Estimating Disease-Free Life Expectancy based on Clinical Data from the French Hospital Discharge Database

TL;DR

This study introduces a method to estimate Disease-Free Life Expectancy using French hospital discharge data, highlighting its advantages and limitations for public health assessment.

Contribution

It demonstrates how large clinical datasets can be used to derive robust health indicators like Dis-FLE, expanding beyond traditional survey-based methods.

Findings

Estimated age-dependent hazard rates for various risk factors.

Identified advantages of using hospital data for health metrics.

Discussed limitations and challenges of data-driven health indicator estimation.

Abstract

The development of health indicators to measure healthy life expectancy (HLE) is an active field of research aimed at summarizing the health of a population. Although many health indicators have emerged in the literature as critical metrics in public health assessments, the methods and data to conduct this evaluation vary considerably in nature and quality. Traditionally, health data collection relies on population surveys. However, these studies, typically of limited size, encompass only a small yet representative segment of the population. This limitation can necessitate the separate estimation of incidence and mortality rates, significantly restricting the available analysis methods. In this article, we leverage an extract from the French National Hospital Discharge database to define health indicators. Our analysis focuses on the resulting Disease-Free Life Expectancy (Dis-FLE) indicator, which provides insights based on the hospital trajectory of each patient admitted to hospital in France during 2008-13. Through this research, we illustrate the advantages and disadvantages of employing large clinical datasets as the foundation for more robust health indicators. We shed light on the opportunities that such data offer for a more comprehensive understanding of the health status of a population. In particular, we estimate age-dependent hazard rates associated with sex, alcohol abuse, tobacco consumption, and obesity, as well as geographic location. Simultaneously, we delve into the challenges and limitations that arise when adopting such a data-driven approach.