Development and validation of a Medicines Comorbidity Index for older people View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-12

AUTHORS

Sujita W. Narayan, Prasad S. Nishtala

ABSTRACT

PURPOSE: An index for estimating multimorbidity based on prescription claims data is important for predicting health outcomes for older people in pharmacoepidemiological studies. We aimed to develop a Medicines Comorbidity Index (MCI) based on nationwide prescription claims data and evaluate its performance in predicting adverse outcomes in older individuals. METHODS: The index was developed on a retrospective cohort comprising of all individuals aged ≥ 65 years old, captured in the claims dataset from 1st January to 31st December 2012. The cohort was followed for 1 year to identify an event of hospitalisation or mortality. A list of medications for 20 comorbidities based on the Chronic Disease Score framework was collated. Predictive performance of the MCI was evaluated against the Charlson Comorbidity Index (CCI) using measures of discrimination (Receiver Operating Characteristic curves), sensitivity and specificity (c-statistic) and calibration (Brier scores) for regression models. RESULTS: The MCI was validated for an outcome of mortality (n = 161,461) and hospitalisation (n = 149,729). For mortality, MCI had a marginally lower c-statistic in comparison to CCI (0.70, 95% CI 0.70-0.71 vs 0.72, 95% CI 0.71-0.72 at p < 0.05) with Brier scores of 0.07 at p < 0.05. For hospitalisation, the Hazard Ratio was higher with MCI (1.08, 95% CI 1.08-1.08, p < 0.001) compared to CCI (0.92, 95% CI 0.91-0.92, p < 0.001). CONCLUSION: Initial testing indicates that the MCI is a valid and appropriate tool for measuring multimorbidity and predicting health outcomes for older individuals, and can be an important index for adjusting comorbidity in pharmacoepidemiological studies. More... »

PAGES

1665-1672

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00228-017-2333-0

DOI

http://dx.doi.org/10.1007/s00228-017-2333-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1091555114

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/28894902


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