area probability sample.

To repeat the main sampling principle, the objective of the design is to give every sample element (i.e. adult citizen) an equal and known chance of being chosen for inclusion in the sample. We strive to reach this objective by (a) strictly applying random selection methods at every stage of sampling and by (b) applying sampling with probability proportionate to population size wherever possible.

In a series of stages, geographically defined sampling units of decreasing size are selected. To ensure that the sample is representative, the probability of selection at various stages is adjusted as follows:

The sample is stratified by key social characteristics in the population such as sub-national area (e.g. region/province) and residential locality (urban or rural). The area stratification reduces the likelihood that distinctive ethnic or language groups are left out of the sample. And the urban/rural stratification is a means to make sure that these localities are represented in their correct proportions. Wherever possible, and always in the first stage of sampling, random sampling is conducted with probability proportionate to population size (PPPS). The purpose is to guarantee that larger (i.e., more populated) geographical units have a proportionally greater probability of being chosen into the sample. The sampling design has four stages

A first-stage to stratify and randomly select primary sampling units;

A second-stage to randomly select sampling start-points;

A third stage to randomly choose households;

A final-stage involving the random selection of individual respondents

We shall deal with each of these stages in turn.

STAGE ONE: Selection of Primary Sampling Units (PSUs)

The primary sampling units (PSU's) are the smallest, well-defined geographic units for which reliable population data are available. In most countries, these will be Census Enumeration Areas (or EAs). Most national census data and maps are broken down to the EA level. In the text that follows we will use the acronyms PSU and EA interchangeably because, when census data are employed, they refer to the same unit.

We strongly recommend that NIs use official national census data as the sampling frame for Afrobarometer surveys. Where recent or reliable census data are not available, NIs are asked to inform the relevant Core Partner before they substitute any other demographic data. Where the census is out of date, NIs should consult a demographer to obtain the best possible estimates of population growth rates. These should be applied to the outdated census data in order to make projections of population figures for the year of the survey. It is important to bear in mind that population growth rates vary by area (region) and (especially) between rural and urban localities. Therefore, any projected census data should include adjustments to take such variations into account.

Indeed, we urge NIs to establish collegial working relationships within professionals in the national census bureau, not only to obtain the most recent census data, projections, and maps, but to gain access to sampling expertise. NIs may even commission a census statistician to draw the sample to Afrobarometer specifications, provided that provision for this service has been made in the survey budget.

Regardless of who draws the sample, the NIs should thoroughly acquaint themselves with the strengths and weaknesses of the available census data and the availability and quality of EA maps. The country and methodology reports should cite the exact census data used, its known shortcomings, if any, and any projections made from the data. At minimum, the NI must know the size of the population and the urban/rural population divide in each region in order to specify how to distribute population and PSU's in the first stage of sampling. National investigators should obtain this written data before they attempt to stratify the sample.

Once this data is obtained, the sample population (either 1200 or 2400) should be stratified, first by area (region/province) and then by residential locality (urban or rural). In each case, the proportion of the sample in each locality in each region should be the same as its proportion in the national population as indicated by the updated census figures.

Having stratified the sample, it is then possible to determine how many PSU's should be selected for the country as a whole, for each region, and for each urban or rural locality.

The total number of PSU's to be selected for the whole country is determined by calculating the maximum degree of clustering of interviews one can accept in any PSU. Because PSUs (which are usually geographically small EAs) tend to be socially homogenous we do not want to select too many people in any one place. Thus, the Afrobarometer has established a standard of no more than 8 interviews per PSU. For a sample size of 1200, the sample must therefore contain 150 PSUs/EAs (1200 divided by 8). For a sample size of 2400, there must be 300 PSUs/EAs.

These PSUs should then be allocated proportionally to the urban and rural localities within each regional stratum of the sample. Let's take a couple of examples from a country with a sample size of 1200. If the urban locality of Region X in this country constitutes 10 percent of the current national population, then the sample for this stratum should be 15 PSUs (calculated as 10 percent of 150 PSUs). If the rural population of Region Y constitutes 4 percent of the current national population, then the sample for this stratum should be 6 PSU's.

The next step is to select particular PSUs/EAs using random methods. Using the above example of the rural localities in Region Y, let us say that you need to pick 6 sample EAs out of a census list that contains a total of 240 rural EAs in Region Y. But which 6? If the EAs created by the national census bureau are of equal or roughly equal population size, then selection is relatively straightforward. Just number all EAs consecutively, then make six selections using a table of random numbers. This procedure, known as simple random sampling (SRS), will

Data was compiled from the CDC Wonder database for each year and combined into a single spreadsheet. Each year has both a death field and a rate of fatalities per 100,000 people. The CDC does not publish the number of fatalities by county if the total is less than 10 in a given year. The CDC does not post a rate of fatalities if the total number of deaths per county is less than 20. The population field contains estimates from 2018 and is NOT the data used to generate the rates over time.The following details are copied directly from the CDC Wonder database text file. Note that the year is different for each data download from the original database."Dataset: Underlying Cause of Death, 1999-2017""Query Parameters:""Drug/Alcohol Induced Causes: Drug poisonings (overdose) Unintentional (X40-X44)""States: Tennessee (47)""Year/Month: 1999""Group By: County""Show Totals: True""Show Zero Values: False""Show Suppressed: False""Calculate Rates Per: 100,000""Rate Options: Default intercensal populations for years 2001-2009 (except Infant Age Groups)""---""Help: See http://wonder.cdc.gov/wonder/help/ucd.html for more information.""---""Query Date: Aug 19, 2019 10:22:15 PM""1. Rows with suppressed Deaths are hidden, but the Deaths and Population values in those rows are included in the totals. Use""Quick Options above to show suppressed rows.""---"Caveats:"1. Data are Suppressed when the data meet the criteria for confidentiality constraints. More information:""http://wonder.cdc.gov/wonder/help/ucd.html#Assurance of Confidentiality.""2. Death rates are flagged as Unreliable when the rate is calculated with a numerator of 20 or less. More information:""http://wonder.cdc.gov/wonder/help/ucd.html#Unreliable.""3. The population figures for year 2017 are bridged-race estimates of the July 1 resident population, from the Vintage 2017""postcensal series released by NCHS on June 27, 2018. The population figures for year 2016 are bridged-race estimates of the July""1 resident population, from the Vintage 2016 postcensal series released by NCHS on June 26, 2017. The population figures for""year 2015 are bridged-race estimates of the July 1 resident population, from the Vintage 2015 postcensal series released by NCHS""on June 28, 2016. The population figures for year 2014 are bridged-race estimates of the July 1 resident population, from the""Vintage 2014 postcensal series released by NCHS on June 30, 2015. The population figures for year 2013 are bridged-race""estimates of the July 1 resident population, from the Vintage 2013 postcensal series released by NCHS on June 26, 2014. The""population figures for year 2012 are bridged-race estimates of the July 1 resident population, from the Vintage 2012 postcensal""series released by NCHS on June 13, 2013. The population figures for year 2011 are bridged-race estimates of the July 1 resident""population, from the Vintage 2011 postcensal series released by NCHS on July 18, 2012. Population figures for 2010 are April 1""Census counts. The population figures for years 2001 - 2009 are bridged-race estimates of the July 1 resident population, from""the revised intercensal county-level 2000 - 2009 series released by NCHS on October 26, 2012. Population figures for 2000 are""April 1 Census counts. Population figures for 1999 are from the 1990-1999 intercensal series of July 1 estimates. Population""figures for the infant age groups are the number of live births.
Note: Rates and population figures for years 2001 -""2009 differ slightly from previously published reports, due to use of the population estimates which were available at the time""of release.""4. The population figures used in the calculation of death rates for the age group 'under 1 year' are the estimates of the""resident population that is under one year of age. More information: http://wonder.cdc.gov/wonder/help/ucd.html#Age Group."

Victims of gang-related homicides (total number of homicide victims; number of homicide victims - unknown gang-relation; number of homicide victims - known gang relation; number of gang-related homicide victims; percentage of gang-related homicide victims; rate (per 100,000 population) of gang-related homicide victims), Canada and regions, 1999 to 2023.

This dataset presents the age-adjusted death rates for the 10 leading causes of death in the United States beginning in 1999. Data are based on information from all resident death certificates filed in the 50 states and the District of Columbia using demographic and medical characteristics. Age-adjusted death rates (per 100,000 population) are based on the 2000 U.S. standard population. Populations used for computing death rates after 2010 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 non-census years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Causes of death classified by the International Classification of Diseases, Tenth Revision (ICD–10) are ranked according to the number of deaths assigned to rankable causes. Cause of death statistics are based on the underlying cause of death. SOURCES 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. 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. Murphy SL, Xu JQ, Kochanek KD, Curtin SC, and Arias E. Deaths: Final data for 2015. National vital statistics reports; vol 66. no. 6. Hyattsville, MD: National Center for Health Statistics. 2017. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_06.pdf.

Background
The Labour Force Survey (LFS) is a unique source of information using international definitions of employment and unemployment and economic inactivity, together with a wide range of related topics such as occupation, training, hours of work and personal characteristics of household members aged 16 years and over. It is used to inform social, economic and employment policy. The LFS was first conducted biennially from 1973-1983. Between 1984 and 1991 the survey was carried out annually and consisted of a quarterly survey conducted throughout the year and a 'boost' survey in the spring quarter (data were then collected seasonally). From 1992 quarterly data were made available, with a quarterly sample size approximately equivalent to that of the previous annual data. The survey then became known as the Quarterly Labour Force Survey (QLFS). From December 1994, data gathering for Northern Ireland moved to a full quarterly cycle to match the rest of the country, so the QLFS then covered the whole of the UK (though some additional annual Northern Ireland LFS datasets are also held at the UK Data Archive). Further information on the background to the QLFS may be found in the documentation.

Longitudinal data
The LFS retains each sample household for five consecutive quarters, with a fifth of the sample replaced each quarter. The main survey was designed to produce cross-sectional data, but the data on each individual have now been linked together to provide longitudinal information. The longitudinal data comprise two types of linked datasets, created using the weighting method to adjust for non-response bias. The two-quarter datasets link data from two consecutive waves, while the five-quarter datasets link across a whole year (for example January 2010 to March 2011 inclusive) and contain data from all five waves. A full series of longitudinal data has been produced, going back to winter 1992. Linking together records to create a longitudinal dimension can, for example, provide information on gross flows over time between different labour force categories (employed, unemployed and economically inactive). This will provide detail about people who have moved between the categories. Also, longitudinal information is useful in monitoring the effects of government policies and can be used to follow the subsequent activities and circumstances of people affected by specific policy initiatives, and to compare them with other groups in the population. There are however methodological problems which could distort the data resulting from this longitudinal linking. The ONS continues to research these issues and advises that the presentation of results should be carefully considered, and warnings should be included with outputs where necessary.

LFS Documentation
The documentation available from the Archive to accompany LFS datasets largely consists of the latest version of each user guide volume alongside the appropriate questionnaire for the year concerned. However, volumes are updated periodically by ONS, so users are advised to check the latest documents on the ONS Labour Force Survey - User Guidance pages before commencing analysis. This is especially important for users of older QLFS studies, where information and guidance in the user guide documents may have changed over time.

Occupation data for 2021 and 2022 data files

The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

2022 Weighting

The population totals used for the latest LFS estimates use projected growth rates from Real Time Information (RTI) data for UK, EU and non-EU populations based on 2021 patterns. The total population used for the LFS therefore does not take into account any changes in migration, birth rates, death rates, and so on since June 2021, and hence levels estimates may be under- or over-estimating the true values and should be used with caution. Estimates of rates will, however, be robust.

This study was deposited in 2008, as a result of the move from seasonal to calendar quarters for the QLFS, and the reweighting process to 2007-2008 population figures. It combines data from previously-available QLFS seasonal five-quarter longitudinal datasets. The depositor has advised that small revisions to the data may have been made during this process, but they should not be significant.

This dataset of U.S. mortality trends since 1900 highlights the differences in age-adjusted death rates and life expectancy at birth by race and sex.

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).

Life expectancy data are available up to 2017. Due to changes in categories of race used in publications, data are not available for the black population consistently before 1968, and not at all before 1960. More information on historical data on age-adjusted death rates is available at https://www.cdc.gov/nchs/nvss/mortality/hist293.htm.

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.

Data for deaths by leading cause of death categories are now available in the death profiles dataset for each geographic granularity.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

Cause of death categories for years 1999 and later are based on tenth revision of International Classification of Diseases (ICD-10) codes. Comparable categories are provided for years 1979 through 1998 based on ninth revision (ICD-9) codes. For more information on the comparability of cause of death classification between ICD revisions see Comparability of Cause-of-death Between ICD Revisions.

The Mortality - Multiple Cause of Death data on CDC WONDER are county-level national mortality and population data spanning the years 1999-2009. 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 (Boolean set analysis), 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., region, state, and county), age group (including infants and single-year-of-age cohorts), race (4 groups), Hispanic ethnicity, sex, year of death, and cause-of-death (4-digit ICD-10 code or group of codes, injury intent and mechanism categories, or drug and alcohol related causes), year, month and week day of death, place of death and whether an autopsy was performed. The data are produced by the National Center for Health Statistics.

Abstract

The Afrobarometer is a comparative series of public attitude surveys that assess African citizen's attitudes to democracy and governance, markets, and civil society, among other topics.

The 12 country datasetis a combined dataset for the 12 African countries surveyed during round 1 of the survey, conducted between 1999-2000 (Botswana, Ghana, Lesotho, Mali, Malawi, Namibia, Nigeria South Africa, Tanzania, Uganda, Zambia and Zimbabwe), plus data from the old Southern African Democracy Barometer, and similar surveys done in West and East Africa.

Geographic coverage

The Round 1 Afrobarometer surveys have national coverage for the following countries: Botswana, Ghana, Lesotho, Malawi, Mali, Namibia, Nigeria, South Africa, Tanzania, Uganda, Zambia, Zimbabwe.

Analysis unit

Individuals

Universe

The sample universe for Afrobarometer surveys includes all citizens of voting age within the country. In other words, we exclude anyone who is not a citizen and anyone who has not attained this age (usually 18 years) on the day of the survey. Also excluded are areas determined to be either inaccessible or not relevant to the study, such as those experiencing armed conflict or natural disasters, as well as national parks and game reserves. As a matter of practice, we have also excluded people living in institutionalized settings, such as students in dormitories and persons in prisons or nursing homes.

What to do about areas experiencing political unrest? On the one hand we want to include them because they are politically important. On the other hand, we want to avoid stretching out the fieldwork over many months while we wait for the situation to settle down. It was agreed at the 2002 Cape Town Planning Workshop that it is difficult to come up with a general rule that will fit all imaginable circumstances. We will therefore make judgments on a case-by-case basis on whether or not to proceed with fieldwork or to exclude or substitute areas of conflict. National Partners are requested to consult Core Partners on any major delays, exclusions or substitutions of this sort.

Kind of data

Sample survey data [ssd]

Sampling procedure

Afrobarometer uses national probability samples designed to meet the following criteria. Samples are designed to generate a sample that is a representative cross-section of all citizens of voting age in a given country. The goal is to give every adult citizen an equal and known chance of being selected for an interview. They achieve this by:

• using random selection methods at every stage of sampling; • sampling at all stages with probability proportionate to population size wherever possible to ensure that larger (i.e., more populated) geographic units have a proportionally greater probability of being chosen into the sample.

The sampling universe normally includes all citizens age 18 and older. As a standard practice, we exclude people living in institutionalized settings, such as students in dormitories, patients in hospitals, and persons in prisons or nursing homes. Occasionally, we must also exclude people living in areas determined to be inaccessible due to conflict or insecurity. Any such exclusion is noted in the technical information report (TIR) that accompanies each data set.

Sample size and design Samples usually include either 1,200 or 2,400 cases. A randomly selected sample of n=1200 cases allows inferences to national adult populations with a margin of sampling error of no more than +/-2.8% with a confidence level of 95 percent. With a sample size of n=2400, the margin of error decreases to +/-2.0% at 95 percent confidence level.

The sample design is a clustered, stratified, multi-stage, area probability sample. Specifically, we first stratify the sample according to the main sub-national unit of government (state, province, region, etc.) and by urban or rural location.

Area stratification reduces the likelihood that distinctive ethnic or language groups are left out of the sample. Afrobarometer occasionally purposely oversamples certain populations that are politically significant within a country to ensure that the size of the sub-sample is large enough to be analysed. Any oversamples is noted in the TIR.

Sample stages Samples are drawn in either four or five stages:

Stage 1: In rural areas only, the first stage is to draw secondary sampling units (SSUs). SSUs are not used in urban areas, and in some countries they are not used in rural areas. See the TIR that accompanies each data set for specific details on the sample in any given country. Stage 2: We randomly select primary sampling units (PSU). Stage 3: We then randomly select sampling start points. Stage 4: Interviewers then randomly select households. Stage 5: Within the household, the interviewer randomly selects an individual respondent. Each interviewer alternates in each household between interviewing a man and interviewing a woman to ensure gender balance in the sample.

To keep the costs and logistics of fieldwork within manageable limits, eight interviews are clustered within each selected PSU.

Data weights For some national surveys, data are weighted to correct for over or under-sampling or for household size. "Withinwt" should be turned on for all national -level descriptive statistics in countries that contain this weighting variable. It is included as the last variable in the data set, with details described in the codebook. For merged data sets, "Combinwt" should be turned on for cross-national comparisons of descriptive statistics. Note: this weighting variable standardizes each national sample as if it were equal in size.

Further information on sampling protocols, including full details of the methodologies used for each stage of sample selection, can be found at https://afrobarometer.org/surveys-and-methods/sampling-principles

Mode of data collection

Face-to-face [f2f]

Research instrument

Because Afrobarometer Round 1 emerged out of several different survey research efforts, survey instruments were not standardized across all countries, there are a number of features of the questionnaires that should be noted, as follows: • In most cases, the data set only includes those questions/variables that were asked in nine or more countries. Complete Round 1 data sets for each individual country have already been released, and are available from ICPSR or from the Afrobarometer website at www.afrobarometer.org. • In the seven countries that originally formed the Southern Africa Barometer (SAB) - Botswana, Lesotho, Malawi, Namibia, South Africa, Zambia and Zimbabwe - a standardized questionnaire was used, so question wording and response categories are the generally the same for all of these countries. The questionnaires in Mali and Tanzania were also essentially identical (in the original English version). Ghana, Uganda and Nigeria each had distinct questionnaires. • This merged dataset combines, into a single variable, responses from across these different countries where either identical or very similar questions were used, or where conceptually equivalent questions can be found in at least nine of the different countries. For each variable, the exact question text from each of the countries or groups of countries ("SAB" refers to the Southern Africa Barometer countries) is listed. • Response options also varied on some questions, and where applicable, these differences are also noted.

The National Health and Nutrition Examination Surveys (NHANES) is a program of studies designed to assess the health and nutritional status of adults and children in the United States. The NHANES combines personal interviews and physical examinations, which focus on different population groups or health topics. These surveys have been conducted by the National Center for Health Statistics (NCHS) on a periodic basis from 1971 to 1994. In 1999, the NHANES became a continuous program with a changing focus on a variety of health and nutrition measurements which were designed to meet current and emerging concerns. The sample for the survey is selected to represent the U.S. population of all ages. Many of the NHANES 2007-2008 questions also were asked in NHANES II 1976-1980, Hispanic HANES 1982-1984, NHANES III 1988-1994, and NHANES 1999-2006. New questions were added to the survey based on recommendations from survey collaborators, NCHS staff, and other interagency work groups. Estimates for previously undiagnosed conditions, as well as those known to and reported by survey respondents, are produced through the survey.

In the 1999-2000 wave, the NHANES includes more than 100 datasets. Most have been combined into three datasets for convenience. Each starts with the Demographic dataset and includes datasets of a specific type.

1. National Health and Nutrition Examination Survey (NHANES), Demographic & Examination Data, 1999-2000 (The base of the Demographic dataset + all data from medical examinations).

2. National Health and Nutrition Examination Survey (NHANES), Demographic & Laboratory Data, 1999-2000 (The base of the Demographic dataset + all data from medical laboratories).

3. National Health and Nutrition Examination Survey (NHANES), Demographic & Questionnaire Data, 1999-2000 (The base of the Demographic dataset + all data from questionnaires)

Not all files from the 1999-2000 wave are included. This is for two reasons, both of which related to the merging variable (SEQN). For a subset of the files, SEQN is not a unique identifier for cases (i.e., some respondents have multiple cases) or SEQN is not in the file at all. The following datasets from this wave of the NHANES are not included in these three files and can be found individually from the "https://www.cdc.gov/nchs/nhanes/index.htm" Target="_blank">NHANES website at the CDC:

Examination: Dietary Interview (Individual Foods File)
Examination: Dual Energy X-ray Absorptiometry (DXX)
Examination: Dual Energy X-ray Absorptiometry (DXX)
Questionnaire: Analgesics Pain Relievers
Questionnaire: Dietary Supplement Use -- Ingredient Information
Questionnaire: Dietary Supplement Use -- Supplement Blend
Questionnaire: Dietary Supplement Use -- Supplement Information
Questionnaire: Drug Information
Questionnaire: Dietary Supplement Use -- Participants Use of Supplement
Questionnaire: Physical Activity Individual Activity File
Questionnaire: Prescription Medications

Variable SEQN is included for merging files within the waves. All data files should be sorted by SEQN.

Additional details of the design and content of each survey are available at the "https://www.cdc.gov/nchs/nhanes/index.htm" Target="_blank">NHANES website.

This data collection provides annual data on prisoners under a sentence of death and prisoners whose offense sentences were commuted or vacated during the period 1973-1999. Information is supplied for basic sociodemographic characteristics such as age, sex, education, and state of incarceration. Criminal history data include prior felony convictions for criminal homicide and legal status at the time of the capital offense. Additional information is available for inmates removed from death row by yearend 1999 and for inmates who were executed.

The benchmark interest rate in Brazil was last recorded at 15 percent. This dataset provides - Brazil Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in South Korea was last recorded at 2.50 percent. This dataset provides - South Korea Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Nurses in the United States increased to 12.71 per 1000 people in 2024 from 12.36 per 1000 people in 2023. This dataset includes a chart with historical data for the United States Nurses.