Dataset Information

A neighborhood wealth metric for use in health studies.

ABSTRACT:

Background

Measures of neighborhood deprivation used in health research are typically based on conventional area-based SES.

Purpose

The aim of this study is to examine new data and measures of SES for use in health research. Specifically, assessed property values are introduced as a new individual-level metric of wealth and tested for their ability to substitute for conventional area-based SES as measures of neighborhood deprivation.

Methods

The analysis was conducted in 2010 using data from 1922 participants in the 2008-2009 survey of the Seattle Obesity Study (SOS). It compared the relative strength of the association between the individual-level neighborhood wealth metric (assessed property values) and area-level SES measures (including education, income, and percentage above poverty as single variables, and as the composite Singh index) on the binary outcome fair/poor general health status. Analyses were adjusted for gender, categoric age, race, employment status, home ownership, and household income.

Results

The neighborhood wealth measure was more predictive of fair/poor health status than area-level SES measures, calculated either as single variables or as indices (lower DIC measures for all models). The odds of having a fair/poor health status decreased by 0.85 (95% CI=0.77, 0.93) per $50,000 increase in neighborhood property values after adjusting for individual-level SES measures.

Conclusions

The proposed individual-level metric of neighborhood wealth, if replicated in other areas, could replace area-based SES measures, thus simplifying analyses of contextual effects on health.

SUBMITTER: Moudon AV

PROVIDER: S-EPMC3118096 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A neighborhood wealth metric for use in health studies.

Moudon Anne Vernez AV Cook Andrea J AJ Ulmer Jared J Hurvitz Philip M PM Drewnowski Adam A

American journal of preventive medicine 20110701 1

<h4>Background</h4>Measures of neighborhood deprivation used in health research are typically based on conventional area-based SES.<h4>Purpose</h4>The aim of this study is to examine new data and measures of SES for use in health research. Specifically, assessed property values are introduced as a new individual-level metric of wealth and tested for their ability to substitute for conventional area-based SES as measures of neighborhood deprivation.<h4>Methods</h4>The analysis was conducted in 20 ...[more]

PMID: 21665069

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Project description:ImportanceData from electronic health records (EHRs) are increasingly used for risk prediction. However, EHRs do not reliably collect sociodemographic and neighborhood information, which has been shown to be associated with health. The added contribution of neighborhood socioeconomic status (nSES) in predicting health events is unknown and may help inform population-level risk reduction strategies.ObjectiveTo quantify the association of nSES with adverse outcomes and the value of nSES in predicting the risk of adverse outcomes in EHR-based risk models.Design, setting, and participantsCohort study in which data from 90 097 patients 18 years or older in the Duke University Health System and Lincoln Community Health Center EHR from January 1, 2009, to December 31, 2015, with at least 1 health care encounter and residence in Durham County, North Carolina, in the year prior to the index date were linked with census tract data to quantify the association between nSES and the risk of adverse outcomes. Machine learning methods were used to develop risk models and determine how adding nSES to EHR data affects risk prediction. Neighborhood socioeconomic status was defined using the Agency for Healthcare Research and Quality SES index, a weighted measure of multiple indicators of neighborhood deprivation.Main outcomes and measuresOutcomes included use of health care services (emergency department and inpatient and outpatient encounters) and hospitalizations due to accidents, asthma, influenza, myocardial infarction, and stroke.ResultsAmong the 90 097 patients in the training set of the study (57 507 women and 32 590 men; mean [SD] age, 47.2 [17.7] years) and the 122 812 patients in the testing set of the study (75 517 women and 47 295 men; mean [SD] age, 46.2 [17.9] years), those living in neighborhoods with lower nSES had a shorter time to use of emergency department services and inpatient encounters, as well as a shorter time to hospitalizations due to accidents, asthma, influenza, myocardial infarction, and stroke. The predictive value of nSES varied by outcome of interest (C statistic ranged from 0.50 to 0.63). When added to EHR variables, nSES did not improve predictive performance for any health outcome.Conclusions and relevanceSocial determinants of health, including nSES, are associated with the health of a patient. However, the results of this study suggest that information on nSES may not contribute much more to risk prediction above and beyond what is already provided by EHR data. Although this result does not mean that integrating social determinants of health into the EHR has no benefit, researchers may be able to use EHR data alone for population risk assessment.

Project description:ImportanceThe association of socioeconomic status and cardiovascular outcomes has been well described, but little is known about whether longitudinal changes in wealth are associated with cardiovascular health status.ObjectiveTo evaluate the association between midlife wealth mobility and risk of cardiovascular events.Design, setting, and participantsThis longitudinal, retrospective cohort study included US adults 50 years or older who participated in the Health and Retirement Study. Participants in the primary analysis had no history of cardiovascular disease and had observations in at least two of three 5-year age intervals (50-54, 55-59, and 60-64 years) and follow-up after 65 years of age. Data were collected from January 1, 1992, to December 31, 2016, and analyzed from November 10, 2020, to April 26, 2021.ExposuresQuintiles of wealth (reflecting total nonhousing assets) were defined within each of 4 birth cohorts (1931-1935, 1936-1940, 1941-1945, and 1946-1950). Wealth mobility was defined as an increase or a decrease of 1 or more wealth quintiles and was compared with wealth stability (same quintile over time) using covariate-adjusted Cox proportional hazards regression models.Main outcomes and measuresComposite outcome of nonfatal cardiovascular event (myocardial infarction, heart failure, cardiac arrhythmia, or stroke) or cardiovascular death.ResultsA total of 5579 participants were included in the primary analysis (mean [SD] age, 54.2 [2.6] years; 3078 women [55.2%]). During a mean (SD) follow-up of 16.9 (5.8) years, 1336 participants (24.0%) experienced a primary end point of nonfatal cardiovascular event or cardiovascular death (14.4 [95% CI, 13.6-15.2] per 1000 patient-years). Higher initial wealth (per quintile) was associated with lower cardiovascular risk (adjusted hazard ratio [aHR] per quintile, 0.89 [95% CI, 0.84-0.95]; P = .001). When compared with stable wealth, participants who experienced upward wealth mobility (by at least 1 quintile) had independently lower hazards of a subsequent nonfatal cardiovascular event or cardiovascular death (aHR, 0.84 [95% CI, 0.73-0.97]; P = .02), and participants who experienced downward wealth mobility had higher risks (aHR, 1.15 [95% CI, 1.00-1.32]; P = .046).Conclusions and relevanceThese findings suggest that upward wealth mobility relative to peers in late middle age is associated with lower risks of cardiovascular events or death after 65 years of age.

Project description:ImportancePrediction models are widely used in health care as a way of risk stratifying populations for targeted intervention. Most risk stratification has been done using a small number of predictors from insurance claims. However, the utility of diverse nonclinical predictors, such as neighborhood socioeconomic contexts, remains unknown.ObjectiveTo assess the value of using neighborhood socioeconomic predictors in the context of 1-year risk prediction for mortality and 6 different health care use outcomes in a large integrated care system.Design, setting, and participantsDiagnostic study using data from all adults age 18 years or older who had Kaiser Foundation Health Plan membership and/or use in the Kaiser Permantente Northern California: a multisite, integrated health care delivery system between January 1, 2013, and June 30, 2014. Data were recorded before the index date for each patient to predict their use and mortality in a 1-year post period using a test-train split for model training and evaluation. Analyses were conducted in fall of 2019.Main outcomes and measuresOne-year encounter counts (doctor office, virtual, emergency department, elective hospitalizations, and nonelective), total costs, and mortality.ResultsA total of 2 951 588 patients met inclusion criteria (mean [SD] age, 47.2 [17.4] years; 47.8% were female). The mean (SD) Neighborhood Deprivation Index was -0.32 (0.84). The areas under the receiver operator curve ranged from 0.71 for emergency department use (using the LASSO method and electronic health record predictors) to 0.94 for mortality (using the random forest method and electronic health record predictors). Neighborhood socioeconomic status predictors did not meaningfully increase the predictive performance of the models for any outcome.Conclusions and relevanceIn this study, neighborhood socioeconomic predictors did not improve risk estimates compared with what is obtainable using standard claims data regardless of model used.

Dataset Information

A neighborhood wealth metric for use in health studies.

Background

Purpose

Methods

Results

Conclusions

Publications

A neighborhood wealth metric for use in health studies.

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