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

Abstract

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.

Identifying Contextual and Spatial Risk Factors for Post-Acute Sequelae of SARS-CoV-2 Infection: An EHR-based Cohort Study from the RECOVER Program

“…Air pollution-induced dysfunction of the insulin signaling system can reduce cognitive function and increase the risk of dementia (H. Paul et al, 2020). People living in neighborhoods of greater deprivation often have fewer financial resources, lower health literacy, and higher food insecurity, leading to the development of diabetes and other conditions (M. D. Kurani et al, 2021). Previous studies found that COVID-19 patients are disproportionately from areas with disadvantaged neighborhood conditions (Y.…”

Aging in Rural Communities

“…In addition, distance to the nearest major metropolitan area or city is increasingly used to measure rurality and remoteness in studies of population health, aging, and development [38][39][40]. Other standard measures of rural-urban status used in health and aging research include several variables produced by the US Department of Agriculture, such as the Rural-Urban Continuum Codes (RUCC) [41,42], Urban Influence Codes (UIC) [43,44], and Rural-Urban Commuting Areas (RUCA) [23,[45][46][47]. However, there is substantial heterogeneity across the measures of rural-urban status [48] with respect to what each measure actually assesses and their associations with other population measures.…”

Optimising transitions of care for acute kidney injury survivors: protocol for a mixed-methods study of nephrologist and primary care provider recommendations

“…The sampling frame will include physicians and advanced practice providers (APPs; nurse practitioner or physician assistant) within the Divisions of Nephrology and Primary Care who provide posthospital care for AKI survivors at Mayo Clinic in Rochester, Florida, Arizona and the Mayo Clinic Health System. The Mayo Clinic Health System includes clinics, hospitals and other healthcare facilities across southern Minnesota, western Wisconsin and northeastern Iowa 38. Facilities include large regional medical centres, community hospitals and rural primary care clinics that collectively offer over 100 medical and surgical services and specialties that serve patients in their communities.…”

“…The Mayo Clinic Health System includes clinics, hospitals and other healthcare facilities across southern Minnesota, western Wisconsin and northeastern Iowa. 38 Facilities include large regional medical centres, community hospitals and rural primary care clinics that collectively offer over 100 medical and surgical services and specialties that serve patients in their communities. Resources are shared across the Mayo Clinic Enterprise, including Florida, Arizona and the Health System.…”