Associations of four indexes of social determinants of health and two community typologies with new onset type 2 diabetes across a diverse geography in Pennsylvania

Adapting the NIMHD Research Framework for Type 2 Diabetes-Related Disparities

PURPOSE OF REVIEW

Type 2 diabetes (T2D) disproportionately impacts minority populations. The National Institute on Minority Health and Health Disparities (NIMHD) developed a research framework to encourage health disparities research that considers a multi-level, multi-domain perspective. The purpose of this review was to describe evidence on the levels and domains that influence T2D disparities among minority populations and use this information to adapt the NIMHD Research Framework for T2D.

RECENT FINDINGS

Screening identified 108 articles published between 2017 and 2023 covering 74,354,597 participants. Articles were classified under the following domains, Biological (18), Behavioral (22), Physical/Built Environment (19), Sociocultural Environment (42), and Health Care System (31). Article levels of influence included Individual (73), Interpersonal (18), Community (36), and Societal (10). Findings were used to adapt the NIMHD Research Framework with an eye towards advancing T2D-related health equity. The results of this review confirm the complex nature of T2D-related disparities and support the notion that drivers operate within and between multiple levels and multiple domains to influence T2D-related outcomes across the lifespan.

Assessing the impact of social determinants of health on diabetes severity and management

Objective

Adverse Social Determinants of Health (SDoH) are considered major obstacles to effective management of type-2 diabetes. This study aims to quantify the impact of SDoH factors on diabetes management outcomes.

Materials and Methods

We quantified the joint impact of multiple SDoH by applying a self-control case series method-which accounts for confounding by using individuals as their own control-to electronic health record data from an academic health system in Maryland.

Results

We found a consistent increase in HbA1c levels associated with SDoH across alternative study designs. The estimated total contributions of SDoH ranged 0.014-0.065 across the alternative designs. Transportation issues demonstrated particularly significant contributions, with estimates of 0.077-0.144. When assuming SDoH's risk window to be ±45 days, for example, the total contribution was estimated to be 0.065 (95% CI [0.010, 0.120]) increase in HbA1c and the transportation issues' contribution 0.134 (95% CI [0.020, 0.249]).

Discussion and Conclusion

Our result suggests that reducing transportation barriers may be an effective SDoH intervention strategy for diabetes management; however, the clinical impact of such interventions warrants further investigation.

Model-based estimation of individual-level social determinants of health and its applications in All of Us

OBJECTIVES

We introduce a widely applicable model-based approach for estimating individual-level Social Determinants of Health (SDoH) and evaluate its effectiveness using the All of Us Research Program.

MATERIALS AND METHODS

Our approach utilizes aggregated SDoH datasets to estimate individual-level SDoH, demonstrated with examples of no high school diploma (NOHSDP) and no health insurance (UNINSUR) variables. Models are estimated using American Community Survey data and applied to derive individual-level estimates for All of Us participants. We assess concordance between model-based SDoH estimates and self-reported SDoHs in All of Us and examine associations with undiagnosed hypertension and diabetes.

RESULTS

Compared to self-reported SDoHs, the area under the curve for NOHSDP is 0.727 (95% CI, 0.724-0.730) and for UNINSUR is 0.730 (95% CI, 0.727-0.733) among the 329 074 All of Us participants, both significantly higher than aggregated SDoHs. The association between model-based NOHSDP and undiagnosed hypertension is concordant with those estimated using self-reported NOHSDP, with a correlation coefficient of 0.649. Similarly, the association between model-based NOHSDP and undiagnosed diabetes is concordant with those estimated using self-reported NOHSDP, with a correlation coefficient of 0.900.

DISCUSSION AND CONCLUSION

The model-based SDoH estimation method offers a scalable and easily standardized approach for estimating individual-level SDoHs. Using the All of Us dataset, we demonstrate reasonable concordance between model-based SDoH estimates and self-reported SDoHs, along with consistent associations with health outcomes. Our findings also underscore the critical role of geographic contexts in SDoH estimation and in evaluating the association between SDoHs and health outcomes.

Realizing the potential of social determinants data in EHR systems: A scoping review of approaches for screening, linkage, extraction, analysis, and interventions

Background

Social determinants of health (SDoH), such as socioeconomics and neighborhoods, strongly influence health outcomes. However, the current state of standardized SDoH data in electronic health records (EHRs) is lacking, a significant barrier to research and care quality.

Methods

We conducted a PubMed search using "SDOH" and "EHR" Medical Subject Headings terms, analyzing included articles across five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions.

Results

Of 685 articles identified, 324 underwent full review. Key findings include implementation of tailored screening instruments, census and claims data linkage for contextual SDoH profiles, NLP systems extracting SDoH from notes, associations between SDoH and healthcare utilization and chronic disease control, and integrated care management programs. However, variability across data sources, tools, and outcomes underscores the need for standardization.

Discussion

Despite progress in identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical for SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately, widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.

Community profiles in northeastern and central Pennsylvania characterized by distinct social, natural, food, and physical activity environments and their relation to type 2 diabetes

Background

Understanding geographic disparities in type 2 diabetes (T2D) requires approaches that account for communities' multidimensional nature.

Methods

In an electronic health record nested case-control study, we identified 15,884 cases of new-onset T2D from 2008 to 2016, defined using encounter diagnoses, medication orders, and laboratory test results, and frequency-matched controls without T2D (79,400; 65,069 unique persons). We used finite mixture models to construct community profiles from social, natural, physical activity, and food environment measures. We estimated T2D odds ratios (OR) with 95% confidence intervals (CI) using logistic generalized estimating equation models, adjusted for sociodemographic variables. We examined associations with the profiles alone and combined them with either community type based on administrative boundaries or Census-based urban/rural status.

Results

We identified four profiles in 1069 communities in central and northeastern Pennsylvania along a rural-urban gradient: "sparse rural," "developed rural," "inner suburb," and "deprived urban core." Urban areas were densely populated with high physical activity resources and food outlets; however, they also had high socioeconomic deprivation and low greenness. Compared with "developed rural," T2D onset odds were higher in "deprived urban core" (1.24, CI = 1.16-1.33) and "inner suburb" (1.10, CI = 1.04-1.17). These associations with model-based community profiles were weaker than when combined with administrative boundaries or urban/rural status.

Conclusions

Our findings suggest that in urban areas, diabetogenic features overwhelm T2D-protective features. The community profiles support the construct validity of administrative-community type and urban/rural status, previously reported, to evaluate geographic disparities in T2D onset in this geography.

Vision Need Profiles for the City of Richmond, Virginia: A Pilot Application of Calibration Methods to Vision Surveillance

Purpose

People with vision problems (VPs) have different needs based on their age, economic resources, housing type, neighborhood, and other disabilities. We used calibration methods to create synthetic data to estimate census tract-level community need profiles (CNPs) for the city of Richmond, Virginia.

Design

Cross-sectional secondary data analysis.

Subjects

Anonymized respondents to the 2015 to 2019 American Community Survey (ACS).

Methods

We used calibration methods to transform the ACS 5-year tabular (2015-2019) and Public Use Microdata estimates into a synthetic data set of person-level records in each census tract, and subset the data to persons who answered yes to the question "Are you blind or do you have serious difficulty seeing even when wearing glasses?" To identify individual need profiles (INPs), we applied divisive clustering to 17 variables measuring individual demographics, nonvision disability status, socioeconomic status (SES), housing, and access and independence. We labeled tracts with CNP names based on their predominant INPs and performed sensitivity analyses. We mapped the CNPs and overlayed information on the number of people with VP, the National Walkability Index, and an uncertainty measure based on our sensitivity analysis.

Main Outcome Measures

Individual need profiles and CNPs.

Results

Compared with people without VP, people with VP exhibited higher rates of disabilities, having low incomes, living alone, and lacking access to the internet or private home vehicles. Among people with VP, we identified 7 INP clusters which we mapped into 6 CNPs: (1) seniors (≥ age 65); (2) low SES younger; (3) low SES older; (4) mixed SES; (5) higher SES; and (6) adults and children in group quarters. Three CNPs had lower-than-average walkability. Community need profile assignments were somewhat sensitive to calibration variables, with 18 tracts changing assignments in 1 sensitivity analysis, and 4 tracts changing assignments in ≥ 2 sensitivity analyses.

Conclusions

This pilot project illustrates the feasibility of using ACS data to better understand the support and service needs of people with VP at the census tract level. However, a subset of categorical CNP assignments were sensitive to variable selection leading to uncertainty in CNP assignment in certain tracts.

Financial Disclosures

The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Realizing the Potential of Social Determinants Data: A Scoping Review of Approaches for Screening, Linkage, Extraction, Analysis and Interventions

Background

Social determinants of health (SDoH) like socioeconomics and neighborhoods strongly influence outcomes, yet standardized SDoH data is lacking in electronic health records (EHR), limiting research and care quality.

Methods

We searched PubMed using keywords "SDOH" and "EHR", underwent title/abstract and full-text screening. Included records were analyzed under five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions.

Results

We identified 685 articles, of which 324 underwent full review. Key findings include tailored screening instruments implemented across settings, census and claims data linkage providing contextual SDoH profiles, rule-based and neural network systems extracting SDoH from notes using NLP, connections found between SDoH data and healthcare utilization/chronic disease control, and integrated care management programs executed. However, considerable variability persists across data sources, tools, and outcomes.

Discussion

Despite progress identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical to fulfill the potential of SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.

Assessing the association between food environment and dietary inflammation by community type: a cross-sectional REGARDS study

BACKGROUND

Communities in the United States (US) exist on a continuum of urbanicity, which may inform how individuals interact with their food environment, and thus modify the relationship between food access and dietary behaviors.

OBJECTIVE

This cross-sectional study aims to examine the modifying effect of community type in the association between the relative availability of food outlets and dietary inflammation across the US.

METHODS

Using baseline data from the REasons for Geographic and Racial Differences in Stroke study (2003-2007), we calculated participants' dietary inflammation score (DIS). Higher DIS indicates greater pro-inflammatory exposure. We defined our exposures as the relative availability of supermarkets and fast-food restaurants (percentage of food outlet type out of all food stores or restaurants, respectively) using street-network buffers around the population-weighted centroid of each participant's census tract. We used 1-, 2-, 6-, and 10-mile (~ 2-, 3-, 10-, and 16 km) buffer sizes for higher density urban, lower density urban, suburban/small town, and rural community types, respectively. Using generalized estimating equations, we estimated the association between relative food outlet availability and DIS, controlling for individual and neighborhood socio-demographics and total food outlets. The percentage of supermarkets and fast-food restaurants were modeled together.

RESULTS

Participants (n = 20,322) were distributed across all community types: higher density urban (16.7%), lower density urban (39.8%), suburban/small town (19.3%), and rural (24.2%). Across all community types, mean DIS was - 0.004 (SD = 2.5; min = - 14.2, max = 9.9). DIS was associated with relative availability of fast-food restaurants, but not supermarkets. Association between fast-food restaurants and DIS varied by community type (P for interaction = 0.02). Increases in the relative availability of fast-food restaurants were associated with higher DIS in suburban/small towns and lower density urban areas (p-values < 0.01); no significant associations were present in higher density urban or rural areas.

CONCLUSIONS

The relative availability of fast-food restaurants was associated with higher DIS among participants residing in suburban/small town and lower density urban community types, suggesting that these communities might benefit most from interventions and policies that either promote restaurant diversity or expand healthier food options.