Measuring the Accumulation of Psychosocial Stressors

Background Environmental and psychosocial stressors—i.e., internal or external events that cause subjective distress and disrupt homeostasis—predict many health-related outcomes. The effects of cumulative stress—exposure to multiple stressors or repeated exposures to the same stressor—often exceed the health detriments of a single exposure. Yet, operationalizing cumulative stress over time remains an outstanding challenge for clinical science, as there is little to no consensus on how to best measure cumulative stress over the life course. Aims & Objectives There are at least two key components to cumulative stress: (1) simultaneous exposure to multiple stressors, and (2) repeated exposure to the same stressors. Traditionally, stress researchers have operationalized cumulative stress by calculating a unit-weighted summary score (i.e. the average of standardized stressors) or a cumulative risk index (i.e. sum of dichotomized stressors). Using three waves of data from the Study of Midlife in the U.S. (MIDUS), the present study extends a recently developed hierarchical model of cumulative stress to capture individual differences in within-individual changes in cumulative stress over time. Method Combining factor analysis with methodological techniques common to neuroendocrinology, the area under the curve with respect to ground (AUCG) and increase (AUCI) were calculated for trajectories of cumulative s-factor scores to operationalize the accumulation of psychosocial stressors over time, aggregating information about simultaneous exposure to multiple stressors, initial-levels, and rate of change. Areas under the curve (AUCG & AUCI) for different dimensions of stress were then compared to intercepts and linear slopes as predictors of physical health outcomes, adjusting for the effects of demographic factors. Relative weights analysis was then used to estimate the contributions of demographic factors and dimensions of stress to the prediction of physical health outcomes. Results The prediction of health outcomes was maximized when cumulative stress was measured using AUCG, compared to AUCI, initial levels, or rates of change. Relative weights analysis suggests that all dimensions of stress make unique contributions to the explained variance in these physical health outcomes. Discussion & Conclusions Similar to endogenous, biological, markers of stress reactivity, the area under the curve for individual stress trajectories shows promise as an analytic approach to operationalizing cumulative stress in longitudinal studies.