An application of Zero-One Inflated Beta regression models for predicting health insurance reimbursement

TL;DR

This paper introduces a Zero-One Inflated Beta regression model using GAMLSS to predict health insurance reimbursements, capturing the complex distribution of reimbursement ratios influenced by contract limitations and risk factors.

Contribution

It proposes a novel GAMLSS-based regression approach for modeling reimbursement ratios with zero-one inflation, improving dependency analysis over traditional Monte Carlo methods.

Findings

The model effectively captures the mixture distribution of reimbursement ratios.

It provides a dependency structure between reimbursements and limitations.

The approach offers a flexible alternative to Monte Carlo simulations.

Abstract

In actuarial practice the dependency between contract limitations (deductibles, copayments) and health care expenditures are measured by the application of the Monte Carlo simulation technique. We propose, for the same goal, an alternative approach based on Generalized Linear Model for Location, Scale and Shape (GAMLSS). We focus on the estimate of the ratio between the one-year reimbursement amount (after the effect of limitations) and the one year expenditure (before the effect of limitations). We suggest a regressive model to investigate the relation between this response variable and a set of covariates, such as limitations and other rating factors related to health risk. In this way a dependency structure between reimbursement and limitations is provided. The density function of the ratio is a mixture distribution, indeed it can continuously assume values mass at 0 and 1, in addition to the probability density within (0, 1) . This random variable does not belong to the exponential family, then an ordinary Generalized Linear Model is not suitable. GAMLSS introduces a probability structure compliant with the density of the response variable, in particular zero-one inflated beta density is assumed. The latter is a mixture between a Bernoulli distribution and a Beta distribution.