Using quantile regression to investigate racial disparities in medication non-adherence

<p>Abstract</p> <p>Background</p> <p>Many studies have investigated racial/ethnic disparities in medication non-adherence in patients with type 2 diabetes using common measures such as medication possession ratio (MPR) or gaps between refills. All these measures including MPR are quasi-continuous and bounded and their distribution is usually skewed. Analysis of such measures using traditional regression methods that model mean changes in the dependent variable may fail to provide a full picture about differential patterns in non-adherence between groups.</p> <p>Methods</p> <p>A retrospective cohort of 11,272 veterans with type 2 diabetes was assembled from Veterans Administration datasets from April 1996 to May 2006. The main outcome measure was MPR with quantile cutoffs Q1-Q4 taking values of 0.4, 0.6, 0.8 and 0.9. Quantile-regression (QReg) was used to model the association between MPR and race/ethnicity after adjusting for covariates. Comparison was made with commonly used ordinary-least-squares (OLS) and generalized linear mixed models (GLMM).</p> <p>Results</p> <p>Quantile-regression showed that Non-Hispanic-Black (NHB) had statistically significantly lower MPR compared to Non-Hispanic-White (NHW) holding all other variables constant across all quantiles with estimates and p-values given as -3.4% (p = 0.11), -5.4% (p = 0.01), -3.1% (p = 0.001), and -2.00% (p = 0.001) for Q1 to Q4, respectively. Other racial/ethnic groups had lower adherence than NHW only in the lowest quantile (Q1) of about -6.3% (p = 0.003). In contrast, OLS and GLMM only showed differences in mean MPR between NHB and NHW while the mean MPR difference between other racial groups and NHW was not significant.</p> <p>Conclusion</p> <p>Quantile regression is recommended for analysis of data that are heterogeneous such that the tails and the central location of the conditional distributions vary differently with the covariates. QReg provides a comprehensive view of the relationships between independent and dependent variables (i.e. not just centrally but also in the tails of the conditional distribution of the dependent variable). Indeed, without performing QReg at different quantiles, an investigator would have no way of assessing whether a difference in these relationships might exist.</p

Longitudinal Differences in Glycemic Control by Race/Ethnicity Among Veterans With Type 2 Diabetes

OBJECTIVE: To examine longitudinal differences in glycemic control between non-Hispanic white and non-Hispanic black veterans with type 2 diabetes. DESIGN: Retrospective cohort study. SETTING: VA facility in the Southeastern United States. PARTICIPANTS: A 3-month person-period dataset was created for 8813 veterans with type 2 diabetes between June 1997 and May 2006. MAIN OUTCOME MEASURES: Primary outcome was mean change in hemoglobin A1c (HbA1c) over time. Secondary outcome was the odds of poor glycemic control over time (HbA1c >8%). For the primary outcome, a linear mixed model (LMM) approach was used to model the relationship of HbA1c levels and race/ethnicity over time. For the secondary outcome, generalized LMMs were used to assess whether glycemic control changed over time and whether change in glycemic control varied by racial/ethnic group. RESULTS: Mean age was 66.3 years, 36% were non-Hispanic black (NHB), 98% were male, 65% were married, and 50% were unemployed. Mean follow-up time was 4.4 years. Least square mean HbA1c levels from LMM adjusted for time and relevant confounders showed that NHBs had higher HbA1c values over time (mean difference of 0.54% [P < 0.001]). The final model with poor versus good glycemic control as the dependent variable, race/ethnicity as primary independent variable adjusted for time, and relevant confounders showed that NHBs were likely to have poor control compared with NHWs (OR: 1.8, 95% CI, 1.7; 2.0, P < 0.0001). CONCLUSIONS: NHB veterans were more likely to have higher mean HbA1c values and less likely to have good glycemic control over time compared with NHW veteran