Social deprivation indices calculated using the 2020 5-year American Community Survey at the census block group level.

Baja California was the state in Mexico with the highest share of population considered vulnerable due to social deprivation in 2022. It was estimated that 38.1 percent of the people living in the state suffered from social deprivation. On the other hand, Tlaxcala was the state with the lowest rate of socially deprived population, with 21 percent. That same year, Chiapas was the Mexican state with the highest number of people living in extreme poverty.

Abstract

Six social deprivation and vulnerability indices (SVI, SDI, NSS7, FDep, ICE) were calculated using the the US Census 2020 5-year American Community Survey data at the census block group, census tract and county geographical levels.

Methodology

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Publicly available geocoded social determinants of health and mobility datasets used in the analysis of "Chronic Acid Suppression and Social Determinants of COVID-19 Infection".These datasets are required for the analytical workflow shared on Github which demonstrates how the analysis in the manuscript was done using randomly generated samples to protect patient privacy.zcta_county_rel_10.txt - Population and housing density from the 2010 decennial census. Obtained from: https://www2.census.gov/geo/docs/maps-data/data/rel/zcta_county_rel_10.txtcre-2018-a11.csv - Community Resilience Estimates which is is the capacity of individuals and households to absorb, endure, and recover from the health, social, and economic impacts of a disaster such as a hurricane or pandemic. Data obtained from: https://www.census.gov/data/experimental-data-products/community-resilience-estimates.htmlzcta_tract_rel_10.txt - Relationship between ZCTA and US Census tracts (used to map census tracts to ZCTA). Data obtained from: https://www.census.gov/geographies/reference-files/time-series/geo/relationship-files.html#par_textimage_674173622mask-use-by-county.txt - Mask Use By County comes from a large number of interviews conducted online by the global data and survey firm Dynata at the request of The New York Times. The firm asked a question about mask use to obtain 250,000 survey responses between July 2 and July 14, enough data to provide estimates more detailed than the state level. Data obtained from: https://github.com/nytimes/covid-19-data/tree/master/mask-usemobility_report_US.txt - Google mobility report which charts movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential. Data obtained from: https://github.com/ActiveConclusion/COVID19_mobility/blob/master/google_reports/mobility_report_US.csvACS2015_zctaallvars.csv - Social Deprivation Index is a composite measure of area level deprivation based on seven demographic characteristics collected in the American Community Survey (https://www.census.gov/programs-surveys/acs/) and used to quantify the socio-economic variation in health outcomes. Factors are: Income, Education, Employment, Housing, Household Characteristics, Transportation, Demographics. Data obtained from: https://www.graham-center.org/rgc/maps-data-tools/sdi/social-deprivation-index.html

SIA77 - Key National Indicators of Poverty, Deprivation and Social Exclusion. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Key National Indicators of Poverty, Deprivation and Social Exclusion...

IntroductionPrevious studies highlight the negative impact of adverse socioeconomic conditions throughout life on motor skills and cognitive health. Factors such as cognitive activity, physical activity, lifestyle, and socioeconomic position significantly affect general health status and brain health. This pilot study investigates the relationships among the Area Deprivation Index (ADI)—a measure of neighborhood-level socioeconomic deprivation, brain structure (cortical volume and thickness), and cognitive status in adults in Arizona. Identifying measures sensitive to ADI could elucidate mechanisms driving cognitive decline.MethodsThe study included 22 adults(mean age = 56.2 ± 15.2) in Arizona, residing in the area for over 10 years(mean = 42.7 ± 15.8). We assessed specific cognitive domains using the NeuroTrax™ cognitive screening test, which evaluates memory, executive function, visual–spatial processing, attention, information processing speed, and motor function. We also measured cortical thickness and volume in 10 cortical regions using FreeSurfer 7.2. Linear regression tests were conducted to examine the relationships between ADI metrics, cognitive status, and brain health measures.ResultsResults indicated a significant inverse relationship between ADI metrics and memory scores, explaining 25% of the variance. Both national and state ADI metrics negatively correlated with motor skills and global cognition (r’s 

Introduction: The spread of Coronavirus Disease 2019 (COVID-19) across the United States has highlighted the long-standing nationwide health inequalities with socioeconomically challenged communities experiencing a higher burden of the disease. We assessed the impact of neighborhood socioeconomic characteristics on the COVID-19 prevalence across seven selected states (i.e., Arizona, Florida, Illinois, Maryland, North Carolina, South Carolina, and Virginia).Methods: We obtained cumulative COVID-19 cases reported at the neighborhood aggregation level by Departments of Health in selected states on two dates (May 3rd, 2020, and May 30th, 2020) and assessed the correlation between the COVID-19 prevalence and neighborhood characteristics. We developed Area Deprivation Index (ADI), a composite measure to rank neighborhoods by their socioeconomic characteristics, using the 2018 US Census American Community Survey. The higher ADI rank represented more disadvantaged neighborhoods.Results: After controlling for age, gender, and the square mileage of each community we identified Zip-codes with higher ADI (more disadvantaged neighborhoods) in Illinois and Maryland had higher COVID-19 prevalence comparing to zip-codes across the country and in the same state with lower ADI (less disadvantaged neighborhoods) using data on May 3rd. We detected the same pattern across all states except for Florida and Virginia using data on May 30th, 2020.Conclusion: Our study provides evidence that not all Americans are at equal risk for COVID-19. Socioeconomic characteristics of communities appear to be associated with their COVID-19 susceptibility, at least among those study states with high rates of disease.

ObjectiveTo describe the development of an area-level measure of children's opportunity, the Ohio Children's Opportunity Index (OCOI).Data Sources/Study SettingSecondary data were collected from US census based-American Community Survey (ACS), US Environmental Protection Agency, US Housing and Urban Development, Ohio Vital Statistics, US Department of Agriculture-Economic Research Service, Ohio State University Center for Urban and Regional Analysis, Ohio Incident Based Reporting System, IPUMS National Historical Geographic Information System, and Ohio Department of Medicaid. Data were aggregated to census tracts across two time periods.Study DesignOCOI domains were selected based on existing literature, which included family stability, infant health, children's health, access, education, housing, environment, and criminal justice domains. The composite index was developed using an equal weighting approach. Validation analyses were conducted between OCOI and health and race-related outcomes, and a national index.Principal FindingsComposite OCOI scores ranged from 0–100 with an average value of 74.82 (SD, 17.00). Census tracts in the major metropolitan cities across Ohio represented 76% of the total census tracts in the least advantaged OCOI septile. OCOI served as a significant predictor of health and race-related outcomes. Specifically, the average life expectancy at birth of children born in the most advantaged septile was approximately 9 years more than those born in least advantaged septile. Increases in OCOI were associated with decreases in proportion of Black (48 points lower in the most advantaged vs. least advantaged septile), p < 0.001) and Minority populations (54 points lower in most advantaged vs. least advantaged septile, p < 0.001). We found R-squared values > 0.50 between the OCOI and the national Child Opportunity Index scores. Temporally, OCOI decreased by 1% between the two study periods, explained mainly by decreases in the children health, accessibility and environmental domains.ConclusionAs the first opportunity index developed for children in Ohio, the OCOI is a valuable resource for policy reform, especially related to health disparities and health equity. Health care providers will be able to use it to obtain holistic views on their patients and implement interventions that can tackle barriers to childhood development using a more tailored approach.

IntroductionCommunity-acquired pneumonia (CAP) is a major health concern in the United States (US), with its incidence, severity, and outcomes influenced by social determinants of health, including socioeconomic status. The impact of neighborhood socioeconomic status, as measured by the Distressed Communities Index (DCI), on CAP-related admissions remains understudied in the literature.ObjectiveTo determine the independent association between DCI and CAP-related admissions in Maryland.MethodsWe conducted a retrospective study using the Maryland State Inpatient Database (SID) to collate data on CAP-related admissions from January 2018 to December 2020. The study included adults aged 18–85 years. We explored the independent association between community-level economic deprivation based on DCI quintiles and CAP-related admissions, adjusting for significant covariates.ResultsIn the study period, 61,467 cases of CAP-related admissions were identified. The patients were predominantly White (49.7%) and female (52.4%), with 48.6% being over 65 years old. A substantive association was found between the DCI and CAP-related admissions. Compared to prosperous neighborhoods, patients living in economically deprived communities had 43% increased odds of CAP-related admissions.ConclusionResidents of the poorest neighborhoods in Maryland have the highest risk of CAP-related admissions, emphasizing the need to develop effective public health strategies beneficial to the at-risk patient population.

In 2020, amidst the COVID-19 pandemic, it was estimated that 8.9 percent of the Mexican population were vulnerable due to a low income or a lack of it. This represents a slight increase when compared with 2018, when eight percent of the population were considered to be in that situation. Baja California was the state in Mexico with the highest share of vulnerable population due to social deprivation in 2022.

Abstract copyright UK Data Service and data collection copyright owner.

The English Longitudinal Study of Ageing (ELSA) study is a longitudinal survey of ageing and quality of life among older people that explores the dynamic relationships between health and functioning, social networks and participation, and economic position as people plan for, move into and progress beyond retirement. The main objectives of ELSA are to:

• construct waves of accessible and well-documented panel data;
• provide these data in a convenient and timely fashion to the scientific and policy research community;
• describe health trajectories, disability and healthy life expectancy in a representative sample of the English population aged 50 and over;
• examine the relationship between economic position and health;
• investigate the determinants of economic position in older age;
• describe the timing of retirement and post-retirement labour market activity; and
• understand the relationships between social support, household structure and the transfer of assets.

Further information may be found on the the ELSA project website or the Natcen Social Research: ELSA web pages.

Health conditions research with ELSA - June 2021

The ELSA Data team have found some issues with historical data measuring health conditions. If you are intending to do any analysis looking at the following health conditions, then please contact elsadata@natcen.ac.uk for advice on how you should approach your analysis. The affected conditions are: eye conditions (glaucoma; diabetic eye disease; macular degeneration; cataract), CVD conditions (high blood pressure; angina; heart attack; Congestive Heart Failure; heart murmur; abnormal heart rhythm; diabetes; stroke; high cholesterol; other heart trouble) and chronic health conditions (chronic lung disease; asthma; arthritis; osteoporosis; cancer; Parkinson's Disease; emotional, nervous or psychiatric problems; Alzheimer's Disease; dementia; malignant blood disorder; multiple sclerosis or motor neurone disease).

Secure Access Data:Secure Access versions of ELSA have more restrictive access conditions than versions available under the standard End User Licence or Special Licence (see 'Access' section below).
Secure Access versions of ELSA include:

• Primary Data from Wave 8 onwards (SN 8444) includes all the variables in the SL primary dataset (SN 8346) as well as day of birth, combined SIC 2003 code (5 digit), combined SOC 2000 code (4 digit), NS-SEC long version including and excluding unclassifiable and non-workers.
• Pension Age Data from Wave 8 onwards (SN 8445) includes all the variables in the SL pension age data (SN 8375) as well as year reached pension age variable.
• Detailed geographical identifier files for each wave, grouped by identifier held under SN 8423 (Index of Multiple Deprivation Score), SN 8424 (Local Authority District Pre-2009 Boundaries), SN 8438 (Local Authority District Post-2009 Boundaries), SN 8425 (Census 2001 Lower Layer Super Output Areas), SN 8434 (Census 2011 Lower Layer Super Output Areas), SN 8426(Census 2001 Middle Layer Super Output Areas), SN 8435 (Census 2011 Middle Layer Super Output Areas), SN 8427 (Population Density for Postcode Sectors), SN 8428 (Census 2001 Rural-Urban Indicators), SN 8436 (Census 2011 Rural-Urban Indicators).

Where boundary changes have occurred, the geographic identifier has been split into two separate studies to reduce the risk of disclosure. Users are also only allowed one version of each identifier:

• either SN 8424 (Local Authority District Pre-2009 Boundaries) or SN 8438 (Local Authority District Post-2009 Boundaries)
• either SN 8425 (Census 2001 Lower Layer Super Output Areas) or SN 8434 (Census 2011 Lower Layer Super Output Areas)
• either SN 8426 (Census 2001 Middle Layer Super Output Areas) or SN 8435 (Census 2011 Middle Layer Super Output Areas)
• either SN 8428 (Census 2001 Rural-Urban Indicators) or SN 8436 (Census 2011 Rural-Urban Indicators)

English Longitudinal Study of Ageing: Wave 8-10, 2016-2023, State Pension Age Data: Secure Access
These datasets include the two SL-level State Pension Age (SPA) related variables (SN 8375), as well as four additional variables specifying the SPA to the level of month year and day. These data have more restrictive access conditions than those available under the standard End User Licence or Special Licence (see 'Access' section).

Latest edition information

For the second edition (September 2024), state pension age data for Waves 9 and 10 were added to the study, along with accompanying documentation.

Main Topics:

ELSA User Guide in SN 5050 includes detailed information about all the ELSA datasets available under different licences. The data...

Abstract copyright UK Data Service and data collection copyright owner.

The English Longitudinal Study of Ageing (ELSA) study is a longitudinal survey of ageing and quality of life among older people that explores the dynamic relationships between health and functioning, social networks and participation, and economic position as people plan for, move into and progress beyond retirement. The main objectives of ELSA are to:

• construct waves of accessible and well-documented panel data;
• provide these data in a convenient and timely fashion to the scientific and policy research community;
• describe health trajectories, disability and healthy life expectancy in a representative sample of the English population aged 50 and over;
• examine the relationship between economic position and health;
• nvestigate the determinants of economic position in older age;
• describe the timing of retirement and post-retirement labour market activity; and
• understand the relationships between social support, household structure and the transfer of assets.

Further information may be found on the the ELSA project website or the Natcen Social Research: ELSA web pages.

Health conditions research with ELSA - June 2021

The ELSA Data team have found some issues with historical data measuring health conditions. If you are intending to do any analysis looking at the following health conditions, then please contact the ELSA Data team at NatCen on elsadata@natcen.ac.uk for advice on how you should approach your analysis. The affected conditions are: eye conditions (glaucoma; diabetic eye disease; macular degeneration; cataract), CVD conditions (high blood pressure; angina; heart attack; Congestive Heart Failure; heart murmur; abnormal heart rhythm; diabetes; stroke; high cholesterol; other heart trouble) and chronic health conditions (chronic lung disease; asthma; arthritis; osteoporosis; cancer; Parkinson's Disease; emotional, nervous or psychiatric problems; Alzheimer's Disease; dementia; malignant blood disorder; multiple sclerosis or motor neurone disease).

Special Licence Data:

Special Licence Access versions of ELSA have more restrictive access conditions than versions available under the standard End User Licence (see 'Access' section below). Users are advised to obtain the latest edition of SN 5050 (the End User Licence version) before making an application for Special Licence data, to see whether that is suitable for their needs. A separate application must be made for each Special Licence study.

Special Licence Access versions of ELSA include:

• Primary data from Wave 8 onwards (SN 8346) includes all the variables in the EUL primary dataset (SN 5050) as well as year and month of birth, consolidated ethnicity and country of birth, marital status, and more detailed medical history variables.
• Wave 8 Pension Age Data (SN 8375) includes all the variables in the EUL pension age data (SN 5050) as well as year and age reached state pension age variables.
• Wave 8 Sexual Self-Completion Data (SN 8376) includes sensitive variables from the sexual self-completion questionnaire.
• Wave 3 (2007) Harmonized Life History (SN 8831) includes retrospective information on previous histories, specifically, detailed data on previous partnership, children, residential, health, and work histories.
• Detailed geographical identifier files for Waves 1-10 which are grouped by identifier held under SN 8429 (Local Authority District Pre-2009 Boundaries), SN 8439 (Local Authority District Post-2009 Boundaries), SN 8430 (Local Authority Type Pre-2009 Boundaries), SN 8441 (Local Authority Type Post-2009 Boundaries), SN 8431 (Quintile Index of Multiple Deprivation Score), SN 8432 (Quintile Population Density for Postcode Sectors), SN 8433 (Census 2001 Rural-Urban Indicators), SN 8437 (Census 2011 Rural-Urban Indicators).

Where boundary changes have occurred, the geographic identifier has been split into two separate studies to reduce the risk of disclosure. Users are also only allowed one version of each identifier:

• either SN 8429 (Local Authority District Pre-2009 Boundaries) or SN 8439 (Local Authority District Post-2009 Boundaries)
• either SN 8430 (Local Authority Type Pre-2009 Boundaries) or SN 8441(Local Authority Type Post-2009 Boundaries)
• either SN 8433 (Census 2001 Rural-Urban Indicators) or SN 8437 (Census 2011 Rural-Urban Indicators)

ELSA Wave 6 and Wave 8 Self-Completion Questionnaires included an open-ended question where respondents could add any other comments they may wish to note down. These responses have been transcribed and anonymised. Researchers can request access to these transcribed responses for research purposes by contacting the...

IntroductionSince February 2020, over 104 million people in the United States have been diagnosed with SARS-CoV-2 infection, or COVID-19, with over 8.5 million reported in the state of Texas. This study analyzed social determinants of health as predictors for readmission among COVID-19 patients in Southeast Texas, United States.MethodsA retrospective cohort study was conducted investigating demographic and clinical risk factors for 30, 60, and 90-day readmission outcomes among adult patients with a COVID-19-associated inpatient hospitalization encounter within a regional health information exchange between February 1, 2020, to December 1, 2022.Results and discussionIn this cohort of 91,007 adult patients with a COVID-19-associated hospitalization, over 21% were readmitted to the hospital within 90  days (n = 19,679), and 13% were readmitted within 30  days (n = 11,912). In logistic regression analyses, Hispanic and non-Hispanic Asian patients were less likely to be readmitted within 90  days (adjusted odds ratio [aOR]: 0.8, 95% confidence interval [CI]: 0.7–0.9, and aOR: 0.8, 95% CI: 0.8–0.8), while non-Hispanic Black patients were more likely to be readmitted (aOR: 1.1, 95% CI: 1.0–1.1, p = 0.002), compared to non-Hispanic White patients. Area deprivation index displayed a clear dose–response relationship to readmission: patients living in the most disadvantaged neighborhoods were more likely to be readmitted within 30 (aOR: 1.1, 95% CI: 1.0–1.2), 60 (aOR: 1.1, 95% CI: 1.2–1.2), and 90  days (aOR: 1.2, 95% CI: 1.1–1.2), compared to patients from the least disadvantaged neighborhoods. Our findings demonstrate the lasting impact of COVID-19, especially among members of marginalized communities, and the increasing burden of COVID-19 morbidity on the healthcare system.

Percentage of emergency admissions to any hospital in England occurring within 30 days of the last, previous discharge from hospital after admission: indirectly standardised by age, sex, method of admission and diagnosis/procedure. The indicator is broken down into the following demographic groups for reporting: ● All years and female only, male only and both male and female (persons). ● <16 years and female only, male only and both male and female (persons). ● 16+ years and female only, male only and both male and female (persons) ● 16-74 years and female only, male only and both male and female (persons) ● 75+ years and female only, male only and both male and female (persons) Results for each of these groups are also split by the following geographical and demographic breakdowns: ● Local authority of residence. ● Region. ● Area classification. ● NHS and private providers. ● NHS England regions. ● Deprivation (Index of Multiple Deprivation (IMD) Quintiles, 2019). ● Sustainability and Transformation Partnerships (STP) & Integrated Care Boards (ICB) from 2016/17. ● Clinical Commissioning Groups (CCG) & sub-Integrated Care Boards (sub-ICB). All annual trends are indirectly standardised against 2013/14.

Abstract copyright UK Data Service and data collection copyright owner.

The English Longitudinal Study of Ageing (ELSA) study is a longitudinal survey of ageing and quality of life among older people that explores the dynamic relationships between health and functioning, social networks and participation, and economic position as people plan for, move into and progress beyond retirement. The main objectives of ELSA are to:

• construct waves of accessible and well-documented panel data;
• provide these data in a convenient and timely fashion to the scientific and policy research community;
• describe health trajectories, disability and healthy life expectancy in a representative sample of the English population aged 50 and over;
• examine the relationship between economic position and health;
• nvestigate the determinants of economic position in older age;
• describe the timing of retirement and post-retirement labour market activity; and
• understand the relationships between social support, household structure and the transfer of assets.

Further information may be found on the the ELSA project website or the Natcen Social Research: ELSA web pages.

Health conditions research with ELSA - June 2021

The ELSA Data team have found some issues with historical data measuring health conditions. If you are intending to do any analysis looking at the following health conditions, then please contact the ELSA Data team at NatCen on elsadata@natcen.ac.uk for advice on how you should approach your analysis. The affected conditions are: eye conditions (glaucoma; diabetic eye disease; macular degeneration; cataract), CVD conditions (high blood pressure; angina; heart attack; Congestive Heart Failure; heart murmur; abnormal heart rhythm; diabetes; stroke; high cholesterol; other heart trouble) and chronic health conditions (chronic lung disease; asthma; arthritis; osteoporosis; cancer; Parkinson's Disease; emotional, nervous or psychiatric problems; Alzheimer's Disease; dementia; malignant blood disorder; multiple sclerosis or motor neurone disease).

Special Licence Data:

Special Licence Access versions of ELSA have more restrictive access conditions than versions available under the standard End User Licence (see 'Access' section below). Users are advised to obtain the latest edition of SN 5050 (the End User Licence version) before making an application for Special Licence data, to see whether that is suitable for their needs. A separate application must be made for each Special Licence study.

Special Licence Access versions of ELSA include:

• Primary data from Wave 8 onwards (SN 8346) includes all the variables in the EUL primary dataset (SN 5050) as well as year and month of birth, consolidated ethnicity and country of birth, marital status, and more detailed medical history variables.
• Wave 8 Pension Age Data (SN 8375) includes all the variables in the EUL pension age data (SN 5050) as well as year and age reached state pension age variables.
• Wave 8 Sexual Self-Completion Data (SN 8376) includes sensitive variables from the sexual self-completion questionnaire.
• Wave 3 (2007) Harmonized Life History (SN 8831) includes retrospective information on previous histories, specifically, detailed data on previous partnership, children, residential, health, and work histories.
• Detailed geographical identifier files for Waves 1-10 which are grouped by identifier held under SN 8429 (Local Authority District Pre-2009 Boundaries), SN 8439 (Local Authority District Post-2009 Boundaries), SN 8430 (Local Authority Type Pre-2009 Boundaries), SN 8441 (Local Authority Type Post-2009 Boundaries), SN 8431 (Quintile Index of Multiple Deprivation Score), SN 8432 (Quintile Population Density for Postcode Sectors), SN 8433 (Census 2001 Rural-Urban Indicators), SN 8437 (Census 2011 Rural-Urban Indicators).

Where boundary changes have occurred, the geographic identifier has been split into two separate studies to reduce the risk of disclosure. Users are also only allowed one version of each identifier:

• either SN 8429 (Local Authority District Pre-2009 Boundaries) or SN 8439 (Local Authority District Post-2009 Boundaries)
• either SN 8430 (Local Authority Type Pre-2009 Boundaries) or SN 8441(Local Authority Type Post-2009 Boundaries)
• either SN 8433 (Census 2001 Rural-Urban Indicators) or SN 8437 (Census 2011 Rural-Urban Indicators)

ELSA Wave 6 and Wave 8 Self-Completion Questionnaires included an open-ended question where respondents could add any other comments they may wish to note down. These responses have been transcribed and anonymised. Researchers can request access to these transcribed responses for research purposes by contacting the...

The repeat absenteeism data is the percentage of pupils missing 15 per cent or more of school sessions. Data are based on all pupils of statutory school age attending a state maintained school.

Dimensions, indicators, deprivation cut-offs, and indicators’ weight.

ELSA Wave 3 Harmonized Life History data: Special Licence Access

In addition to the main interview, ELSA also conducted a life history interview in its third wave. The ELSA Life History interview includes retrospective information on previous histories, specifically, detailed data on previous partnerships, children, residential, health, and work histories. The data collection of ELSA Wave 3 Life History interview took place between March and October 2007.

In order to make the ELSA Life History survey more accessible to researchers and to facilitate such comparisons, the Harmonized ELSA Life History was created as a user-friendly version of a subset of the ELSA Wave 3 Life History survey. The Harmonized ELSA Life History includes variables with a similar data structure and naming conventions to other Harmonized Life History variables.

Overview of complications accounting for transitions between transient states for the study population throughout the study period lasting from 01 January 2013 to 31 December 2018.