Abstract

BackgroundStudies have indicated variability around prevalence estimates of multimorbidity due to poor consensus regarding its definition and measurement. Medication-based measures of morbidity may be valuable resources in the primary-care setting where access to medical data can be limited. We compare the agreement between patient self-reported and medication-based morbidity; and examine potential patient-level predictors of discordance between these two measures of morbidity in an older (≥ 50 years) community-based population.MethodsA retrospective cohort study was performed using national pharmacy claims data linked to The Irish LongituDinal study on Ageing (TILDA). Morbidity was measured by patient self-report (TILDA) and two medication-based measures, the Rx-Risk (< 65 years) and Rx-Risk-V (≥65 years), which classify drug claims into chronic disease classes. The kappa statistic measured agreement between self-reported and medication-based morbidity at the individual patient-level. Multivariate logistic regression was used to examine patient-level characteristics associated with discordance between measures of morbidity.ResultsTwo thousand nine hundred twenty-five patients were included (< 65 years: N = 1095, 37.44%; and ≥ 65 years: N = 1830 62.56%). Hypertension and high cholesterol were the most prevalent self-reported morbidities in both age cohorts. Agreement was good or very good (κ = 0.61–0.81) for diabetes, osteoporosis and glaucoma; and moderate for high cholesterol, asthma, Parkinson’s and angina (κ = 0.44–0.56). All other conditions had fair or poor agreement. Age, gender, marital status, education, poor-delayed recall, depression and polypharmacy were significantly associated with discordance between morbidity measures.ConclusionsMost conditions achieved only moderate or fair agreement between self-reported and medication-based morbidity. In order to improve the accuracy in prevalence estimates of multimorbidity, multiple measures of multimorbidity may be necessary. Future research should update the current Rx-Risk algorithms in-line with current treatment guidelines, and re-assess the feasibility of using these indices alone, or in combination with other methods, to yield more accurate estimates of multimorbidity.

Highlights

  • Studies have indicated variability around prevalence estimates of multimorbidity due to poor consensus regarding its definition and measurement

  • The results of our study indicate that neither measure of morbidity is completely reliable, and we suggest that researchers may require multiple measures to fully capture accurate prevalence estimates of multimorbidity

  • High cholesterol was found to be highly prevalent in the Rx-Risk-V conditions included arthritis (Rx-Risk) (N = 454, 41.46%) and Rx-Risk-V (N = 1002, 54.75%) measures of morbidity

Read more

Summary

Introduction

Studies have indicated variability around prevalence estimates of multimorbidity due to poor consensus regarding its definition and measurement. Multimorbidity is commonly defined as the presence of two or more chronic medical conditions and its prevalence has been shown to increase with age [1]. To date, studies in the literature reveal wide disparities in prevalence estimates of multimorbidity, ranging from 3.5 to 95.1% [2, 3]. This large variability is thought to be due to the lack of standards defining multimorbidity and validated methods for how it should be measured [4]. The appropriateness of different measures of multimorbidity is variable depending on both the outcome of interest as well as the type of data that is available [6]

Objectives
Methods
Results
Discussion
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call