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

> **NIH NIH R01** · UNIVERSITY OF COLORADO DENVER · 2022 · $18,257

## Abstract

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.

## Key facts

- **NIH application ID:** 10523628
- **Project number:** 3R01AG061189-03S1
- **Recipient organization:** UNIVERSITY OF COLORADO DENVER
- **Principal Investigator:** Luohua Jiang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $18,257
- **Award type:** 3
- **Project period:** 2019-04-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10523628

## Citation

> US National Institutes of Health, RePORTER application 10523628, Dementia epidemiology, health service utilization and treatment costs among American Indian and Alaska Native Elders (3R01AG061189-03S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10523628. Licensed CC0.

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