Dementia epidemiology, health service utilization and treatment costs among American Indian and Alaska Native Elders

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Project Summary Electronic health records (EHRs) have grown in popularity for health research because they provide relatively easy access to large amounts of longitudinal health data in real­world healthcare scenarios. However, establishing causal relationships between potential disease risk factors and mortality is subject to multiple limitations. Among these are selection bias, misclassification bias, informed presence bias, and unmeasured confounding. Another potential, yet largely unstudied, bias arises from patients seeking care outside of the system being studied, what we refer to as system migration. By definition, system migration leads to intermittent missing data at the subject level. Further complicating this issue is the fact that most migration of patients is unknown to the researcher as there is generally no indication of a patient leaving one healthcare system and seeking care at another. This problem is particularly true in the Indian Health System (IHS), where it is common for patients to receive care by outside providers as well as the IHS. When modeling a time­to­event endpoint such as time to ADRD diagnosis, the resulting missingness due to system migration can be characterized by (potentially unobserved) left­, right­, or interval­censoring. My proposed training and research consider the implications of potentially unobserved intermittent missingness when modeling censored time­to­event outcomes and proposes methodological solutions to reduce bias in such cases. Specifically, we propose statistical methods that can be used to 1) more accurately estimate covariate effects on time­to­ event outcomes under unknown system migration patterns; 2) more accurately estimate covariate effects on time­to­event outcomes under a mis­specified model and unknown system migration patterns; and 3) improve assessment of prediction accuracy for recurrent event right­censored survival data. Our proposed work will provide the researchers with methods to better understand and minimize the impact of concerns related to system migration, thereby leading to increased validity and replicability of our research findings.