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Algorithmic fairness in predictive models to eliminate disparities in adverse infant outcomes

Award Number: K01MD018072
ORGANIZATION: NATIONAL CENTER ON MINORITY HEALTH AND HEALTH DISPARITIES
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP
PERIOD OF PERFORMANCE START DATE: 09/26/2022
PERIOD OF PERFORMANCE END DATE: 06/30/2026

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Algorithmic fairness in predictive models to eliminate disparities in adverse infant outcomes - Non-Hispanic Black infants have twice the rates of low birthweight births as non-Hispanic White infants. As adverse birth outcomes increase risk of infant mortality and adverse outcomes across the life course, improving birth outcomes is a national priority. Despite this important public health issue, many public and private payers are unable to adequately reduce adverse infant outcomes because of a lack of race/ethnicity data. This K01 fills a critical need for evidence-based recommendations for collection and use of racial/ethnic data among payers to enable population health management programs to develop predictive algorithms that could be used to reduce adverse birth outcomes. Failure to include race/ethnicity in predictive models used for resource allocation may ultimately lead to biased algorithms that preclude adequate resource allocation. Aim 1 of this study will use an algorithmic fairness framework to test multiple algorithms for developing predictive models for low birthweight birth. In addition to testing model accuracy, predictive models will be tested for seven measures of algorithmic fairness to assess whether having race/ethnicity improves algorithmic fairness (e.g., equal [or better] predictive accuracy for non-White relative to White women) after applying four fairness-enhancing approaches. This project will utilize medical claims, birth certificates, and beneficiary information from the Arkansas All Payer Claims Database. Linkage to the birth certificates uniquely allows this study to have race/ethnicity, which are absent in the commercial claims given lack of collection by most payers. The seminal Institute of Medicine Report Unequal Treatment recommended collection of race/ethnicity to improve health and healthcare delivery; however, it is well known that payers fear accusations of redlining and rarely collect race/ethnicity in most states. Research on payer and provider views regarding collection of race/ethnicity has been conducted, but similar research on the views of minority beneficiaries are severely lacking. Aim 2 of this study will conduct raciallyhomogenous focus groups among Black, Hispanic, and Marshallese women in Arkansas regarding attitudes on acceptability of collecting and using race/ethnicity data as well as administrative aspects (e.g., when to collect the data), with an emphasis on perinatal programs. These aims will provide an evidence-base and serve as a national model for collecting and using racial/ethnic data with community input. Large third-party payers have the infrastructure to improve health outcomes, but lack a community-engaged approach to inform collection and use of these data to guide development of algorithms. The K01 will allow the investigator to build on her expertise in insurance claims analysis to acquire skillsets in predictive modeling, community engagement, and qualitative methodologies. These important skillsets will allow the researcher to achieve her long-term goals of becoming a productive and independent researcher with a focus on identifying and mitigating factors that serve as drivers of r adverse infant and maternal outcomes.


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: 2026 ( Subtotal = $0 )
2026	2025	UNIVERSITY OF ARKANSAS FOR MEDICAL SCIENCES	4301 W MARKHAM ST	LITTLE ROCK	AR	72205	PULASKI	USA	Minority Health and Health Disparities Research	000	4	6/5/2026	NON-COMPETING CONTINUATION	$0
														Subtotal = $0

Issue Date FY: 2025 ( Subtotal = $125,185 )
2025	2025	UNIVERSITY OF ARKANSAS FOR MEDICAL SCIENCES	4301 W MARKHAM ST	LITTLE ROCK	AR	72205	PULASKI	USA	Minority Health and Health Disparities Research	000	4	8/18/2025	NON-COMPETING CONTINUATION	$125,185
														Subtotal = $125,185

Issue Date FY: 2024 ( Subtotal = $131,428 )
2024	2024	UNIVERSITY OF ARKANSAS FOR MEDICAL SCIENCES	4301 W MARKHAM ST	LITTLE ROCK	AR	72205	PULASKI	USA	Minority Health and Health Disparities Research	000	3	7/5/2024	NON-COMPETING CONTINUATION	$131,428
														Subtotal = $131,428

Issue Date FY: 2023 ( Subtotal = $114,503 )
2023	2023	UNIVERSITY OF ARKANSAS FOR MEDICAL SCIENCES	4301 W MARKHAM ST	LITTLE ROCK	AR	72205	PULASKI	USA	Minority Health and Health Disparities Research	002	2	7/3/2023	NON-COMPETING CONTINUATION	$114,503
2023	2022	UNIVERSITY OF ARKANSAS FOR MEDICAL SCIENCES	4301 W MARKHAM ST	LITTLE ROCK	AR	72205	PULASKI	USA	Minority Health and Health Disparities Research	001	1	1/4/2023	NEW	$0
2023	2022	UNIVERSITY OF ARKANSAS SYSTEM	4301 W MARKHAM ST SLOT 812 BIO-MED BLDG 1 STE 102	LITTLE ROCK	AR	72205	PULASKI	USA	Minority Health and Health Disparities Research	000	1	11/17/2022	NEW	$0
														Subtotal = $114,503

Issue Date FY: 2022 ( Subtotal = $125,047 )
2022	2022	UNIVERSITY OF ARKANSAS SYSTEM	4301 W MARKHAM ST SLOT 812 BIO-MED BLDG 1 STE 102	LITTLE ROCK	AR	72205	PULASKI	USA	Minority Health and Health Disparities Research	000	1	9/26/2022	NEW	$125,047
														Subtotal = $125,047

Grand Total All Awards = $496,163

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Algorithmic fairness in predictive models to eliminate disparities in adverse infant outcomes

Award Number: K01MD018072

ORGANIZATION: NATIONAL CENTER ON MINORITY HEALTH AND HEALTH DISPARITIES

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP

PERIOD OF PERFORMANCE START DATE: 09/26/2022

PERIOD OF PERFORMANCE END DATE: 06/30/2026

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