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Abstract
Objectives To provide a map of Anatomical Therapeutic Chemical (ATC) Classification System codes to individual Rx-Risk comorbidities and to validate the Rx-Risk Comorbidity Index.
Design The 46 comorbidities in the Rx-Risk Index were mapped to dispensing’s indicative of each condition using ATC codes. Prescription dispensing claims in 2014 were used to calculate the Rx-Risk. A baseline logistic regression model was fitted using age and gender as covariates. Rx-Risk was added to the base model as an (1) unweighted score, (2) weighted score and as (3) individual comorbidity categories indicating the presence or absence of each condition. The Akaike information criterion and c-statistic were used to compare the models.
Setting Models were developed in the Australian Government Department of Veterans’ Affairs health claims data, and external validation was undertaken in a 10% sample of the Australian Pharmaceutical Benefits Scheme Data.
Participants Subjects aged 65 years or older.
Outcome measures Death within 1 year (eg, 2015).
Results Compared with the base model (c-statistic 0.738, 95% CI 0.734 to 0.742), including Rx-Risk improved prediction of mortality; unweighted score 0.751, 95% CI 0.747 to 0.754, weighted score 0.786, 95% CI 0.782 to 0.789 and individual comorbidities 0.791, 95% CI 0.788 to 0.795. External validation confirmed the utility of the weighted index (c-statistic=0.833).
Conclusions The updated Rx-Risk Comorbidity Score was predictive of 1-year mortality and may be useful in practice to adjust for confounding in observational studies using medication claims data.
- chronic disease burden
- claims data
- comorbidity
- weighting
- mortality prediction
- model validation
This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
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Footnotes
Contributors Research area and study design: NLP, MK, ER and EER. Data acquisition: NLP, MK and JDB. Data analysis and interpretation: NLP, MK, ER, AK-C and LMKE. Statistical analysis: NLP and MK. Mapping of the ATC codes to the Rx-risk categories: JDB, LMKE and EER. All authors drafted, edited and approved the final manuscript.
Funding This work was funded by the Australian Government Department of Veterans’ Affairs (DVA) as part of the Veterans’ Medicines Advice and Therapeutics Education Services (Veterans’ MATES) programme. EER is supported by NHMRC GNT 1110139. DVA reviewed this manuscript before submission but had no role in the design or conduct of this research.
Competing interests None declared.
Patient consent Not required.
Ethics approval This research was approved by the Australian Government Department of Veterans’ Affairs Human Research Ethics Committee and the University of South Australia Human Research Ethics Committee.
Provenance and peer review Not commissioned; externally peer reviewed.
Data sharing statement Data are available through the Australian Government Department of Veterans’ Affairs.