Estimation of mortality rate ratios for chronic conditions with misclassification of disease status at death.

Autor: Voß S; Chair for Medical Biometry and Epidemiology, Faculty of Health, Witten/Herdecke University, Witten, Germany. sabrina.voss@uni-wh.de., Hoyer A; Biostatistics and Medical Biometry, Medical School EWL, Bielefeld University, Bielefeld, Germany., Landwehr S; Regional Association of Statutory Health Insurance Physicians, Strategic Data Analysis Unit, Düsseldorf, Germany., Pavkov ME; Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Diseases Control and Prevention, Atlanta, GA, USA., Gregg E; Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK., Brinks R; Chair for Medical Biometry and Epidemiology, Faculty of Health, Witten/Herdecke University, Witten, Germany.; German Diabetes Center, Institute for Biometry and Epidemiology, Düsseldorf, Germany.
Jazyk: angličtina
Zdroj: BMC medical research methodology [BMC Med Res Methodol] 2024 Jan 03; Vol. 24 (1), pp. 2. Date of Electronic Publication: 2024 Jan 03.
DOI: 10.1186/s12874-023-02111-3
Abstrakt: Estimation of mortality rates and mortality rate ratios (MRR) of diseased and non-diseased individuals is a core metric of disease impact used in chronic disease epidemiology. Estimation of mortality rates is often conducted through retrospective linkage of information from nationwide surveys such as the National Health Interview Survey (NHIS) and death registries. These surveys usually collect information on disease status during only one study visit. This infrequency leads to missing disease information (with right censored survival times) for deceased individuals who were disease-free at study participation, and a possibly biased estimation of the MRR because of possible undetected disease onset after study participation. This occurrence is called "misclassification of disease status at death (MicDaD)" and it is a potentially common source of bias in epidemiologic studies. In this study, we conducted a simulation analysis with a high and a low incidence setting to assess the extent of MicDaD-bias in the estimated mortality. For the simulated populations, MRR for diseased and non-diseased individuals with and without MicDaD were calculated and compared. Magnitude of MicDaD-bias depends on and is driven by the incidence of the chronic disease under consideration; our analysis revealed a noticeable shift towards underestimation for high incidences when MicDaD is present. Impact of MicDaD was smaller for lower incidence (but associated with greater uncertainty in the estimation of MRR in general). Further research can consider the amount of missing information and potential influencers such as duration and risk factors of the disease.
(© 2023. The Author(s).)
Databáze: MEDLINE
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