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Cost sharing: (i) Copay increases across multiple services, low-income and chronically-ill population

Healthcare: Healthcare System Efficiency
  Literature review updated November 2015.

Evaluations of health care policies and programs often measure two broad types of outcomes: (1) those that reflect the health status of people (e.g., disease incidence) and (2) those that reflect health care system costs and utilization. Cost and utilization measures may or may not be an indication of health status or well-being.

The effect reported below reflects changes in medical costs resulting from increases in patient copays for multiple services (prescription drugs, office visits, emergency department visits, and outpatient surgery). The effect size is the price elasticity for medical expenditures. Estimates are derived from data for low-income adults (< 300% Federal Poverty Line) with a chronic condition in a subsidized health plan.
 
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META-ANALYSIS
CITATIONS

**The effect size for this outcome represents an elasticity, not a standardized mean difference effect size.

Meta-analysis is a statistical method to combine the results from separate studies on a program, policy, or topic to estimate its effect on an outcome. WSIPP systematically evaluates all credible evaluations we can locate on each topic. The outcomes measured are the program impacts measured in the research literature (for example, impacts on crime or educational attainment). Treatment N represents the total number of individuals or units in the treatment group across the included studies.

An effect size (ES) is a standard metric that summarizes the degree to which a program or policy affects a measured outcome. If the effect size is positive, the outcome increases. If the effect size is negative, the outcome decreases. See Estimating Program Effects Using Effect Sizes for additional information on how we estimate effect sizes.

The effect size may be adjusted from the unadjusted effect size estimated in the meta-analysis. Historically, WSIPP adjusted effect sizes to some programs based on the methodological characteristics of the study. For programs reviewed in 2024 or later, we do not make additional adjustments, and we use the unadjusted effect size whenever we run a benefit-cost analysis.

Research shows the magnitude of effects may change over time. For those effect sizes, we estimate outcome-based adjustments, which we apply between the first time ES is estimated and the second time ES is estimated. More details about these adjustments can be found in our Technical Documentation.

Meta-Analysis of Program Effects
Outcomes measured No. of effect sizes Treatment N Effect sizes (ES) and standard errors (SE) Unadjusted effect size (random effects model)
ES SE Age ES p-value
Healthcare costs**
Percent change in total medical costs.
0 1 37961 -0.057 0.094 41 -0.057 0.545

Citations Used in the Meta-Analysis

Chandra, A., Gruber, J., & McKnight, R. (2014). The impact of patient cost-sharing on low-income populations: evidence from Massachusetts. Journal of Health Economics, 33, 57-66.