AI Article Synopsis

  • Estimation of mortality rates and mortality rate ratios (MRR) is crucial in understanding the impact of chronic diseases in epidemiology, often using retrospective data from surveys and death registries.
  • The study highlights a problem called "misclassification of disease status at death (MicDaD)", which can lead to biased MRR estimates, particularly when individuals who appeared disease-free at the time of the study later die from an undetected disease.
  • Through simulations, the research found that the extent of MicDaD bias varies with the incidence of the chronic disease and tends to underestimate MRR significantly in high-incidence scenarios, while lower incidence cases showed less bias accompanied by greater uncertainty.

Article Abstract

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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10765798PMC
http://dx.doi.org/10.1186/s12874-023-02111-3DOI Listing

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