Table 5 of Actuarial Note 159 presents death rates experienced by the general population in the Social Security coverage area consistent with estimates in the 2017 Trustees Reports.

The following tables provide historical and projected probabilities of death by age, sex, and year for the period 2011 - 2090. Death probabilities for males.

Abstract

The Health Inequality Project uses big data to measure differences in life expectancy by income across areas and identify strategies to improve health outcomes for low-income Americans.

Section 7

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution. Both race-adjusted and unadjusted estimates are reported.

Source

Section 13

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution separately by year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 6

This dataset was created on 2020-01-10 18:53:00.508 by merging multiple datasets together. The source datasets for this version were:

Commuting Zone Life Expectancy Estimates by year: CZ-level by-year life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy: Commuting zone (CZ)-level life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy Trends: CZ-level estimates of trends in life expectancy for men and women, by income quartile

Commuting Zone Characteristics: CZ-level characteristics

Commuting Zone Life Expectancy for larger populations: CZ-level life expectancy estimates for men and women, by income ventile

Section 15

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by state of residence and year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 11

This table reports US mortality rates by gender, age, year and household income percentile. Household incomes are measured two years prior to the mortality rate for mortality rates at ages 40-63, and at age 61 for mortality rates at ages 64-76. The “lag” variable indicates the number of years between measurement of income and mortality.

Observations with 1 or 2 deaths have been masked: all mortality rates that reflect only 1 or 2 deaths have been recoded to reflect 3 deaths

Source

Section 3

This table reports coefficients and standard errors from regressions of life expectancy estimates for men and women at age 40 for each quartile of the national income distribution on calendar year by commuting zone of residence. Only the slope coefficient, representing the average increase or decrease in life expectancy per year, is reported. Trend estimates for both race-adjusted and unadjusted life expectancies are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 9

This table reports life expectancy estimates at age 40 for Males and Females for all countries. Source: World Health Organization, accessed at: http://apps.who.int/gho/athena/

Source

Section 10

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by county of residence. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for counties with populations larger than 25,000 only

Source

Section 2

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by commuting zone of residence and year. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 8

This table reports US population and death counts by age, year, and sex from various sources. Counts labelled “dm1” are derived from the Social Security Administration Data Master 1 file. Counts labelled “irs” are derived from tax data. Counts labelled “cdc” are derived from NCHS life tables.

Source

Section 12

This table reports numerous county characteristics, compiled from various sources. These characteristics are described in the county life expectancy table.

Two variables constructed by the Cen

The following tables provide historical and projected probabilities of death by single year of age, sex, and year for the period 1900 through 2010. Death Probabilities for Females.

Abstract It has been shown that under the social security factor rule current contribution rates are insufficient to cover social security benefits, since the actuarially fair rates are 30.69% and 35.27% for men and women, respectively. However, if the social security reform were approved as submitted, the fair rates would be reduced to 22.25% and 21.60%, respectively. Besides the minimum age, part of this reduction is due to the proposed rules allowing pension values lower than the minimum wage. These results served the objective of this work, which was to compare the actuarially fair social security rates for the General Social Welfare Policy (GSWP), based on the social security factor rules and the minimum age proposal present in Proposed Constitutional Amendment n. 287/2016. The demographic changes that have taken place in Brazil in recent years raise questions about the sustainability of the national social security system and approving social security reform has been a government priority. Therefore, there is an undisputed need for an actuarial study that calculates actuarially fair rates and compares the current scenario with the reform proposals. Multiple decrement actuarial models were used to calculate the fair rates considering a standard family (25-year-old worker, spouse, and two children), in which the man is three years older than the woman. The IBGE 2015 Extrapolated (mortality) and Álvaro Vindas (disability) tables were adopted as biometric assumptions, and a real wage growth rate of 2% p.a. and real interest rate of 3% p.a. were used.

Infant mortality statistics (including rates with confidence intervals) stratified by geography, Medicaid status, age group, racial identity, and Hispanic ethnicity.

We introduce a new framework for forecasting age-sex-country-cause-specific mortality rates that incorporates considerably more information, and thus has the potential to forecast much better, than any existing approach. Mortality forecasts are used in a wide variety of academic fields, and for global and national health policy making, medical and pharmaceutical research, and social security and retirement planning. As it turns out, the tools we developed in pursuit of this goal also have broader statistical implications, in addition to their use for forecasting mortality or other variables with similar statistical properties. First, our methods make it possible to include different explanatory variables in a time series regression for each cross-section, while still borrowing strength from one regression to improve the estimation of all. Second, we show that many existing Bayesian (hierarchi cal and spatial) models with explanatory variables use prior densities that incorrectly formalize prior knowledge. Many demographers and public health researchers have fortuitously avoided this problem so prevalent in other fields by using prior knowledge only as an ex post check on empirical results, but this approach excludes considerable information from their models. We show how to incorporate this demographic knowledge into a model in a statistically appropriate way. Finally, we develop a set of tools useful for developing models with Bayesian priors in the presence of partial prior ignorance. This approach also provides many of the attractive features claimed by the empirical Bayes approach, but fully within the standard Bayesian theory of inference. See also: Mortality Studies , Event Counts and Durations

Table 6 of Actuarial Note 159 presents the ratio of the adjusted death rates for claimants with cases pending an Administrative Law Judge's (ALJ) determination to the similarly adjusted death rates for the general population.

Final adjusted model for the 10-year competing risk of other cause mortality.

Range of 10-year other cause mortality risk predictions.

Table 7 of Actuarial Note 159 provides a comparison of death rates for claimants with cases pending an Administrative Law Judge's (ALJ) determination, to the death rates for Disability Insurance (DI) disabled worker beneficiaries who are in their first two years of entitlement.

Description of the functions in qlifetable for building quarterly life tables.

Examples of time and age coordinates of the event as a function of the length of the year utilised to calculate the exact age at event.

The Population Projections for the State of Tennessee, produced for the Tennessee State Data Center, contain projections for each county in Tennessee by race, age, and sex for each year from 2020 to 2070. Age is defined by five-year bands, starting with an “age 0-4” group and ending with an “age 85+” group. Race is delineated as one of four categories that combine race and ethnic definitions:White Non-HispanicBlack Non-HispanicAll HispanicOther non-Hispanic, including two or more races. Our forecast implements a cohort-component methodology. We specify the base year as 2000 and the launch year as 2021. Thus, we inform the forecast with trends from 2000 to 2020. Using vital statistics data from the Tennessee Department of Health, we project the population change resulting from natural components (births minus deaths). Differences between actual population values as reported by the Census and values predicted using births and deaths are used to establish net migration patterns. The forecast used these predicted net migration patterns; life tables from the Social Security Administration; recent average birth rates by county, race, and age of female; and forecast future U.S. populations.The 2020 base year population estimates for Tennessee Counties are from the 2020 Vintage Estimates of Population and Housing Units produced by the US Census Bureau.

A. SUMMARY This dataset includes unintentional drug overdose death rates by race/ethnicity by year. This dataset is created using data from the California Electronic Death Registration System (CA-EDRS) via the Vital Records Business Intelligence System (VRBIS). Substance-related deaths are identified by reviewing the cause of death. Deaths caused by opioids, methamphetamine, and cocaine are included. Homicides and suicides are excluded. Ethnic and racial groups with fewer than 10 events are not tallied separately for privacy reasons but are included in the “all races” total.

Unintentional drug overdose death rates are calculated by dividing the total number of overdose deaths by race/ethnicity by the total population size for that demographic group and year and then multiplying by 100,000. The total population size is based on estimates from the US Census Bureau County Population Characteristics for San Francisco, 2022 Vintage by age, sex, race, and Hispanic origin.

These data differ from the data shared in the Preliminary Unintentional Drug Overdose Death by Year dataset since this dataset uses finalized counts of overdose deaths associated with cocaine, methamphetamine, and opioids only.

B. HOW THE DATASET IS CREATED This dataset is created by copying data from the Annual Substance Use Trends in San Francisco report from the San Francisco Department of Public Health Center on Substance Use and Health.

C. UPDATE PROCESS This dataset will be updated annually, typically at the end of the year.

D. HOW TO USE THIS DATASET N/A

E. RELATED DATASETS Overdose-Related 911 Responses by Emergency Medical Services Preliminary Unintentional Drug Overdose Deaths San Francisco Department of Public Health Substance Use Services

F. CHANGE LOG

• 12/16/2024 - Updated with 2023 data. Asian/Pacific Islander race/ethnicity group was changed to Asian.
• 12/16/2024 - Past year totals by race/ethnicity were revised after obtaining accurate race/ethnicity for some decedents that were previously marked as “unknown” race/ethnicity.

Examples of exact ages at events as a function of the length of the year utilised to calculate them when births and events happen at exactly the same moment in two different time years.

Analysis of ‘NCHS - Potentially Excess Deaths from the Five Leading Causes of Death’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/3d1da62a-9f1c-47e8-b5a1-b473f57d7fdc on 28 January 2022.

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

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

1. 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.

2. 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.

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

The Population Projections for the State of Tennessee, produced for the Tennessee State Data Center, contain projections for each county in Tennessee by race, age, and sex for each year from 2018 to 2070. Age is defined by five-year bands, starting with an “age 0-4” group and ending with an “age 85+” group. Race is delineated as one of four categories that combine race and ethnic definitions:White Non-HispanicBlack Non-HispanicAll HispanicOther non-Hispanic, including two or more races. Our forecast implements a cohort-component methodology. We specify the base year as 2000 and the launch year as 2018. Thus, we inform the forecast with trends from 2000 to 2018. Using vital statistics data from the Tennessee Department of Health, we project the population change resulting from natural components (births minus deaths). Differences between actual population values as reported by the Census and values predicted using births and deaths are used to establish net migration patterns.The forecast used these predicted net migration patterns; life tables from the Social Security Administration; recent average birth rates by county, race, and age of female; and forecast future U.S. populations.The 2018 base year population estimates for Tennessee Counties are from the 2018 Vintage Estimates of Population and Housing Units produced by the US Census Bureau.

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

1. 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.

2. 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.

Abstract

The 1992 Namibia Demographic and Health Survey (NDHS) is a nationally representative survey conducted by the Ministry of Health and Social Services, assisted by the Central Statistical Office, with the aim of gathering reliable information on fertility, family planning, infant and child mortality, maternal mortality, maternal and child health and nutrition. Interviewers collected information on the reproductive histories of 5,421 women 15-49 years and on the health of 3,562 children under the age of five years.

The Namibia Demographic and Health Survey (NDHS) is a national sample survey of women of reproductive age designed to collect data on mortality and fertility, socioeconomic characteristics, marriage patterns, breastfeeding, use of contraception, immunisation of children, accessibility to health and family planning services, treatment of children during episodes of illness, and the nutritional status of women and children. More specifically, the objectives of NDHS are: - To collect data at the national level which will allow the calculation of demographic rates, particularly fertility rates and child mortality rates, and maternal mortality rates; To analyse the direct and indirect factors which determine levels and trends in fertility and childhood mortality, Indicators of fertility and mortality are important in planning for social and economic development; - To measure the level of contraceptive knowledge and practice by method, region, and urban/rural residence; - To collect reliable data on family health: immunisations, prevalence and treatment of diarrhoea and other diseases among children under five, antenatal visits, assistance at delivery and breastfeeding; - To measure the nutritional status of children under five and of their mothers using anthropometric measurements (principally height and weight).

Geographic coverage

The sample for the NDHS was designed to be nationally representative. The design involved a two- stage stratified sample which is self-weighting within each of the three health regions for which estimates of fertility and mortality were required--Northwest, Northeast, and the combined Central/South region. In order to have a sufficient number of cases for analysis, oversampling was necessary for the Northeast region, which has only 14.8 percent of the population. Therefore, the sample was not allocated proportionally across regions and is not completely self-weighting.

Analysis unit

• Household
• Women age 15-49

Universe

All women age 15-49 years who were either usual residents of the households in the sample or visitors present in the household on the night before the survey were eligible to be interviewed in the survey.

Kind of data

Sample survey data

Sampling procedure

The sample for the Namibia Demographic and Health Survey (NDHS) was designed to yield a nationally representative probability sample of 5000 completed interviews with women between the ages of 15 and 49, regardless of their marital status, selected from 175 area units throughout the country. The design involved a two-stage stratified sample, which is self-weighting in each of the three main reporting domains: the Northwest region, the Northeast region, and the combined Central and South region.

AREA SAMPLING FRAME

The Republic of Namibia undertook a population and housing census in 1991 (the census dates were from 21 to 30 October). For this purpose, the country was divided into 27 census districts. Each district was in turn demarcated into enumeration areas (EAs). A list of 2177 EAs, together with their measure of size, which is the EA population as recorded manually from the Enumerator's Record Books, was compiled and used to select the area units for the NDHS.

SAMPLE DESIGN

Within each of the three domains (Northwest, Northeast, and Central/South), the sampling frame for the NDHS was stratified by urban and rural, and then by census district. The sample was then selected in two stages: at the first stage, 175 primary sampling units (PSU) were selected from the frame with probability proportional to size, the size being the population in the PSU. In general, a PSU corresponds to an EA as defined for the 1991 population and housing census. For each selected PSU, the Enumerator's Record Books obtained from the census was used as the frame for selecting the households to be included in the survey.

SAMPLING PARAMETERS

The objective of the sample design was to obtain 5000 completed individual interviews with women between the ages of 15 and 49 regardless of their marital status. To allow for nonresponse and other losses, an appropriate number of households was selected so as to obtain 5500 eligible women. A proportional allocation of the 5500 women to the three domains would have yielded approximately 2400, 800, and 2300 to the Northwest, Northeast and Central/South regions, respectively. While the samples for the Northwest and Central/South regions would have been sufficiently large for providing reliable estimates, it was not the case for the Northeast region. For this reason, it was necessary to double the sampling rate for the Northeast region relative to the other two regions. Table B.1 shows the allocation of the sample to the three regions as well as the implied number of households and PSUs to be selected in each region.

Mode of data collection

Face-to-face

Research instrument

Two types of questionnaires were used in the NDHS: the Household Questionnaire and the Individual Questionnaire. The content of these questionnaires were based on the DHS model B questionnaire, which was designed for use in countries with low contraceptive prevalence. Additions and modifications to the model questionnaire were made in order to collect information particularly relevant to Namibia. Verbal autopsy and maternal mortality modules were added. The questionnaires were developed in English whereafter it was translated by experienced translators into six languages (Oshiwambo, Herere, Afrikaans, Lozi, Kwangali and Damara/Nama). The translation in the indigenous languages was necessary as it makes interviewing much less susceptible to interviewers interpretations. The prepared translation in the Damara/Nama language was not printed since the translated version would be required only in a small number of households, of which the majority speaks Afrikaans. All teams, however, carried a master copy of this questionnaire to serve as a reference should need arise.

a) The Household Questionnaire was used to enumerate all usual members of and visitors to the selected households and to obtain information on each individual's age, sex, relationship to the head of the household, and educational attainment. In addition, questions were asked about indicators of the socioeconomic position of the household, such as the source of water, sanitation facilities, and the availability of electricity and durable goods. Information recorded on the Household Questionnaire was used to identify respondents eligible for the individual interview.

b) The individual questionnaire was administered to women age 15-49 who spent the night preceding the household interview in the selected household. Information in the following areas was obtained during the individual interview: 1. Background characteristics of the respondent 2. Health services utilisation and availability 3. Reproductive behaviour and intentions 4. Knowledge and use of contraception 5. Breastfeeding, health, and vaccination status of children 6. Marriage 7. Fertility preferences 8. Husband's background and woman's work 9. Height and weight of children under five and their mothers 10. Causes of death in childhood 11. Maternal mortality

Cleaning operations

Data processing staff for the NDHS consisted of five data entry clerks of which one was used to control all incoming completed EAs from the field, and one supervisor (the head of data processing) from the Epidemiology Section. Periodic assistance was given by the Macro International staff. Four microcomputers were installed in the project office, Epidemiology Section, MOHSS, and were used to process the data utilizing ISSA software for processing. All data entry occurred in the project office in Windhoek.

Before questionnaires were passed for data entry, office editing was conducted. This entailed checking for intemal consistency of responses recorded in the questionnaire, that skip instructions were properly followed, that there were no omissions, and that all entries were legible. This secured completeness of the questionnaires and speeded up the work of data entry staff.

Data entry started in July and was completed in the second week of December 1992. As data entry continued, editing was carried out every second week by running the ISSA program to check for inconsistencies, and corrections were made (when possible) by referencing the original questionnaire. A standard set of data quality tables were run every second week. These tables provided data on the performance of each team and were taken into the field to discuss the results with the supervisors to improve data collection. The staff from the Epidemiology Section visited the teams in the field every second week.

The staff from the Epidemiology section with assistance from the Macro International staff completed the final editing in December 1992, and secondary editing was done by Macro International staff. Preparation and presentation of the Preliminary report was conducted in November and December 1992. The preliminary report was published in December 1992.

Response rate

A total of 5,006