Stanford Health Policy is a joint effort of the Freeman Spogli Institute for International Studies and the Stanford School of Medicine
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.
Measuring the expression of most or all of the genes in a biological system raises major analytic challenges. A wealth of recent reports uses microarray expression data to examine diverse biological phenomena - from basic processes in model organisms to complex aspects of human disease. After an initial flurry of methods for clustering the data on the basis of similarity, the field has recognized some longer-term challenges. Firstly, there are efforts to understand the sources of noise and variation in microarray experiments in order to increase the biological signal. Secondly, there are efforts to combine expression data with other sources of information to improve the range and quality of conclusions that can be drawn. Finally, techniques are now emerging to reconstruct networks of genetic interactions in order to create integrated and systematic models of biological systems.