The Center for Population Health Sciences advances population health and health equity research by expanding access to population-level datasets, reducing barriers to their use through training and technical support, and collaborating with academic, community, and government partners to generate evidence that strengthens policies and programs to improve health for all.
Exploring How Local Policies Shape Community Health Across the Bay Area
Utilizing a novel policy surveillance approach, PHS partnered with the Solano County Public Health Department to code and analyze local ordinances, plans, and resolutions in seven Bay Area Counties.
Exploring How Local Policies Shape Community Health Across the Bay Area
It is critical to understand how local social and health policies influence the well-being of communities. Policy surveillance is an approach that enables us to do this by systematically collecting codes and laws across jurisdictions, turning complex legal language into data that can reveal how policy shapes health. Tracy Lam-Hine, a PHS post-doctoral fellow, in partnership with the Solano County Public Health Department, utilized this approach to code and analyze local ordinances, plans, and resolutions in seven Bay Area Counties.
Leveraging Area-Level Measures to Better Predict Health Outcomes and Mortality
This study compares the predictive value of area-level measures of social risk to individual-level socio-economic measures.
Leveraging Area-Level Measures to Better Predict Health Outcomes and Mortality
How well do area-based measures of social risk, compared with individual-level socioeconomic measures, predict health outcomes and mortality? David Rehkopf, Director of PHS, and his colleagues conducted a cross-sectional study including more than 2.8 million patients evaluated 8 area-based measures of social risk for health and found that the Area Deprivation Index was the most consistent predictor of health outcomes and mortality across different subpopulations compared with other area- and individual-level social risk measures.
Read the study findings.
Learn more about area-level measures.
AI Breakthrough Promises Early Detection of Silent Artery Killer
An AI-driven study from Stanford PHS and UC San Diego is tackling the hidden burden of peripheral artery disease by developing a scalable screening tool that could transform early detection and treatment for millions.
Artificial Intelligence for Early Detection of Peripheral Artery Disease (AID-PAD)
Peripheral artery disease (PAD) affects over 10 million U.S. adults over 40 years old, causing high morbidity, mortality, and healthcare costs. PAD often goes undetected due to low awareness, atypical symptoms, and lack of routine screening—particularly among women, minorities, and underserved populations. Primary care settings play a crucial role in early detection, thereby improving PAD outcomes. In partnership with UC San Diego, PHS has launched a study to develop a well-validated AI-based PAD screening tool. Using electronic health records (EHRs) from the American Family Cohort, a research dataset from the PRIME Registry, PHS will train AI models based on patient-level data, including sociodemographic and clinical patterns. The findings will inform scalable strategies to improve the PAD diagnosis and treatment across diverse populations and clinical environments. This study highlights a collaborative effort between Stanford PHS and external partners to test hypotheses and address key research questions using a robust, real-world EHR longitudinal dataset hosted in the PHS data portal.
Income Supplementation and Cancer Risk Factors in Areas of Persistent Poverty
The Upstream Research Center, representing a partnership between Stanford, UCSF, and UC Davis, is conducting two research projects aimed at determining whether income supplementation can reduce cancer risk factors.
Income Supplementation and Cancer Risk Factors in Areas of Persistent Poverty
The Upstream Research Center, representing a partnership between Stanford, UCSF, and UC Davis, is conducting two research projects aimed at answering this important question. The first project is assessing the impact of seven basic income pilots across California on modifiable cancer risk factors. The second project is evaluating the effect of increased income support through the Earned Income Tax Credit (EITC), estimating the impact of the 2015 CalEITC expansion on colorectal risk factors.
Understanding the Misalignment Between Spending on Social Services and Medical Costs
The Wrong Pocket Project, with data support from PHS, is linking New York All-Payer Claims to social services data to better understand the misalignment between spending on social services and medical costs.
The Misalignment Between Spending on Social Services and Medical Costs
The "wrong pocket problem," where one organization or sector pays for an investment, but another sector receives the financial or health benefits, can create perverse incentives that discourage spending. This is especially true in health, where social factors drive costs. With data support from PHS, this project is linking New York All-Payer Claims data to social services data to understand and address the misalignment between spending on social services and medical costs.
Training Future Leaders to Use Real-World Data to Improve Population Health
PHS offers several training programs designed to build the real-world data capacity of researchers and partners.
Training Future Leaders to Use Real-World Data to Improve Population Health
Examples include:
Persistent Poverty Initiative RWD Seminar Series and Workshop. Designed to build the RWD knowledge and capacity of early-career scholars dedicated to promoting cancer equity.
Using Real-World Data for Translational Research Course. Will train scholars how to use RWD to advance translational science and research.
Community Partner Meetings & Talks. PHS hosts meetings, events, and talks on different real-world data topics of interest to its community partners.
AHEAD Program. A summer internship program that trained undergraduates on how to use real-world data for population health research.
Enhancing and Validating Primary Care Data to Improve U.S. Health
The American Family Cohort-Health Project aims to create a revolutionary new data platform based on a national primary care electronic health record data resource, enabling a more comprehensive, accurate, equitable, and rapid assessment of U.S. health.
Enhancing and Validating Primary Care Data to Improve U.S. Health
Accurate and impact-oriented health data systems are the microscope of the 21st century. We are at the nascent stage of developing these critical tools, and as a result, failure to have adequate data effectively hinders our progress in improving U.S. population health. The American Family Cohort-Health Project aims to create a revolutionary new data platform based on a national primary care electronic health record data resource, enabling a more comprehensive, accurate, equitable, and rapid assessment of U.S. health.
Providing Coordinated Care and Cash to Improve the Health of Children in San Mateo County
The Baby Bonus Project, a unique community-funded intervention, is the first study to assess the impact of combining basic income with coordinated care.
Providing Coordinated Care and Cash to Improve the Health of Children in San Mateo County
The Baby Bonus Program is a unique community-funded intervention that combines guaranteed income with integrated social and health services to support San Mateo families with limited resources during the early years of their child’s life. We are evaluating its impact on health care utilization, mental health, child development, social service utilization, and social determinants of health.
Study Reveals Who Gets Diagnosed with Long COVID—and Who Gets Left Behind
A new analysis of more than 10,000 primary care patients uncovers who is receiving Post-COVID Condition diagnoses, how their care needs evolve, and where disparities persist—offering critical insights to guide future healthcare planning and equity efforts.
Study Reveals Who Gets Diagnosed with Long COVID—and Who Gets Left Behind
This study investigates Post-COVID Conditions (PCC) in over 10,000 U.S. primary care patients, leveraging the American Family Cohort (AFC) dataset. Findings reveal high clinical complexity and frequent healthcare use among diagnosed individuals, with notable disparities in diagnosis rates among rural and socioeconomically deprived populations. By identifying potential diagnostic gaps and shifts in care demands, this research informs healthcare planning and resource allocation for PCC management. Policymakers, healthcare providers, and public health officials may benefit from insights into who receives a PCC diagnosis and how care needs evolve.