Created with a 500 meter side hexagon grid, we undertook a regression analysis creating a correlation matrix utilising a number of demographic indicators from the Local Insight OCSI platform. This dataset is showing the distribution of the metrics that were found to have the strongest relationships, with the base comparison metric of Indices of Deprivation 2019 income deprivation affecting older people. This dataset contains the following metrics: IoD 2019 Income Deprivation Affecting Older People (IDAOPI) Score (rate) - The Indices of Deprivation (IoD) 2019 Income Deprivation Affecting Older People Index captures deprivation affecting older people defined as those adults aged 60 or over receiving Income Support or income-based Jobseekers Allowance or income-based Employment and Support Allowance or Pension Credit (Guarantee) or Universal Credit (in the 'Searching for work', 'No work requirements', 'Planning for work', 'Working with requirements' and 'Preparing for work' conditionality groups) or families not in receipt of these benefits but in receipt of Working Tax Credit or Child Tax Credit with an equivalised income (excluding housing benefit) below 60 per cent of the national median before housing costs. Asylum seekers aged 60 and over are not included in the Income Deprivation Affecting Older People Index. Rate calculated as = (ID 2019 Income Deprivation Affecting Older People Index (IDAOPI) numerator)/(ID 2019 Older population aged 60 and over: mid 2015 (excluding prisoners))*100.Pension Credit claimants who are single - Shows the proportion of people receiving Pension Credit who are single (as a % of all of pensionable age). Pension Credit provides financial help for people aged 60 or over whose income is below a certain level set by the law. Rate calculated as = (Pension Credit claimants, single)/(Population aged 65+)*100.Pension Credit claimants, Guarantee Element - Shows the proportion of people of retirement age receiving Pension Credit Guarantee Element. Pension Credit provides financial help for people aged 60 or over whose income is below a certain level set by the law. The Guarantee Element is payable to tops up incomes that are below a minimum threshold. Rate calculated as = (Pension Credit claimants, Guarantee Element)/(Population aged 65+)*100.Working-age DWP benefit claimants aged 50 and over - Shows the proportion of people aged 50-64 receiving DWP benefits. DWP Benefits are benefits payable to all people who need additional financial support due to low income, worklessness, poor health, caring responsibilities, bereavement or disability. The following benefits are included: Bereavement Benefit, Carers Allowance, Disability Living Allowance, Incapacity Benefit/Severe Disablement Allowance, Income Support, Jobseekers Allowance, Pension Credit and Widows Benefit. Figure are derived from 100% sample of administrative records from the Work and Pensions Longitudinal Study (WPLS), with all clients receiving more than one benefit counted only by their primary reason for interacting with the benefits system (to avoid double counting). Universal Credit (UC) and Personal Independence Payment (PIP) started to replace the benefits included in this measure from April 2013 when new Jobseeker's Allowance and Disability Living Allowance claimants started to move onto the new benefits in selected geographical areas. This rollout intensified from March 2016 onwards to capture all of the other Working age DWP Benefits. As UC and PIP are not included in this measure it no longer represent a complete count of working age people receiving DWP Benefits. As a result the measure was discontinued in November 2016. Rate calculated as = (Working-age DWP benefit claimants aged 50 and over) /(Population aged 50+)*100.People with numeracy skills at entry level 1 or below (2011) (%) - Shows the proportion of people with numeracy skills at entry level 1 or below. The Skills for Life Survey 2011 was commissioned by the Department for Business Innovation and Skills. The survey aimed to produce a national profile of adult literacy, numeracy and Information and Communication Technology (ICT) skills, and to assess the impact different skills had on people's lives. Each figure is a mean estimate of the number of adults with each skill level (or who do / do not speak English as a first language). The survey was conducted at regional level as a part interview part questionnaire. The interview comprised a background questionnaire followed by a pre-assigned random combination of two of the three skills assessments: literacy, numeracy and ICT. The background questionnaire was designed to collect a broad set of relevant demographic and behavioural data. This demographic data was used to model the information down to neighbourhood level using the neighbourhood characteristics of each MSOA to create a likely average skill level of the population within each MSOA. survey. Respondents who completed the questions allocated to the literacy and numeracy assessments were assigned to one of the five lowest levels of the National Qualifications Framework: Entry Level 1 or below; Entry Level 2; Entry Level 3; Level 1; or Level 2 or above. Each figure is a mean estimate of the number of adults with each skill level (or who do / do not speak English as a first language).IoD 2015 Housing affordability indicator -Social Grade (N-SEC): 8. Never worked and long-term unemployed - Shows the proportion of people in employment (aged 16-74) in the Approximated Social grade (N-SEC) category: 8. Never worked and long-term unemployed. An individual's approximated social grade is determined by their response to the occupation questions in the 2011 Census. Rate calculated as = (Never worked and long-term unemployed (census KS611))/(All usual residents aged 16 to 74 (census KS611))*100.Female healthy life expectancy at birth - Female healthy life expectancy at birth. Healthy life expectancy (HLE) is the average number of years that an individual might expect to live in "good" health in their lifetime. The 'good' health state used for estimation of HLE was based on self-reports of general health at the 2011 Census; specifically those reporting their general health as 'very good' or 'good' were defined as in 'Good' health in this context. The HLE estimates are a snapshot of the health status of the population, based on self-reported health status and mortality rates for each area in that period. They are not a guide to how long someone will actually expect to live in "good" health, both because mortality rates and levels of health status are likely to change in the future, and because many of those born in an area will live elsewhere for at least part of their lives.Sport England Market Segmentation: Pub League Team Mates - Shows the proportion of people living in the area that are classified as Pub League Team Mates in the Sports Market Segmentation tool developed by Sport England. The Pub League Team Mates classification group are predominantly aged 36-45 are a mix of married/single child and childless and likely to be engaged in a vocational job. For more details about the characteristics of this group see http://segments.sportengland.org/pdf/penPortrait-9.pdf. Sports Market Segmentation is a web-based tool developed by Sport England to help all those delivering sport to better understand their local markets and target them more effectively.IoD 2010 Income Domain, score - The Indices of Deprivation (IoD) 2010 Income Deprivation Domain measures the proportion of the population in an area experiencing deprivation relating to low income. The definition of low income used includes both those people that are out-of-work, and those that are in work but who have low earnings (and who satisfy the respective means tests). The domain forms part of the overall Index of Multiple Deprivation (IMD) 2010. The IMD 2010 is the most comprehensive measure of multiple deprivation available. Drawn primarily from 2008 data and presented at small area level, the IMD 2010 is a unique and invaluable tool for measuring deprivation nationally and across local areas. The concept of multiple deprivation upon which the IMD 2010 is based is that separate types of deprivation exist, which are separately recognised and measurable.People over the age of 65 with bad or very bad health - Shows the proportion of people over the age of 65 that reported to have bad or very bad health. Figures are self-reported and taken from the 2011 Census. Rate calculated as = (Bad or very bad health (census LC3206)/(Population aged 65+)*100

Families of tax filers; Census families by total income, family type and number of children (final T1 Family File; T1FF).

Life expectancy at birth is the average number of years a group of infants would live if they were to experience, throughout their lives, the age-specific death rates prevailing during a specified period. Life expectancy at birth estimates were calculated using abridged period life tables according to the Chiang method. Estimates are based on provisional data and subject to change. Unstable estimates are excluded and are defined as having confidence intervals greater than 6 years, i.e., +/-3.0 years. The average life expectancy of a population is one of the most basic and important measures of the health of a community. Life expectancy is heavily driven by the social determinants of health, including social, economic, and environmental conditions, with Black and low-income individuals experiencing much lower life expectancies compared to White and more affluent individuals.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

Association of sociodemographic factors with childlessness (having no children) and having 3 or more children among men born 1971–1975.

Kuwait KW: Prevalence of Overweight: Weight for Height: % of Children Under 5 data was reported at 6.000 % in 2015. This records a decrease from the previous number of 8.700 % for 2014. Kuwait KW: Prevalence of Overweight: Weight for Height: % of Children Under 5 data is updated yearly, averaging 8.400 % from Dec 1996 (Median) to 2015, with 16 observations. The data reached an all-time high of 11.000 % in 2001 and a record low of 6.000 % in 2015. Kuwait KW: Prevalence of Overweight: Weight for Height: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kuwait – Table KW.World Bank: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's new child growth standards released in 2006.; ; UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.; Linear mixed-effect model estimates; Estimates of overweight children are also from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues

The relative deprivation index can reflect the income inequality faced by different individuals, which is helpful to understand the relationship between income inequality and the variability of fertility intentions. But previous studies have almost focused on the macro indicators of income inequality, ignoring individual heterogeneity in income inequality. In this study, we explore the causal relationship and potential mechanisms between income inequality and fertility intentions from the perspective of relative deprivation in income. The findings are as follows: (1) An increase in income inequality boosts individuals’ fertility intentions, and the results are still robust after using the instrumental variables two-stage least squares (2SLS) model to deal with endogeneity. (2) Mechanism analysis reveals that income inequality improves individuals’ fertility intentions through the channels of “Build hopes on children”, “Allocate more time to families” and “Put less value on children’s education”. (3) Heterogeneity analysis indicates that income inequality has a more pronounced positive impact on fertility intentions of individuals with poor education, low household assets and without pension insurance. (4) Further analysis reveals that an increase in income inequality at macro level also promote individuals’ fertility intentions. Our findings hold significant policy implications for promoting a rebound in fertility rates. When developing policies to adjust income distribution, it is necessary to consider the response of individuals’ fertility decisions to income inequality. Policymakers should ensure that efforts to improve income distribution do not inadvertently reduce the willingness of individuals to have more children.

Mali ML: Prevalence of Overweight: Weight for Height: % of Children Under 5 data was reported at 1.900 % in 2015. This records an increase from the previous number of 1.000 % for 2010. Mali ML: Prevalence of Overweight: Weight for Height: % of Children Under 5 data is updated yearly, averaging 2.100 % from Dec 1987 (Median) to 2015, with 6 observations. The data reached an all-time high of 4.700 % in 2006 and a record low of 0.500 % in 1987. Mali ML: Prevalence of Overweight: Weight for Height: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mali – Table ML.World Bank: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's new child growth standards released in 2006.; ; UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.; Linear mixed-effect model estimates; Estimates of overweight children are also from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues

Cameroon CM: Prevalence of Overweight: Weight for Height: Male: % of Children Under 5 data was reported at 12.200 % in 2018. This records an increase from the previous number of 7.100 % for 2014. Cameroon CM: Prevalence of Overweight: Weight for Height: Male: % of Children Under 5 data is updated yearly, averaging 8.200 % from Dec 1991 (Median) to 2018, with 7 observations. The data reached an all-time high of 12.200 % in 2018 and a record low of 6.000 % in 1991. Cameroon CM: Prevalence of Overweight: Weight for Height: Male: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Cameroon – Table CM.World Bank.WDI: Social: Health Statistics. Prevalence of overweight, male, is the percentage of boys under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.;;Estimates of overweight children are from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues.

Macedonia MK: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data was reported at 11.400 % in 2011. This records a decrease from the previous number of 15.800 % for 2005. Macedonia MK: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data is updated yearly, averaging 10.100 % from Dec 1999 (Median) to 2011, with 4 observations. The data reached an all-time high of 15.800 % in 2005 and a record low of 7.600 % in 2004. Macedonia MK: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Macedonia – Table MK.World Bank: Health Statistics. Prevalence of overweight, female, is the percentage of girls under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's new child growth standards released in 2006.; ; World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries.; Linear mixed-effect model estimates; Estimates of overweight children are also from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues

People experiencing homelessness have historically had high mortality rates compared to housed individuals in Canada, a trend believed to have become exacerbated during the COVID-19 pandemic. In this matched cohort study conducted in Toronto, Canada, we investigated all-cause mortality over a one-year period by following a random sample of people experiencing homelessness (n = 640) alongside matched housed (n = 6,400) and low-income housed (n = 6,400) individuals. Matching criteria included age, sex-assigned-at-birth, and Charlson comorbidity index. Data were sourced from the Ku-gaa-gii pimitizi-win cohort study and administrative databases from ICES. People experiencing homelessness had 2.7 deaths/100 person-years, compared to 0.7/100 person-years in both matched unexposed groups, representing an all-cause mortality unadjusted hazard ratio (uHR) of 3.7 (95% CI, 2.1–6.5). Younger homeless individuals had much higher uHRs than older groups (ages 25–44 years uHR 16.8 [95% CI 4.0–70.2]; ages 45–64 uHR 6.8 [95% CI 3.0–15.1]; ages 65+ uHR 0.35 [95% CI 0.1–2.6]). Homeless participants who died were, on average, 17 years younger than unexposed individuals. After adjusting for number of comorbidities and presence of mental health or substance use disorder, people experiencing homelessness still had more than twice the hazard of death (aHR 2.2 [95% CI 1.2–4.0]). Homelessness is an important risk factor for mortality; interventions to address this health disparity, such as increased focus on homelessness prevention, are urgently needed.

This table contains 2394 series, with data for years 1991 - 1991 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Population group (19 items: Entire cohort; Income adequacy quintile 1 (lowest);Income adequacy quintile 2;Income adequacy quintile 3 ...), Age (14 items: At 25 years; At 30 years; At 40 years; At 35 years ...), Sex (3 items: Both sexes; Females; Males ...), Characteristics (3 items: Life expectancy; High 95% confidence interval; life expectancy; Low 95% confidence interval; life expectancy ...).

Low-income cut-offs, after tax (LICO-AT) - The Low-income cut-offs, after tax refers to an income threshold, defined using 1992 expenditure data, below which economic families or persons not in economic families would likely have devoted a larger share of their after-tax income than average to the necessities of food, shelter and clothing. More specifically, the thresholds represented income levels at which these families or persons were expected to spend 20 percentage points or more of their after-tax income than average on food, shelter and clothing. These thresholds have been adjusted to current dollars using the all-items Consumer Price Index (CPI).The LICO-AT has 35 cut-offs varying by seven family sizes and five different sizes of area of residence to account for economies of scale and potential differences in cost of living in communities of different sizes. These thresholds are presented in Table 4.3 Low-income cut-offs, after tax (LICO-AT - 1992 base) for economic families and persons not in economic families, 2015, Dictionary, Census of Population, 2016.When the after-tax income of an economic family member or a person not in an economic family falls below the threshold applicable to the person, the person is considered to be in low income according to LICO-AT. Since the LICO-AT threshold and family income are unique within each economic family, low-income status based on LICO-AT can also be reported for economic families.Return to footnote1referrerFootnote 2For more information on generation status variables, including information on their classifications, the questions from which they are derived, data quality and their comparability with other sources of data, please refer to the Place of Birth, Generation Status, Citizenship and Immigration Reference Guide, Census of Population, 2016.Return to footnote2referrerFootnote 3Low-income status - The income situation of the statistical unit in relation to a specific low-income line in a reference year. Statistical units with income that is below the low-income line are considered to be in low income.For the 2016 Census, the reference period is the calendar year 2015 for all income variables.Return to footnote3referrerFootnote 4The low-income concepts are not applied in the territories and in certain areas based on census subdivision type (such as Indian reserves). The existence of substantial in-kind transfers (such as subsidized housing and First Nations band housing) and sizeable barter economies or consumption from own production (such as product from hunting, farming or fishing) could make the interpretation of low-income statistics more difficult in these situations.Return to footnote4referrerFootnote 5Prevalence of low income - The proportion or percentage of units whose income falls below a specified low-income line.Return to footnote5referrerFootnote 6For more information on the Visible minority variable, including information on its classification, the questions from which it is derived, data quality and its comparability with other sources of data, please refer to the Visible Minority and Population Group Reference Guide, Census of Population, 2016.Return to footnote6referrerFootnote 7The Employment Equity Act defines visible minorities as 'persons, other than Aboriginal peoples, who are non-Caucasian in race or non-white in colour.'Return to footnote7referrerFootnote 8For example, 'East Indian,' 'Pakistani,' 'Sri Lankan,' etc.Return to footnote8referrerFootnote 9For example, 'Vietnamese,' 'Cambodian,' 'Laotian,' 'Thai,' etc.Return to footnote9referrerFootnote 10For example, 'Afghan,' 'Iranian,' etc.Return to footnote10referrerFootnote 11The abbreviation 'n.i.e.' means 'not included elsewhere.' Includes persons with a write-in response such as 'Guyanese,' 'West Indian,' 'Tibetan,' 'Polynesian,' 'Pacific Islander,' etc.Return to footnote11referrerFootnote 12Includes persons who gave more than one visible minority group by checking two or more mark-in responses, e.g., 'Black' and 'South Asian.'Return to footnote12referrerFootnote 13Includes persons who reported 'Yes' to the Aboriginal group question (Question 18), as well as persons who were not considered to be members of a visible minority group.

Maternal mortality ratio is defined as the number of female deaths due to obstetric causes (ICD-10 codes: A34, O00-O95, O98-O99) while pregnant or within 42 days of termination of pregnancy. The maternal mortality ratio indicates the likelihood of a pregnant person dying of obstetric causes. It is calculated by dividing the number of deaths among birthing people attributable to obstetric causes in a calendar year by the number of live births registered for the same period and is presented as a rate per 100,000 live births. The number of live births used in the denominator approximates the population of pregnant and birthing people who are at risk. Data are not presented for geographies with number of maternal deaths less than 11.Compared to other high-income countries, women in the US are more likely to die from childbirth or problems related to pregnancy. In addition, there are persistent disparities by race and ethnicity, with Black pregnant persons experiencing a much higher rate of maternal mortality compared to White pregnant persons. Improving the quality of medical care for pregnant individuals before, during, and after pregnancy can help reduce maternal deaths.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

Kuwait KW: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data was reported at 8.600 % in 2014. This records an increase from the previous number of 7.600 % for 2013. Kuwait KW: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data is updated yearly, averaging 8.000 % from Dec 2001 (Median) to 2014, with 14 observations. The data reached an all-time high of 11.000 % in 2001 and a record low of 6.000 % in 2003. Kuwait KW: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kuwait – Table KW.World Bank: Health Statistics. Prevalence of overweight, female, is the percentage of girls under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's new child growth standards released in 2006.; ; World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries.; Linear mixed-effect model estimates; Estimates of overweight children are also from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues

This longitudinal survey was designed to add significantly to the amount of detailed information available on the economic situation of households and persons in the United States. These data examine the level of economic well-being of the population and also provide information on how economic situations relate to the demographic and social characteristics of individuals. There are three basic elements contained in the survey. The first is a control card that records basic social and demographic characteristics for each person in a household, as well as changes in such characteristics over the course of the interviewing period. The second element is the core portion of the questionnaire, with questions repeated at each interview on labor force activity, types and amounts of income, participation in various cash and noncash benefit programs, attendance in postsecondary schools, private health insurance coverage, public or subsidized rental housing, low-income energy assistance, and school breakfast and lunch participation. The third element consists of topical modules which are series of supplemental questions asked during selected household visits. No topical modules were created for the first or second waves. The Wave III Rectangular Core and Topical Module File offers both the core data and additional data on (1) education and work history and (2) health and disability. In the areas of education and work history, data are supplied on the highest level of schooling attained, courses or programs studied in high school and after high school, whether the respondent received job training, and if so, for how long and under what program (e.g., CETA or WIN). Other items pertain to the respondent's general job history and include a description of selected previous jobs, duration of jobs, and reasons for periods spent not working. Health and disability variables present information on the general condition of the respondent's health, functional limitations, work disability, and the need for personal assistance. Data are also provided on hospital stays or periods of illness, health facilities used, and whether health insurance plans (private or Medicare) were available. Respondents whose children had physical, mental, or emotional problems were questioned about the causes of the problems and whether the children attended regular schools. The Wave IV Rectangular Core and Topical Module file contains both the core data and sets of questions exploring the subjects of (1) assets and liabilities, (2) retirement and pension coverage, and (3) housing costs, conditions, and energy usage. Some of the major assets for which data are provided are savings accounts, stocks, mutual funds, bonds, Keogh and IRA accounts, home equity, life insurance, rental property, and motor vehicles. Data on unsecured liabilities such as loans, credit cards, and medical bills also are included. Retirement and pension information covers such items as when respondents expect to stop working, whether they will receive retirement benefits, whether their employers have retirement plans, if so whether they are eligible, and how much they expect to receive per year from these plans. In the category of housing costs, conditions, and energy usage, variables pertain to mortgage payments, real estate taxes, fire insurance, principal owed, when the mortgage was obtained, interest rates, rent, type of fuel used, heating facilities, appliances, and vehicles. The Wave V topical modules explore the subject areas of (1) child care, (2) welfare history and child support, (3) reasons for not working/reservation wage, and (4) support for nonhousehold members/work-related expenses. Data on child care include items on child care arrangements such as who provides the care, the number of hours of care per week, where the care is provided, and the cost. Questions in the areas of welfare history and child support focus on receipt of aid from specific welfare programs and child support agreements and their fulfillment. The reasons for not working/reservation wage module presents data on why persons are not in the labor force and the conditions under which they might join the labor force. Additional variables cover job search activities, pay rate required, and reason for refusal of a job offer. The set of questions dealing with nonhousehold members/work-related expenses contains items on regular support payments for nonhousehold members and expenses associated with a job such as union dues, licenses, permits, special tools, uniforms, or travel expenses. Information is supplied in the Wave VII Topical Module file on (1) assets and liabilities, (2) pension plan coverage, and (3) real estate property and vehicles. Variables pertaining to assets and liabilities are similar to those contained in the topical module for Wave IV. Pension plan coverage items include whether the respondent will receive retirement benefits, whether the employer offers a retirement plan and if the respondent is included in the plan, and contributions by the employer and the employee to the plan. Real estate property and vehicles data include information on mortgages held, amount of principal still owed and current interest rate on mortgages, rental and vacation properties owned, and various items pertaining to vehicles belonging to the household. Wave VIII Topical Module includes questions on support for nonhousehold members, work-related expenses, marital history, migration history, fertility history, and household relationships. Support for nonhousehold members includes data for children and adults not in the household. Weekly and annual work-related expenses are documented. Widowhood, divorce, separation, and marriage dates are part of the marital history. Birth expectations as well as dates of birth for all the householder's children, in the household or elsewhere, are recorded in the fertility history. Migration history data supplies information on birth history of the householder's parents, number of times moved, and moving expenses. Household relationships lists the exact relationships among persons living in the household. Part 49, Wave IX Rectangular Core and Topical Module Research File, includes data on annual income, retirement accounts, taxes, school enrollment, and financing. This topical module research file has not been edited nor imputed, but has been topcoded or bottomcoded and recoded if necessary by the Census Bureau to avoid disclosure of individual respondents' identities. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR08317.v2. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

Abstract We aimed to analyze the effects of family income trajectories on the increase in dental caries from childhood to young adulthood. Data from the 1993 Pelotas (Brazil) birth cohort study, in which dental caries was measured at ages 6, 12, and 18 years, were analyzed. Family income of 302 participants was assessed at birth, and at 4, 11, 15, and 18 years of age. Mother's education, toothbrushing frequency, dental visiting, dental caries in primary dentition, and birth weight were covariates. A latent class growth analysis was conducted to characterize trajectories of time-varying variables. The influence of income trajectories on the increase in dental caries from age 6 to age 18 was evaluated by a generalized linear mixed model. After adjustment, the increases in numbers of decayed and missing teeth (DMT) from age 6 to age 18 were associated with family income trajectory. The incident rate ratios (IRR) of DMT compared with the group of stable high incomes were 2.36 for stable low incomes, 1.71 for downward, and 1.64 for upward. The IRR of teeth being filled in stable low-income groups compared with stable high-income groups was 0.55. Family income mobility affected treatment patterns of dental caries. Differences across income trajectory groups were found in the components of dental caries indices rather than in the experience of disease.

Low-income cut-offs, after tax (LICO-AT) - The Low-income cut-offs, after tax refers to an income threshold, defined using 1992 expenditure data, below which economic families or persons not in economic families would likely have devoted a larger share of their after-tax income than average to the necessities of food, shelter and clothing. More specifically, the thresholds represented income levels at which these families or persons were expected to spend 20 percentage points or more of their after-tax income than average on food, shelter and clothing. These thresholds have been adjusted to current dollars using the all-items Consumer Price Index (CPI).The LICO-AT has 35 cut-offs varying by seven family sizes and five different sizes of area of residence to account for economies of scale and potential differences in cost of living in communities of different sizes. These thresholds are presented in Table 4.3 Low-income cut-offs, after tax (LICO-AT - 1992 base) for economic families and persons not in economic families, 2015, Dictionary, Census of Population, 2016.When the after-tax income of an economic family member or a person not in an economic family falls below the threshold applicable to the person, the person is considered to be in low income according to LICO-AT. Since the LICO-AT threshold and family income are unique within each economic family, low-income status based on LICO-AT can also be reported for economic families.Return to footnote1referrerFootnote 2For more information on generation status variables, including information on their classifications, the questions from which they are derived, data quality and their comparability with other sources of data, please refer to the Place of Birth, Generation Status, Citizenship and Immigration Reference Guide, Census of Population, 2016.Return to footnote2referrerFootnote 3Low-income status - The income situation of the statistical unit in relation to a specific low-income line in a reference year. Statistical units with income that is below the low-income line are considered to be in low income.For the 2016 Census, the reference period is the calendar year 2015 for all income variables.Return to footnote3referrerFootnote 4The low-income concepts are not applied in the territories and in certain areas based on census subdivision type (such as Indian reserves). The existence of substantial in-kind transfers (such as subsidized housing and First Nations band housing) and sizeable barter economies or consumption from own production (such as product from hunting, farming or fishing) could make the interpretation of low-income statistics more difficult in these situations.Return to footnote4referrerFootnote 5Prevalence of low income - The proportion or percentage of units whose income falls below a specified low-income line.Return to footnote5referrerFootnote 6For more information on the Visible minority variable, including information on its classification, the questions from which it is derived, data quality and its comparability with other sources of data, please refer to the Visible Minority and Population Group Reference Guide, Census of Population, 2016.Return to footnote6referrerFootnote 7The Employment Equity Act defines visible minorities as 'persons, other than Aboriginal peoples, who are non-Caucasian in race or non-white in colour.'Return to footnote7referrerFootnote 8For example, 'East Indian,' 'Pakistani,' 'Sri Lankan,' etc.Return to footnote8referrerFootnote 9For example, 'Vietnamese,' 'Cambodian,' 'Laotian,' 'Thai,' etc.Return to footnote9referrerFootnote 10For example, 'Afghan,' 'Iranian,' etc.Return to footnote10referrerFootnote 11The abbreviation 'n.i.e.' means 'not included elsewhere.' Includes persons with a write-in response such as 'Guyanese,' 'West Indian,' 'Tibetan,' 'Polynesian,' 'Pacific Islander,' etc.Return to footnote11referrerFootnote 12Includes persons who gave more than one visible minority group by checking two or more mark-in responses, e.g., 'Black' and 'South Asian.'Return to footnote12referrerFootnote 13Includes persons who reported 'Yes' to the Aboriginal group question (Question 18), as well as persons who were not considered to be members of a visible minority group.

World Development Indicators (WDI) is the primary World Bank collection of development indicators, compiled from officially recognized international sources. It presents the most current and accurate global development data available, and includes national, regional and global estimates. [Note: Even though Global Development Finance (GDF) is no longer listed in the WDI database name, all external debt and financial flows data continue to be included in WDI. The GDF publication has been renamed International Debt Statistics (IDS), and has its own separate database, as well.

Last Updated:01/28/2025

Data contains Following 20 Countries 'Argentina', 'Australia', 'Brazil', 'China', 'France', 'Germany', 'India', 'Indonesia', 'Italy', 'Japan', 'Korea, Rep.', 'Mexico', 'Netherlands', 'Russian Federation', 'Saudi Arabia', 'Spain', 'Switzerland', 'Turkiye', 'United Kingdom', 'United States'

Dataset contains below Development Indicators 'Adolescent fertility rate (births per 1,000 women ages 15-19)', 'Agriculture, forestry, and fishing, value added (% of GDP)', 'Annual freshwater withdrawals, total (% of internal resources)', 'Births attended by skilled health staff (% of total)', 'Contraceptive prevalence, any method (% of married women ages 15-49)', 'Domestic credit provided by financial sector (% of GDP)', 'Electric power consumption (kWh per capita)', 'Energy use (kg of oil equivalent per capita)', 'Exports of goods and services (% of GDP)', 'External debt stocks, total (DOD, current US$)', 'Fertility rate, total (births per woman)', 'Foreign direct investment, net inflows (BoP, current US$)', 'Forest area (sq. km)', 'GDP (current US$)', 'GDP growth (annual %)', 'GNI per capita, Atlas method (current US$)', 'GNI per capita, PPP (current international $)', 'GNI, Atlas method (current US$)', 'GNI, PPP (current international $)', 'Gross capital formation (% of GDP)', 'High-technology exports (% of manufactured exports)', 'Immunization, measles (% of children ages 12-23 months)', 'Imports of goods and services (% of GDP)', 'Income share held by lowest 20%', 'Industry (including construction), value added (% of GDP)', 'Inflation, GDP deflator (annual %)', 'Life expectancy at birth, total (years)', 'Merchandise trade (% of GDP)', 'Military expenditure (% of GDP)', 'Mobile cellular subscriptions (per 100 people)', 'Mortality rate, under-5 (per 1,000 live births)', 'Net barter terms of trade index (2015 = 100)', 'Net migration', 'Net official development assistance and official aid received (current US$)', 'Personal remittances, received (current US$)', 'Population density (people per sq. km of land area)', 'Population growth (annual %)', 'Population, total', 'Poverty headcount ratio at $2.15 a day (2017 PPP) (% of population)', 'Poverty headcount ratio at national poverty lines (% of population)', 'Prevalence of HIV, total (% of population ages 15-49)', 'Prevalence of underweight, weight for age (% of children under 5)', 'Primary completion rate, total (% of relevant age group)', 'Revenue, excluding grants (% of GDP)', 'School enrollment, primary (% gross)', 'School enrollment, primary and secondary (gross), gender parity index (GPI)', 'School enrollment, secondary (% gross)', 'Surface area (sq. km)', 'Tax revenue (% of GDP)', 'Terrestrial and marine protected areas (% of total territorial area)', 'Time required to start a business (days)', 'Total debt service (% of exports of goods, services and primary income)', 'Urban population growth (annual %)

This dataset presents information about total income distribution. The data covers the financial year of 2017-2018, and is based on Statistical Area Level 2 (SA2) according to the 2016 edition of the Australian Statistical Geography Standard (ASGS).

Total Income is the sum of all reported income derived from Employee income, Own unincorporated business, Superannuation, Investments and Other income. Total income does not include the non-lodger population.

Government pensions, benefits or allowances are excluded from the Australian Bureau of Statistics (ABS) income data and do not appear in Other income or Total income. Pension recipients can fall below the income threshold that necessitates them lodging a tax return, or they may only receive tax free pensions or allowances. Hence they will be missing from the personal income tax data set. Recent estimates from the ABS Survey of Income and Housing (which records Government pensions and allowances) suggest that this component can account for between 9% to 11% of Total income.

All monetary values are presented as gross pre-tax dollars, as far as possible. This means they reflect income before deductions and loses, and before any taxation or levies (e.g. the Medicare levy or the temporary budget repair levy) are applied. The amounts shown are nominal, they have not been adjusted for inflation. The income presented in this release has been categorised into income types, these categories have been devised by the ABS to closely align to ABS definitions of income.

The statistics in this release are compiled from the Linked Employer Employee Dataset (LEED), a cross-sectional database based on administrative data from the Australian taxation system. The LEED includes more than 120 million tax records over seven consecutive years between 2011-12 and 2017-18.

Please note:

• All personal income tax statistics included in LEED were provided in de-identified form with no home address or date of birth. Addresses were coded to the ASGS and date of birth was converted to an age at 30 June of the reference year prior to data provision.

• To minimise the risk of identifying individuals in aggregate statistics, perturbation has been applied to the statistics in this release. Perturbation involves small random adjustment of the statistics and is considered the most satisfactory technique for avoiding the release of identifiable statistics, while maximising the range of information that can be released. These adjustments have a negligible impact on the underlying pattern of the statistics. Some cells have also been suppressed due to low counts.

• Totals may not align with the sum of their components due to missing or unpublished information in the underlying data and perturbation.

For further information please visit the Australian Bureau of Statistics.

AURIN has made the following changes to the original data:

• Spatially enabled the original data.

• Set 'np' (not published to protect the confidentiality of individuals or businesses) values to Null.

Reliable statistics on maternal morbidity and mortality are scarce in low and middle-income countries, especially in rural areas. This is the case in Mozambique where many births happen at home. Furthermore, a sizeable number of facility births have inadequate registration. Such information is crucial for developing effective national and global health policies for maternal and child health. The aim of this study was to generate reliable baseline socio-demographic information on women of reproductive age as well as to establish a demographic surveillance platform to support the planning and implementation of the Community Level Intervention for Pre-eclampsia (CLIP) study, a cluster randomized controlled trial. This study represents a census of all women of reproductive age (12–49 years) in twelve rural communities in Maputo and Gaza provinces of Mozambique. The data were collected through electronic forms implemented in Open Data Kit (ODK) (an app for android based tablets) and household and individual characteristics. Verbal autopsies were conducted on all reported maternal deaths to determine the underlying cause of death. Between March and October 2014, 50,493 households and 80,483 women of reproductive age (mean age 26.9 years) were surveyed. A total of 14,617 pregnancies were reported in the twelve months prior to the census, resulting in 9,029 completed pregnancies. Of completed pregnancies, 8,796 resulted in live births, 466 resulted in stillbirths and 288 resulted in miscarriages. The remaining pregnancies had not yet been completed during the time of the survey (5,588 pregnancies). The age specific fertility indicates that highest rate (188 live births per 1,000 women) occurs in the age 20–24 years old. The estimated stillbirth rate was 50.3/1,000 live and stillbirths; neonatal mortality rate was 13.3/1,000 live births and maternal mortality ratio was 204.6/100,000 live births. The most common direct cause of maternal death was eclampsia and tuberculosis was the most common indirect cause of death. This study found that fertility rate is high at age 20–24 years old. Pregnancy in the advanced age (>35 years of age) in this study was associated with higher poor outcomes such as miscarriage and stillbirth. The study also found high stillbirth rate indicating a need for increased attention to maternal health in southern Mozambique. Tuberculosis and HIV/AIDS are prominent indirect causes of maternal death, while eclampsia represents the number one direct obstetric cause of maternal deaths in these communities. Additional efforts to promote safe motherhood and improve child survival are crucial in these communities.