ABSTRACT
OBJECTIVE: School-based health centers (SBHCs) improve health care access, but associations with educational outcomes are mixed and limited for elementary and middle school students. We investigated whether students enrolled in a comprehensive SBHC demonstrated more growth in standardized math and reading assessments over 4 school years versus nonenrolled students. We also explored changes in absenteeism. METHODS: Participants were students enrolled in 2 co-located Title I schools from 2015-19 (1 elementary, 1 middle, n = 2480). Analysis of math and reading was limited to students with baseline and postbaseline scores (math n = 1622; reading n = 1607). Longitudinal regression models accounting for within-subject clustering were used to estimate the association of SBHC enrollment with academic scores and daily absenteeism, adjusting for grade, sex, body mass index category, health conditions, baseline outcomes (scores or absenteeism), and outcome pretrends. RESULTS: More than 70% of SBHC-enrolled students had math (1194 [73.6%]) and reading 1186 [73.8%]) scores. Enrollees were more likely than nonenrollees to have asthma (39.7% vs 19.6%) and overweight/obesity (42.4% vs 33.6%). Adjusted baseline scores were significantly lower in math and reading for enrollees. Mean change from baseline for enrollees exceeded nonenrollees by 3.5 points (95% confidence interval [CI]: 2.2, 4.8) in math and 2.1 points (95% CI: 0.9, 3.3) in reading. The adjusted rate of decrease in daily absenteeism was 10.8% greater for enrollees (incident rate ratio 0.772 [95% CI: 0.623, 0.956]) than nonenrollees (incident rate ratio 0.865 [95% CI: 0.696, 1.076]). CONCLUSIONS: SBHC enrollees had greater health and educational risk but demonstrated more growth in math and reading and less absenteeism than nonenrollees.
ABSTRACT
Web-based survey data collection has become increasingly popular, and limitations on in-person data collection during the COVID-19 pandemic have fueled this growth. However, the anonymity of the online environment increases the risk of fraudulent responses provided by bots or those who complete surveys to receive incentives, a major risk to data integrity. As part of a study of COVID-19 and the return to in-person school, we implemented a web-based survey of parents in Maryland between December 2021 and July 2022. Recruitment relied, in part, on social media advertisements. Despite implementing many existing best practices, we found the survey challenged by sophisticated fraudsters. In response, we iteratively improved survey security. In this paper, we describe efforts to identify and prevent fraudulent online survey responses. Informed by this experience, we provide specific, actionable recommendations for identifying and preventing online survey fraud in future research. Some strategies can be deployed within the data collection platform such as careful crafting of survey links, Internet Protocol address logging to identify duplicate responses, and comparison of client-side and server-side time stamps to identify responses that may have been completed by respondents outside of the survey's target geography. Other strategies can be implemented during the survey design phase. These approaches include the use of a 2-stage design in which respondents must be eligible on a preliminary screener before receiving a personalized link. Other design-based strategies include within-survey and cross-survey validation questions, the addition of "speed bump" questions to thwart careless or computerized responders, and the use of optional open-ended survey questions to identify fraudsters. We describe best practices for ongoing monitoring and post-completion survey data review and verification, including algorithms to expedite some aspects of data review and quality assurance. Such strategies are increasingly critical to safeguarding survey-based public health research.
