Diabetes care quality is highly correlated with patient panel characteristics

Abstract

Introduction: Health care reimbursement is increasingly based on quality. Little is known about how clinic-level patient characteristics affect quality, particularly in community health centers (CHCs).

Methods: Using data from electronic health records for 4019 diabetic patients from 23 primary care CHCs in the OCHIN practice-based research network, we calculated correlations between a clinic's patient panel characteristics and rates of delivery of diabetes preventive services in 2007. Using regression models, we estimated the proportion of variability in clinics' preventive services rates associated with the variability in the clinics' patient panel characteristics. We also explored whether clinics' performance rates were affected by how patient panel denominators were defined.

Results: Clinic rates of hemoglobin testing, influenza immunizations, and lipid screening were positively associated with the percentage of patients with continuous health insurance coverage and negatively associated with the percentage of uninsured patients. Microalbumin screening rates were positively associated with the percentage of racial minorities in a clinic's panel. Associations remained consistent with different panel denominators.

Conclusions: Clinic variability in delivery rates of preventive services correlates with differences in clinics' patient panel characteristics, particularly the percentage of patients with continuous insurance coverage. Quality scores that do not account for these differences could create disincentives to clinics providing diabetes care for vulnerable patients.

Keywords: Diabetes Mellitus; Health Care Systems; Health Insurance; Health Policy.

Figure 1. Variability in clinic-level percent of patients provided diabetes preventive services, full versus restricted patient panels

LDL: Low-density lipoprotein cholesterol screening; FLU: influenza vaccination; Micro-Alb: urine microalbumin screening; HbA1c: hemoglobin A1c monitoring

Figure 2. Proportion of variability in clinic-level percent of patients provided diabetes preventive services accounted for by clinic-level patient panel characteristics

Note. The percent variability associated with Clinic Patient Panel characteristics was determined from the change in r² when the variable was added to a model already containing the variables with stronger association with delivery of the service. The variable order was determined through stepwise selection based on the variable addition resulting in the maximum change in r². The proportion of clinic variability in delivery of a service that is not explained by a model including all patient characteristics in this study is shown in gray. *p<0.05.

Figure 3. Observed rates of diabetes preventive services by clinic, as compared to rates predicted by the percentage of each clinic’s patient panel with full insurance coverage

Note. Predicted values and 95% confidence intervals were estimated from regression models of delivery of preventive service rates on the percent of patient panel with full insurance coverage. The model used logit transformed rates to keep the predicted rates bounded between 0 and 100%. Graphed values are the logit transformed values (y axis) of observed values labeled with the actual rates (%) to facilitate interpretation.