scholarly journals Comparisons of individual- and area-level socioeconomic status as proxies for individual-level measures: evidence from the Mortality Disparities in American Communities study

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Jennifer L. Moss ◽  
Norman J. Johnson ◽  
Mandi Yu ◽  
Sean F. Altekruse ◽  
Kathleen A. Cronin
Keyword(s):  
Socioeconomic Status ◽  
Census Tract ◽  
Household Income ◽  
County Level ◽  
Individual Level ◽  
Census Tract Level ◽  
Level Data ◽  
The Us ◽  
Mortality Disparities ◽  
Household Incomes

Abstract Background Area-level measures are often used to approximate socioeconomic status (SES) when individual-level data are not available. However, no national studies have examined the validity of these measures in approximating individual-level SES. Methods Data came from ~ 3,471,000 participants in the Mortality Disparities in American Communities study, which links data from 2008 American Community Survey to National Death Index (through 2015). We calculated correlations, specificity, sensitivity, and odds ratios to summarize the concordance between individual-, census tract-, and county-level SES indicators (e.g., household income, college degree, unemployment). We estimated the association between each SES measure and mortality to illustrate the implications of misclassification for estimates of the SES-mortality association. Results Participants with high individual-level SES were more likely than other participants to live in high-SES areas. For example, individuals with high household incomes were more likely to live in census tracts (r = 0.232; odds ratio [OR] = 2.284) or counties (r = 0.157; OR = 1.325) whose median household income was above the US median. Across indicators, mortality was higher among low-SES groups (all p < .0001). Compared to county-level, census tract-level measures more closely approximated individual-level associations with mortality. Conclusions Moderate agreement emerged among binary indicators of SES across individual, census tract, and county levels, with increased precision for census tract compared to county measures when approximating individual-level values. When area level measures were used as proxies for individual SES, the SES-mortality associations were systematically underestimated. Studies using area-level SES proxies should use caution when selecting, analyzing, and interpreting associations with health outcomes.

scholarly journals Associations between Greenspace and Gentrification-Related Sociodemographic and Housing Cost Changes in Major Metropolitan Areas across the United States

2021 ◽  
Vol 18 (6) ◽  
pp. 3315
Author(s):  
Leah H. Schinasi ◽  
Helen V. S. Cole ◽  
Jana A. Hirsch ◽  
Ghassan B. Hamra ◽  
Pedro Gullon ◽  
...  
Keyword(s):  
United States ◽  
Census Tract ◽  
Household Income ◽  
The United States ◽  
Effect Modification ◽  
Ethnic Composition ◽  
Housing Cost ◽  
Census Tract Level ◽  
Specific Distribution ◽  
Meta Analyses

Neighborhood greenspace may attract new residents and lead to sociodemographic or housing cost changes. We estimated relationships between greenspace and gentrification-related changes in the 43 largest metropolitan statistical areas (MSAs) of the United States (US). We used the US National Land Cover and Brown University Longitudinal Tracts databases, as well as spatial lag models, to estimate census tract-level associations between percentage greenspace (years 1990, 2000) and subsequent changes (1990–2000, 2000–2010) in percentage college-educated, percentage working professional jobs, race/ethnic composition, household income, percentage living in poverty, household rent, and home value. We also investigated effect modification by racial/ethnic composition. We ran models for each MSA and time period and used random-effects meta-analyses to derive summary estimates for each period. Estimates were modest in magnitude and heterogeneous across MSAs. After adjusting for census-tract level population density in 1990, compared to tracts with low percentage greenspace in 1992 (defined as ≤50th percentile of the MSA-specific distribution in 1992), those with high percentage greenspace (defined as >75th percentile of the MSA-specific distribution) experienced higher 1990–2000 increases in percentage of the employed civilian aged 16+ population working professional jobs (β: 0.18, 95% confidence interval (CI): 0.11, 0.26) and in median household income (β: 0.23, 95% CI: 0.15, 0.31). Adjusted estimates for the 2000–2010 period were near the null. We did not observe evidence of effect modification by race/ethnic composition. We observed evidence of modest associations between greenspace and gentrification trends. Further research is needed to explore reasons for heterogeneity and to quantify health implications.

scholarly journals A COVID-19 Risk Assessment for the US Labor Force

2020 ◽  
Author(s):  
Samantha Maher ◽  
Alexandra E Hill ◽  
Peter Britton ◽  
Eli P. Fenichel ◽  
Peter Daszak ◽  
...  
Keyword(s):  
At Risk ◽  
Health Risk ◽  
Return To Work ◽  
Control Measures ◽  
Social Risk ◽  
Social Risk Factors ◽  
Individual Level ◽  
Important Variation ◽  
Level Data ◽  
The Us

AbstractThe consequences of COVID-19 infection varies substantially based on individual social risk factors and predisposing health conditions. Understanding this variability may be critical for targeting COVID-19 control measures, resources and policies, including efforts to return people back to the workplace. We compiled individual level data from the National Health Information Survey and Quarterly Census of Earnings and Wages to estimate the number of at-risk workers for each US county and industry, accounting for both social and health risks. Nearly 80% of all workers have at least one health risk and 11% are over 60 with an additional health risk. We document important variation in the at-risk population across states, counties, and industries that could provide a strategic underpinning to a staged return to work.One Sentence SummaryThere is important variability in the proportion of the US workforce at risk for COVID-19 complications across regions, counties, and industries that should be considered when targeting control and relief policies, and a staged return to work.

Self-employment and Civic Inclination

2020 ◽  
Vol 63 (5) ◽  
pp. 719-737
Author(s):  
F. Carson Mencken ◽  
Bethany Smith ◽  
Charles M. Tolbert
Keyword(s):  
Regression Models ◽  
Political Activism ◽  
The Self ◽  
Data Sets ◽  
County Level ◽  
Cross Sectional ◽  
Individual Level ◽  
Self Employment ◽  
Level Data ◽  
Building Community

We test whether the self-employed have higher levels of civic inclination (trust, political activism, community closeness, community participation) compared to workers from the private sector. We examine the civic inclinations of the self-employed with two national cross-sectional data sets. We use a variety of discrete and continuous regression models. We find that the self-employed have higher levels of political activism, feel closer to neighbors and family, and have greater odds of engaging to solve community problems. We fail to detect differences in donating money, attending community events, and closeness to friends. Previous research has concluded with county-level data that the self-employed are important actors in building community and creating social capital. Our results add to this literature by showing that the self-employed have higher levels of civic inclination with individual-level data. Implications for theory and research are discussed.

The impact of census-tract socioeconomic status on survival in stage III colon cancer.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 7032-7032
Author(s):  
Amina Dhahri ◽  
Jori Lee Kaplan ◽  
Shana Ntiri ◽  
Iman Imanirad ◽  
Seth Felder ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Socioeconomic Status ◽  
Census Tract ◽  
Predictor Variable ◽  
Stage Iii ◽  
National Database ◽  
Lower Grade ◽  
Stage Iii Colon Cancer ◽  
Census Tract Level ◽  
The Impact

7032 Background: Socioeconomic status (SES) has been associated with worse outcomes in stage III colon cancer. However, these studies have used large geographic areas (zip codes or counties) as a proxy for SES which may bias results. To overcome this challenge, we used a national database with census-tract level SES to assess the impact on cancer-specific (CSS) and overall survival (OS). Methods: Using the SEER Census-Tract Dataset from 2004-2015, we identified 8th edition AJCC stage III colon adenocarcinoma patients who underwent curative-intent surgery and initiated adjuvant chemotherapy. The predictor variable was census-tract level SES, consisting of 7 variables such as income, housing, and education. SES was analyzed as quartiles. Statistical analysis included chi square tests for association and Kaplan-Meier and Cox regression for survival analysis. Results: We identified 27,222 patients who met inclusion criteria. Lower SES was associated with younger age, Black or Hispanic race/ethnicity, Medicaid or uninsured status, higher T stage, <12 lymph nodes examined and lower grade tumors. Median CSS was not reached; the 25th percentile CSS time was 54 months for the lowest SES (LSES) quartile and 80 months for the highest (HSES). Median OS was 113 months for LSES and not reached for HSES. The 5-year CSS rate was 72.4% for the LSES quartile compared to 78.9% in the HSES (p<0.001). The 5-year OS rate was 66.5% for LSES and 74.6% in the HSES (p<0.001). After adjusting for potential confounders (age, sex, race, insurance, pathologic T and N stage and grade), LSES was associated with increased cancer-specific death relative to the HSES (HR 1.22; 95% CI [1.114-1.327]) Conclusions: This is the first study to evaluate CSS and OS in a national cohort of stage III colon cancer patients using a granular, standardized measure of SES. Despite receipt of guideline-based treatment, low SES remained a predictor of increased cancer-specific mortality. These data suggest that investigating treatment barriers beyond adjuvant therapy is needed to address colon cancer survival disparities. [Table: see text]

scholarly journals Individual and neighbourhood socioeconomic status increase risk of avoidable hospitalizations among Canadian adults: A retrospective cohort study of linked population health data

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Lauren E Wallar ◽  
Laura C Rosella
Keyword(s):  
Socioeconomic Status ◽  
Hospital Discharge ◽  
Household Income ◽  
Joint Effect ◽  
Material Deprivation ◽  
Income Quintile ◽  
Individual Level ◽  
Avoidable Hospitalizations ◽  
Neighbourhood Level ◽  
Increase Risk

IntroductionAvoidable hospitalizations refer to acute care use for conditions that should normally be managed inprimary care settings. Lower socioeconomic status that is often measured using area-based indicators(e.g. median household income) has been shown to increase risk of avoidable hospitalizations.However, both area- and individual-level socioeconomic status can contribute to hospitalization risk,but previous data limitations have prevented separate analyses. Further, the joint effect of individualand neighbourhood socioeconomic status has not been established in the Canadian population. Toaddress this, this study links individual-level household income and neighbourhood-level materialdeprivation data within a population-based Canadian cohort. ObjectivesTo determine the individual and joint effect of individual-level household income and neighbourhood-level material deprivation on risk of hospitalization for a set of chronic ambulatory care sensitiveconditions using linked health survey, hospital discharge, and census-derived data. MethodsA pooled cohort was created by linking sociodemographic and health information from eight cycles ofthe Canadian Community Health Survey (2000/2001 - 2010) to hospital discharge records and Cana-dian Marginalization Indices (2001, 2006) (N = 354,595). The primary outcome variable was riskof index hospitalization with a primary diagnosis of angina, asthma, congestive heart failure, chronicobstructive pulmonary disease, diabetes, epilepsy, or hypertension. The primary exposure variablewas joint individual-level national household income quintile and neighbourhood-level material de-privation quintile. Relative risk (RR) was estimated by constructing modified Poisson regressionmodels with robust error variance. ResultsIn fully adjusted models with income and deprivation considered separately, individuals in the lowesthousehold income quintile and highest material deprivation quintile were at increased risk of hospi-talization (Income RR: 1.82 (95% CI 1.56-2.13) Deprivation RR: 1.67 (1.44-1.95)). When incomeand deprivation were jointly considered, those with low individual income living in high deprivationneighbourhoods were at greatest risk of hospitalization (RR 1.83 (95% CI 1.63 - 2.05)). ConclusionsBoth individual income and neighbourhood deprivation separately and jointly increase risk of avoid-able hospitalizations. Additional research is needed to understand their mechanisms of action.However, both levels should be considered when designing effective policies and interventions toreduce avoidable hospitalizations.

scholarly journals County-level recruitment in online samples: Applications to COVID-19 and beyond

2020 ◽  
Author(s):  
Leib Litman ◽  
Robert Hartman ◽  
Shalom Noach Jaffe ◽  
Jonathan Robinson
Keyword(s):  
Infection Rate ◽  
Face Mask ◽  
The Self ◽  
Data Sets ◽  
County Level ◽  
Sampling Methodology ◽  
Individual Level ◽  
Level Data ◽  
Potential Applications ◽  
Present Effort

Thousands of readily downloadable county-level data sets offer untapped potential for linking geo-social influences to individual-level human behavior. In this study we describe a methodology for county-level sampling of online participants, allowing us to link the self-reported behavior of N = 1084 online respondents to contemporaneous county-level data on COVID-19 infection rate density. Using this approach, we show that infection rate density predicts person-level self-reported face mask wearing beyond multiple other demographic and attitudinal covariates. Using the present effort as a demonstration project, we describe the underlying sampling methodology and discuss the wider range of potential applications.

scholarly journals State-Level Income Inequality and County-Level Social Capital in Relation to Individual-Level Depression in Middle-Aged Adults: A Lagged Multilevel Study

2020 ◽  
Vol 17 (15) ◽  
pp. 5386
Author(s):  
Saloni Dev ◽  
Daniel Kim
Keyword(s):  
Social Capital ◽  
Income Inequality ◽  
State Level ◽  
Social Determinant ◽  
County Level ◽  
Middle Aged ◽  
Effective Prevention ◽  
Individual Level ◽  
The Us ◽  
Middle Aged Adults

In the US, the incidence of depression and suicide have followed escalating trends over the past several years. These trends call for greater efforts towards identifying their underlying drivers and finding effective prevention strategies and treatments. One social determinant of health that plausibly influences the risk of depression is income inequality, the gap between the rich and poor. However, research on this association is still sparse. We used data from the National Longitudinal Survey of Youth 1979 and the US Census to investigate the multilevel lagged associations of state-level income inequality with the individual-level odds of depression in middle-aged adults, controlling for state- and individual-level factors. We also examined the independent associations of county-level social capital with depression and explored whether it mediated the income inequality relationship. Higher income inequality at the state level predicted higher odds of individual-level depression nearly 2 decades later [OR for middle vs. lowest tertile of income inequality = 1.35 (95% CI: 1.02, 1.76), OR for highest vs. lowest tertile = 1.34 (95% CI: 1.01, 1.78)]. This association was stronger among men than women. Furthermore, there was evidence that county-level social capital independently predicted depression and that it mediated the income inequality association. Overall, our findings suggest that policies attenuating levels of income inequality at the US state level and that leverage social capital may protect against one’s likelihood of developing depression.

scholarly journals Pantries and Policy Implementation: Using Nonprofit Priorities to Understand Variation in Emergency Food Assistance

2018 ◽  
Vol 47 (4_suppl) ◽  
pp. 11S-33S ◽  
Author(s):  
Rachel Fyall ◽  
Jamie Levine Daniel
Keyword(s):  
Policy Implementation ◽  
Census Tract ◽  
Service Providers ◽  
Food Assistance ◽  
Administrative Records ◽  
Food Pantries ◽  
Census Tract Level ◽  
Level Data ◽  
Emergency Food Assistance ◽  
Food Needs

Public and nonprofit actors have long partnered to carry out emergency food assistance, particularly through the use of nonprofit food pantries. Although nonprofit pantries fulfill an important function in policy implementation, they differ with respect to specific mission and organizational priorities. To what extent do organizational priorities explain variation in emergency food? Our analyses examine this question using survey data from 95 nonprofit food pantries associated with a Midwestern Foodbank, administrative records, and census tract-level data. Findings indicate that the priorities of nonprofit pantries help explain variation in food assistance provided by pantries, even after taking into consideration measures of need, accessibility, capacity, and processes. Our results imply that policymakers may be better equipped to meet community food needs by knowing more about the organizational priorities of nonprofit service providers.

scholarly journals Employment, Wages, and Voter Turnout

2013 ◽  
Vol 5 (4) ◽  
pp. 111-143 ◽  
Author(s):  
Kerwin Kofi Charles ◽  
Melvin Stephens
Keyword(s):  
Panel Data ◽  
Voter Turnout ◽  
House Of Representatives ◽  
Political Knowledge ◽  
County Level ◽  
Labor Market Conditions ◽  
Individual Level ◽  
Media Usage ◽  
Us Congress ◽  
Level Data

Using county-level data across several decades, and various OLS and TSLS models, we find that higher local wages and employment lower turnout in elections for governor, senator, US Congress and state House of Representatives, but have no effect on presidential turnout. We also find that the share of people voting in one election but not in another on the same ballot increases as local labor market conditions improve. We argue that these results are most consistent with information-based models of voting, and use individual level panel data to show that increased employment lowers media usage and political knowledge. (JEL D72, D83, J22, J31, R23)

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