Life Course Assessment of Area-Based Social Disadvantage: A Systematic Review.

Area-based social disadvantage, which measures the income, employment, and housing quality in one's community, can impact an individual's health above person-level factors. A life course approach examines how exposure to disadvantage can affect health in later life. This systematic review aimed to summarize the approaches used to assess exposure to area-based disadvantage over a life course, specifically those that define the length and timing of exposure. We reviewed the abstracts of 831 articles based on the following criteria: (1) whether the abstract described original research; (2) whether the study was longitudinal; (3) whether area-based social disadvantage was an exposure variable; (4) whether area-based social disadvantage was assessed at multiple points; and (5) whether exposure was assessed from childhood to older adulthood. Zero articles met all the above criteria, so we relaxed the fifth criterion in a secondary review. Six studies met our secondary criteria and were eligible for data extraction. The included studies followed subjects from childhood into adulthood, but none assessed disadvantages in late life. The approaches used to assess exposure included creating a cumulative disadvantage score, conducting a comparison between life course periods, and modeling the trajectory of disadvantage over time. Additional research was needed to validate the methodologies described here, specifically in terms of measuring the impact of area-based social disadvantage on health.

A systematic review of geographic indices of disadvantage with implications for older adults.

BACKGROUNDNeighborhood-level socioeconomic disadvantage has wide-ranging impacts on health outcomes, particularly in older adults. Although indices of disadvantage are a widely used tool, research conducted to date has not codified a set of standard variables that should be included in these indices for the United States. The objective of this study was to conduct a systematic review of literature describing the construction of geographic indices of neighborhood-level disadvantage and to summarize and distill the key variables included in these indices. We also sought to demonstrate the utility of these indices for understanding neighborhood-level disadvantage in older adults.METHODSWe conducted a systematic review of existing indices in the English-language literature.RESULTSWe identified 6021 articles, of which 130 met final study inclusion criteria. Our review identified 7 core domains across the surveyed papers, including income, education, housing, employment, neighborhood structure, demographic makeup, and health. Although not universally present, the most prevalent variables included in these indices were education and employment.CONCLUSIONIdentifying these 7 core domains is a key finding of this review. These domains should be considered for inclusion in future neighborhood-level disadvantage indices, and at least 5 domains are recommended to improve the strength of the resulting index. Targeting specific domains offers a path forward toward the construction of a new US-specific index of neighborhood disadvantage with health policy applications. Such an index will be especially useful for characterizing the life-course impact of lived disadvantage in older adults.

Differences in Exclusive Breastfeeding Rates in US Hospitals According to Baby-Friendly Hospital Initiative Designation and Area Deprivation Index Category.

Background: Disparities in breastfeeding persist placing a greater burden of disease on non-Hispanic black and Hispanic women and infants. Targeted implementation of the Baby-Friendly Hospital Initiative (BFHI) in areas at risk for poor breastfeeding outcomes has been shown to improve disparities in breastfeeding. The area deprivation index (ADI), a measure of the relative socioeconomic disadvantage of a neighborhood, may be useful in exploring the accessibility of BFHI hospitals in highly deprived areas and the differences in exclusive breastfeeding (EBF) rates in hospitals with and without the BFHI designation across deprivation categories. Objective: To evaluate the geographical distribution of BFHI and non-BFHI hospitals across ADI categories and explore the differences in EBF rates in BFHI and non-BFHI hospitals across ADI categories. Methods: Hospital EBF rates obtained from the Joint Commission included 414 BFHI and 1,532 non-BFHI hospitals. State ADI rank scores were determined for each hospital's census block group. Descriptive statistics were used to describe the geographic distribution of BFHI hospitals across three ADI categories (low, medium, and high). EBF rates across ADI categories and BFHI designations were compared using multiway analysis of variance. Results: The distribution of BFHI was similar across all ADI categories, ranging from 18% to 24%. EBF rates were 4.9% lower in highly deprived areas compared to areas with lower deprivation (p < 0.01). BFHI was associated with significantly higher EBF rates across all ADI categories (6.9%-11.2%, p < 0.01). Conclusion: ADI may be a useful tool for targeting the implementation of BFHI in hospitals in highly deprived areas to reduce breastfeeding disparities.

Association of Neighborhood Context, Cognitive Decline, and Cortical Change in an Unimpaired Cohort.

OBJECTIVE: To test the hypothesis that neighborhood-level disadvantage is associated with longitudinal measures of neurodegeneration and cognitive decline in an unimpaired cohort. METHODS: Longitudinal MRI and cognitive testing data were collected from 601 cognitively unimpaired participants in the Wisconsin Registry for Alzheimer's Prevention Study and the Wisconsin Alzheimer's Disease Research Center clinical cohort. Area Deprivation Index was geospatially determined based on participant residence geocode and ranked relative to state of residence. Linear regression models were fitted to test associations between neighborhood-level disadvantage and longitudinal change in cortical thickness and cognitive test performance. Mediation tests were used to assess whether neurodegeneration and cognitive decline were associated with neighborhood-level disadvantage along the same theoretical causal path. RESULTS: In our middle- to older-aged study population (mean baseline age 59 years), living in the 20% most disadvantaged neighborhoods (n = 19) relative to state of residence was associated with cortical thinning in Alzheimer signature regions (p = 0.002) and decline in the Preclinical Alzheimer's Disease Cognitive Composite (p = 0.04), particularly the Trail-Making Test, part B (p < 0.001), but not Rey Auditory Verbal Learning Test (p = 0.77) or Story Memory Delayed Recall (p = 0.49) subtests. Associations were attenuated but remained significant after controlling for racial and demographic differences between neighborhood-level disadvantage groups. Cortical thinning partially mediated the association between neighborhood-level disadvantage and cognitive decline. CONCLUSIONS: In this longitudinal study of cognitively unimpaired adults, living in the most highly disadvantaged neighborhoods was associated with accelerated degeneration in Alzheimer signature regions and cognitive decline. This study provides further evidence for neighborhood-level disadvantage as a risk factor for preclinical neurodegeneration and cognitive decline in certain populations. Limitations of the present study, including a small number of participants from highly disadvantaged neighborhoods and a circumscribed geographic setting, should be explored in larger and more diverse study cohorts.

Thirty-Day Re-observation, Chronic Re-observation, and Neighborhood Disadvantage.

OBJECTIVE: To determine whether neighborhood socioeconomic disadvantage, as determined by the Area Deprivation Index, increases 30-day hospital re-observation risk. PARTICIPANTS AND METHODS: This retrospective study of 20% Medicare fee-for-service beneficiary observation stays from January 1, 2014, to November 30, 2014, included 319,980 stays among 273,308 beneficiaries. We evaluated risk for a 30-day re-observation following an index observation stay for those living in the 15% most disadvantaged compared with the 85% least disadvantaged neighborhoods. RESULTS: Overall, 4.5% (270,600 of 6,080,664) of beneficiaries had index observation stays, which varied by disadvantage (4.3% [232,568 of 5,398,311] in the least disadvantaged 85% compared with 5.6% [38,032 of 682,353] in the most disadvantaged 15%). Patients in the most disadvantaged neighborhoods had a higher 30-day re-observation rate (2857 of 41,975; 6.8%) compared with least disadvantaged neighborhoods (13,543 of 278,005; 4.9%); a 43% increased risk (unadjusted odds ratio [OR], 1.43; 95% CI, 1.31 to 1.55). After adjustment, this risk remained (adjusted OR, 1.13; 95% CI, 1.04 to 1.22). Discharge to a skilled nursing facility reduced 30-day re-observation risk (OR, 0.63; 95% CI, 0.57 to 0.69), whereas index observation length of stay of 4 or more days (3 midnights) conferred increased risk (OR, 1.29; 95% CI, 1.09 to 1.52); those living in disadvantaged neighborhoods were less likely to discharge to skilled nursing facilities and more likely to have long index stays. Beneficiaries with more than one 30-day re-observation (chronic re-observation) had progressively greater disadvantage by number of stays (adjusted incident rate ratio, 1.08; 95% CI, 1.02 to 1.14). Observation prevalence varied nationally. CONCLUSION: Thirty-day re-observation, especially chronic re-observation, is highly associated with socioeconomic neighborhood disadvantage, even after accounting for factors such as race, disability, and Medicaid eligibility. Beneficiaries least able to pay are potentially most vulnerable to costs from serial re-observations and challenges of Medicare observation policy, which may discourage patients from seeking necessary care.

The Area Deprivation Index: A novel tool for harmonizable risk assessment in Alzheimer's disease research.

INTRODUCTION: Residence in a disadvantaged neighborhood associates with adverse health exposures and outcomes, and may increase risk for cognitive impairment and dementia. Utilization of a publicly available, geocoded disadvantage metric could facilitate efficient integration of social determinants of health into models of cognitive aging. METHODS: Using the validated Area Deprivation Index and two cognitive aging cohorts, we quantified Census block-level poverty, education, housing, and employment characteristics for the neighborhoods of 2119 older adults. We assessed relationships between neighborhood disadvantage and cognitive performance in domains sensitive to age-related change. RESULTS: Participants in the most disadvantaged neighborhoods (n = 156) were younger, more often female, and less often college-educated or white than those in less disadvantaged neighborhoods (n = 1963). Disadvantaged neighborhood residence associated with poorer performance on tests of executive function, verbal learning, and memory. DISCUSSION: This geospatial metric of neighborhood disadvantage may be valuable for exploring socially rooted risk mechanisms, and prioritizing high-risk communities for research recruitment and intervention.

Association of Neighborhood-Level Disadvantage With Alzheimer Disease Neuropathology.

Importance: Social determinants of health, such as income, education, housing quality, and employment, are associated with disparities in Alzheimer disease and health generally, yet these determinants are rarely incorporated within neuropathology research. Objective: To establish the feasibility of linking neuropathology data to social determinants of health exposures using neighborhood disadvantage metrics (the validated Area Deprivation Index) and to evaluate the association between neighborhood disadvantage and Alzheimer disease-related neuropathology. Design, Setting, and Participants: This cross-sectional study consisted of decedents with a known home address who donated their brains to 1 of 2 Alzheimer disease research center brain banks in California and Wisconsin between January 1, 1990, and December 31, 2016. Neither site had preexisting social metrics available for their decedents. Neuropathologic features were obtained from each site for data collected using the standardized Neuropathology Data Set form and from autopsy reports. Data were analyzed from June 7 to October 10, 2019. Exposures: Geocoded decedent addresses linked to neighborhood disadvantage as measured by the Area Deprivation Index calculated for the year of death. Main Outcomes and Measures: Presence of Alzheimer disease neuropathology. The association between neighborhood disadvantage and Alzheimer disease neuropathology was evaluated via logistic regression, adjusting for age, sex, and year of death. Results: The sample consisted of 447 decedents (249 men [56%]; mean [SD] age, 80.3 [9.5] years; median year of death, 2011) spanning 24 years of donation. Fewer decedents (n = 24 [5.4%]) originated from the top 20% most disadvantaged neighborhood contexts. Increasing neighborhood disadvantage was associated with an 8.1% increase in the odds of Alzheimer disease neuropathology for every decile change on the Area Deprivation Index (adjusted odds ratio, 1.08; 95% CI, 1.07-1.09). As such, living in the most disadvantaged neighborhood decile was associated with a 2.18 increased odds of Alzheimer disease neuropathology (adjusted odds ratio, 2.18; 95% CI, 1.99-2.39). Conclusions and Relevance: The findings of this cross-sectional study suggest that social determinants of health data can be linked to preexisting autopsy samples as a means to study sociobiological mechanisms involved in neuropathology. This novel technique has the potential to be applied to any brain bank within the United States. To our knowledge, this is the first time Alzheimer disease neuropathology has been associated with neighborhood disadvantage.

Neighborhood disadvantage and chronic disease management.

OBJECTIVE: To assess the relationship between a composite measure of neighborhood disadvantage, the Area Deprivation Index (ADI), and control of blood pressure, diabetes, and cholesterol in the Medicare Advantage (MA) population. DATA SOURCES: Secondary analysis of 2013 Medicare Healthcare Effectiveness Data and Information Set, Medicare enrollment data, and a neighborhood disadvantage indicator. STUDY DESIGN: We tested the association of neighborhood disadvantage with intermediate health outcomes. Generalized estimating equations were used to adjust for geographic and individual factors including region, sex, race/ethnicity, dual eligibility, disability, and rurality. DATA COLLECTION: Data were linked by ZIP+4, representing compact geographic areas that can be linked to Census block groups. PRINCIPAL FINDINGS: Compared with enrollees residing in the least disadvantaged neighborhoods, enrollees in the most disadvantaged neighborhoods were 5 percentage points (P < 0.05) less likely to have controlled blood pressure, 6.9 percentage points (P < 0.05) less likely to have controlled diabetes, and 9.9 percentage points (P < 0.05) less likely to have controlled cholesterol. Adjustment attenuated this relationship, but the association remained. CONCLUSIONS: The ADI is a strong, independent predictor of diabetes and cholesterol control, a moderate predictor of blood pressure control, and could be used to track neighborhood-level disparities and to target disparities-focused interventions in the MA population.

Impact Of Risk Adjustment For Socioeconomic Status On Medicare Advantage Plan Quality Rankings.

Sociodemographically disadvantaged patients have worse outcomes on some quality measures that inform Medicare Advantage plan ratings. Performance measurement that does not adjust for sociodemographic factors may penalize plans that disproportionately serve disadvantaged populations. We assessed the impact of adjusting for socioeconomic and demographic factors (sex, race/ethnicity, dual eligibility, disability, rurality, and neighborhood disadvantage) on Medicare Advantage plan rankings for blood pressure, diabetes, and cholesterol control. After adjustment, 20.3 percent, 19.5 percent, and 11.4 percent of Medicare Advantage plans improved by one or more quintiles in rank on the diabetes, cholesterol, and blood pressure measures, respectively. Plans that improved in ranking after adjustment enrolled higher proportions of disadvantaged enrollees. Adjusting quality measures for socioeconomic factors is important for equitable payment and quality reporting. Our study suggests that plans serving disadvantaged populations would have improved relative rankings for three important outcome measures if socioeconomic factors were included in risk-adjustment models.

Sparse modeling of spatial environmental variables associated with asthma.

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors.

The potential and pitfalls of geocoding electronic health records.

BACKGROUND: Geocoding electronic health records (EHRs) provides novel insights for clinicians, but it is important to understand and address key issues, including privacy and protection of patient records, in order to realize potential benefits. METHODS: This paper discusses the issues surrounding geocoding and illustrates potential benefits through 3 case studies of no-shows to clinical appointments, patient analysis for a merged clinic site, and multi-clinic patient overlap. CONCLUSION: Geocoding EHRs provides a new contextual understanding for clinicians to understand patients and provide targeted interventions that patients can implement. While geocoding EHRs presents a need for high data security, the benefits outweigh the risks when proper protections are observed.

The effect of prenatal support on birth outcomes in an urban midwestern county.

OBJECTIVES: In Dane County, Wisconsin, the black-white infant mortality gap started decreasing from 2000 and was eliminated from 2004 to 2007. Unfortunately, it has reappeared since 2008. This paper examines risk factors and levels of prenatal care to identify key contributors to the dramatic decline and recent increase in black infant mortality and extremely premature birth rates. METHODS: This retrospective cohort study analyzed approximately 100,000 Dane County birth, fetal, and infant death records from 1990 to 2007. Levels of prenatal care received were categorized as "less-than-standard," "standard routine" or "intensive." US Census data analysis identified demographic and socioeconomic changes. Infant mortality rates and extremely premature ( < or = 28 weeks gestation) birth rates were main outcome measures. Contributions to improved outcomes were measured by calculating relative risk, risk difference and population attributable fraction (PAF). Mean income and food stamp use by race were analyzed as indicators of general socioeconomic changes suspected to be responsible for worsening outcomes since 2008. RESULTS: Risk of extremely premature delivery for black women receiving standard routine care and intensive care decreased from 1990-2000 to 2001-2007 by 77.8% (95% CI = 49.9-90.1%) and 57.3% (95% CI = 27.6-74.8%) respectively. Women receiving less-than-standard care showed no significant improvement over time. Racial gaps in mean income and food stamp use narrowed 2002-2007 and widened since 2008. CONCLUSIONS: Prenatal support played an important role in improving black birth outcomes and eliminating the Dane County black-white infant mortality gap. Increasing socioeconomic disparities with worsening US economy since 2008 likely contributed to the gap's reappearance.