Instrumental variables in the cost of illness featuring type 2 diabetes
Kyle Kole1, Cathleen D Zick1, Barbara B Brown1
1Department of Family and Consumer Studies, University of Utah, Salt Lake City, Utah, USA.
Health Services Research
|November 26, 2024
Summary
Instrumental variables (IV) models can correct bias in cost-of-illness (COI) studies for chronic diseases like type 2 diabetes mellitus (T2DM). This improves healthcare cost estimates, crucial for intervention studies.
Area of Science:
- Health Economics
- Biostatistics
- Epidemiology
Background:
- Traditional cost-of-illness (COI) models may produce biased estimates for chronic diseases.
- Unobserved characteristics can confound the relationship between illness and healthcare costs.
Purpose of the Study:
- To evaluate the effectiveness of instrumental variables (IV) models in improving cost-of-illness (COI) estimates.
- To compare IV model estimates against traditional COI models using type 2 diabetes mellitus (T2DM) data.
Main Methods:
- A simulation study compared IV and traditional COI models with an introduced unobservable confounder.
- The study applied both models to real-world data on T2DM patients using a kinship-weighted family history as an instrument.
Main Results:
- Simulation confirmed bias in traditional COI models due to unobserved confounders.
- An IV model estimated that T2DM increases healthcare costs by 27% for individuals aged 45-64.
- The IV approach demonstrated a capacity to correct for bias in COI estimates.
Conclusions:
- Researchers should consider potential bias in traditional COI studies for chronic diseases.
- Accurate cost-of-illness (COI) estimates are vital for evaluating prevention and intervention strategies.
- Instrumental variables (IV) offer a more reliable method for estimating disease-related healthcare costs.
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