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

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.

Data on death rates for suicide, by selected population characteristics. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://www.cdc.gov/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time.

SOURCE: NCHS, National Vital Statistics System (NVSS); Grove RD, Hetzel AM. Vital statistics rates in the United States, 1940–1960. National Center for Health Statistics. 1968; numerator data from NVSS annual public-use Mortality Files; denominator data from U.S. Census Bureau national population estimates; and Murphy SL, Xu JQ, Kochanek KD, Arias E, Tejada-Vera B. Deaths: Final data for 2018. National Vital Statistics Reports; vol 69 no 13. Hyattsville, MD: National Center for Health Statistics. 2021. Available from: https://www.cdc.gov/nchs/products/nvsr.htm. For more information on the National Vital Statistics System, see the corresponding Appendix entry at https://www.cdc.gov/nchs/data/hus/hus19-appendix-508.pdf.

Analysis of ‘NCHS - Leading Causes of Death: United States’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/a6bfec1a-0f36-4691-9d0b-888ca8d5ee13 on 26 January 2022.

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

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

1. 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.

2. 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.

--- Original source retains full ownership of the source dataset ---

Data on drug overdose death rates, by drug type and selected population characteristics. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://www.cdc.gov/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time. SOURCE: NCHS, National Vital Statistics System, numerator data from annual public-use Mortality Files; denominator data from U.S. Census Bureau national population estimates; and Murphy SL, Xu JQ, Kochanek KD, Arias E, Tejada-Vera B. Deaths: Final data for 2018. National Vital Statistics Reports; vol 69 no 13. Hyattsville, MD: National Center for Health Statistics.2021. Available from: https://www.cdc.gov/nchs/products/nvsr.htm. For more information on the National Vital Statistics System, see the corresponding Appendix entry at https://www.cdc.gov/nchs/data/hus/hus19-appendix-508.pdf.

This publication of the SHMI relates to discharges in the reporting period April 2024 - March 2025. The SHMI is the ratio between the actual number of patients who die following hospitalisation at the trust and the number that would be expected to die on the basis of average England figures, given the characteristics of the patients treated there. The SHMI covers patients admitted to hospitals in England who died either while in hospital or within 30 days of being discharged. To help users of the data understand the SHMI, trusts have been categorised into bandings indicating whether a trust's SHMI is 'higher than expected', 'as expected' or 'lower than expected'. For any given number of expected deaths, a range of observed deaths is considered to be 'as expected'. If the observed number of deaths falls outside of this range, the trust in question is considered to have a higher or lower SHMI than expected. The expected number of deaths is a statistical construct and is not a count of patients. The difference between the number of observed deaths and the number of expected deaths cannot be interpreted as the number of avoidable deaths or excess deaths for the trust. The SHMI is not a measure of quality of care. A higher than expected number of deaths should not immediately be interpreted as indicating poor performance and instead should be viewed as a 'smoke alarm' which requires further investigation. Similarly, an 'as expected' or 'lower than expected' SHMI should not immediately be interpreted as indicating satisfactory or good performance. Trusts may be located at multiple sites and may be responsible for 1 or more hospitals. A breakdown of the data by site of treatment is also provided, as well as a breakdown of the data by diagnosis group. Further background information and supporting documents, including information on how to interpret the SHMI, are available on the SHMI homepage (see Related Links).

MMWR Surveillance Summary 66 (No. SS-1):1-8 found that nonmetropolitan areas have significant numbers of potentially excess deaths from the five leading causes of death. These figures accompany this report by presenting information on potentially excess deaths in nonmetropolitan and metropolitan areas at the state level. They also add additional years of data and options for selecting different age ranges and benchmarks. Potentially excess deaths are defined in MMWR Surveillance Summary 66(No. SS-1):1-8 as deaths that exceed the numbers that would be expected if the death rates of states with the lowest rates (benchmarks) occurred across all states. They are calculated by subtracting expected deaths for specific benchmarks from observed deaths. Not all potentially excess deaths can be prevented; some areas might have characteristics that predispose them to higher rates of death. However, many potentially excess deaths might represent deaths that could be prevented through improved public health programs that support healthier behaviors and neighborhoods or better access to health care services. Mortality data for U.S. residents come from the National Vital Statistics System. Estimates based on fewer than 10 observed deaths are not shown and shaded yellow on the map. Underlying cause of death is based on the International Classification of Diseases, 10th Revision (ICD-10) Heart disease (I00-I09, I11, I13, and I20–I51) Cancer (C00–C97) Unintentional injury (V01–X59 and Y85–Y86) Chronic lower respiratory disease (J40–J47) Stroke (I60–I69) Locality (nonmetropolitan vs. metropolitan) is based on the Office of Management and Budget’s 2013 county-based classification scheme. Benchmarks are based on the three states with the lowest age and cause-specific mortality rates. Potentially excess deaths for each state are calculated by subtracting deaths at the benchmark rates (expected deaths) from observed deaths. Users can explore three benchmarks: “2010 Fixed” is a fixed benchmark based on the best performing States in 2010. “2005 Fixed” is a fixed benchmark based on the best performing States in 2005. “Floating” is based on the best performing States in each year so change from year to year. 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 Moy E, Garcia MC, Bastian B, Rossen LM, Ingram DD, Faul M, Massetti GM, Thomas CC, Hong Y, Yoon PW, Iademarco MF. Leading Causes of Death in Nonmetropolitan and Metropolitan Areas – United States, 1999-2014. MMWR Surveillance Summary 2017; 66(No. SS-1):1-8. Garcia MC, Faul M, Massetti G, Thomas CC, Hong Y, Bauer UE, Iademarco MF. Reducing Potentially Excess Deaths from the Five Leading Causes of Death in the Rural United States. MMWR Surveillance Summary 2017; 66(No. SS-2):1–7.

Analysis of ‘Death Rate & Life-Expectancy Over The Years’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/death-rate-and-life-expectancye on 13 February 2022.

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

About this dataset

This storyboard of U.S. mortality trends over the past 113 years highlights the differences in age-adjusted death rates and life expectancy at birth by race and sex; neonatal mortality and infant mortality rates by race; childhood mortality rates by age; and trends in age-adjusted death rates for five selected major causes of death.

• Age-adjusted death rates (deaths per 100,000) are based on the 2000 U.S. standard population.
• Populations used for computing death rates for 2011–2013 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 before 2010 are revised using updated intercensal population estimates and may differ from rates previously published.

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National Center for Health Statistics Data Visualization of Deaths in the United States, 1900–2013 (6/01/15)

Attribution: Centers for Disease Control and Prevention.

This dataset was created by Health and contains around 2000 samples along with Sex, Race, technical information and other features such as: - Year - Measure Names - and more.

How to use this dataset

• Analyze Mortality in relation to Average Life Expectancy
• Study the influence of Sex on Race
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Health

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--- Original source retains full ownership of the source dataset ---

Footnotes: 1 Sources: Statistics Canada, Canadian Vital Statistics, Death Database and Demography Division (population estimates). The table 13-10-0743-01 is an update of table 13-10-0412-01. This is because of the adoption of the 2015 version of the Health Region Geography. For more information, consult Statistics Canada's publication Health Regions: Boundaries and Correspondence with Census Geography" (catalogue number 82-402-X)." 2 Mortality is the death rate, which can be measured as total mortality (all causes of death combined) or by selected cause of death. All counts and rates are calculated using the total population (all age groups). 3 Potential years of life lost (PYLL) is the number of years of potential life not lived when a person dies prematurely" defined for this indicator as before age 75. All counts and rates in this table are calculated using the population aged 0 to 74." 4 Counts and rates in this table are based on three consecutive years of death data. Rates are per 100,000 population and were calculated by dividing the counts by three consecutive years of population data. 5 Rates are age-standardized using the direct method and the 2011 Canadian Census population structure. The use of a standard population results in more meaningful rate comparisons because it adjusts for variations in population age distributions over time and across geographic areas. 6 Counts and rates in this table exclude: deaths of non-residents of Canada; deaths of residents of Canada whose province or territory of residence was unknown; deaths for which age of decedent was unknown. 7 Rates in this table are based on place of residence for indicators derived from death events. 8 The number of deaths in Ontario for 2016 is considered preliminary. 9 Health regions are administrative areas defined by provincial ministries of health according to provincial legislation. The health regions presented in this table are based on boundaries and names in effect as of December 2017. For complete Canadian coverage, each northern territory represents a health region. 10 Peer groups are aggregations of health regions that share similar socio-economic and demographic characteristics, based on data from the 2011 Census of Population and 2011 National Household Survey. These are useful in the analysis of health regions, where important differences may be detected by comparing health regions within a peer group. The nine peer groups are identified by the letters A through I, which are appended to the health region 4-digit code. Caution should be taken when comparing data for the Peer Groups over time due to changes in the Peer Groups. In an analysis involving the peer groups, only one level of geography in Ontario should be used. For more information on the peer groups classification, consult Statistics Canada's publication Health Regions: Boundaries and Correspondence with Census Geography" (catalogue number 82-402-X)." 11 Before 2010, missing data on sex of the deceased were imputed based on death registration number. Starting with 2010 data year, missing data on sex of the deceased were imputed based on the cause of death information and a logistic regression. 12 The cause of death tabulated is the underlying cause of death. This is defined as (a) the disease or injury which initiated the train of events leading directly to death, or (b) the circumstances of the accident or violence which produced the fatal injury. The underlying cause is selected from the conditions listed on the medical certificate of cause of death. 13 Confidence intervals for age-standardized rates for selected causes of death data were produced using the Spiegelman method. Source: Spiegelman, M., Introduction to Demography" Revised Edition Cambridge14 Confidence intervals for crude rates for selected causes of death data were produced using the Fleiss method. Source: Fleiss, JL., Statistical Methods for Rates and Proportions" Second Edition New York15 The 95% confidence interval (CI) illustrates the degree of variability associated with a number or a rate. 16 Wide confidence intervals (CIs) indicate high variability, thus, these numbers or rates should be interpreted and compared with due caution. 17 The following standard symbols are used in this Statistics Canada table: (..) for figures not available for a specific reference period, (...) for figures not applicable and (x) for figures suppressed to meet the confidentiality requirements of the Statistics Act. 18 The figures shown in the tables have been subjected to a confidentiality procedure known as controlled rounding to prevent the possibility of associating statistical data with any identifiable individual. Under this method, all figures, including totals and margins, are rounded either up or down to a multiple of 5. Controlled rounding has the advantage over other types of rounding of producing additive tables as well as offering more protection. 19 Premature deaths are those of individuals who are younger than age 75.

The New York City Department of Health and Mental Hygiene (NYC DOHMH) has shared vital statistics data (birth and mortality data) online. Birth data includes demographic information on the mother, including age, race, and education. Mortality data includes demographic information on the deceased, such as age, sex, race, and education. The publicly-available birth and death micro-SAS datasets provide aggregate data on the community district, zip code, and census tract levels. Researchers may also complete an application process to request line-listed and de-identified vital statistics data from NYC DOHMH.

Footnotes: 1 Sources: Statistics Canada, Canadian Vital Statistics, Death Database and Demography Division (population estimates). The table 13-10-0743-01 is an update of table 13-10-0412-01. This is because of the adoption of the 2015 version of the Health Region Geography. For more information, consult Statistics Canada's publication Health Regions: Boundaries and Correspondence with Census Geography" (catalogue number 82-402-X)." 2 Mortality is the death rate, which can be measured as total mortality (all causes of death combined) or by selected cause of death. All counts and rates are calculated using the total population (all age groups). 3 Potential years of life lost (PYLL) is the number of years of potential life not lived when a person dies prematurely" defined for this indicator as before age 75. All counts and rates in this table are calculated using the population aged 0 to 74."4 Counts and rates in this table are based on three consecutive years of death data. Rates are per 100,000 population and were calculated by dividing the counts by three consecutive years of population data. 5 Rates are age-standardized using the direct method and the 2011 Canadian Census population structure. The use of a standard population results in more meaningful rate comparisons because it adjusts for variations in population age distributions over time and across geographic areas. 6 Counts and rates in this table exclude: deaths of non-residents of Canada; deaths of residents of Canada whose province or territory of residence was unknown; deaths for which age of decedent was unknown. 7 Rates in this table are based on place of residence for indicators derived from death events. 8 The number of deaths in Ontario for 2016 is considered preliminary. 9 Health regions are administrative areas defined by provincial ministries of health according to provincial legislation. The health regions presented in this table are based on boundaries and names in effect as of December 2017. For complete Canadian coverage, each northern territory represents a health region. 10 Peer groups are aggregations of health regions that share similar socio-economic and demographic characteristics, based on data from the 2011 Census of Population and 2011 National Household Survey. These are useful in the analysis of health regions, where important differences may be detected by comparing health regions within a peer group. The nine peer groups are identified by the letters A through I, which are appended to the health region 4-digit code. Caution should be taken when comparing data for the Peer Groups over time due to changes in the Peer Groups. In an analysis involving the peer groups, only one level of geography in Ontario should be used. For more information on the peer groups classification, consult Statistics Canada's publication Health Regions: Boundaries and Correspondence with Census Geography" (catalogue number 82-402-X)." 11 Before 2010, missing data on sex of the deceased were imputed based on death registration number. Starting with 2010 data year, missing data on sex of the deceased were imputed based on the cause of death information and a logistic regression. 12 The cause of death tabulated is the underlying cause of death. This is defined as (a) the disease or injury which initiated the train of events leading directly to death, or (b) the circumstances of the accident or violence which produced the fatal injury. The underlying cause is selected from the conditions listed on the medical certificate of cause of death. 13 Confidence intervals for age-standardized rates for selected causes of death data were produced using the Spiegelman method. Source: Spiegelman, M., Introduction to Demography" Revised Edition14 Confidence intervals for crude rates for selected causes of death data were produced using the Fleiss method. Source: Fleiss, JL., Statistical Methods for Rates and Proportions" Second Edition15 The 95% confidence interval (CI) illustrates the degree of variability associated with a number or a rate. 16 Wide confidence intervals (CIs) indicate high variability, thus, these numbers or rates should be interpreted and compared with due caution. 17 The following standard symbols are used in this Statistics Canada table: (..) for figures not available for a specific reference period, (...) for figures not applicable and (x) for figures suppressed to meet the confidentiality requirements of the Statistics Act. 18 The figures shown in the tables have been subjected to a confidentiality procedure known as controlled rounding to prevent the possibility of associating statistical data with any identifiable individual. Under this method, all figures, including totals and margins, are rounded either up or down to a multiple of 5. Controlled rounding has the advantage over other types of rounding of producing additive tables as well as offering more protection. 19 Premature deaths are those of individuals who are younger than age 75.

BackgroundAlthough excessive alcohol-related mortality in the post-Soviet countries remains the major public health threat, determinants of this phenomenon are still poorly understood.AimsWe assess simultaneously individual- and area-level factors associated with an elevated risk of alcohol-related mortality among Lithuanian males aged 30–64.MethodsOur analysis is based on a census-linked dataset containing information on individual- and area-level characteristics and death events which occurred between March 1st, 2011 and December 31st, 2013. We limit the analysis to a few causes of death which are directly linked to excessive alcohol consumption: accidental poisonings by alcohol (X45) and liver cirrhosis (K70 and K74). Multilevel Poisson regression models with random intercepts are applied to estimate mortality rate ratios (MRR).ResultsThe selected individual-level characteristics are important predictors of alcohol-related mortality, whereas area-level variables show much less pronounced or insignificant effects. Compared to married men, never married (MRR = 1.9, CI:1.6–2.2), divorced (MRR = 2.6, CI:2.3–2.9), and widowed (MRR = 2.4, CI: 1.8–3.1) men are disadvantaged groups. Men who have the lowest level of educational attainment have the highest mortality risk (MRR = 1.7 CI:1.4–2.1). Being unemployed is associated with a five-fold risk of alcohol-related death (MRR = 5.1, CI: 4.4–5.9), even after adjusting for all other individual variables. Lithuanian males have an advantage over Russian (MRR = 1.3, CI:1.1–1.6) and Polish (MRR = 1.8, CI: 1.5–2.2) males. After adjusting for all individual characteristics, only two out of seven area-level variables—i.e., the share of ethnic minorities in the population and the election turnout—have statistically significant direct associations. These variables contribute to a higher risk of alcohol-related mortality at the individual level.ConclusionsThe huge and increasing socio-economic disparities in alcohol-related mortality indicate that recently implemented anti-alcohol measures in Lithuania should be reinforced by specific measures targeting the most disadvantaged population groups and geographical areas.

The objective of the annual survey on deaths is to collect data on demographic and socio-economic features of dead person, data on origin and cause of death, as well as data on demographic and socio-economic characteristics of mother of dead infant.

The following data are collected: for every dead person (sex, date of death, date of birth, place of birth, citizenship, ethnicity, place of usual residence, educational attainment, activity, occupation, where the death event took place, informant on cause of death, underlying cause of death, origin of death), for dead infant (child born within wedlock or out of wedlock, body mass at birth, gestation age, date of birth of mother, number of children that mother has born up to the moment), for violent deaths (origin of violent death, nature of injury).

Footnotes: 1 Sources: Statistics Canada, Canadian Vital Statistics, Death Database and Demography Division (population estimates). The table 13-10-0743-01 is an update of table 13-10-0412-01. This is because of the adoption of the 2015 version of the Health Region Geography. For more information, consult Statistics Canada's publication Health Regions: Boundaries and Correspondence with Census Geography" (catalogue number 82-402-X)." 2 Mortality is the death rate, which can be measured as total mortality (all causes of death combined) or by selected cause of death. All counts and rates are calculated using the total population (all age groups). 3 Potential years of life lost (PYLL) is the number of years of potential life not lived when a person dies prematurely" defined for this indicator as before age 75. All counts and rates in this table are calculated using the population aged 0 to 74." 4 Counts and rates in this table are based on three consecutive years of death data. Rates are per 100,000 population and were calculated by dividing the counts by three consecutive years of population data. 5 Rates are age-standardized using the direct method and the 2011 Canadian Census population structure. The use of a standard population results in more meaningful rate comparisons because it adjusts for variations in population age distributions over time and across geographic areas. 6 Counts and rates in this table exclude: deaths of non-residents of Canada; deaths of residents of Canada whose province or territory of residence was unknown; deaths for which age of decedent was unknown. 7 Rates in this table are based on place of residence for indicators derived from death events. 8 The number of deaths in Ontario for 2016 is considered preliminary. 9 Health regions are administrative areas defined by provincial ministries of health according to provincial legislation. The health regions presented in this table are based on boundaries and names in effect as of December 2017. For complete Canadian coverage, each northern territory represents a health region. 10 Peer groups are aggregations of health regions that share similar socio-economic and demographic characteristics, based on data from the 2011 Census of Population and 2011 National Household Survey. These are useful in the analysis of health regions, where important differences may be detected by comparing health regions within a peer group. The nine peer groups are identified by the letters A through I, which are appended to the health region 4-digit code. Caution should be taken when comparing data for the Peer Groups over time due to changes in the Peer Groups. In an analysis involving the peer groups, only one level of geography in Ontario should be used. For more information on the peer groups classification, consult Statistics Canada's publication Health Regions: Boundaries and Correspondence with Census Geography" (catalogue number 82-402-X)." 11 Before 2010, missing data on sex of the deceased were imputed based on death registration number. Starting with 2010 data year, missing data on sex of the deceased were imputed based on the cause of death information and a logistic regression. 12 The cause of death tabulated is the underlying cause of death. This is defined as (a) the disease or injury which initiated the train of events leading directly to death, or (b) the circumstances of the accident or violence which produced the fatal injury. The underlying cause is selected from the conditions listed on the medical certificate of cause of death. 13 Confidence intervals for age-standardized rates for selected causes of death data were produced using the Spiegelman method. Source: Spiegelman, M., Introduction to Demography" Revised Edition Cambridge14 Confidence intervals for crude rates for selected causes of death data were produced using the Fleiss method. Source: Fleiss, JL., Statistical Methods for Rates and Proportions" Second Edition New York15 The 95% confidence interval (CI) illustrates the degree of variability associated with a number or a rate. 16 Wide confidence intervals (CIs) indicate high variability, thus, these numbers or rates should be interpreted and compared with due caution. 17 The following standard symbols are used in this Statistics Canada table: (..) for figures not available for a specific reference period, (...) for figures not applicable and (x) for figures suppressed to meet the confidentiality requirements of the Statistics Act. 18 The figures shown in the tables have been subjected to a confidentiality procedure known as controlled rounding to prevent the possibility of associating statistical data with any identifiable individual. Under this method, all figures, including totals and margins, are rounded either up or down to a multiple of 5. Controlled rounding has the advantage over other types of rounding of producing additive tables as well as offering more protection. 19 Premature deaths are those of individuals who are younger than age 75.

Data on infant, neonatal, postneonatal, fetal, and perinatal mortality rates by selected characteristics of the mother. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://res1wwwd-o-tcdcd-o-tgov.vcapture.xyz/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time. SOURCE: NCHS, National Vital Statistics System, public-use Linked Birth/Infant Death Data Set, public-use Fetal Death File, and public-use Birth File. For more information on the National Vital Statistics System, see the corresponding Appendix entry at https://res1wwwd-o-tcdcd-o-tgov.vcapture.xyz/nchs/data/hus/hus19-appendix-508.pdf.

a The sampling units for the Mozambique survey were deaths identified from the 2007 census not households. The relevant number of households from which deaths were identified is the total number of households in the selected CSA segments, which is unavailable.b Fieldwork was conducted Jan–Aug 2010. Only deaths 1–36 months before the household interview are included in all subsequent analyses (15,857 household deaths; 768 deaths to WRA; 108 maternal deaths).c Not all deaths occurring in the latter part of 2010 are expected to be included due to the lag time between a death being identified by a key informant and a verbal autopsy being conducted.d This table includes all deaths identified. Subsequent tables exclude deaths with missing information on age (0 in Bangladesh, 4 in Mozambique, and 46 in Zambia) or incomplete verbal autopsy data (2 in Bangladesh).e Maternal death statistics include late maternal deaths (1 in Bangladesh, 46 in Mozambique, 0 in Zambia) and maternal deaths with an underlying cause of HIV/AIDS (0 in Bangladesh, 33 in Mozambique, 3 in Zambia).Comparison of sample characteristics (unweighted).

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.

This dataset describes drug poisoning deaths at the county level by selected demographic characteristics and includes age-adjusted death rates for drug poisoning from 1999 to 2015.

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 2011–2015 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.

Estimate does not meet standards of reliability or precision. Death rates are flagged as “Unreliable” in the chart when the rate is calculated with a numerator of 20 or less.

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. Estimates should be interpreted with caution.

Smoothed county age-adjusted death rates (deaths per 100,000 population) were obtained according to methods described elsewhere (3–5). Briefly, two-stage hierarchical models were used to generate empirical Bayes estimates of county age-adjusted death rates due to drug poisoning for each year during 1999–2015. These annual county-level estimates “borrow strength” across counties to generate stable estimates of death rates where data are sparse due to small population size (3,5). Estimates are unavailable for Broomfield County, Colo., and Denali County, Alaska, before 2003 (6,7). Additionally, Bedford City, Virginia was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. County boundaries are consistent with the vintage 2005-2007 bridged-race population file geographies (6).

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

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 from 1999 to 2015.

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 2011–2015 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.

Estimate does not meet standards of reliability or precision. Death rates are flagged as “Unreliable” in the chart when the rate is calculated with a numerator of 20 or less.

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. Estimates should be interpreted with caution.

Smoothed county age-adjusted death rates (deaths per 100,000 population) were obtained according to methods described elsewhere (3–5). Briefly, two-stage hierarchical models were used to generate empirical Bayes estimates of county age-adjusted death rates due to drug poisoning for each year during 1999–2015. These annual county-level estimates “borrow strength” across counties to generate stable estimates of death rates where data are sparse due to small population size (3,5). Estimates are unavailable for Broomfield County, Colo., and Denali County, Alaska, before 2003 (6,7). Additionally, Bedford City, Virginia was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. County boundaries are consistent with the vintage 2005-2007 bridged-race population file geographies (6).

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Note: MRR = Mortality Rate Ratio; CI = confidence interval; AIC = Akaike Information Criteria; RL = Relative Likelihood.Among ≥ 25 years of age. Race-specific age and sex-adjusted mortality rates weighted using the US 2000 standard population per 100,000 person-years from death certificates and mid-year population counts collated by the National Center for Health Statistics, 2004–2009. Area characteristics at the designated market area level from the American Community Survey, 2004–2009 for urbanicity (% living in a city with ≥ 50,000 people); % Black; education among Blacks (% with up to high school education); and poverty among Blacks (% households in poverty). All models adjusted for individual age, sex, year of death, and Census region.Nested negative binomial regression models estimating associations with Black all-cause mortality rates.