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Improving the representativeness of American Indian Tribal Behavioral Risk Factor Surveillance System (TBRFSS) by machine learning and propensity score based data integration approach A1

Award Number: R21MD014658
ORGANIZATION: NATIONAL CENTER ON MINORITY HEALTH AND HEALTH DISPARITIES
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

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Improving the representativeness of American Indian Tribal Behavioral Risk Factor Surveillance System (TBRFSS) by machine learning and propensity score based data integration approach A1 - PROJECT SUMMARY Previous studies showed discrepancies of health and behavior prevalence between American Indians (AI) population and other racial or ethnic groups. Most health surveys have certain limitations when studying AI population due to the small sample sizes for AI population. Data collected by AI Tribal Epidemiology Centers (TECs) provides an excellent opportunity to conduct research for AI population due to sufficient sample size and extensive information. However, most surveys conducted by TECs used non-probability sampling design (e.g. convenient sample) due to its lower cost and increased time efficiency. Non-probability sample may suffer from sampling, coverage and nonresponse errors without further proper adjustments. Such difficulties greatly hampers the analysis of AI population in health and behavior research. Our general hypothesis is that data integration by combining information from non-probability and probability samples can reduce sampling, coverage and nonresponse errors in original non-probability sample. The Goal of this project is to develop an accurate and robust data integration methodology for AI population analysis specifically tailored to health and behavior research. During the past years, we have 1) studied data integration using calibration and parametric modeling approaches; 2) investigated machine learning and propensity score modeling methods in survey sampling and other fields; and 3) assembled an experienced team of multi-disciplinary team of experts. In this project, we propose to capitalize on our expertise and fulfill the following Specific Aims: Aim 1. Develop a data integration approach using machine learning and propensity score modeling We will develop machine learning and propensity score based data integration approaches to combine information from non-probability and probability samples. Compared to existing methods (i.e., Calibration, Parametric approach), our proposed approaches are more robust against the failure of underlying model assumptions. The inference is more general and multi-purpose (e.g. one can estimate most parameters such as means, totals and percentiles). Simulation studies will be performed to compare our proposed methods with other existing methods. A computing package will be built to implement the method in other settings. Aim 2. Evaluate the accuracy and robustness of the proposed method in AI health and behavior research We will use real data to validate the proposed methods in terms of accuracy and robustness to the various data types. The performance will also be assessed by comparing with results from existing data integration methods such as calibration and parametric modeling approaches. The planned study takes advantage of a unique data source and expands the impact of the Indian Health Service (IHS)-funded research. We expect this novel integration method will vertically advance the field by facilitating the analysis based on non-probability sample, which can provide in-depth understanding regarding the AI population health and behavior studies.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2024 ( Subtotal = -$1,738 )
2024	2021	UNIVERSITY OF OKLAHOMA	865 RESEARCH PKWY	OKLAHOMA CITY	OK	73104	OKLAHOMA	USA	Minority Health and Health Disparities Research	000	2	10/10/2023	NON-COMPETING CONTINUATION	-$1,738
														Subtotal = -$1,738

Issue Date FY: 2021 ( Subtotal = $109,613 )
2021	2021	UNIVERSITY OF OKLAHOMA	865 RESEARCH PKWY STE 530	OKLAHOMA CITY	OK	73104	OKLAHOMA	USA	Minority Health and Health Disparities Research	001	2	6/16/2021	NON-COMPETING CONTINUATION	$109,613
2021	2020	UNIVERSITY OF OKLAHOMA	865 RESEARCH PKWY STE 530	OKLAHOMA CITY	OK	73104	OKLAHOMA	USA	Minority Health and Health Disparities Research	000	1	10/29/2020	NEW	$0
														Subtotal = $109,613

Issue Date FY: 2020 ( Subtotal = $115,176 )
2020	2020	UNIVERSITY OF OKLAHOMA	865 RESEARCH PKWY STE 530	OKLAHOMA CITY	OK	73104	OKLAHOMA	USA	Minority Health and Health Disparities Research	000	1	9/26/2020	NEW	$115,176
														Subtotal = $115,176

Grand Total All Awards = $223,051

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Improving the representativeness of American Indian Tribal Behavioral Risk Factor Surveillance System (TBRFSS) by machine learning and propensity score based data integration approach A1

Award Number: R21MD014658

ORGANIZATION: NATIONAL CENTER ON MINORITY HEALTH AND HEALTH DISPARITIES

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

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