These datasets contain measures of socioeconomic and demographic characteristics by U.S. census tract for the years 1990-2022 and ZIP code tabulation area (ZCTA) for the years 2008-2022. Example measures include population density; population distribution by race, ethnicity, age, and income; income inequality by race and ethnicity; and proportion of population living below the poverty level, receiving public assistance, and female-headed or single parent families with kids. The datasets also contain a set of theoretically derived measures capturing neighborhood socioeconomic disadvantage and affluence, as well as a neighborhood index of Hispanic, foreign born, and limited English.

This dataset contains the data on which the conclusions of the study "Impact of neighbourhood-level socioeconomic status, traditional coronary risk factors, and ancestry on age at myocardial infarction onset: A population-based register study" rely. We collected data registered in the Norwegian Myocardial Infarction Register for all patients admitted to Diakonhjemmet Hospital with a non-ST elevation myocardial infarction (NSTEMI) in 2014-2017 (n=840). Using the patients' registered postal codes, we identified in which city district in Oslo, Norway the patients were residing. Patients from districts other than Frogner, Vestre Aker, Ullern, Stovner, Grorud, and Alna were excluded (n=60), and the remaining patients were grouped according to whether they were residing in the western (high neighbourhood-level socioeconomic status (SES)) or north-eastern (low neighbourhood-level SES) city districts. Using the patients' registered social security numbers and the electronic medical record system at Diakonhjemmet Hospital, patients were grouped according to whether or not they had presumed Northwest-European ancestry based on their names and other information found in their medical records. Patients with undecidable ancestry (n=2) were excluded. Furthermore, patients with type 2 myocardial infarction (n=117) were excluded since we aimed to investigate the risk for coronary heart disease (CHD). Re-admissions in the period (n=55) were excluded, and we were left with 606 patients. The dataset contains patient data on city district group, presumed ancestry group, age at hospital admission with NSTEMI, history of previous acute myocardial infarction (AMI), prior diagnosis of diabetes, prior diagnosis of hypertension, cigarette smoking status, use of statins, body mass index (BMI), and serum levels of low-density lipoprotein (LDL) cholesterol. Raw data from the Norwegian Myocardial Infarction Register, which was used to generate variables on the patients' presumed ancestry and city-district group, is not made available as it contains personal data, but can be applied for at helsedata.no. Previous AMI was defined regardless of infarction type and ECG diagnosis, prior diagnosis of diabetes was defined as known diagnosis with diabetes mellitus type 1 or 2, prior diagnosis of hypertension was defined as prior or ongoing treatment for hypertension, and cigarette smoking was defined as patients that had been smoking the last month. BMI and LDL cholesterol were measured at hospital admission. Registration of all cases of AMI in Norway in the Norwegian Myocardial Infarction Register is mandatory and does not require informed consent. The Norwegian Myocardial Infarction Register is part of the National Register of Cardiovascular Diseases and is authorized in the Section 11 h of the Norwegian Health Register Act. The study was approved by the Institutional Review Board of Diakonhjemmet Hospital and the data privacy representative for Diakonhjemmet Hospital, and all methods were in accordance with the ethical standards of the institution and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

A more recent web map on this same topic is available for ArcGIS Online subscribers here.This map shows the socioeconomic status of each census tract. Data come from the US Census Bureau's 2011-2015 American Community Survey. Neighborhood Socioeconomic Status, over and above individual socioeconomic status, is a predictor of many health outcomes. The Neighborhood Socioeconomic Status (NSES) Index is on a scale from 0 to 100 with 50 being the national average around 2010. The Index incorporates the following indicators (fields in this layer's attribute table):Median Household Income (from Table B19013)Percent of individuals with income below the Federal Poverty Line (from Table S1701)The educational attainment of adults (age 25+) (from Table B15003)Unemployment Rate (from Table S2301)Percent of households with children under the age of 18 that are "female-headed" (no male present) (from Table B11005)NSES = log(median household income) + (-1.129 * (log(percent of female-headed households))) + (-1.104 * (log(unemployment rate))) + (-1.974 * (log(percent below poverty))) + .451*((high school grads)+(2*(bachelor's degree holders)))To learn more about how the NSES Index was developed, please explore this journal articleMiles, Jeremy and Weden, Margaret; Lavery, Diana; Escarce, José; Kathleen Cagney; Shih, Regina. 2016. “Constructing a Time-Invariant Measure of the Socio-Economic Status of U.S. Census Tracts.” Journal of Urban Health, vol. 93, issue no.1, pp. 213-232. or this PPT presentation presented at the University of Texas at San Antonio's Applied Demography Conference in 2014.

"BACKGROUND: Early social experiences are believed to shape neurodevelopment, with potentially lifelong consequences. Yet minimal evidence exists regarding the role of the social environment on children's neural functioning, a core domain of neurodevelopment.

METHODS: We analysed data from 36?443 participants in the United States Collaborative Perinatal Project, a socioeconomically diverse pregnancy cohort conducted between 1959 and 1974. Study outcomes included: physician (neurologist or paediatrician)-rated neurological abnormality neonatally and thereafter at 4 months and 1 and 7 years； indicators of neurological hard signs and soft signs； and indicators of autonomic nervous system function.

RESULTS: Children born to socioeconomically disadvantaged parents were more likely to exhibit neurological abnormalities at 4 months [odds ratio (OR)?=?1.20； 95% confidence interval (CI)?=?1.06, 1.37], 1 year (OR?=?1.35； CI?=?1.17, 1.56), and 7 years (OR?=?1.67； CI?=?1.48, 1.89), and more likely to exhibit neurological hard signs (OR?=?1.39； CI?=?1.10, 1.76), soft signs (OR?=?1.26； CI?=?1.09, 1.45) and autonomic nervous system dysfunctions at 7 years. Pregnancy and delivery complications, themselves associated with socioeconomic disadvantage, did not account for the higher risks of neurological abnormalities among disadvantaged children.

CONCLUSIONS: Parental socioeconomic disadvantage was, independently from pregnancy and delivery complications, associated with abnormal child neural development during the first 7 years of life. These findings reinforce the importance of the early environment for neurodevelopment generally, and expand knowledge regarding the domains of neurodevelopment affected by environmental conditions. Further work is needed to determine the mechanisms linking socioeconomic disadvantage with children's neural functioning, the timing of such mechanisms and their potential reversibility."

Data spreadsheets (xls), based on the Inequality Graphs, and presenting the latest Social Health Atlas indicators, where available, are produced by Quintile of Socioeconomic Disadvantage of Area (Inequality Graphs). The Quintiles of Socioeconomic Disadvantage of Area, referred to as Inequality Graphs, and associated data are based on either the 2006 ASGC or 2011 ASGC ABS Index of Relative Socioeconomic Disadvantage, as noted for each data indicator. More information can be found on http://phidu.torrens.edu.au/social-health-atlases Dataset to be attributed to Public Health Information Unit (PHIDU) located at The Torrens University Adelaide.

IntroductionReduced sleep health has been consistently linked with increased negative emotion in children. While sleep characteristics have been associated with neural function in adults and adolescents, much less is known about these associations in children while considering socioeconomic context. In this study, we examined the associations among socioeconomic factors, sleep duration and timing, and resting-state functional connectivity (rsFC) of the amygdala in children.MethodsParticipants were typically-developing 5- to 9-year-olds from socioeconomically diverse families (61% female; N = 94). Parents reported on children’s weekday and weekend bedtimes and wake-up times, which were used to compute sleep duration and midpoint. Analyses focused on amygdala-anterior cingulate cortex (ACC) connectivity followed by amygdala-whole brain connectivity.ResultsLower family income-to-needs ratio and parental education were significantly associated with later weekday and weekend sleep timing and shorter weekday sleep duration. Shorter weekday sleep duration was associated with decreased amygdala-ACC and amygdala-insula connectivity. Later weekend sleep midpoint was associated with decreased amygdala-paracingulate cortex and amygdala-postcentral gyrus connectivity. Socioeconomic factors were indirectly associated with connectivity in these circuits via sleep duration and timing.DiscussionThese results suggest that socioeconomic disadvantage may interfere with both sleep duration and timing, in turn possibly altering amygdala connectivity in emotion processing and regulation circuits in children. Effective strategies supporting family economic conditions may have benefits for sleep health and brain development in children.

Socioeconomic disadvantage is associated with larger COVID-19 disease burdens and pandemic-related economic impacts. We utilized the longitudinal Adolescent Brain Cognitive Development Study to understand how family- and neighborhood-level socioeconomic disadvantage relate to disease burden, family communication, and preventative responses to the pandemic in over 6,000 youth-caregiver dyads. Data were collected at three timepoints (May–August 2020). Here, we show that both family- and neighborhood-level disadvantage were associated with caregivers' reports of greater family COVID-19 disease burden, less perceived exposure risk, more frequent caregiver-youth conversations about COVID-19 risk/prevention and reassurance, and greater youth preventative behaviors. Families with more socioeconomic disadvantage may be adaptively incorporating more protective strategies to reduce emotional distress and likelihood of COVID-19 infection. The results highlight the importance of caregiver-youth communication and disease-preventative practices for buffering the economic and disease burdens of COVID-19, along with policies and programs that reduce these burdens for families with socioeconomic disadvantage.

In 2021, 20.1% of people from the Indian ethnic group were in higher managerial and professional occupations – the highest percentage out of all ethnic groups in this socioeconomic group.

Data spreadsheets (xls), based on the Inequality Graphs, and presenting the latest Social Health Atlas indicators, where available, are produced by Quintile of Socioeconomic Disadvantage of Area …Show full descriptionData spreadsheets (xls), based on the Inequality Graphs, and presenting the latest Social Health Atlas indicators, where available, are produced by Quintile of Socioeconomic Disadvantage of Area (Inequality Graphs). The Quintiles of Socioeconomic Disadvantage of Area, referred to as Inequality Graphs, and associated data are based on either the 2006 ASGC or 2011 ASGC ABS Index of Relative Socioeconomic Disadvantage, as noted for each data indicator. More information can be found on http://phidu.torrens.edu.au/social-health-atlases Dataset to be attributed to Public Health Information Unit (PHIDU) located at The Torrens University Adelaide.

This dataset contains measures of socioeconomic and demographic characteristics by US census tract 1990-2010. Example measures include population density; population distribution by race, ethnicity, age, and income; and proportion of population living below the poverty level, receiving public assistance, and female-headed families. The dataset also contains a set of index variables to represent neighborhood disadvantage and affluence.

COVID-19 has had worse health, education and labor market effects on groups with low socio-economic status (SES) than on those with high SES. Little is known, however, about whether COVID-19 has also had differential effects on non-cognitive skills that are important for life outcomes. Using panel data from before and during the pandemic, we show that COVID-19 affects one key non-cognitive skill, i.e., prosociality. While prosociality is already lower for low-SES students prior to the pandemic, we show that COVID-19 infections within families amplify the prosociality gap between French high-school students of high- and low-SES by almost tripling its size in comparison to pre-COVID-19 levels.

What is CDC Social Vulnerability Index?ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) created the Social Vulnerability Index (SVI) to help emergency response planners and public health officials identify and map the communities that will most likely need support before, during, and after a hazardous event.SVI uses U.S Census Data to determine the social vulnerability of every county and tract. CDC SVI ranks each county and tract on 16 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes:Theme 1 - Socioeconomic StatusTheme 2 - Household CharacteristicsTheme 3 - Racial & Ethnic Minority StatusTheme 4 - Housing Type & Transportation VariablesFor a detailed description of variable uses, please refer to the full SVI 2020 Documentation.RankingsWe ranked counties and tracts for the entire United States against one another. This feature layer can be used for mapping and analysis of relative vulnerability of counties in multiple states, or across the U.S. as a whole. Rankings are based on percentiles. Percentile ranking values range from 0 to 1, with higher values indicating greater vulnerability. For each county and tract, we generated its percentile rank among all counties and tracts for 1) the sixteen individual variables, 2) the four themes, and 3) its overall position. Overall Rankings:We totaled the sums for each theme, ordered the counties, and then calculated overall percentile rankings. Please note: taking the sum of the sums for each theme is the same as summing individual variable rankings.The overall tract summary ranking variable is RPL_THEMES. Theme rankings:For each of the four themes, we summed the percentiles for the variables comprising each theme. We ordered the summed percentiles for each theme to determine theme-specific percentile rankings. The four summary theme ranking variables are: Socioeconomic Status - RPL_THEME1Household Characteristics - RPL_THEME2Racial & Ethnic Minority Status - RPL_THEME3Housing Type & Transportation - RPL_THEME4FlagsCounties and tracts in the top 10%, i.e., at the 90th percentile of values, are given a value of 1 to indicate high vulnerability. Counties and tracts below the 90th percentile are given a value of 0. For a theme, the flag value is the number of flags for variables comprising the theme. We calculated the overall flag value for each county as the total number of all variable flags. SVI Informational VideosIntroduction to CDC Social Vulnerability Index (SVI)Methods for CDC Social Vulnerability Index (SVI)More Questions?CDC SVI 2020 Full DocumentationSVI Home PageContact the SVI Coordinator

Low socioeconomic status (SES) and living in a disadvantaged neighborhood are associated with poor cardiovascular health. Multiple lines of evidence have linked DNA methylation to both cardiovascular risk factors and social disadvantage indicators. However, limited research has investigated the role of DNA methylation in mediating the associations of individual- and neighborhood-level disadvantage with multiple cardiovascular risk factors in large, multi-ethnic, population-based cohorts. We examined whether disadvantage at the individual level (childhood and adult SES) and neighborhood level (summary neighborhood SES as assessed by Census data and social environment as assessed by perceptions of aesthetic quality, safety, and social cohesion) were associated with 11 cardiovascular risk factors including measures of obesity, diabetes, lipids, and hypertension in 1,154 participants from the Multi-Ethnic Study of Atherosclerosis (MESA). For significant associations, we conducted epigenome-wide mediation analysis to identify methylation sites mediating the relationship between individual/neighborhood disadvantage and cardiovascular risk factors using the JT-Comp method that assesses sparse mediation effects under a composite null hypothesis. In models adjusting for age, sex, race/ethnicity, smoking, medication use, and genetic principal components of ancestry, epigenetic mediation was detected for the associations of adult SES with body mass index (BMI), insulin, and high-density lipoprotein cholesterol (HDL-C), as well as for the association between neighborhood socioeconomic disadvantage and HDL-C at FDR q < 0.05. The 410 CpG mediators identified for the SES-BMI association were enriched for CpGs associated with gene expression (expression quantitative trait methylation loci, or eQTMs), and corresponding genes were enriched in antigen processing and presentation pathways. For cardiovascular risk factors other than BMI, most of the epigenetic mediators lost significance after controlling for BMI. However, 43 methylation sites showed evidence of mediating the neighborhood socioeconomic disadvantage and HDL-C association after BMI adjustment. The identified mediators were enriched for eQTMs, and corresponding genes were enriched in inflammatory and apoptotic pathways. Our findings support the hypothesis that DNA methylation acts as a mediator between individual- and neighborhood-level disadvantage and cardiovascular risk factors, and shed light on the potential underlying epigenetic pathways. Future studies are needed to fully elucidate the biological mechanisms that link social disadvantage to poor cardiovascular health.

The Index of Educational Disadvantage for SA Government schools, each year from 2017 (not 2019). The Index of Educational Disadvantage is a socio-economic index, used by the Department for Education to allocate resources to schools to address educational disadvantage related to socio-economic status. The most disadvantaged schools have an index of 1, the least disadvantaged have an index of 7. More information on the Index of Educational Disadvantage is available at https://www.education.sa.gov.au/sites/g/files/net691/f/educational_disadvantage_index_explanation.pdf

This data is SA1 based SEIFA data on The Index of Relative Socio-economic Advantage and Disadvantage, 2016. Data is based upon 2016 ASGS boundaries. Socio-Economic Indexes for Areas (SEIFA) is an ABS product that ranks areas in Australia according to relative socio-economic advantage and disadvantage. The indexes are based on information from the five-yearly Census of Population and Housing. SEIFA 2016 has been created from Census 2016 data and consists of four indexes: The Index of Relative Socio-economic Disadvantage (IRSD); The Index of Relative Socio-economic Advantage and Disadvantage (IRSAD); The Index of Education and Occupation (IEO); The Index of Economic Resources (IER). Each index is a summary of a different subset of Census variables and focuses on a different aspect of socio-economic advantage and disadvantage. This data is ABS data (catalogue number: 2033.0.55.001) used with permission from the Australian Bureau of Statistics. For more information on this data please visit the Australian Bureau of Statistics.

The German Index of Socioeconomic Deprivation (GISD) is an index developed at the Robert Koch Institute to measure regional socioeconomic disadvantage. It is used to highlight regional socio-economic inequalities in health and to identify starting points for explaining regional differences in health. The GISD makes it possible to investigate socio-economic differences in health chances, disease and death risks in Germany even if the relevant health data do not contain information on socio-economic status at the individual level. Information on the education, employment and income situation in districts and municipalities from the INKAR database is used to generate the GISD. It is generated at the level of the municipalities and is provided for the spatial references municipalities, municipal associations, urban and rural districts, spatial planning regions, NUTS 2 and postcode areas in a population-weighted aggregated manner. The weighting of the indicators is carried out via main component analyses within the subdimensions. The currently available data refer to the area as at 31.12.2019 and contain values from 1998 to 2019.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. The study includes data collected with the purpose of creating an integrated dataset that would allow researchers to address significant, policy-relevant gaps in the literature--those that are best answered with cross-jurisdictional data representing a wide array of economic and social factors. The research addressed five research questions: What is the impact of gentrification and suburban diversification on crime within and across jurisdictional boundaries? How does crime cluster along and around transportation networks and hubs in relation to other characteristics of the social and physical environment? What is the distribution of criminal justice-supervised populations in relation to services they must access to fulfill their conditions of supervision? What are the relationships among offenders, victims, and crimes across jurisdictional boundaries? What is the increased predictive power of simulation models that employ cross-jurisdictional data?

This dataset offers census tract level estimates for the number of uninsured noninstitutionalized civilians, number of persons below poverty line, unemployed population, number of persons with no high school diploma, which are socioeconomic characteristics with a negative impact on the access to healthcare services.

BackgroundSocioeconomic disadvantage is a risk factor for dementia, but longitudinal studies suggest that it does not affect the rate of cognitive decline. Our objective is to understand the manner in which socioeconomic disadvantage shapes dementia risk by examining its associations with midlife cognitive performance and cognitive decline from midlife to old age, including cognitive decline trajectories in those with dementia.Methods and findingsData are drawn from the Whitehall II study (N = 10,308 at study recruitment in 1985), with cognitive function assessed at 4 waves (1997, 2002, 2007, and 2012). Sociodemographic, behavioural, and cardiometabolic risk factors from 1985 and chronic conditions until the end of follow-up in 2015 (N dementia/total = 320/9,938) allowed the use of inverse probability weighting to take into account data missing because of loss to follow-up between the study recruitment in 1985 and the introduction of cognitive tests to the study in 1997. Generalized estimating equations and Cox regression were used to assess associations of socioeconomic markers (height, education, and midlife occupation categorized as low, intermediate, and high to represent hierarchy in the socioeconomic marker) with cognitive performance, cognitive decline, and dementia (N dementia/total = 195/7,499). In those with dementia, we examined whether retrospective trajectories of cognitive decline (backward timescale) over 18 years prior to diagnosis differed as a function of socioeconomic markers. Socioeconomic disadvantage was associated with poorer cognitive performance (all p < 0.001). Using point estimates for the effect of age, the differences between the high and low socioeconomic groups corresponded to an age effect of 4, 15, and 26 years, for height, education, and midlife occupation, respectively. There was no evidence of faster cognitive decline in socioeconomically disadvantaged groups. Low occupation, but not height or education, was associated with risk of dementia (hazard ratio [HR] = 2.03 [95% confidence interval (CI) 1.23–3.36]) in an analysis adjusted for sociodemographic factors; the excess risk was unchanged after adjustment for cognitive decline but was completely attenuated after adjustment for cognitive performance. In further analyses restricted to those with dementia, retrospective cognitive trajectories over 18 years prior to dementia diagnosis showed faster cognitive decline in the high education (p = 0.006) and occupation (p = 0.001) groups such that large differences in cognitive performance in midlife were attenuated at dementia diagnosis. A major limitation of our study is the use of electronic health records rather than comprehensive dementia ascertainment.ConclusionsOur results support the passive or threshold cognitive reserve hypothesis, in that high cognitive reserve is associated with lower risk for dementia because of its association with cognitive performance, which provides a buffer against clinical expression of dementia.

This dataset is derived from an upcoming systematic review. The review examines the effects of school meal programs on socioeconomically disadvantaged populations. It compiles a non-exhaustive list of outcomes from 40 studies and 83 publications. The outcomes include academic achievement, attendance/absenteeism, anemia, BAZ, behavior, dropout, enrollment, ferritin, food insecurity, HAZ, height, hemoglobin, IQ, math achievement, working memory (digit span), obesity, reading achievement, stunting, thinness, vitamin B12, WAZ, weight, and WHZ. Subgroups are also included by sex and relative social disadvantage (measured either through economic measures or malnutrition). Any data transformations are noted within several columns and the notes section.