RESUMEN
Background: Early detection of community health risk factors such as stress is of great interest to health policymakers, but representative data collection is often expensive and time-consuming. It is important to investigate the use of alternative means of data collection such as crowdsourcing platforms. Methods: An online sample of Amazon Mechanical Turk (MTurk) workers (N = 500) filled out, for themselves and their child, demographic information and the 10-item Perceived Stress Scale (PSS-10), designed to measure the degree to which situations in one's life are appraised as stressful. Internal consistency reliability of the PSS-10 was examined via Cronbach's alpha. Analysis of variance (ANOVA) was utilized to explore trends in the average perceived stress of both adults and their children. Last, Rasch trees were utilized to detect differential item functioning (DIF) in the set of PSS-10 items. Results: The PSS-10 showed adequate internal consistency reliability (Cronbach's alpha = 0.73). ANOVA results suggested that stress scores significantly differed by education (p = 0.024), employment status (p = 0.0004), and social media usage (p = 0.015). Rasch trees, a recursive partitioning technique based on the Rasch model, indicated that items on the PSS-10 displayed DIF attributable to physical health for adults and social media usage for children. Conclusion: The key conclusion is that this data collection scheme shows promise, allowing public health officials to examine health risk factors such as perceived stress quickly and cost effectively.
RESUMEN
In response to the 2009 H1N1 pandemic and subsequent vaccination program, the Department of Health and Human Services and collaborators developed the Post-Licensure Rapid Immunization Safety Monitoring (PRISM) Program as a demonstration project to detect rare adverse events rapidly. The program monitored three million people who had received the H1N1 vaccine by linking data from large private health plans and from public immunization registries that had originally not been designed to share data, and on a larger scale than had been previously attempted. The program generated safety data in two weeks rather than three to six monty 10ths-the standard time frame achievable using health plan data. PRISM substantially contributed to the understanding of the safety of H1N1 vaccines. Its use in the case of H1N1 highlights the necessity of proactive planning, scalable infrastructure, and public-private partnerships in tracking adverse events after vaccination in epidemics. It also illustrates how data could be integrated to produce policy-relevant information for other medical products.