The ADI-3: a revised neighborhood risk index of the social determinants of health over time and place

Berg, Kristen A.; Dalton, Jarrod E.; Gunzler, Douglas D.; Coulton, Claudia J.; Freedman, Darcy A.; Krieger, Nikolas I.; Dawson, Neal V.; Perzynski, Adam T.

doi:10.1007/s10742-021-00248-6

Cited by 23 publications

(23 citation statements)

References 36 publications

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“…Area deprivation index is a zip-code based indicator of neighbourhood socio-economic disadvantage consisting of neighbourhood financial strength, economic hardship and inequality, and educational attainment. 9 Socioeconomic data for evaluating ADI were gathered from American community survey 5-year estimates data (2015–19). The ADI has been examined in various studies for its association with health outcomes.…”

Section: Methodsmentioning

confidence: 99%

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Smartphone-based cardiac implantable electronic device remote monitoring: improved compliance and connectivity

Manyam

Burri

Casado‐Arroyo

et al. 2022

European Heart Journal - Digital Health

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Objective Remote monitoring (RM) is the standard of care for follow up of patients with cardiac implantable electronic devices. The aim of this study was to compare smartphone-based RM (SM-RM) using patient applications (myMerlinPulseTM app) with traditional bedside monitor RM (BM-RM). Methods The retrospective study included de-identified U.S. patients who received either SM-RM or BM-RM capable implantable cardioverter defibrillators or cardiac resynchronization therapy defibrillators (Abbott, USA). Patients in SM-RM and BM-RM groups were propensity-score matched on age and gender, device type, implant year and month. Compliance with RM was quantified as the proportion of patients enrolling in the RM system (Merlin.netTM) and transmitting data at least once. Connectivity was measured by the median number of days between consecutive transmissions per patient. Results Of the initial 9,714 patients with SM-RM and 26,679 patients with BM-RM, 9,397 patients from each group were matched. RM compliance was higher in SM-RM; significantly more patients with SM-RM were enrolled in RM compared to BM-RM (94.4% vs. 85.0%, p < 0.001), similar number of patients in the SM-RM group paired their device (95.1% vs. 95.0%, p = 0.77), but more SM-RM patients transmitted at least once (98.1% vs. 94.3%, p < 0.001). Connectivity was significantly higher in the SM-RM, with patients transmitting data every 1.2 [1.1, 1.7] vs. every 1.7 [1.5, 2.0] days with BM-RM (p < 0.001) and remained better over time. Significantly more SM-RM patients utilized patient-initiated transmissions compared to BM-RM (55.6% vs. 28.1%, p < 0.001). Conclusion In this large real-world study, patients with SM-RM demonstrated improved compliance and connectivity compared to BM-RM.

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Section: Methodsmentioning

confidence: 99%

“…The ADI has been examined in various studies for its association with health outcomes. 9–12 The ADI scores were categorized into tertiles, with the first ADI tertile representing the highest SES level, and the third ADI tertile representing the lowest SES level.…”

Section: Methodsmentioning

confidence: 99%

Smartphone-based cardiac implantable electronic device remote monitoring: improved compliance and connectivity

Manyam

Burri

Casado‐Arroyo

et al. 2022

European Heart Journal - Digital Health

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“…This retrospective cohort study used data from the Scientific Reg- from the "sociome" R package, which derives ADI from American Community Survey 5-year estimates. 13 Area deprivation is a widely used small-area measure of socioeconomic disadvantage based on education, income, housing, and employment. 14 ADI estimates were linked to SRTR data and shapefiles via ZCTA identification code.…”

Section: Methodsmentioning

confidence: 99%

“…ZCTA‐level area deprivation index (ADI) values were obtained from 2010 to 2019 estimates from the “sociome” R package, which derives ADI from American Community Survey 5‐year estimates. 13 Area deprivation is a widely used small‐area measure of socioeconomic disadvantage based on education, income, housing, and employment. 14 ADI estimates were linked to SRTR data and shapefiles via ZCTA identification code.…”

Section: Methodsmentioning

confidence: 99%

Geographic hot spots of kidney transplant candidates wait‐listed post‐dialysis

Buchalter

Huml

Poggio

et al. 2022

Clinical Transplantation

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Introduction:Preemptive wait-listing of deceased donor kidney transplant (DDKT) candidates before maintenance dialysis increases the likelihood of transplantation and improves outcomes among transplant patients. Previous studies have identified substantial disparities in rates of preemptive listing, but a gap exists in examining geographic sources of disparities, particularly for sub-regional units. Identifying small area hot spots where delayed listing is particularly prevalent may more effectively inform both health policy and regionally appropriate interventions. Methods:We conducted a retrospective cohort study utilizing 2010-2020 Scientific Registry of Transplant Recipients (SRTR) data for all DDKT candidates to examine overall and race-stratified geospatial hot spots of post-dialysis wait-listing in U.S. zip code tabulation areas (ZCTA). Three geographic clustering methods were utilized to identify robust statistically significant hot spots of post-dialysis wait-listing.Results: Novel sub-regional hot spots were identified in the southeast, southwest, Appalachia, and California, with a majority existing in the southeast. Race-stratified results were more nuanced, but broadly reflected similar patterns. Comparing transplant candidates in hot spots to candidates in non-clusters indicated a strong association between residence in hot spots and high area deprivation (OR: 6.76, 95%CI: 6.52-7.02), indicating that improving access healthcare in these areas may be particularly beneficial. Conclusion:Our study identified overall and race-stratified hot spots with low rates of preemptive wait list placement in the U.S., which may be useful for prospective healthcare policy and interventions via targeting of these narrowly defined geographical areas.

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“…Details are provided elsewhere. [17][18][19][20] For most analyses, we analyzed ADI values based on quintiles of the distribution among Ohio block groups: ADI quintile 1 corresponded to the lowest level of neighborhood socioeconomic deprivation, while ADI quintile 5 corresponded to the highest level of neighborhood socioeconomic deprivation. However, for our spatial analysis, we used (continuous) ADI values.…”

Section: Demonstration: Analysis Of Local Mortality Patterns Among Ol...mentioning

confidence: 99%

Evaluating and Modeling Neighborhood Diversity and Health Using Electronic Health Records

et al. 2022

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Background Electronic health records (EHRs) provide researchers with abundant sample sizes, detailed clinical data, and other advantages for performing high-quality observational health research on diverse populations. We review and demonstrate strategies for the design and analysis of cohort studies on neighborhood diversity and health, including evaluation of the effects of race, ethnicity, and neighborhood socioeconomic position on disease prevalence and health outcomes, using localized EHR data. Methods Design strategies include integrating and harmonizing EHR data across multiple local health systems and defining the population(s) of interest and cohort extraction procedures for a given analysis based on the goal(s) of the study. Analysis strategies address inferential goals, including the mechanistic study of social risks, statistical adjustment for differences in distributions of social and neighborhood-level characteristics between available EHR data and the underlying local population, and inference on individual neighborhoods. We provide analyses of local variation in mortality rates within Cuyahoga County, Ohio. Results When the goal of the analysis is to adjust EHR samples to be more representative of local populations, sampling and weighting are effective. Causal mediation analysis can inform effects of racism (through racial residential segregation) on health outcomes. Spatial analysis is appealing for large-scale EHR data as a means for studying heterogeneity among neighborhoods even at a given level of overall neighborhood disadvantage. Conclusions The methods described are a starting point for robust EHR-derived cohort analysis of diverse populations. The methods offer opportunities for researchers to pursue detailed analyses of current and historical underlying circumstances of social policy and inequality. Investigators can employ combinations of these methods to achieve greater robustness of results. Highlights EHR data are an abundant resource for studying neighborhood diversity and health. When using EHR data for these studies, careful consideration of the goals of the study should be considered in determining cohort specifications and analytic approaches. Causal mediation analysis, stratification, and spatial analysis are effective methods for characterizing social mechanisms and heterogeneity across localized populations.

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The ADI-3: a revised neighborhood risk index of the social determinants of health over time and place

Cited by 23 publications

References 36 publications

Smartphone-based cardiac implantable electronic device remote monitoring: improved compliance and connectivity

Smartphone-based cardiac implantable electronic device remote monitoring: improved compliance and connectivity

Geographic hot spots of kidney transplant candidates wait‐listed post‐dialysis

Evaluating and Modeling Neighborhood Diversity and Health Using Electronic Health Records

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