Cities and population health.

A majority of the world's population will live in urban areas by 2007 and cities are exerting growing influence on the health of both urban and non-urban residents. Although there long has been substantial interest in the associations between city living and health, relatively little work has tried to understand how and why cities affect population health. This reflects both the number and complexity of determinants and of the absence of a unified framework that integrates the multiple factors that influence the health of urban populations. This paper presents a conceptual framework for studying how urban living affects population health. The framework rests on the assumption that urban populations are defined by size, density, diversity, and complexity, and that health in urban populations is a function of living conditions that are in turn shaped by municipal determinants and global and national trends. The framework builds on previous urban health research and incorporates multiple determinants at different levels. It is intended to serve as a model to guide public health research and intervention

INFERENCE FOR SURVIVAL CURVES WITH INFORMATIVELY COARSENED DISCRETE EVENT-TIME DATA: APPLICATION TO ALIVE

In many prospective studies, including AIDS Link to the Intravenous Experience (ALIVE), researchers are interested in comparing event-time distributions (e.g.,for human immunodeficiency virus seroconversion) between a small number of groups (e.g., risk behavior categories). However, these comparisons are complicated by participants missing visits or attending visits off schedule and seroconverting during this absence. Such data are interval-censored, or more generally,coarsened. Most analysis procedures rely on the assumption of non-informative censoring, a special case of coarsening at random that may produce biased results if not valid. Our goal is to perform inference for estimated survival functions across a small number of goups in the presence of informative coarsening. To do so, we propose methods for frequentist and Bayesian inference of ALIVE data utilizing information elicited from ALIVE scientists and an AIDS epidemiology expert about the visit compliance process

Considering Bias in the Assessment of Respiratory Symptoms among Residents of Lower Manhattan following the Events of September 11, 2001

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/40270/2/Vlahov_Invited Commentary - Considering Bias in the Assessment_2005.pd

War and Anxiety Disorders

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/40278/2/Vlahov_War and Anxiety Disorders_2004.pd

Epidemiologic Research and Disasters

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/40267/2/Vlahov_Epidemiologic Research and Disasters_2004.PD

Alternative causal inference methods in population health research: Evaluating tradeoffs and triangulating evidence.

Population health researchers from different fields often address similar substantive questions but rely on different study designs, reflecting their home disciplines. This is especially true in studies involving causal inference, for which semantic and substantive differences inhibit interdisciplinary dialogue and collaboration. In this paper, we group nonrandomized study designs into two categories: those that use confounder-control (such as regression adjustment or propensity score matching) and those that rely on an instrument (such as instrumental variables, regression discontinuity, or differences-in-differences approaches). Using the Shadish, Cook, and Campbell framework for evaluating threats to validity, we contrast the assumptions, strengths, and limitations of these two approaches and illustrate differences with examples from the literature on education and health. Across disciplines, all methods to test a hypothesized causal relationship involve unverifiable assumptions, and rarely is there clear justification for exclusive reliance on one method. Each method entails trade-offs between statistical power, internal validity, measurement quality, and generalizability. The choice between confounder-control and instrument-based methods should be guided by these tradeoffs and consideration of the most important limitations of previous work in the area. Our goals are to foster common understanding of the methods available for causal inference in population health research and the tradeoffs between them; to encourage researchers to objectively evaluate what can be learned from methods outside one's home discipline; and to facilitate the selection of methods that best answer the investigator's scientific questions

Social Determinants and the Health of Drug Users: Socioeconomic Status, Homelessness, and Incarceration

Objectives: This article reviews the evidence on the adverse health consequences of low socioeconomic status, homelessness, and incarceration among drug users. Observations: Social and economic factors shape risk behavior and the health of drug users. They affect health indirectly by shaping individual drug-use behavior; they affect health directly by affecting the availability of resources, access to social welfare systems, marginalization, and compliance with medication. Minority groups experience a disproportionately high level of the social factors that adversely affect health, factors that contribute to disparities in health among drug users. Conclusion: Public health interventions aimed at improving the health of drug users must address the social factors that accompany and exacerbate the health consequences of illicit drug use.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/40329/2/Galea_Social Determinants and the Health of_2002.pd

Urbanization, Urbanicity, and Health

A majority of the world’s population will live in urban areas by 2007. The most rapidly urbanizing cities are in less-wealthy nations, and the pace of growth varies among regions. There are few data linking features of cities to the health of populations. We suggest a framework to guide inquiry into features of the urban environment that affect health and well-being. We consider two key dimensions: urbanization and urbanicity. Urbanization refers to change in size, density, and heterogeneity of cities. Urbanicity refers to the impact of living in urban areas at a given time. A review of the published literature suggests that most of the important factors that affect health can be considered within three broad themes: the social environment, the physical environment, and access to health and social services. The development of urban health as a discipline will need to draw on the strengths of diverse academic areas of study (e.g., ecology, epidemiology, sociology). Cross-national research may provide insights about the key features of cities and how urbanization influences population health.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/40277/2/Vlahov_Urbanization, Urbanicity, and Health_2002.pd