The Veterans’ Grandchildren Mortality Plus sample consists of the records of more than 35,700 total grandchildrenboth male and female in nearly equal numbers,about 28,000 of which survived to age 45,who were born after the war to 16,791 children of 2,825 veterans,and contains an oversample of ex-POW veterans.The primary purpose of the project was to explore how grandfathers’ trauma affects the longevity and overweight of descendants. The dataset contains birth and death dates of grandchildren, census information on their parents' household, select socioeconomic and education information from the 1930 and 1940 census, and height and weight information from WWII draft cards for the grandsons. This multigenerational dataset can be used for researching the intergenerational transmission of longevity, overweight and socioeconomic status and the sex-specific pathways of this transmission and for testing mechanical linkage algorithms. Researchers built on a previously collected NIA-funded project containing census and death information of children of ex-POW and non-POW veterans (“Early Indicators, Intergenerational Processes, and Aging,” NIA grant P01AG10120, PI: Costa). The Veterans’ Grandchildren Mortality Plus data set contains the newly collected records of the veterans’ grandchildren, as well as the previously collected data of the veterans and their children.

This data includes several different tables presenting counts of births by race (total, Black, white) by Census Tract aggregated over a five-year period (2014-18). Data extracted from Pennsylvania's Vital Statistics Database with the following disclaimer: "These data were provided by the Pennsylvania Department of Health. The Department specifically disclaims responsibility for any analyses, interpretations, or conclusions."

Census tract of residence was determined using address-level data. Records were excluded from analysis if address was missing or unmatched to a census tract (≈1% records). Census tracts starting with 980x.xx, 981x.xx, and 982x.xx were also excluded due to a geocoding error. 2014 used a different methodology to assign census tract compared to years 2015-2018.

Counts < 5 are censored and displayed as "None". Census-tract-level counts may not equal county-level counts when summed due to censored data or missing data. For cause of death, underlying cause of death from the death certificate is used and is categorized based on ICD-10 codes, defined below.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

This dataset assembles all final birth data for females aged 15–19, 15–17, and 18–19 for the United States and each of the 50 states.

Data are based on 100% of birth certificates filed in all 50 states. All the teen birth rates in this dashboard reflect the latest revisions to Census populations (i.e., the intercensal populations) and thus provide a consistent series of accurate rates for the past 25 years. The denominators of the teen birth rates for 1991–1999 have been revised to incorporate the results of the 2000 Census. The denominators of the teen birth rates for 2001–2009 have revised to incorporate the results of the 2010 Census.

Ratio: Number of live births to resident females aged 15-17

Definition: The number of live births to resident females aged 15-17, per 1,000 females in the age group.

Data Sources:

(1) Birth Certificate Database, Office of Vital Statistics and Registry, New Jersey Department of Health;

(2) National Center for Health Statistics and U.S. Census Bureau. Vintage 2014 bridged-race postcensal population estimates;

(3) National Center for Health Statistics and U.S. Census Bureau. Revised 2000-2009 bridged-race intercensal population estimates.

This dataset was created from the CDC's National Vital Statistics Reports Volume 56, Number 6. The dataset includes all data available from this report by state level and includes births by race and Hispanic origin, births to unmarried women, rates of cesarean delivery, and twin and multiple birth rates. The data are final for 2005. No value is represented by a -1. "Descriptive tabulations of data reported on the birth certificates of the 4.1 million births that occurred in 2005 are presented. Denominators for population-based rates are postcensal estimates derived from the U.S. 2000 census".

The registration of births plays a significant role in managing the health system and offers valuable information for enhancing the effectiveness of the health system in monitoring advancements towards health-related development objectives. In Cameroon, approximately 40% of children below the age of 5 are not officially registered at the civil registry office. The objective of this study was to identify the factors that influence the low registration of births in Yaoundé II Subdivision, Cameroon. Between April 22 and May 21, 2022; a descriptive study took place at the Yaoundé II City Council as an integral part of a citizen sensitization campaign led by the mayor. The campaign aimed to raise awareness about the census and issuance of birth certificates. Data were collected through in-person questionnaires administered to parents involved in the campaign.

This polygon shapefile contains statistics on age groups, length of residence, and birth certificate holders in Peru taken from the Censos Nacionales 2007: XI de Poblacion y VI de Vivienda. These data are derived from the PERU Instituto Nacional de Estadistica e Informatica and were released in 2007. The data contain 1850 census boundary polygons and the lowest administrative boundary level represented is the Distrito.

The Population and Dwellings data from the 2021 Federal Census covers population in private households by age and gender.

For questions, please contact socialresearch@calgary.ca. Please visit Data about Calgary's population for more information.

Population in private households refers to all persons or group of persons who occupy the same dwelling and do not have a usual place of residence elsewhere in Canada or abroad. For census purposes, households are classified into three groups: private households, collective households, and households outside Canada. Unless otherwise specified, all data in census products are for private households only. Population in private households includes Canadian citizens and landed immigrants whose usual place of residence is Canada. Also includes refugee claimants, holders of work and study permits, Canadian citizens and landed immigrants at sea or in port aboard merchant or government vessels, and Canadian citizens away from Canada on military or diplomatic business. Excludes government representatives and military members of other countries and residents of other countries visiting Canada.

Age refers to the age of a person (or subject) of interest at last birthday (or relative to a specified, well‑defined reference date).

Gender refers to an individual's personal and social identity as a man, woman, or non‑binary person (a person who is not exclusively a man or a woman). A person's gender may differ from their sex at birth, and from what is indicated on their current identification or legal documents such as their birth certificate, passport, or driver's licence. A person's gender may change over time. Statistics Canada collected data about transgender and non-binary populations for the first time on the 2021 Census. The category "Men+" includes men (and/or boys), as well as some non-binary persons. The category "Women+" also includes women (and/or girls), as well as some non-binary persons.

This is a one-time load of Statistics Canada federal census data from 2021 applied to the Communities, Wards, and City geographical boundaries current as of 2022 (so they will likely not match the current year's boundaries). Update frequency is every 5 years. Data Steward: Business Unit Community Strategies (Demographics and Evaluation). This dataset is for general public and internal City business groups.

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.

We conducted an unmatched case-control study of 1,225,285 infants from a North Carolina Birth Cohort (2003-2015). Ozone and PM2.5 during critical exposure periods (gestational weeks 3-8) were estimated using residential address and a national spatiotemporal model at census tract centroid. Here we describe data sources for outcome (i.e., congenital heart defects) and exposure (i.e., ozone and PM2.5) data. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: The North Carolina Birth Cohort data are not publicly available as it contains personal identifiable information. Data may be requested through the NCDHHS, Division of Public Health with proper approvals. Air pollutant concentrations for ozone and PM2.5 from the national spatiotemporal model are publicly available from EPA's website. Format: Birth certificate data from the State Center for Health Statistics of the NC Department of Health and Human Services linked with data from the Birth Defects Monitoring Program (NC BDMP) to create a birth cohort of all infants born in NC between 2003-2015. The NC BDMP is an active surveillance system that follows NC births to obtain birth defect diagnoses up to 1 year after the date of birth as well as identify infant deaths during the first year of life and include relevant information from the death certificate. A national spatiotemporal model provided data on predicted ozone PM2.5 concentrations over critical prenatal and time periods. The prediction model used data from research and regulatory monitors as well as a large (>200) array of geographic covariates to create fine scale spatial and temporal predictions. The model has a cross-validated R2 of 0.89 for PM2.5. Concentrations were predicted for daily throughout the study period at the centroid of each 2010 census tract in NC. This dataset is associated with the following publication: Arogbokun, O., T. Luben, J. Stingone, L. Engel, C. Martin, and A. Olshan. Racial disparities in maternal exposure to ambient air pollution during pregnancy and prevalence of congenital heart defects. AMERICAN JOURNAL OF EPIDEMIOLOGY. Johns Hopkins Bloomberg School of Public Health, 194(3): 709-721, (2025).

The LIFE-M project combines of U.S. vital records (birth, marriage, death certificates) with census information into longitudinal and intergenerational micro-data. Using cutting-edge, machine learning techniques, the resulting dataset consists of four generations and millions of high-quality links for 20th century Americans. For more details about the project, check out the website (https://life-m.org/). Additionally, these data can be linked to the LIFE-M Ohio Causes of Death Project (https://doi.org/10.3886/E149841).

We conducted an unmatched case-control study of 5,992 infant mortality cases and 60,000 randomly selected controls from a North Carolina Birth Cohort (2003-2015). PM2.5 during critical exposure periods (trimesters, pregnancy, first month alive) were estimated using residential address and a national spatiotemporal model at census block centroid. Here we describe data sources for outcome (i.e., infant mortality) and exposure (i.e., PM2.5) data. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: The North Carolina Birth Cohort data are not publicly available as it contains personal identifiable information. Data may be requested through the NCDHHS, Division of Public Health with proper approvals. Air pollutant concentrations for PM2.5 from the national spatiotemporal model are available upon request and may require a processing fee. Air monitoring data questions can be directed to Ms. Amanda Gassett at the University of Washington. Format: Birth certificate data from the State Center for Health Statistics of the NC Department of Health and Human Services linked with data from the Birth Defects Monitoring Program (NC BDMP) to create a birth cohort of all infants born in NC between 2003-2015. The NC BDMP is an active surveillance system that follows NC births to obtain birth defect diagnoses up to 1 year after the date of birth as well as identify infant deaths during the first year of life and include relevant information from the death certificate. A national spatiotemporal model provided data on predicted PM2.5 concentrations over critical prenatal and postnatal time periods. The prediction model used data from research and regulatory monitors as well as a large (>200) array of geographic covariates to create fine scale spatial and temporal predictions. The model has a cross-validated R2 of 0.89 for PM2.5. Concentrations were predicted for every 2 weeks in the study period at the centroid of each 2010 census block in NC. This dataset is associated with the following publication: Jampel, S., J. Kaufman, D. Enquobahrie, A. Wilkie, A. Gassett, and T. Luben. Association between fine particulate matter (PM2.5) and infant mortality in a North Carolina Birth Cohort (2003-2015). Environmental Epidemiology. Wolters Kluwer, Alphen aan den Rijn, NETHERLANDS, 8(6): e350, (2024).

Introduction

This report presents projections of population from 2015 to 2025 by age and sex for Illinois, Chicago and Illinois counties produced for the Certificate of Need (CON) Program. As actual future population trends are unknown, the projected numbers should not be considered a precise prediction of the future population; rather, these projections, calculated under a specific set of assumptions, indicate the levels of population that would result if our assumptions about each population component (births, deaths and net migration) hold true. The assumptions used in this report, and the details presented below, generally assume a continuation of current trends.

Methodology These projections were produced using a demographic cohort-component projection model. In this model, each component of population change – birth, death and net migration – is projected separately for each five-year birth cohort and sex. The cohort – component method employs the following basic demographic balancing equation: P1 = P0 + B – D + NM Where: P1 = Population at the end of the period; P0 = Population at the beginning of the period; B = Resident births during the period; D = Resident deaths during the period; and NM = Net migration (Inmigration – Outmigration) during the period. The model roughly works as follows: for every five-year projection period, the base population, disaggregated by five-year age groups and sex, is “survived” to the next five-year period by applying the appropriate survival rates for each age and sex group; next, net migrants by age and sex are added to the survived population. The population under 5 years of age is generated by applying age specific birth rates to the survived females in childbearing age (15 to 49 years).

Base Population These projections began with the July 1, 2010 population estimates by age and sex produced by the U.S. Census Bureau. The most recent census population of April 1, 2010 was the base for July 1, 2010 population estimates.

Special Populations In 19 counties, the college dormitory population or adult inmates in correctional facilities accounted for 5 percent or more of the total population of the county; these counties were considered as special counties. There were six college dorm counties (Champaign, Coles, DeKalb, Jackson, McDonough and McLean) and 13 correctional facilities counties (Bond, Brown, Crawford, Fayette, Fulton, Jefferson, Johnson, Lawrence, Lee, Logan, Montgomery, Perry and Randolph) that qualified as special counties. When projecting the population, these special populations were first subtracted from the base populations for each special county; then they were added back to the projected population to produce the total population projections by age and sex. The base special population by age and sex from the 2010 population census was used for this purpose with the assumption that this population will remain the same throughout each projection period.

Mortality Future deaths were projected by applying age and sex specific survival rates to each age and sex specific base population. The assumptions on survival rates were developed on the basis of trends of mortality rates in the individual life tables constructed for each level of geography for 1989-1991, 1999-2001 and 2009-2011. The application of five-year survival rates provides a projection of the number of persons from the initial population expected to be alive in five years. Resident deaths data by age and sex from 1989 to 2011 were provided by the Illinois Center for Health Statistics (ICHS), Illinois Department of Public Health.

Fertility Total fertility rates (TFRs) were first computed for each county. For most counties, the projected 2015 TFRs were computed as the average of the 2000 and 2010 TFRs. 2010 or 2015 rates were retained for 2020 projections, depending on the birth trend of each county. The age-specific birth rates (ASBR) were next computed for each county by multiplying the 2010 ASBR by each projected TFR. Total births were then projected for each county by applying age-specific birth rates to the projected female population of reproductive ages (15 to 49 years). The total births were broken down by sex, using an assumed sex-ratio at birth. These births were survived five years applying assumed survival ratios to get the projected population for the age group 0-4. For the special counties, special populations by age and sex were taken out before computing age-specific birth rates. The resident birth data used to compute age-specific birth rates for 1989-1991, 1999-2001 and 2009-2011 came from ICHS. Births to females younger than 15 years of age were added to those of the 15-19 age group and births to women older than 49 years of age were added to the 45-49 age group.

Net Migration Migration is the major component of population change in Illinois, Chicago and Illinois counties. The state is experiencing a significant loss of population through internal (domestic migration within the U.S.) net migration. Unlike data on births and deaths, migration data based on administrative records are not available on a regular basis. Most data on migration are collected through surveys or indirectly from administrative records (IRS individual tax returns). For this report, net migration trends have been reviewed using data from different sources and methods (such as residual method) from the University of Wisconsin, Madison, Illinois Department of Public Health, individual exemptions data from the Internal Revenue Service, and survey data from the U.S. Census Bureau. On the basis of knowledge gained through this review and of levels of net migration from different sources, assumptions have been made that Illinois will have annual net migrants of -40, 000, -35,000 and -30,000 during 2010-2015, 2015-2020 and 2020-2025, respectively. These figures have been distributed among the counties, using age and sex distribution of net migrants during 1995-2000. The 2000 population census was the last decennial census, which included the question “Where did you live five years ago?” The age and sex distribution of the net migrants was derived, using answers to this question. The net migration for Chicago has been derived independently, using census survival method for 1990-2000 and 2000-2010 under the assumption that the annual net migration for Chicago will be -40,000, -30,000 and -25,000 for 2010-2015, 2015-2020 and 2020-2025, respectively. The age and sex distribution from the 2000-2010 net migration was used to distribute the net migrants for the projection periods.

Conclusion These projections were prepared for use by the Certificate of Need (CON) Program; they are produced using evidence-based techniques, reasonable assumptions and the best available input data. However, as assumptions of future demographic trends may contain errors, the resulting projections are unlikely to be free of errors. In general, projections of small areas are less reliable than those for larger areas, and the farther in the future projections are made, the less reliable they may become. When possible, these projections should be regularly reviewed and updated, using more recent birth, death and migration data.

VITAL SIGNS INDICATOR Life Expectancy (EQ6)

FULL MEASURE NAME Life Expectancy

LAST UPDATED April 2017

DESCRIPTION Life expectancy refers to the average number of years a newborn is expected to live if mortality patterns remain the same. The measure reflects the mortality rate across a population for a point in time.

DATA SOURCE State of California, Department of Health: Death Records (1990-2013) No link

California Department of Finance: Population Estimates Annual Intercensal Population Estimates (1990-2010) Table P-2: County Population by Age (2010-2013) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

U.S. Census Bureau: Decennial Census ZCTA Population (2000-2010) http://factfinder.census.gov

U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2013) http://factfinder.census.gov

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Life expectancy is commonly used as a measure of the health of a population. Life expectancy does not reflect how long any given individual is expected to live; rather, it is an artificial measure that captures an aspect of the mortality rates across a population that can be compared across time and populations. More information about the determinants of life expectancy that may lead to differences in life expectancy between neighborhoods can be found in the Bay Area Regional Health Inequities Initiative (BARHII) Health Inequities in the Bay Area report at http://www.barhii.org/wp-content/uploads/2015/09/barhii_hiba.pdf. Vital Signs measures life expectancy at birth (as opposed to cohort life expectancy). A statistical model was used to estimate life expectancy for Bay Area counties and ZIP Codes based on current life tables which require both age and mortality data. A life table is a table which shows, for each age, the survivorship of a people from a certain population.

Current life tables were created using death records and population estimates by age. The California Department of Public Health provided death records based on the California death certificate information. Records include age at death and residential ZIP Code. Single-year age population estimates at the regional- and county-level comes from the California Department of Finance population estimates and projections for ages 0-100+. Population estimates for ages 100 and over are aggregated to a single age interval. Using this data, death rates in a population within age groups for a given year are computed to form unabridged life tables (as opposed to abridged life tables). To calculate life expectancy, the probability of dying between the jth and (j+1)st birthday is assumed uniform after age 1. Special consideration is taken to account for infant mortality.

For the ZIP Code-level life expectancy calculation, it is assumed that postal ZIP Codes share the same boundaries as ZIP Code Census Tabulation Areas (ZCTAs). More information on the relationship between ZIP Codes and ZCTAs can be found at http://www.census.gov/geo/reference/zctas.html. ZIP Code-level data uses three years of mortality data to make robust estimates due to small sample size. Year 2013 ZIP Code life expectancy estimates reflects death records from 2011 through 2013. 2013 is the last year with available mortality data. Death records for ZIP Codes with zero population (like those associated with P.O. Boxes) were assigned to the nearest ZIP Code with population. ZIP Code population for 2000 estimates comes from the Decennial Census. ZIP Code population for 2013 estimates are from the American Community Survey (5-Year Average). ACS estimates are adjusted using Decennial Census data for more accurate population estimates. An adjustment factor was calculated using the ratio between the 2010 Decennial Census population estimates and the 2012 ACS 5-Year (with middle year 2010) population estimates. This adjustment factor is particularly important for ZCTAs with high homeless population (not living in group quarters) where the ACS may underestimate the ZCTA population and therefore underestimate the life expectancy. The ACS provides ZIP Code population by age in five-year age intervals. Single-year age population estimates were calculated by distributing population within an age interval to single-year ages using the county distribution. Counties were assigned to ZIP Codes based on majority land-area.

ZIP Codes in the Bay Area vary in population from over 10,000 residents to less than 20 residents. Traditional life expectancy estimation (like the one used for the regional- and county-level Vital Signs estimates) cannot be used because they are highly inaccurate for small populations and may result in over/underestimation of life expectancy. To avoid inaccurate estimates, ZIP Codes with populations of less than 5,000 were aggregated with neighboring ZIP Codes until the merged areas had a population of more than 5,000. ZIP Code 94103, representing Treasure Island, was dropped from the dataset due to its small population and having no bordering ZIP Codes. In this way, the original 305 Bay Area ZIP Codes were reduced to 217 ZIP Code areas for 2013 estimates. Next, a form of Bayesian random-effects analysis was used which established a prior distribution of the probability of death at each age using the regional distribution. This prior is used to shore up the life expectancy calculations where data were sparse.

Abstract

SAVVY is a demographic surveillance system built around vital events monitoring. It operates in a similar way to existing Health and Demographic Surveillance (HDSS) but is distributed across the country and sampled to generate estimates that are nationally representative. The system is based on a periodic census of the sample population that provides information on population age, sex, household characteristics and migration. During the year, community key informants report births and deaths and probable cause of death is determined through verbal autopsy.

Geographic coverage

SAVVY is part of the Sentinel Panel of Districts (SPD), a nationally-representative sample of 23 districts (plus an additional 4) in Mainland Tanzania for health monitoring, evaluation and research. Attention: the totality of distrcits has been reached only in March 2014!

Universe

Resident population (nationally representative), longitudinal.

Kind of data

Event/transaction data [evn]

Sampling procedure

A two-stage probability sampling approach was employed. District sampling aims to permit disaggregation of results by residence (urban/rural) as well as by zone. Within selected districts, enumeration areas were randomly selected from the national master sample frame, to yield a total sample of 167,000 households comprising about 800,000 individuals (~2% of Mainland Tanzania population).

SAVVY data collection is grouped into three categories: census enumeration, birth and death notifications, and VA interviews. During initial setup of the SAVVY arm, baseline censuses were conducted in all districts enumerating all households within the selected enumeration areas and captured a snapshot of the population. Each household was visited and family structure data were collected including details of the head of household, each member's name, gender, occupation, and education. Follow up questions were asked for female household members on number of children. During baseline census, retrospective death events of the past 12 months were also collected. A notification system of vital events was set up following the baseline censuses. Each birth or death event occurring in SAVVY enumeration areas triggered a notification message sent by a community key informant using a mobile phone. In addition to reporting of vital events, SAVVY also promotes vital registration through use of government registers provided by the Registration Insolvency and Trusteeship Agency (RITA).

SAVVY started with baseline enumeration censuses in March 2011 and continued in phases until it reached a full scale of all 23 districts in March 2014. Follow-up enumeration censuses will be conducted from 2015. Monitoring of vital events and conducting verbal autopsy (VA) interviews in enumeration areas began shortly after commencement of baseline censuses and is done prospectively. FBIS data collection began in January 2010 and is conducted regularly on monthly basis from all health facilities in SPD districts.

Research instrument

Census enumeration, birth and death notifications, and VA interviews.

Data collection instruments include two registers (births, deaths) and three questionnaires (household census, and verbal autopsy questionnaires for neonates, children and adults). The household census questionnaire includes household identification, location, household members, dates of birth, highest educational attainment, occupation and births in the past twelve months. The births and deaths registers record individual and household identity, location and date of event. The verbal autopsy questionnaires have an identification section; history of chronic illness; verbal account of the events leading to death; symptoms checklist; lifestyle (use of alcohol, drugs and smoking), and sequential use of health services prior to death.

Each death notification event is followed by a VA interview with the head of household or a person who took care of the deceased. Interviewers use the three standard World Health Organisation’s 2002 VA questionnaires: for newborns (0-28 days), children (29 days -14 years) and adults (15 years and above).9 These questionnaires are designed to collect background information of the deceased including their age, sex, marital status, and health data prior to death. Other information collected in verbal autopsy interviews include history of chronic illness, a narrative account of events leading to death, symptom checklist and duration, lifestyle (use of alcohol, drugs and smoking) and a sequence of use of health services prior to death. All information on verbal autopsy interviews (those captured retrospectively and prospectively during baseline census) are sent to trained physicians in order to establish a probable cause of death. Each death is coded independently using the World Health Organisation International Classification of Diseases and Health Related Conditions version 10 (ICD 10).

Response rate

Number of districts 23 districts Total Population 644,217 people Males (%) 48% Population rural (%) 70%

Series Name: Countries with birth registration data that are at least 90 percent complete (1 = YES; 0 = NO)Series Code: SG_REG_BRTH90NRelease Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 17.19.2: Proportion of countries that (a) have conducted at least one population and housing census in the last 10 years; and (b) have achieved 100 per cent birth registration and 80 per cent death registrationTarget 17.19: By 2030, build on existing initiatives to develop measurements of progress on sustainable development that complement gross domestic product, and support statistical capacity-building in developing countriesGoal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable DevelopmentFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Data quality:Hamilton, City (C)Total non-response (TNR) rate, short-form census questionnaire: 2.5%Total non-response (TNR) rate, long-form census questionnaire: 3.5%Notes:Gender refers to an individual's personal and social identity as a man, woman or non-binary person (a person who is not exclusively a man or a woman).Gender includes the following concepts: gender identity, which refers to the gender that a person feels internally and individually; gender expression, which refers to the way a person presents their gender, regardless of their gender identity, through body language, aesthetic choices or accessories (e.g., clothes, hairstyle and makeup), which may have traditionally been associated with a specific gender. A person's gender may differ from their sex at birth, and from what is indicated on their current identification or legal documents such as their birth certificate, passport or driver's licence. A person's gender may change over time. Some people may not identify with a specific gender. Given that the non-binary population is small, data aggregation to a two-category gender variable is sometimes necessary to protect the confidentiality of responses provided. In these cases, individuals in the category "non-binary persons" are distributed into the other two gender categories and are denoted by the "?" symbol. "Men?" includes men (and/or boys), as well as some non-binary persons. "Women?" includes women (and/or girls), as well as some non-binary persons.

This dataset compromises all country data included in the UN Inter-agency Group for Child Mortality Estimation (IGME) database (https://childmortality.org/data, downloaded June 2019).

It includes:

Reference area: name of the country

Indicator: child mortality indicator (neonatal mortality, infant mortality, under-5 mortality and mortality rate age 5 to 14)

Sex: sex of the child (male, female and total)

Series name: name of survey/census/VR [note: UN IGME estimates, i.e. not source data, are identified as "UN IGME estimate" in this field]

Series year: year of survey/census/VR series

Observation value: value of indicator from survey/census/VR

Observation status: indicates whether the data point is included or excluded for estimation [status of "normal" indicates UN IGME estimate, i.e. not source data]

Series Category: category of survey/census/VR, and can be:

DHS [Demographic and Health Survey]

MIS [Malaria Indicator Survey]

AIS [AIDS Indicator Survey]

Interim DHS

Special DHS

NDHS [National DHS]

WFS [World Fertility Survey]

MICS [Multiple Indicator Cluster Survey]

NMICS [National MICS]

RHS [Reproductive Health Survey]

PAP [Pan Arab Project for Child or Pan Arab Project for Family Health or Gulf Famly Health Survey]

LSMS [Living Standard Measurement Survey]

Panel [Dual record, multiround/follow-up survey and longitudinal/panel survey]

Census

VR [Vital Registration]

SVR [Sample Vital Registration]

Others [e.g. Life Tables]

Series type: the type of calculation method used to derive the indicator value (direct, indirect, household deaths, life table and vital records)

Standard error: sampling standard error of the observation value

Series method: data collection method, and can be:

Survey/census with Full Birth Histories

Survey/census with Summary Birth Histories

Survey/census with Household death

Vital Registration

Other

Lower and upper bound: the lower and upper bounds of 90% uncertainty interval of UN IGME estimates (for estimates only, i.e., not source data).

The dataset is used in the following paper:

Ezbakhe, F. and Pérez-Foguet, A. (2019) Levels and trends in child mortality: a compositional approach. Demographic Research (Under Review)

This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.

VITAL SIGNS INDICATOR Life Expectancy (EQ6)

FULL MEASURE NAME Life Expectancy

LAST UPDATED April 2017

DESCRIPTION Life expectancy refers to the average number of years a newborn is expected to live if mortality patterns remain the same. The measure reflects the mortality rate across a population for a point in time.

DATA SOURCE State of California, Department of Health: Death Records (1990-2013) No link

California Department of Finance: Population Estimates Annual Intercensal Population Estimates (1990-2010) Table P-2: County Population by Age (2010-2013) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Life expectancy is commonly used as a measure of the health of a population. Life expectancy does not reflect how long any given individual is expected to live; rather, it is an artificial measure that captures an aspect of the mortality rates across a population. Vital Signs measures life expectancy at birth (as opposed to cohort life expectancy). A statistical model was used to estimate life expectancy for Bay Area counties and Zip codes based on current life tables which require both age and mortality data. A life table is a table which shows, for each age, the survivorship of a people from a certain population.

Current life tables were created using death records and population estimates by age. The California Department of Public Health provided death records based on the California death certificate information. Records include age at death and residential Zip code. Single-year age population estimates at the regional- and county-level comes from the California Department of Finance population estimates and projections for ages 0-100+. Population estimates for ages 100 and over are aggregated to a single age interval. Using this data, death rates in a population within age groups for a given year are computed to form unabridged life tables (as opposed to abridged life tables). To calculate life expectancy, the probability of dying between the jth and (j+1)st birthday is assumed uniform after age 1. Special consideration is taken to account for infant mortality. For the Zip code-level life expectancy calculation, it is assumed that postal Zip codes share the same boundaries as Zip Code Census Tabulation Areas (ZCTAs). More information on the relationship between Zip codes and ZCTAs can be found at https://www.census.gov/geo/reference/zctas.html. Zip code-level data uses three years of mortality data to make robust estimates due to small sample size. Year 2013 Zip code life expectancy estimates reflects death records from 2011 through 2013. 2013 is the last year with available mortality data. Death records for Zip codes with zero population (like those associated with P.O. Boxes) were assigned to the nearest Zip code with population. Zip code population for 2000 estimates comes from the Decennial Census. Zip code population for 2013 estimates are from the American Community Survey (5-Year Average). The ACS provides Zip code population by age in five-year age intervals. Single-year age population estimates were calculated by distributing population within an age interval to single-year ages using the county distribution. Counties were assigned to Zip codes based on majority land-area.

Zip codes in the Bay Area vary in population from over 10,000 residents to less than 20 residents. Traditional life expectancy estimation (like the one used for the regional- and county-level Vital Signs estimates) cannot be used because they are highly inaccurate for small populations and may result in over/underestimation of life expectancy. To avoid inaccurate estimates, Zip codes with populations of less than 5,000 were aggregated with neighboring Zip codes until the merged areas had a population of more than 5,000. In this way, the original 305 Bay Area Zip codes were reduced to 218 Zip code areas for 2013 estimates. Next, a form of Bayesian random-effects analysis was used which established a prior distribution of the probability of death at each age using the regional distribution. This prior is used to shore up the life expectancy calculations where data were sparse.

This dataset provides a range of demographic and socio-economic variables for Registration Sub-Districts (RSDs) in England and Wales, 1851-1911. The measures have mainly been derived from the computerised individual level census enumerators' books (and household schedules for 1911) for England and Wales enhanced under the I-CeM project. I-CeM does not currently include data for 1871, although the project has been able to access a version of the data for that year it does not contain information necessary to calculate many of the variables presented here. Users should therefore beware that 1871 does not contain data for many of the variables. Additional data, for some indicators, has been derived from the tables summarising numbers of births and deaths by year and areas, which were published by the Registrar General in his quarterly, annual and decennial reports of births, deaths and marriages. More information on the data, including overviews of the geographical patterns and changes over time, can be found on the Populations Past – Atlas of Victorian and Edwardian Population website, which provides an interactive mapping facility for these data. The second half of the nineteenth century was a period of major change in the dynamics of the British population. This was a time of transformation from a relatively 'high pressure' demographic regime characterised by medium to high birth and death rates towards a 'low pressure' regime of low birth and death rates, a transformation known as the 'demographic transition'. This transition was not uniform across England and Wales: certain places and social groups appear to have led the declines while others lagged behind. Exploring these geographical patterns can provide insights into the process of change and the influence of economic and geographical factors. This project aimed to utilise the individual-level data of the Integrated Census Microdata (I-CeM) project to calculate age-specific fertility rates both for a range of fine geographical units covering England and Wales and for occupational groups and then to investigate the relationships between these rates and other socioeconomic variables. This was to provide, for the first time, widespread information of the age patterns of fertility which render insight into ‘starting’, ‘spacing’ or ‘stopping’ fertility regulating behaviour. A time series of such measures across geographical and social space is also vital when trying to identify how new forms of behaviour spread through the population. This database contains a variety of measures of fertility, marriage and infant and child mortality, and also a range of socio-economic indicators (related to households, age structure, and social class) for the 2000+ Registration Sub Districts (RSDs) in both England and Wales, for each census year between 1851 and 1871. Most of these data can be mapped using our interactive website www.populationspast.org. This data collection was derived from near complete count individual level census data, from which we have created demographic and socio-economic indicators at a Registration Sub-District level, using a variety of demographic and statistical techniques. For a few variables, birth and death summary data (at Sub-Registration District level) were also used.