Enrollment choices in different types of HMOs: a multivariate analysis

Area of Science:

• Health Services Research
• Health Economics
• Biostatistics

Background:

• Analyzing enrollment decisions in healthcare plans is complex.
• Previous studies often relied on simpler bivariate analyses.
• Understanding factors influencing choice between different health insurance models (HMOs, BC/BS) is crucial for planning.

Purpose of the Study:

• To analyze enrollment decisions among employed individuals choosing between open-panel and closed-panel Health Maintenance Organizations (HMOs) and Blue Cross/Blue Shield (BC/BS).
• To identify consistent predictors of health plan selection using a multivariate logistic probability model.

Main Methods:

• Utilized a multivariate logistic probability model (logit) to overcome limitations of bivariate analysis.
• Analyzed enrollment choices based on access (previous source of care), health risk (family life stage, chronic conditions), economic vulnerability (income), and health concern.

Main Results:

• Previous source of care was the strongest predictor: absence favored closed-panel HMOs, presence favored open-panel HMOs.
• Higher health risk (younger families with more children), higher income, and more chronic conditions increased likelihood of joining open-panel HMOs.
• Greater health concern predicted higher probability of choosing closed-panel HMOs.

Conclusions:

• The multivariate logistic model accurately predicts enrollment choices, with over 50% accuracy for 60% of the sample.
• Predictive accuracy for distinguishing between open and closed-panel HMOs exceeded 50% for 80% of individuals.
• The findings support the applicability of this approach for Health Maintenance Organization (HMO) feasibility analysis and strategic planning.