The Europe Center is jointly housed in the Freeman Spogli Institute for International Studies and the Stanford Global Studies Division.
FSI’s researchers assess health and medicine through the lenses of economics, nutrition and politics. They’re studying and influencing public health policies of local and national governments and the roles that corporations and nongovernmental organizations play in providing health care around the world. Scholars look at how governance affects citizens’ health, how children’s health care access affects the aging process and how to improve children’s health in Guatemala and rural China. They want to know what it will take for people to cook more safely and breathe more easily in developing countries.
FSI professors investigate how lifestyles affect health. What good does gardening do for older Americans? What are the benefits of eating organic food or growing genetically modified rice in China? They study cost-effectiveness by examining programs like those aimed at preventing the spread of tuberculosis in Russian prisons. Policies that impact obesity and undernutrition are examined; as are the public health implications of limiting salt in processed foods and the role of smoking among men who work in Chinese factories. FSI health research looks at sweeping domestic policies like the Affordable Care Act and the role of foreign aid in affecting the price of HIV drugs in Africa.
Federal policy changes in 2002 and 2009 led some states to expand public health insurance coverage to non-US-born children and pregnant women who are lawful permanent residents during their first 5 years of residency in the United States. In other states, there were concerns that insurance expansion could attract immigrants to relocate to gain free health insurance coverage.
Research has consistently identified firearm availability as a risk factor for suicide. However, existing studies are relatively small in scale, estimates vary widely, and no study appears to have tracked risks from commencement of firearm ownership.
Background Virtually all existing evidence linking access to firearms to elevated risks of mortality and morbidity comes from ecological and case–control studies. To improve understanding of the health risks and benefits of firearm ownership, we launched a cohort study: the Longitudinal Study of Handgun Ownership and Transfer (LongSHOT).
Methods Using probabilistic matching techniques we linked three sources of individual-level, state-wide data in California: official voter registration records, an archive of lawful handgun transactions and all-cause mortality data. There were nearly 28.8 million unique voter registrants, 5.5 million handgun transfers and 3.1 million deaths during the study period (18 October 2004 to 31 December 2016). The linkage relied on several identifying variables (first, middle and last names; date of birth; sex; residential address) that were available in all three data sets, deploying them in a series of bespoke algorithms.
Results Assembly of the LongSHOT cohort commenced in January 2016 and was completed in March 2019. Approximately three-quarters of matches identified were exact matches on all link variables. The cohort consists of 28.8 million adult residents of California followed for up to 12.2 years. A total of 1.2 million cohort members purchased at least one handgun during the study period, and 1.6 million died.
Conclusions Three steps taken early may be particularly useful in enhancing the efficiency of large-scale data linkage: thorough data cleaning; assessment of the suitability of off-the-shelf data linkage packages relative to bespoke coding; and careful consideration of the minimum sample size and matching precision needed to support rigorous investigation of the study questions.
An introduction to the new area of ignorance studies that examines how science produces ignorance—both actively and passively, intentionally and unintentionally.
Modelling of emerging vector borne diseases serves as an important complement to clinical studies of modern zoonoses. This article presents an archaeo‐historic epidemiological modelling study of Rift Valley fever (RVF), using data‐driven neural network technology. RVF affects both human and animal populations, can rapidly decimate herds causing catastrophic economic hardship, and is identified as a Category A biodefense pathogen by the US Center for Disease Control. Despite recent origins circa the early 1900s, little is known about the circumstances of its inception nor the relationships between factors that affect transmission. This evidence could be vital as the disease continues to expand from its epicentre in Kenya to other parts of Africa and the Arabian Peninsula. RVF is a relevant case for archaeological/palaeopathological investigations of disease as it intersects between numerous human, animal, spatial, temporal, and sociopolitical dimensions. By integrating landscape archaeology, historical evidence, and climatic data, with evidence of human behaviour gathered through ethnoarchaeological study, this article presents an applied framework for human–animal palaeopathology. This framework aligns with the One Health approach that observes disease to be intrinsically tied to ecological and societal factors. We provide a useable alternative way of thinking about disease modelling in the present and the past, ultimately seeking to support efforts to accurately predict future impacts. Tapping into longitudinal evidence from the last 50–300 years offers a powerful way to respond to the threat zoonoses will pose to human populations around the world as the climate warms.
Immigrants, once settled in a particular state, will not move to another state in search of public health benefits, Stanford researchers find.
Whether it’s designing equipment or developing drugs, scientists often fail to consider how gendered preferences, biases and assumptions can lead to unintended consequences.
According to Stanford historian Londa Schiebinger, it’s time for science to catch up.
The goal of sex and gender analysis is to promote rigorous, reproducible and responsible science. Incorporating sex and gender analysis into experimental design has enabled advancements across many disciplines, such as improved treatment of heart disease and insights into the societal impact of algorithmic bias. Here we discuss the potential for sex and gender analysis to foster scientific discovery, improve experimental efficiency and enable social equality. We provide a roadmap for sex and gender analysis across scientific disciplines and call on researchers, funding agencies, peer-reviewed journals and universities to coordinate efforts to implement robust methods of sex and gender analysis.
