Abstract
BackgroundFamily health history (FHH) inherently involves collecting proxy reports of health statuses of related family members. Traditionally, such information has been collected from a single informant. More recently, research has suggested that a multiple informant approach to collecting FHH results in improved individual risk assessments. Likewise, recent work has emphasized the importance of incorporating health-related behaviors into FHH-based risk calculations. Integrating both multiple accounts of FHH with behavioral information on family members represents a significant methodological challenge as such FHH data is hierarchical in nature and arises from potentially error-prone processes.MethodsIn this paper, we introduce a statistical model that addresses these challenges using informative priors for background variation in disease prevalence and the effect of other, potentially correlated, variables while accounting for the nested structure of these data. Our empirical example is drawn from previously published data on families with a history of diabetes.ResultsThe results of the comparative model assessment suggest that simply accounting for the structured nature of multiple informant FHH data improves classification accuracy over the baseline and that incorporating family member health-related behavioral information into the model is preferred over alternative specifications.ConclusionsThe proposed modelling framework is a flexible solution to integrate multiple informant FHH for risk prediction purposes.
Highlights
Health history (FHH) inherently involves collecting proxy reports of health statuses of related family members
Many complex diseases are believed to result from the joint influence of genetic, socio-environmental, and lifestyle risk factors that are clustered within families [1], thereby making family health history (FHH) a powerful predictor of varied health outcomes, such as heart disease [2, 3], type 2 diabetes [4,5,6], and colorectal cancer [7]
We present a statistical model that improves estimation for reconciling discrepant accounts of multiple informant family health histories into a unified Family health history (FHH) that can be used to calculate risk by adjusting for errors arising from the informants, their family members, and background noise
Summary
Health history (FHH) inherently involves collecting proxy reports of health statuses of related family members. A patient or research subject reports on their FHH independently and autonomously by informing on the health and disease status of their biological first- and second-degree relatives (e.g., children, siblings, parents, aunts/uncles, and grandparents). This single informant, may not have accurate or complete knowledge about their relatives’ disease diagnoses, age at diagnosis, causes of death, and health-related behaviors, leading to an inaccurate risk assessment.
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