Estimation of excess mortality in a chronic condition from current status data with disease duration: simulation study about need for long-term care

TL;DR

This paper introduces a method to estimate excess mortality in chronic conditions using current status data with disease duration, demonstrated through simulation, highlighting the need for large sample sizes for accuracy.

Contribution

The paper presents a novel differential equation-based approach to estimate mortality rate ratios from cross-sectional data incorporating disease duration, applicable with accessible population data.

Findings

Requires large sample sizes (100,000+) for low error in estimates

Method is feasible with claims data and vital statistics

Simulation based on illness-death model demonstrates applicability

Abstract

This article describes a method to estimate the mortality rate ratio R from current status data with duration in a chronic condition in case the general mortality of the overall population is known. Apart from the general mortality, the method requires four pieces of information from the study participants: age and time at the survey/interview, whether the chronic condition is present (current status) and if so, for how long the condition is present (duration). The method uses a differential equation that relates prevalence, incidence and mortality to estimate R of the people with the chronic condition compared to those without the condition. To demonstrate feasibility, a simulation based on the illness-death model (multi-state model) with transition rates motivated from long-term care is run. It is found that the method requires a large number of study participants (100000 or more) to estimate R with a reasonably low relative error. Despite the large sample size, the method can be useful in settings when cross-sectional information are easily available, e.g., in claims data, and national age-specific general mortality rates are accessible from vital statistics.