Interdisciplinary research on global health problems through the lenses of economics, nutrition and politics.
Research Spotlight
Tackling the Health of Women and Children in Global Conflict Settings
A new four-paper series in The Lancet exposes the far-reaching effects of modern warfare on women’s and children’s health. Stanford researchers, including SHP's Paul Wise and Eran Bendavid, have joined other academics and health-care experts in calling for an international commitment from humanitarian actors and donors to confront political and security challenges.
Many countries have taken digital epidemiology to the next level in responding to COVID-19. Focusing on core public health functions of case detection, contact tracing, and isolation and quarantine, the authors explore ethical concerns raised by digital technologies and new data sources in public health surveillance during epidemics.
Does Diversity Matter for Health? Experimental Evidence from Oakland
African-American men have the lowest life expectancy of any major demographic group in
the United States and live on average 4.5 fewer years than non-Hispanic white men. This paper finds that the mortality disparity is partly related to underutilized preventive
healthcare services.
Generative artificial intelligence (AI) tools have made it easy to create realistic disinformation that is hard to detect by humans and may undermine public trust. Some approaches used for assessing the reliability of online information may no longer work in the AI age. We offer suggestions for how research can help to tackle the threats of AI-generated disinformation.
In low- and middle-income countries, urbanization has spurred the expansion of peri-urban communities, or urban communities of formerly rural residents with low socioeconomic status. The growth of these communities offers researchers an opportunity to measure the associations between the level of urbanization and the home language environment (HLE) among otherwise similar populations. Data were collected in 2019 using Language Environment Analysis observational assessment technology from 158 peri-urban and rural households with Han Chinese children (92 males, 66 females) aged 18–24 months in China. Peri-urban children scored lower than rural children in measures of the HLE and language development. In both samples, child age, gender, maternal employment, and sibling number were positively correlated with the HLE, which was in turn correlated with language development.
As the U.S., EU and China are taking divergent leads in new AI regulations, a new framework for AI diplomacy is emerging, all under the shadow of strategic technological competition.
Renée DiResta is the technical research manager at Stanford Internet Observatory. Dave Willner is a Non-Resident Fellow in the Program on Governance of Emerging Technologies at Stanford Cyber Policy Center.
The home language environment is a significant correlate of early childhood development outcomes; however, less is known about this mechanism in rural and peri-urban China where rates of developmental delay are as high as 52%. This study examines associations between the home language environment and child development in a sample of 158 children (58% boys) aged 18–24 months (Mage = 21.5) from rural and peri-urban households in Western China. Results show a significant association between adult-child conversation count and language development, suggesting the home language environment may be a mechanism for child development in rural and peri-urban China. 22.5% of the sample were at risk of language delay. Mother’s employment and child’s age were significant factors in the home language environment.
Many U.S. states have legislated to allow nurse practitioners (NPs) to independently prescribe drugs. Critics contend that these moves will adversely affect quality of care.
Compartmental infectious disease (ID) models are often used to evaluate non-pharmaceutical interventions (NPIs) and vaccines. Such models rarely separate within-household and community transmission, potentially introducing biases in situations where multiple transmission routes exist. We formulated an approach that incorporates household structure into ID models, extending the work of House and Keeling.
