Estimation of the incidence rate and mortality rate ratio for chronic conditions based on aggregated current status data

TL;DR

This paper presents a new method to estimate age-specific incidence and mortality rate ratios for chronic conditions using aggregated current status data, validated through a simulation study on diabetes in Germany.

Contribution

It introduces a novel estimation approach for the illness-death model using ACS data, which does not require follow-up studies, and demonstrates its effectiveness via simulation.

Findings

Good agreement between estimates and simulation parameters

Method effectively estimates incidence and mortality rate ratios

Applicable to epidemiological studies with aggregated data

Abstract

Recently, it has been shown that the transition rates of the illness-death model (IDM) for chronic conditions are related to the percentages of people in the states by a three-dimensional system of differential equations [Bri24]. The aim of this article is to introduce a method to estimate the age-specific incidence rate together with the mortality rate ratio from aggregated current status (ACS) data. By ACS data we mean counts of (non-necessarily different) people in the three states of the IDM at different points in time. ACS data stem from epidemiological studies where only current disease status and vital status data need to be collected without following-up people (as, for example, in cohort studies). As an application, we use the theory in a simulation study about diabetes in Germany with 600 study subjects at eleven repeated cross-sections each of which with 50% participation quote. Special focus is given to stochastic dependency of the sampled participants. We find a good agreement between the estimates and the input parameters used for the simulation.