Mapping Census Tract Clusters of Type 2 Diabetes in a Primary Care Population

Kolak, Marynia; Abraham, Geethi; Talen, Mary R.

doi:10.5888/pcd16.180502

Cited by 12 publications

(11 citation statements)

References 10 publications

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“…Rates of type 2 diabetes are substantially higher in neighborhoods that are characterized by lower income, lower educational attainment, single-parent households, and crowded housing. 27 , 35 Individuals living in areas of greater deprivation often have fewer financial resources, lower health literacy, greater comorbidity burden, and higher food insecurity. 8 In addition, deprived neighborhoods are associated with an increased rate of obesity and decreased physical activity.…”

Section: Discussionmentioning

confidence: 99%

Association Between Area-Level Socioeconomic Deprivation and Diabetes Care Quality in US Primary Care Practices

et al. 2021

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IMPORTANCE Diabetes management operates under a complex interrelationship between behavioral, social, and economic factors that affect a patient's ability to self-manage and access care.OBJECTIVE To examine the association between 2 complementary area-based metrics, area deprivation index (ADI) score and rurality, and optimal diabetes care. DESIGN, SETTING, AND PARTICIPANTSThis cross-sectional study analyzed the electronic health records of patients who were receiving care at any of the 75 Mayo Clinic or Mayo Clinic Health System primary care practices in Minnesota, Iowa, and Wisconsin in 2019. Participants were adults with diabetes aged 18 to 75 years. All data were abstracted and analyzed between June 1 and November 30, 2020. MAIN OUTCOMES AND MEASURESThe primary outcome was the attainment of all 5 components of the D5 metric of optimal diabetes care: glycemic control (hemoglobin A 1c <8.0%), blood pressure (BP) control (systolic BP <140 mm Hg and diastolic BP <90 mm Hg), lipid control (use of statin therapy according to recommended guidelines), aspirin use (for patients with ischemic vascular disease), and no tobacco use. The proportion of patients receiving optimal diabetes care was calculated as a function of block group-level ADI score (a composite measure of 17 US Census indicators) and zip code-level rurality (calculated using Rural-Urban Commuting Area codes). Odds of achieving the D5 metric and its components were assessed using logistic regression that was adjusted for demographic characteristics, coronary artery disease history, and primary care team specialty. RESULTS Among the 31 934 patients included in the study (mean [SD] age, 59 [11.7] years; 17 645 men [55.3%]), 13 138 (41.1%) achieved the D5 metric of optimal diabetes care. Overall, 4090 patients (12.8%) resided in the least deprived quintile (quintile 1) of block groups and 1614 (5.1%) lived in the most deprived quintile (quintile 5), while 9193 patients (28.8%) lived in rural areas and 2299 (7.2%) in highly rural areas. The odds of meeting the D5 metric were lower for individuals residing in quintile 5 vs quintile 1 block groups (odds ratio [OR], 0.72; 95% CI, 0.67-0.78). Patients residing in rural (OR, 0.84; 95% CI, 0.73-0.97) and highly rural (OR, 0.81; 95% CI, 0.72-0.91) zip codes were also less likely to attain the D5 metric compared with those in urban areas. CONCLUSIONS AND RELEVANCEThis cross-sectional study found that patients living in more deprived and rural areas were significantly less likely to attain high-quality diabetes care compared with those living in less deprived and urban areas. The results call for geographically targeted population health management efforts by health systems, public health agencies, and payers.

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Section: Discussionmentioning

confidence: 99%

Association Between Area-Level Socioeconomic Deprivation and Diabetes Care Quality in US Primary Care Practices

et al. 2021

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“…Recent immigrant communities may experience additional barriers to language, health insurance, and social service safety nets [49,50]. A previous study of T2D at the census tract-level for a clinical health population also found increased incidence in communities with more residents with limited English proficiency and persons without health insurance [51]. Areas with more concentrated seniors and disabled adult populations may also reflect additional challenges between aging, mobility, and transit that serve as risk factors for T2D onset.…”

Section: Plos Onementioning

confidence: 97%

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

et al. 2022

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Evaluation of geographic disparities in type 2 diabetes (T2D) onset requires multidimensional approaches at a relevant spatial scale to characterize community types and features that could influence this health outcome. Using Geisinger electronic health records (2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016), we conducted a nested case-control study of new onset T2D in a 37-county area of Pennsylvania. The study included 15,888 incident T2D cases and 79,435 controls without diabetes, frequency-matched 1:5 on age, sex, and year of diagnosis or encounter. We characterized patients' residential census tracts by four dimensions of social determinants of health (SDOH) and into a 7-category SDOH census tract typology previously generated for the entire United States by dimension reduction techniques. Finally, because the SDOH census tract typology classified 83% of the study region's census tracts into two heterogeneous categories, termed rural affordable-like and suburban affluent-like, to further delineate geographies relevant to T2D, we subdivided these two typology categories by administrative community types (U.S. Census Bureau minor civil divisions of township, borough, city). We used generalized estimating equations to examine associations of 1) four SDOH indexes, 2) SDOH census tract typology, and 3) modified typology, with odds of new onset T2D, controlling for individual-level confounding variables. Two SDOH dimensions, higher socioeconomic advantage and higher mobility (tracts with fewer seniors and disabled adults) were independently associated with lower odds of T2D. Compared to rural affordable-like as the reference group, residence in tracts categorized as extreme poverty (odds ratio [95% confidence interval] = 1.11 [1.02, 1.21]) or multilingual working (1.07 [1.03, 1.23]) were associated with higher odds of new onset T2D. Suburban affluent-like was associated

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“…Obesity is a modifiable risk factor for type 2 diabetes [28], and there is consensus that nutrition therapy is an efficacious and cost-effective component of type 2 diabetic care [67]. Thus, we plan future data analytics to connect census tract level datasets on food access to census tract level diabetes-related hospital use and diabetes related complications [68,69].…”

Section: Opportunities For Census Tract Level Research On Nutritionalmentioning

confidence: 99%

Knowledge Visualizations to Inform Decision Making for Improving Food Accessibility and Reducing Obesity Rates in the United States

Isokpehi

Johnson

Campos

et al. 2020

IJERPH

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The aim of this article is to promote the use of knowledge visualization frameworks in the creation and transfer of complex public health knowledge. The accessibility to healthy food items is an example of complex public health knowledge. The United States Department of Agriculture Food Access Research Atlas (FARA) dataset contains 147 variables for 72,864 census tracts and includes 16 food accessibility variables with binary values (0 or 1). Using four-digit and 16-digit binary patterns, we have developed data analytical procedures to group the 72,684 U.S. census tracts into eight and forty groups respectively. This value-added FARA dataset facilitated the design and production of interactive knowledge visualizations that have a collective purpose of knowledge transfer and specific functions including new insights on food accessibility and obesity rates in the United States. The knowledge visualizations of the binary patterns could serve as an integrated explanation and prediction system to help answer why and what-if questions on food accessibility, nutritional inequality and nutrition therapy for diabetic care at varying geographic units. In conclusion, the approach of knowledge visualizations could inform coordinated multi-level decision making for improving food accessibility and reducing chronic diseases in locations defined by patterns of food access measures.

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Mapping Census Tract Clusters of Type 2 Diabetes in a Primary Care Population

Cited by 12 publications

References 10 publications

Association Between Area-Level Socioeconomic Deprivation and Diabetes Care Quality in US Primary Care Practices

Association Between Area-Level Socioeconomic Deprivation and Diabetes Care Quality in US Primary Care Practices

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

Knowledge Visualizations to Inform Decision Making for Improving Food Accessibility and Reducing Obesity Rates in the United States

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