Prevention Science最新文献

Many conventional research methods employed in randomized controlled trials were not possible during the height of the COVID-19 pandemic. In particular, behavioral observations are nearly universally gathered in-person. Observational methods are valued for the rich, informative data they produce in comparison to non-observational methods and are a cornerstone of parenting and family research. COVID provided the opportunity to, and indeed necessitated, the transition to fully remote observation. However, little to no studies have investigated whether remotely collected observational data are methodologically sound. This paper assesses the feasibility of remote data collection by describing the transition between in-person and fully remote observational data collection during a Sequential, Multiple Assignment, Randomized Trial (SMART) of a parenting program that took place both before and during the pandemic. Using mixed-methods data from coders, the overall quality of video-recorded data collected both before and during COVID was examined. Coder reliability over time was assessed with intraclass correlation coefficients. Results suggest that the frequency of audio problems, the severity of visual problems, and the level of administration challenges decreased after transitioning to remote data collection. Additionally, coders showed good to excellent reliability coding remotely collected data, and reliability even improved on some measured tasks. Although challenges to remote data collection exist, this study demonstrated that observational data can be collected feasibly and reliably. As observational data collection is a key method to assess parenting practices, these findings should improve researcher confidence in utilizing remote observational methods in prevention science.

The quality of parenting program implementation significantly affects the extent to which a program is delivered effectively as well as the likelihood of it becoming embedded in everyday services. The group based Parenting for Lifelong Health for Young Children (PLH-YC) program for parents of children aged 2-9 years was developed specifically for implementation in low- and middle-income contexts, has been tested in five randomized trials, and incorporates a number of strategies to encourage fidelity of delivery. This paper reports on the introduction of PLH-YC to Montenegro, including initial work to engage government agencies and service providers, adapt the program and, following initial evidence of effectiveness, implement strategies to promote effective delivery and embed the program. Following program adaptation and initial facilitator training, eight groups were run, supported with resources and supervision and independently evaluated. The successful pilot led to program training accreditation by national professional agencies and a series of steps to successfully further embed it into routine settings in Montenegro, including by recognizing the program in national policy documents. This led to further facilitator trainings, now numbering 97 facilitators and the certification of ten coaches and two trainers. By the end of 2023, 1278 parents, across 13 municipalities (half of all municipalities in Montenegro) and a range of service providers, have received the program. The paper describes the project phases and key fidelity components that underpinned the successful introduction and embedding of the program in Montenegro. The plan has resulted in Montenegro having its own domestic resources to continue to implement the program effectively and further plan for widespread dissemination.

Adolescence is a critical period during which youth develop and shape their behaviors. Because differences between youths are strongly connected to environmental factors, we aimed to elucidate possible pathways from home-school regulation and atmosphere to youths' prosocial and antisocial behaviors. Data were derived from the China Education Panel Survey. This study involved a total of 9291 students aged 14-15 years (4834 boys, 4457 girls). We used structural equation modeling (SEM) with LISREL 8.80 and Monte Carlo resampling with R to conduct the analysis strategy. In the home-school regulation, parental supervision on the one hand and teacher criticism on the other hand have direct positive and negative predictive effects on youths' prosocial behaviors, respectively, while their direct effects on antisocial behavior are the opposite; teachers praise does not directly affect adolescents' prosocial and antisocial behaviors. In the home-school atmosphere, family interaction and perceived good class climate directly positively affect youths' prosocial behaviors, while the direct effects of both on antisocial behavior are not significant. The SEM results reveal that academic self-efficacy and depressive symptoms may be underlying mediating mechanisms through which home-school regulation and atmosphere during adolescence affect students' prosocial and antisocial behaviors. Intervention programs targeting home-school supportive environments and prevention programs targeting positive emotion and self-awareness may yield benefits for proper social behavior in adolescents. For example, by enhancing the way and frequency of parent-child interaction, teachers and students jointly create a good class climate of care and friendship to strengthen a home-school supportive environment. Improve adolescents' positive emotions such as contentment, optimism, and hope to reduce the possibility of depression.

Cluster-randomized trials (CRTs) often allocate intact clusters of participants to treatment or control conditions and are increasingly used to evaluate healthcare delivery interventions. While previous studies have developed sample size methods for testing confirmatory hypotheses of treatment effect heterogeneity in CRTs (i.e., targeting the difference between subgroup-specific treatment effects), sample size methods for testing the subgroup-specific treatment effects themselves have not received adequate attention-despite a rising interest in health equity considerations in CRTs. In this article, we develop formal methods for sample size and power analyses for testing subgroup-specific treatment effects in parallel-arm CRTs with a continuous outcome and a binary subgroup variable. We point out that the variances of the subgroup-specific treatment effect estimators and their covariance are given by weighted averages of the variance of the overall average treatment effect estimator and the variance of the heterogeneous treatment effect estimator. This analytical insight facilitates an explicit characterization of the requirements for both the omnibus test and the intersection-union test to achieve the desired level of power. Generalizations to allow for subgroup-specific variance structures are also discussed. We report on a simulation study to validate the proposed sample size methods and demonstrate that the empirical power corresponds well with the predicted power for both tests. The design and setting of the Umea Dementia and Exercise (UMDEX) CRT in older adults are used to illustrate our sample size methods.

In June 2022, the NIH Office of Disease Prevention (ODP) issued a Call for Papers for a Supplemental Issue to Prevention Science on Design and Analytic Methods to Evaluate Multilevel Interventions to Reduce Health Disparities. ODP sought to bring together current thinking and new ideas about design and analytic methods for studies aimed at reducing health disparities, including strategies for balancing methodological rigor with design feasibility, acceptability, and ethical considerations. ODP was particularly interested in papers on design and analytic methods for parallel group- or cluster-randomized trials (GRTs), stepped-wedge GRTs, group-level regression discontinuity trials, and other methods appropriate for evaluating multilevel interventions. In this issue, we include 12 papers that report new methods, provide examples of strong applications of existing methods, or provide guidance on developing multilevel interventions to reduce health disparities. These papers provide examples showing that rigorous methods are available for the design and analysis of multilevel interventions to reduce health disparities.

Reservation-dwelling American Indian adolescents are at exceedingly high risk for cannabis use. Prevention initiatives to delay the onset and escalation of use are needed. The risk and promotive factors approach to substance use prevention is a well-established framework for identifying the timing and targets for prevention initiatives. This study aimed to develop predictive models for the usage of cannabis using 22 salient risk and promotive factors. Models were developed using data from a cross-sectional study and further validated using data from a separate longitudinal study with three measurement occasions (baseline, 6-month follow-up, 1-year follow-up). Application of the model to longitudinal data showed an acceptable performance contemporaneously but waning prospective predictive utility over time. Despite the model's high specificity, the sensitivity was low, indicating an effective prediction of non-users but poor performance in correctly identifying users, particularly at the 1-year follow-up. This divergence can have significant implications. For example, a model that misclassifies future adolescent cannabis use could fail to provide necessary intervention for those at risk, leading to negative health and social consequences. Moreover, supplementary analysis points to the importance of considering change in risk and promotive factors over time.