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Machine learning in publicly available geotagged data to allow monitoring of maternal and child health

Award Number: R01HD111547
ORGANIZATION: NATIONAL INSTITUTE OF CHILD HEALTH AND HUMAN DEVELOPMENT
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 08/21/2024
PERIOD OF PERFORMANCE END DATE: 04/30/2029

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Machine learning in publicly available geotagged data to allow monitoring of maternal and child health - Over 95 percent of maternal and child deaths globally occur in low- and middle-income countries (LMICs) where reliable death registries are mostly unavailable and other dependable data are scarce. Moreover, within LMICs, the most disadvantaged and remote communities tend to have the highest mortality rates but the least reliable data. Knowledge on the state of maternal and child health (MCH) in LMICs relies mostly on household surveys. These expensive and time-consuming surveys cover merely a small minority (usually <2%) of all communities in a country and are (at best) carried out only every couple of years. We propose a new approach to measuring MCH indicators that would provide up-to-date estimates at a very high geographic resolution and at little to no cost. Specifically, we hypothesize that machine learning in geotagged big data sources, with a key source being satellite imagery, can accurately estimate critical MCH indicators for each village and neighborhood in a country. Satellite imagery is a key data source in this project because it is updated frequently (at least monthly), covers all areas of a country, and is available free of charge. We will pursue three specific aims: 1) determining whether machine learning in satellite imagery and other publicly available geotagged data can accurately estimate key indicators of MCH status in a village or neighborhood at a snapshot in time and longitudinally over time; 2) determining whether this approach can also accurately estimate coverage with critical MCH services at a snapshot in time and over time; and 3) achieving sufficient interpretability of our machine learning models to inform on data needed to improve predictions, which interventions to target where, and generalizability. The approach to achieving our aims is to use household surveys that are geocoded at the village and neighborhood level as the ground truth against which we will train machine learning models in satellite images and publicly available geotagged data. Our extensive preliminary data, along with high-impact publications (e.g., in Science and Nature) by our team on using satellite images to predict important determinants of MCH in LMICs (such as community-level poverty, surface water quality, water and sanitation infrastructure, crop yields, travel time to the nearest healthcare facility, and air pollution), demonstrate that this approach is feasible. In addition to a unique dataset as well as open-access code for our machine learning models, this project will provide maps with an extremely high geographic resolution of key MCH indicators to inform policymakers and researchers on the current state of MCH. This project is significant because it can inform the geographically precise planning and targeting of MCH interventions, may enable the evaluation of past interventions and policies, and can serve as a blueprint for extending a precision public health approach to health outcomes beyond MCH. The methods developed here may also serve to forewarn of emerging infectious epidemics overseas that may eventually affect the United States. The same satellite-based approach could also be applied domestically — including in the aftermath of natural disasters such as Hurricane Katrina and during rapidly emerging health threats — to rapidly estimate mortality and health outcomes where reliable data are lacking.


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 = $618,930 )
2026	2026	THE LELAND STANFORD JUNIOR UNIVERSITY	450 JANE STANFORD WAY	STANFORD	CA	94305	SANTA CLARA	USA	Child Health and Human Development Extramural Research	000	3	6/30/2026	NON-COMPETING CONTINUATION	$618,930
														Subtotal = $618,930

Issue Date FY: 2025 ( Subtotal = $632,996 )
2025	2025	THE LELAND STANFORD JUNIOR UNIVERSITY	450 JANE STANFORD WAY	STANFORD	CA	94305	SANTA CLARA	USA	Child Health and Human Development Extramural Research	001	2	7/17/2025	NON-COMPETING CONTINUATION	$632,996
2025	2024	THE LELAND STANFORD JUNIOR UNIVERSITY	450 JANE STANFORD WAY	STANFORD	CA	94305	SANTA CLARA	USA	Child Health and Human Development Extramural Research	000	1	5/1/2025	NEW	$0
														Subtotal = $632,996

Issue Date FY: 2024 ( Subtotal = $643,468 )
2024	2024	THE LELAND STANFORD JUNIOR UNIVERSITY	450 JANE STANFORD WAY	STANFORD	CA	94305	SANTA CLARA	USA	Child Health and Human Development Extramural Research	000	1	8/21/2024	NEW	$643,468
														Subtotal = $643,468

Grand Total All Awards = $1,895,394

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Machine learning in publicly available geotagged data to allow monitoring of maternal and child health

Award Number: R01HD111547

ORGANIZATION: NATIONAL INSTITUTE OF CHILD HEALTH AND HUMAN DEVELOPMENT

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

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

PERIOD OF PERFORMANCE START DATE: 08/21/2024

PERIOD OF PERFORMANCE END DATE: 04/30/2029

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