Dataset Information

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

ABSTRACT: 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.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).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.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.

SUBMITTER: Gebregziabher M

PROVIDER: S-EPMC3121729 | biostudies-other | 2011 Jun

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

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

Gebregziabher Mulugeta M Lynch Cheryl P CP Mueller Martina M Gilbert Gregory E GE Echols Carrae C Zhao Yumin Y Egede Leonard E LE

BMC medical research methodology 20110606

<h4>Background</h4>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 ...[more]

PMID: 21645379

Similar Datasets

Project description:BackgroundSubstantial research has documented inequalities between US minorities and whites in meeting the eligibility criteria for the Medicare Part D medication therapy management (MTM) program. Even though the Centers for Medicare & Medicaid Services attempted to relax the eligibility criteria, a critical barrier to effective MTM reform is a lack of stronger evidence about the effects of MTM on minorities' health outcomes.ObjectiveTo examine the effects of comprehensive medication review (CMR), an MTM core component, on racial and ethnic disparities in adherence to diabetes, hypertension, and hyperlipidemia medications among Medicare beneficiaries aged ≥65 years.MethodsThis study used full-year 2017 Medicare Parts A, B, and D claims data, including MTM data, linked to the Area Health Resources Files. Racial and ethnic disparities in nonadherence to diabetes, hypertension, and hyperlipidemia medications were compared between CMR recipients and nonrecipients matched by their propensity scores. To determine the changes in racial and ethnic disparities after receiving CMR, a difference-in-differences framework was applied, by including in logistic regression analyses interaction terms between dummy variables for CMR receipt and each racial or ethnic minority group.ResultsCompared with CMR nonrecipients, CMR recipients had significantly lower racial and ethnic disparities across the 3 outcome measures, with the exception of the difference between whites and blacks in nonadherence to diabetes medications. For example, compared with CMR nonrecipients, among CMR recipients the differences in the odds of nonadherence to hypertension medications were reduced, respectively, by 8% (95% confidence interval [CI], 0.88-0.96) between whites and blacks; by 18% (95% CI, 0.78-0.86) between whites and Hispanics; by 16% (95% CI, 0.77-0.91) between whites and Asians; and by 9% (95% CI, 0.85-0.98) between whites and other racial and ethnic groups.ConclusionReceiving a CMR reduced the racial and ethnic disparities in adherence to diabetes, hypertension, and hyperlipidemia medications among Medicare beneficiaries aged ≥65 years. These findings provide critical empirical evidence that may inform the future design of the Medicare Part D MTM program, which is valuable for improving pharmacotherapy outcomes and could further realize its potential when additional people from racial and ethnic minorities are enrolled.

Project description:Studies of the health effects of the microbiome often measure overall associations by using diversity metrics, and individual taxa associations in separate analyses, but do not consider the correlated relationships between taxa in the microbiome. In this study, we applied random subset weighted quantile sum regression with repeated holdouts (WQSRSRH), a mixture method successfully applied to ‘omic data to account for relationships between many predictors, to processed amplicon sequencing data from the Human Microbiome Project. We simulated a binary variable associated with 20 operational taxonomic units (OTUs). WQSRSRH was used to test for the association between the microbiome and the simulated variable, adjusted for sex, and sensitivity and specificity were calculated. The WQSRSRH method was also compared to other standard methods for microbiome analysis. The method was further illustrated using real data from the Growth and Obesity Cohort in Chile to assess the association between the gut microbiome and body mass index. In the analysis with simulated data, WQSRSRH predicted the correct directionality of association between the microbiome and the simulated variable, with an average sensitivity and specificity of 75% and 70%, respectively, in identifying the 20 associated OTUs. WQSRSRH performed better than all other comparison methods. In the illustration analysis of the gut microbiome and obesity, the WQSRSRH analysis identified an inverse association between body mass index and the gut microbe mixture, identifying Bacteroides, Clostridium, Prevotella, and Ruminococcus as important genera in the negative association. The application of WQSRSRH to the microbiome allows for analysis of the mixture effect of all the taxa in the microbiome, while simultaneously identifying the most important to the mixture, and allowing for covariate adjustment. It outperformed other methods when using simulated data, and in analysis with real data found results consistent with other study findings.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets