Literature DB >> 26683784

Multiple-domain Versus Single-domain Measurements of Socioeconomic Status (SES) for Predicting Nonadherence to Statin Medications: An Observational Population-based Cohort Study.

Mhd Wasem Alsabbagh1, Lisa M Lix, Dean Eurich, Thomas W Wilson, David F Blackburn.   

Abstract

INTRODUCTION: Low socioeconomic status (SES) should be a robust predictor of medication nonadherence because it shares key features with the theoretical origins of this phenomenon. However, population-based studies have demonstrated weak associations overall, possibly because SES is inadequately represented. We compared the performance of multiple versus single-domain measures of SES as predictors of statin adherence.
METHODS: This retrospective cohort study used population-based administrative data mapped to area-level census information of individuals who received a statin medication following a hospitalization for coronary heart disease. One-year adherence was calculated by dividing the sum of all tablets dispensed by the total number of days in the observation period (365 d following the first statin dispensation). Logistic regression models were constructed and the relative impact of each SES measure was assessed by its adjusted odds ratio (OR) and improvement over the predictive accuracy of a reference model that included non-SES factors only.
RESULTS: More than two thirds (ie, 68.8%; 6517/9478) of eligible individuals exhibited optimal adherence (ie, ≥80%). The estimated impact of SES on optimal adherence differed depending on the SES measure tested. The highest performing single-domain measure, household income (OR=0.75; 95% confidence interval, 0.63-0.90; model c-statistic improvement 0.5%, P=0.04) generated a similar result to the multiple-domain measure (adjusted OR=0.74; 95% confidence interval, 0.62-0.88; model c-statistic improvement 0.7%, P=0.01).
CONCLUSION: Multidomain measurements of SES using administrative databases mapped to census data are not associated with better performance in predicting statin medication adherence compared with single-domain measures such as household income.

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Year:  2016        PMID: 26683784     DOI: 10.1097/MLR.0000000000000468

Source DB:  PubMed          Journal:  Med Care        ISSN: 0025-7079            Impact factor:   2.983


  4 in total

Review 1.  Improving Medication Adherence in Coronary Heart Disease.

Authors:  Leah L Zullig; Katherine Ramos; Hayden B Bosworth
Journal:  Curr Cardiol Rep       Date:  2017-09-22       Impact factor: 2.931

2.  The Impact of Age and Sex Concordance Between Patients and Physicians on Medication Adherence: A Population-Based Study.

Authors:  Shenzhen Yao; Lisa Lix; Gary Teare; Charity Evans; David Blackburn
Journal:  Patient Prefer Adherence       Date:  2022-01-21       Impact factor: 2.711

3.  An integrated continuity of care measure improves performance in models predicting medication adherence using population-based administrative data.

Authors:  Shenzhen Yao; Lisa Lix; Gary Teare; Charity Evans; David Blackburn
Journal:  PLoS One       Date:  2022-03-03       Impact factor: 3.240

4.  A mathematical approach to study and forecast racial groups interactions: deterministic modeling and scenario method.

Authors:  Goran Dominioni; Addolorata Marasco; Alessandro Romano
Journal:  Qual Quant       Date:  2017-10-03
  4 in total

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