Impact of diagnostic accuracy on the estimation of excess mortality from incidence and prevalence: simulation study and application to diabetes in German men.

Ralph Brinks,Annika Hoyer,Thaddäus Tönnies

doi:10.12688/f1000research.28023.1

Abstract

Aggregated data about the prevalence and incidence of chronic conditions is becoming more and more available. We recently proposed a method to estimate the age-specific excess mortality in chronic conditions from aggregated age-specific prevalence and incidence data. Previous works showed that in age groups below 50 years, estimates from this method were unstable or implausible. In this article, we examine how limited diagnostic accuracy in terms of sensitivity and specificity affects the estimates. We use a simulation study with two settings, a low and a high prevalence setting, and assess the relative importance of sensitivity and specificity. It turns out that in both settings, specificity, especially in the younger age groups, dominates the quality of the estimated excess mortality. The findings are applied to aggregated claims data comprising the diagnoses of diabetes from about 35 million men in the German Statutory Health Insurance. Key finding is that specificity in the lower age groups (<50 years) can be derived without knowing the sensitivity. The false-positive ratio in the claims data increases linearly from 0.5 per mil at age 25 to 2 per mil at age 50. As a conclusion, our findings stress the importance of considering diagnostic accuracy when estimating excess mortality from aggregated data using the method to estimate excess mortality. Especially the specificity in the younger age-groups should be carefully taken into account.

Highlights

For research purposes, aggregated data about the prevalence and incidence of chronic conditions become more and more available
Based on the illness-death model for chronic diseases (Figure 1), it can be shown that the temporal change, ∂p 1⁄4 ð∂t þ ∂aÞ p, of the age-specific prevalence p is related to the incidence rate i, and the mortality rates m0 and m1 of the people with and without the chronic condition, respectively
For age groups > 50, we can see an upper bound for the false positive ratio (FPR) that continues linearly, while the lower bound can reach 0 at ages between 60 and 70 years

Summary

Introduction

For research purposes, aggregated data about the prevalence and incidence of chronic conditions become more and more available. We proposed a new method to estimate the age-specific excess mortality in chronic conditions from aggregated age-specific prevalence and incidence data based on a differential equation [Tönnies et al, 2018; Brinks et al, 2019]. In age groups below 50 years of age, estimates from this method have been proven to be unstable or implausible [Brinks et al, 2020]. We obtained estimates of the mortality rate ratio in type 2 diabetes with values greater than 100 in ages below 40 years [Brinks et al, 2020]. In [Brinks et al, 2020] it was hypothesized “that the diagnostic accuracy of the claims data plays a crucial role for the proposed methods of estimating excess mortality.”

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