# Improving statistical methods for small area estimates of public health indicators and demographic characteristics

> **NIH NIH R01** · EMORY UNIVERSITY · 2020 · $523,104

## Abstract

SPATIAL UNCERTAINTY IN SMALL AREA ANAYLSIS FROM SURVEY AND ADMINISTRATIVE DATA
ABSTRACT
Small area data are ubiquitous in public health including incident or prevalent disease counts, population sizes,
socioeconomic covariates, and environmental exposures. Geographic information systems (GISs) link and
merge such data but do not account for multiple sources of uncertainty. Small area estimation and disease
mapping techniques provide tools for maintaining geographic precision of smaller areas and statistical
precision of larger sample sizes via “borrowing information” from neighboring areas. To date, these methods
do not readily incorporate design-based uncertainty from the components. Our proposed research project
involves the development of statistical methodology and associated software tools as part of an evolving
reproducible workflow to incorporate local population and covariate uncertainty into spatial models of disease
risk.

## Key facts

- **NIH application ID:** 9993634
- **Project number:** 5R01HD092580-03
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** Lance A Waller
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $523,104
- **Award type:** 5
- **Project period:** 2018-09-13 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9993634

## Citation

> US National Institutes of Health, RePORTER application 9993634, Improving statistical methods for small area estimates of public health indicators and demographic characteristics (5R01HD092580-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9993634. Licensed CC0.

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