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Screening for chronic diseases: optimizing lead time through balancing prescribed frequency and individual adherence
John D Rice1, Brent A Johnson2, Robert L Strawderman3
1Department of Biostatistics and Informatics, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA.
Lifetime Data Analysis
|June 23, 2022
Summary
Optimizing cancer screening requires considering patient adherence. Lower adherence necessitates decreasing screening frequency to maintain program effectiveness, as shown in breast cancer screening models.
Area of Science:
- Public Health
- Biostatistics
- Epidemiology
Background:
- Screening for chronic diseases like cancer is crucial for public health.
- Traditional screening focus is on frequency/rate, neglecting adherence impact.
- Imperfect adherence to screening policies reduces program efficacy.
Purpose of the Study:
- Develop methodology to optimize screening policies under imperfect adherence.
- Quantify the impact of adherence on screening program efficacy.
- Determine optimal screening frequency considering adherence levels.
Main Methods:
- Utilized a four-state progressive disease model (healthy, undetectable preclinical, detectable preclinical, clinical).
- Integrated a stochastic screening-behavior model using renewal processes theory.
- Modeled imperfect adherence to screening programs transparently.
Main Results:
- Decreased adherence significantly reduces screening program efficacy.
- Lead time distribution elements quantify the reduction in efficacy.
- Optimal screening frequency inversely correlates with adherence levels.
Conclusions:
- Imperfect adherence necessitates a decrease in optimal screening frequency.
- Accounting for adherence is essential for effective screening policy optimization.
- Breast cancer screening example illustrates the impact of adherence on recommended frequency.
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