University of Connecticut University of UC Title Fallback Connecticut


SHARP exists to learn meaningful facts relevant to human health. As described in the history page, much of the work SHARP is doing in the near future relates to the network individual resource (NIR) model. In our meta-analyses of HIV prevention risk outcomes, we are examining how community level support can enable or interfere with the success of HIV prevention interventions. Building on the success of our prior analyses focused on community-level stigma, these projects are focusing especially on heterosexual adults in the U.S. and African Americans. A separate project examines interventions for adolescents. We also are completing work related to the use of behavior change techniques in HIV prevention interventions and in relation to needle-exchange programs to reduce HIV risk for injection-drug users.

Many of our current projects merge big databases to address issues of currency to health and public health. For example, we are interested in trends in anxiety and depression in the U.S., using proprietary and public survey databases. Another study examines changes in body mass index and isolates profiles of individuals who believe they are healthy but actually have unhealthy behavior habits. Because we have information on where these individuals live, we can examine how other factors may play important roles. For example, income and income inequality information drawn from the American Community Surveys is known at the county level and can be related to critical health outcomes. As well, SHARP has been quite interested in levels of stigma toward minority groups as a factor related to community health, drawing this information from the American National Election Studies (ANES) and other sources (e.g., General Social Survey, Roper Center archive of polls). See the Resources page for more information.

SHARP team members often generate their own big databases in relation to particular issues. For example, we have geocoded acts of interpersonal violence so that we can use other databases to predict where these acts are more likely to occur and who is the most likely to commit them or fall prey to them.

Science is a method of gaining knowledge about the actions of particular entities in their environments. All of our projects rest on methodological assumptions. When we can evaluate whether differences in assumptions matter, we do so. To the extent that our conclusions generalize even in the face of changing assumptions, they are more likely to reflect the truth. SHARP team members have recently done quite a lot of work on methodological problems. Some of these efforts have taken Monte Carlo procedures to evaluate the robustness of different meta-analytic statistics. SHARP has been particularly innovative in generating new applications of meta-analysis; for example, we are perhaps the first team to join health promotion study outcomes with specific information known about the communities where the studies took place.

Of note, have been intensive qualitative examinations of systematic reviews dedicated to particular subjects. Although SHARP is predominately a quantitative data modeling project, qualitative aspects are never far from view. Indeed, quantitative investigations rest on transforming qualitative information into numbers that can be examined for trends.

There are numerous possible new projects that may be drawn from these resources; for more details, or to see how you can play a role in this work, please contact Prof. Johnson (contact information here).