Audrey J Weiss

Predictors of medication adherence in patients with type 2 diabetes mellitus.

Abstract Objective: Medical professionals are often challenged by lack of patient compliance with pharmaceutical treatments. Research has shown that patients with diabetes have one of the lowest medication adherence rates at 65% to 85%. Some causes have been identified in the literature, but the influence of type of medication is unknown. This study assessed the impact of a broad range of factors on medication adherence and persistence among adult patients with type 2 diabetes mellitus. Methods: Patients were selected from the Truven Health MarketScan Research Databases of healthcare administrative claims (2009 through 2012), assigned to mutually exclusive cohorts based on initiation of saxagliptin (a dipeptidyl peptidase-4 [DPP-4] inhibitor), or a glucagon-like peptide 1 (GLP-1) receptor agonist (daily or twice daily formulation), sulfonylurea (SU), or thiazolidinedione (TZD), and screened for continuous enrollment 1 year before and after drug initiation. Adherence and persistence were measured using proportion of days covered and time to discontinuation, respectively. Multivariate models were used to examine the impact of study drug and demographic and clinical factors. Results: Overall, 45.1% of patients were adherent with their study drug over the 1 year follow-up period. Adherence was higher among patients who were male, older, or residing in non-Southern states. Adherence was better with mail-order use and lower levels of cost sharing. Patients taking a GLP-1 (OR = 0.40, 95% CI = 0.37, 0.42), SU (OR = 0.49, 95% CI = 0.46, 0.52), or TZD (OR = 0.54, 95% CI = 0.51, 0.57) were less likely to be adherent compared with those taking saxagliptin. Results were mixed regarding the impact of comorbidities and polypharmacy on medication adherence. Influencing factors may be the type of comorbidity, overall health level, number of drugs, and complexity of the drug regimen. Key limitations: Adherence was measured using data for prescriptions dispensed and it is not known whether patients actually took the medications, hence adherence may be overestimated. Whether patients who discontinued the study drugs switched to other diabetes medications or discontinued treatment completely was not measured. Conclusion: Identified risk factors can guide medical professionals in their attempts to increase the likelihood of patient adherence to drug treatment regimens.

Impact of Race/Ethnicity and Socioeconomic Status on Risk-Adjusted Hospital Readmission Rates Following Hip and Knee Arthroplasty

BACKGROUND Readmission rates following total hip arthroplasty (THA) and total knee arthroplasty (TKA) are increasingly used to measure hospital performance. Readmission rates that are not adjusted for race/ethnicity and socioeconomic status, patient risk factors beyond a hospitals control, may not accurately reflect a hospitals performance. In this study, we examined the extent to which risk-adjusting for race/ethnicity and socioeconomic status affected hospital performance in terms of readmission rates following THA and TKA. METHODS We calculated 2 sets of risk-adjusted readmission rates by (1) using the Centers for Medicare & Medicaid Services standard risk-adjustment algorithm that incorporates patient age, sex, comorbidities, and hospital effects and (2) adding race/ethnicity and socioeconomic status to the model. Using data from the Healthcare Cost and Utilization Project, 2011 State Inpatient Databases, we compared the relative performances of 1,194 hospitals across the 2 methods. RESULTS Addition of race/ethnicity and socioeconomic status to the risk-adjustment algorithm resulted in (1) little or no change in the risk-adjusted readmission rates at nearly all hospitals; (2) no change in the designation of the readmission rate as better, worse, or not different from the population mean at >99% of the hospitals; and (3) no change in the excess readmission ratio at >97% of the hospitals. CONCLUSIONS Inclusion of race/ethnicity and socioeconomic status in the risk-adjustment algorithm led to a relative-performance change in readmission rates following THA and TKA at <3% of the hospitals. We believe that policymakers and payers should consider this result when deciding whether to include race/ethnicity and socioeconomic status in risk-adjusted THA and TKA readmission rates used for hospital accountability, payment, and public reporting. LEVEL OF EVIDENCE Prognostic Level III. See instructions for Authors for a complete description of levels of evidence.

Impact of Race/Ethnicity and Socioeconomic Status on Risk-Adjusted Readmission Rates Implications for the Hospital Readmissions Reduction Program

Under the Hospital Readmissions Reduction Program (HRRP) of the Centers for Medicare & Medicaid Services (CMS), hospitals with excess readmissions for select conditions and procedures are penalized. However, readmission rates are not risk adjusted for socioeconomic status (SES) or race/ethnicity. We examined how adding SES and race/ethnicity to the CMS risk-adjustment algorithm would affect hospitals’ excess readmission ratios and potential penalties under the HRRP. For each HRRP measure, we compared excess readmission ratios with and without SES and race/ethnicity included in the CMS standard risk-adjustment algorithm and estimated the resulting effects on overall penalties across a number of hospital characteristics. For the 5 HRRP measures (heart failure, acute myocardial infarction, chronic obstructive pulmonary disease, pneumonia, and total hip or knee arthroplasty), we used data from the Healthcare Cost and Utilization Project’s State Inpatient Databases for 2011-2012 to calculate the excess readmission ratio with and without SES and race/ethnicity included in the model. With these ratios, we estimated the impact on HRRP penalties and found that risk adjusting for SES and race/ethnicity would affect Medicare payments for 83.8% of hospitals. The effect on the size of HRRP penalties ranged from −14.4% to 25.6%, but the impact on overall Medicare base payments was small—ranging from −0.09% to 0.06%. Including SES and race/ethnicity in the calculation had a disproportionately favorable effect on safety-net and rural hospitals. Any financial effects on hospitals and on the Medicare program of adding SES and race/ethnicity to the HRRP risk-adjustment calculation likely would be small.