Examining Health Inequities in A1C Control over Time across Individual, Geospatial, and Geopolitical Factors among Adults with Type 2 Diabetes: Analyses of a Sample from One Commercial Insurer in a Southern State.

INTRODUCTION: Type 2 diabetes impacts millions and poor maintenance of diabetes can lead to preventable complications, which is why achieving and maintaining target A1C levels is critical. Thus, we aimed to examine inequities in A1C over time, place, and individual characteristics, given known inequities across these indicators and the need to provide continued surveillance. METHODS: Secondary de-identified data from medical claims from a single payer in Texas was merged with population health data. Generalized Estimating Equations were utilized to assess multiple years of data examining the likelihood of having non-target (>7% and ≥7%, two slightly different cut points based on different sources) and separately uncontrolled (>9%) A1C. Adults in Texas, with a Type 2 Diabetes (T2D) flag and with A1C reported in first quarter of the year using data from 2016 and 2019 were included in analyses. RESULTS: Approximately 50% had A1Cs within target ranges (<7% and ≤7%), with 50% considered having non-target (>7% and ≥7%) A1Cs; with 83% within the controlled ranges (≤9%) as compared to approximately 17% having uncontrolled (>9%) A1Cs. The likelihood of non-target A1C was higher among those individuals residing in rural (vs urban) areas (P < .0001); similar for the likelihood of reporting uncontrolled A1C, where those in rural areas were more likely to report uncontrolled A1C (P < .0001). In adjusted analysis, ACA enrollees in 2016 were approx. 5% more likely (OR = 1.049, 95% CI = 1.002-1.099) to have non-target A1C (≥7%) compared to 2019; in contrast non-ACA enrollees were approx. 4% more likely to have non-target A1C (≥7%) in 2019 compared to 2016 (OR = 1.039, 95% CI = 1.001-1.079). In adjusted analysis, ACA enrollees in 2016 were 9% more likely (OR = 1.093, 95% CI = 1.025-1.164) to have uncontrolled A1C compared to 2019; whereas there was no significant change among non-ACA enrollees. CONCLUSIONS: This study can inform health care interactions in diabetes care settings and help health policy makers explore strategies to reduce health inequities among patients with diabetes. Key partners should consider interventions to aid those enrolled in ACA plans, those in rural and border areas, and who may have coexisting health inequities.

Group-based trajectory analysis identifies varying diabetes-related cost trajectories among type 2 diabetes patients in Texas: an empirical study using commercial insurance.

BACKGROUND: The trend of Type 2 diabetes-related costs over 4 years could be classified into different groups. Patient demographics, clinical factors (e.g., A1C, short- and long-term complications), and rurality could be associated with different trends of cost. Study objectives are to: (1) understand the trajectories of cost in different groups; (2) investigate the relationship between cost and key factors in each cost trajectory group; and (3) assess significant factors associated with different cost trajectories. METHODS: Commercial claims data in Texas from 2016 to 2019 were provided by a large commercial insurer and were analyzed using group-based trajectory analysis, longitudinal analysis of cost, and logistic regression analyses of different trends of cost. RESULTS: Five groups of distinct trends of Type 2 diabetes-related cost were identified. Close to 20% of patients had an increasing cost trend over the 4 years. High A1C values, diabetes complications, and other comorbidities were significantly associated with higher Type 2 diabetes costs and higher chances of increasing trend over time. Rurality was significantly associated with higher chances of increasing trend over time. CONCLUSIONS: Group-based trajectory analysis revealed distinct patient groups with increased cost and stable cost at low, medium, and high levels in the 4-year period. The significant associations found between the trend of cost and A1C, complications, and rurality have important policy and program implications for potentially improving health outcomes and constraining healthcare costs.

Factors associated with higher hemoglobin A1c and type 2 diabetes-related costs: Secondary data analysis of adults 18 to 64 in Texas with commercial insurance.

OBJECTIVE: This study will identify factors associated with higher hemoglobin A1c (A1c) values and diabetes-related costs among commercially insured adults in Texas diagnosed with type 2 diabetes. RESEARCH DESIGN AND METHODS: This secondary data analysis was based on claims data from commercially insured individuals 18-64 years of age residing in Texas with diagnosed type 2 diabetes during the 2018-2019 study period. The final analysis sample after all the exclusions consisted of 34,992 individuals. Measures included hemoglobin A1c, diabetes-related costs, Charlson Comorbidity Index, diabetes-related complications, rurality and other socioeconomic characteristics. Longitudinal A1c measurements were modeled using age, sex, rurality, comorbidity, and diabetes-related complications in generalized linear longitudinal regression models adjusting the observation time, which was one of the 8 quarters in 2018 and 2019. The diabetes-related costs were similarly modeled in both univariable and multivariable generalized linear longitudinal regression models adjusting the observation time by calendar quarters and covariates. RESULTS: The median A1c value was 7, and the median quarterly diabetes-related cost was $120. A positive statistically significant relationship (p = < .0001) was found between A1c levels and diabetes-related costs, although this trend slowed down as A1c levels exceeded 8.0%. Higher A1c values were associated with being male, having diabetes-related complications, and living in rural areas. Higher costs were associated with higher A1c values, older age, and higher Charlson Comorbidity Index scores. CONCLUSION: The study adds updated analyses of the interrelationships among demographic and geographic factors, clinical indicators, and health-related costs, reinforcing the role of higher A1c values and complications as diabetes-related cost drivers.