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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Multimorbidity in Australia: Comparing estimates derived using administrative data sources and survey data.

Sanja Lujic1, Judy M Simpson2, Nicholas Zwar3

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Estimating multimorbidity using administrative data varies by source. Self-report data identified more cases than claims or hospital records, highlighting the need for caution with single data sources.

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Area of Science:

  • Epidemiology
  • Health Services Research
  • Biostatistics

Background:

  • Estimating multimorbidity using administrative data is increasingly common.
  • Investigating concordance between self-report and administrative data for chronic conditions is crucial.
  • Understanding data source discrepancies in multimorbidity identification is essential.

Purpose of the Study:

  • To assess the agreement between self-report and administrative data for identifying chronic conditions and multimorbidity.
  • To compare characteristics of individuals with multimorbidity across different data sources.
  • To determine the overlap in multimorbidity classification using various datasets.

Main Methods:

  • Linked baseline survey data (90,352 participants) with pharmaceutical claims and hospital admission records.
  • Examined concordance using sensitivity, positive predictive value, and kappa statistics for eight chronic conditions.
  • Compared characteristics of multimorbid individuals using logistic regression.

Main Results:

  • Agreement was highest for diabetes (κ=0.79 for hospital, κ=0.78 for claims).
  • Multimorbidity prevalence varied: self-report (37.4%), claims (36.1%), hospital (19.3%).
  • Combining datasets identified 52% with multimorbidity; 20% identified across all three sources.

Conclusions:

  • Different data sources identify distinct individuals as multimorbid.
  • Caution is advised when using a single data source due to poor agreement.
  • Future research should focus on disease combinations rather than simple counts for better insights.