A Habitat Model for Disease Vector Aedes aegypti in the Tampa Bay Area, FloridA.

Within the contiguous USA, Florida is unique in having tropical and subtropical climates, a great abundance and diversity of mosquito vectors, and high rates of human travel. These factors contribute to the state being the national ground zero for exotic mosquito-borne diseases, as evidenced by local transmission of viruses spread by Aedes aegypti, including outbreaks of dengue in 2022 and Zika in 2016. Because of limited treatment options, integrated vector management is a key part of mitigating these arboviruses. Practical knowledge of when and where mosquito populations of interest exist is critical for surveillance and control efforts, and habitat predictions at various geographic scales typically rely on ecological niche modeling. However, most of these models, usually created in partnership with academic institutions, demand resources that otherwise may be too time-demanding or difficult for mosquito control programs to replicate and use effectively. Such resources may include intensive computational requirements, high spatiotemporal resolutions of data not regularly available, and/or expert knowledge of statistical analysis. Therefore, our study aims to partner with mosquito control agencies in generating operationally useful mosquito abundance models. Given the increasing threat of mosquito-borne disease transmission in Florida, our analytic approach targets recent Ae. aegypti abundance in the Tampa Bay area. We investigate explanatory variables that: 1) are publicly available, 2) require little to no preprocessing for use, and 3) are known factors associated with Ae. aegypti ecology. Out of our 4 final models, none required more than 5 out of the 36 predictors assessed (13.9%). Similar to previous literature, the strongest predictors were consistently 3- and 4-wk temperature and precipitation lags, followed closely by 1 of 2 environmental predictors: land use/land cover or normalized difference vegetation index. Surprisingly, 3 of our 4 final models included one or more socioeconomic or demographic predictors. In general, larger sample sizes of trap collections and/or citizen science observations should result in greater confidence in model predictions and validation. However, given disparities in trap collections across jurisdictions, individual county models rather than a multicounty conglomerate model would likely yield stronger model fits. Ultimately, we hope that the results of our assessment will enable more accurate and precise mosquito surveillance and control of Ae. aegypti in Florida and beyond.

Relationship between effortful control and facets of mindfulness in meditators, non-meditators and individuals with borderline personality disorder.

As dimensions of effortful control (EC), activation control, attentional control, and inhibitory control could mediate the relationship between mindfulness meditation practice and the facets of mindfulness (i.e., observing, describing, acting with awareness, non-judging and non-reacting to inner experience). In this study, we tested whether participant status: meditators (n = 330), healthy non-meditators (n = 254) and individuals with borderline personality disorder (BPD) diagnosis (n = 46) predicted the facets of mindfulness and if these potential effects were mediated through the three effortful control (EC) dimensions (activation, attentional and inhibitory control). Meditators scored higher than non-meditators on attentional and inhibitory control and on the facets of mindfulness with attentional and inhibitory control partially mediating this relationship between meditation status and mindfulness facets. Participants with BPD showed lower scores on EC along with lower scores on the facets of mindfulness compared with healthy non-meditators. All three aspects of EC partially mediated the relationship between BPD and mindfulness facets. Copyright © 2018 John Wiley & Sons, Ltd.

Reliability and validity of the SE-HEPA: examining physical activity--and healthy eating-specific self-efficacy among a sample of preadolescents.

OBJECTIVE: The purpose of this study was to examine the psychometric properties of the self-efficacy for healthy eating and physical activity measure (SE-HEPA) for preadolescents. METHOD: The reliability of the measure was examined to determine if the internal consistency of the measure was adequate (i.e., αs > .70). Next, in an effort to determine if a two-factor model was a better fit than a one-factor model, as hypothesized, an exploratory factor analysis in a confirmatory factor analysis framework was used to determine model fit. Additionally, the criterion-related validity of the measure was evaluated by conducting correlational analyses to determine if SE-HEPA scores were associated with preadolescent body mass index. RESULTS: Consistent with the hypotheses, Cronbach's alphas indicated good reliability for the measure (i.e., αs > .70) and factor analyses indicated the two-factor model provided a better fit than the one-factor model. Additionally, correlations revealed a significant relationship between the two factors (i.e., healthy eating, physical activity) and preadolescent body mass index. CONCLUSIONS: The SE-HEPA will allow researchers and clinicians to better understand self-efficacy for behaviors important to weight loss among preadolescents. This could, in turn, inform future efforts aimed at increasing behaviors that promote healthy weight status among this population within the context of family-based weight loss programs.