Inequalities and Health Disparities

At the heart of population research lies the question of how population processes affect patterns of human health, and how health dynamics in turn affect population processes. This theme examines the influence of place-based context and social influences on health and health behaviors, in particular examining the role of context and society in patterning health disparities across groups. The health disparities theme at BPC builds on Berkeley's historic public health strength as the birthplace of social epidemiology, and the modern .Berkeley difference. that combines biology, behavior, and environment to study the production of patterns of population health. There is a vast amount of research taking place at Berkeley in the area of health disparities. At Berkeley we demonstrate how highlight innovative biology-based work understanding fundamental mechanisms of how socioeconomic status "gets under the skin" by relating BPC work in health disparities with research in formal biodemography carried out at Berkeley, to new advances in understanding social network influences on behavior, and the cutting-edge work on effects of placed-based physical environmental factors.

Social and economic inequalities in the United States and globally are the structures underlying social mobility and health disparities. This theme addresses the interface with, and reciprocal relationships inherent in, demographic processes with economy and society. Our research contributes to basic science in economics and sociology and improves our practical knowledge of social change and public policy. Differences among populations in inequalities and opportunities contribute to our understanding of why and how those populations have different health outcomes and behaviors.

Berkeley population researchers bring the data and modeling tools of demography, economics, and quantitative sociology to an ever-wider array of population outcomes. As documented in the follow subsections, we have made creative use of existing materials, enriched them by merging data from different sources, created new data sources through traditional data collection techniques, and developed field experiments. In addition our members continue to publish papers that contain innovations in the processing of data and procedures for statistical inference