Evidence map›Paper›PMID 40124172›Full record

ArticleClinical interventions in aging2025

Validity of the Updated Rx-Risk Index as a Disease Identification and Risk-Adjustment Tool for Use in Observational Health Studies.

Imaina Widagdo, Mhairi Kerr, Lisa Kalisch Ellett, Clement Schlegel, Elham Sadeqzadeh, Alvin Wang, Allison Louise Clarke, Nicole Pratt

Abstract readValidation Study
In one paragraph

Article in Clinical interventions in aging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Imaina WidagdoQuality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Adelaide, South Australia, Australia.
Mhairi KerrQuality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Adelaide, South Australia, Australia.
Lisa Kalisch EllettQuality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Adelaide, South Australia, Australia.
Clement SchlegelData and Analytics Branch, Health Economics and Research Division, Department of Health and Aged Care, Canberra, Australian Capital Territory, Australia.
Elham SadeqzadehData and Analytics Branch, Health Economics and Research Division, Department of Health and Aged Care, Canberra, Australian Capital Territory, Australia.
Alvin WangData and Analytics Branch, Health Economics and Research Division, Department of Health and Aged Care, Canberra, Australian Capital Territory, Australia.
Allison Louise ClarkeData and Analytics Branch, Health Economics and Research Division, Department of Health and Aged Care, Canberra, Australian Capital Territory, Australia.
Nicole PrattQuality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Adelaide, South Australia, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Identifying patient health conditions in observational studies is essential for accurately measuring healthcare practices and planning effective health policy interventions. This analysis evaluates the validity of the Rx-Risk Index, a tool that uses medication dispensing data to identify patient comorbidities and measure overall health. We examined an updated version of the Rx-Risk Index, reflecting changes in treatment practices, to assess its validity as a tool for identifying specific health conditions and as a measure of overall health to aid in risk adjustment in observational studies. Patients and Methods: We conducted a validation study using two Australian linked health datasets, the Person-Level Integrated Data Asset (PLIDA) and the National Health Data Hub (NHDH), from 2010 to 2018, focusing on individuals aged 65 years or older. The sensitivity, specificity, PPV/NPV, Cohen's kappa, and F1 scores were used to assess agreement between Rx-Risk Index conditions and two reference standards: patient self-reported conditions and hospital diagnosis. The Rx-Risk Index's predictive validity for one-year mortality was also evaluated using logistic regression, with model fit assessed by AIC and c-statistic. Results: Data were analysed from 3,959 individuals in PLIDA and 157,709 individuals in NHDH. The Rx-Risk Index showed high sensitivity (≥75%) for diabetes, chronic airways disease, hyperlipidemia, and epilepsy against both self-reported conditions and hospital diagnoses. However, hyperlipidemia and hypertension showed lower specificity (<70%). High PPVs (≥78%) were observed for diabetes and renal failure. The agreement between the Rx-Risk Index and self-reported conditions was stronger (Cohen's kappa: 0.41-0.81 for 7 conditions) than between Rx-Risk Index and ICD10-AM diagnoses (kappa: 0.73 for one condition). The Rx-Risk Index was a strong predictor of one-year mortality, with c-statistic of 0.820 (95% CI: 0.817-0.825). Conclusion: Selected Rx-Risk Index conditions are reasonable proxies for identifying specific conditions, particularly those requiring pharmacological management. The Rx-Risk Index was a strong predictor of one-year mortality, suggesting it is a valid measure of overall health. This study demonstrates the Rx-Risk Index's potential to enhance disease classification and risk adjustment in observational studies, supporting informed decision-making in health policy planning.

Indexed as

Observational Studies as TopicRisk AdjustmentAgedAged, 80 and overAustraliaComorbidityFemaleHumansMaleReproducibility of ResultsRisk Assessmentdisease classificationobservational studypharmacoepidemiologypredictive validityprescription claims datarisk adjustment

Identifiers

PMID40124172
PMCPMC11930017

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