Jane E. Clougherty, MSc, ScD
- Assistant Professor, Department of Environmental and Occupational Health
- Graduate School of Public Health
“Adapting Geospatial Modeling Methods to Assess Individual-level Variability in Urban Chronic Stress”
Chronic stress has been linked to a range of illnesses, including respiratory infection, cardiovascular disease, diabetes, and the common cold. More recent evidence suggests that chronic stress may, through alterations in immune, endocrine, and metabolic function, also shape individual´s susceptibility and response to environmental pollution. A persistent problem in the epidemiology of chronic stress is that stress must be individually assessed – the cost and logistical burden of doing so has routinely limited epidemiological sample size, and reduced the efficiency with which epidemiologists can examine effects of chronic stress on rare health outcomes. Chronic stress, however, is also distinctly geographically patterned, in so far as it is influenced by neighborhood factors (e.g., crime rate), access to resources, and proximity to physical stressors associated with noise or air pollution (e.g., airports, construction sites).
Dr. Clougherty and her co-investigators propose a novel adaptation of spatial modeling methods developed for air pollution epidemiology (known as “land-use regression” (LUR)), to predict individual-level stress as a function of community-level stressor exposures. The “Stress LUR” models will enable: (1) exploration of the proportion of individual-level chronic stress which may be attributable to community stressors; (2) identification of key community stressors most associated with individual stress experience; and — if the models reasonably predict individual stress — (3) the extrapolation of chronic stress exposure estimates across large urban cohorts for epidemiological analyses.
The investigators will leverage preliminary analyses of community social stressors in New York City, which reveal wide variation in strength and direction of spatial correlation among stressors, and between stressors and air pollution. Related sensitivity analyses indicated that: (1) administrative areas (to which public data is aggregated) may not accurately capture social and environmental processes; and (2) unit of aggregation may influence observed effect estimates and autocorrelation (i.e. clustering) structures, signaling the need for more refined spatial analytic tools.
By adapting these “LUR” techniques, the investigators will be able to estimate the percentage of a response (here, individual stress), which can be explained by spatially-distributed stressors, such as crime or ambient noise. Equally important, the process of covariate selection allows disaggregation of the effects of multiple stressors, and identification of the most important stressors contributing to individual stress – which has great relevance for urban social science research, and for policy and intervention design. LUR methods allows for moving beyond blunt administrative areas (long known to present challenges for interpreting public data sets for health research) in stressor exposure assessment, wherein the investigators can leverage the unique constellation of data that applies to a specific residential location.
Fengyan Tang, PhD
- Assistant Professor
- School of Social Work
“Retirement Transition, Volunteer Engagement, and Physical Health”
Current cohorts of older adults are productively engaging in increasing amounts of paid work and volunteer activity. As baby boomers age, there will be significant potential for even greater productive engagement in the older population. Continued participation in employment and a phased-out connection to the workplace may boost volunteering for organizations. Most importantly, productive engagement in paid employment and volunteering may have protective effects against health decline for older adults. This outcome reinforces the importance to society in harnessing the desire of many older adults to continue working and/or to provide volunteer services beyond the traditional retirement age.
This study will use the Health and Retirement Study panel data (1998-2008) to investigate the dynamic process of retirement transitions, any associated change in volunteer engagement during such transitions, and related physical health change among middle-aged and older adults. This study will also examine group differences based on gender, race, and social class and contextual effects of birth cohorts and time periods on the relationship between productive engagement and physical health change. According to the life course perspective that highlights the dynamic process of productive engagement change over time, this study will test the following hypotheses: (1) transition to part-time work and/or fully-retired status is related to increased likelihood of volunteering; (2) productive engagement in paid work and/or volunteering is related to slower rates of physical health decline over time; (3) concurrent engagement in paid work and volunteering has an even stronger positive association with physical health change; and (4) productive engagement is related to slower rates of physical health decline among the following groups: the oldest old, women, non-Whites, and those with low socioeconomic status. The study sample will include respondents who reported full-time work hours in 1998 (N=5,531). Latent transition analysis will be used to identify the latent structure of retirement transition and the probabilities of change in work status among these individuals over the subsequent decade. Generalized linear mixed models will be applied to examine the change in volunteer engagement following changes in work status during retirement transitions. Growth curve modeling will be used to estimate physical health change related to productive engagement and to assess group differences. Age-period-cohort analysis will be used to identify the separate effects of age, period, and cohort on retirement transition, productive engagement, and physical health change.