Preventing eye injuries among citrus harvesters: the community health worker model.

OBJECTIVES: Although eye injuries are common among citrus harvesters, the proportion of workers using protective eyewear has been negligible. We focused on adoption of worker-tested safety glasses with and without the presence and activities of trained peer-worker role models on harvesting crews. METHODS: Observation of 13 citrus harvesting crews established baseline use of safety eyewear. Nine crews subsequently were assigned a peer worker to model use of safety glasses, conduct eye safety education, and treat minor eye injuries. Safety eyewear use by crews was monitored up to 15 weeks into the intervention. RESULTS: Intervention crews with peer workers had significantly higher rates of eyewear use than control crews. Intervention exposure time and level of worker use were strongly correlated. Among intervention crews, workers with 1 to 2 years of experience (odds ratio [OR] = 2.89; 95% confidence interval [CI] = 1.11, 7.55) and who received help from their peer worker (OR = 3.73; 95% CI = 1.21, 11.57) were significantly more likely to use glasses than were other intervention crew members. CONCLUSIONS: Adaptation of the community health worker model for this setting improved injury prevention practices and may have relevance for similar agricultural settings.

From plan to action: Adapting evaluation to serve the developmental needs of a newly-funded multidisciplinary research center.

The Southeastern Coastal Center for Agricultural Health and Safety (SCCAHS) is one of many newly-funded federal research centers, housing five multidisciplinary research projects and seven pilot projects, and serving a multi-state region. In the early stages of such a complex project, with multiple teams separated by geography and disciplines, the evaluation program has been integral in connecting internal and external stakeholders at the center and project levels. We used a developmental evaluation (DE) framework to respond to the complex political environment surrounding agricultural health and safety in the southeast; to engage external stakeholders in guiding the center's research and outreach trajectories; to support center research teams in a co-creation process to develop logic models and tailored indicators; and to provide timely and feedback within the center to address communications gaps identified by the evaluation program. By using DE principles to shape monitoring and evaluation approaches, our evaluation program has adapted to the dynamic circumstances presented as our center's progress has been translated from a plan in a grant proposal to implementation.

Machine learning approaches in GIS-based ecological modeling of the sand fly Phlebotomus papatasi, a vector of zoonotic cutaneous leishmaniasis in Golestan province, Iran.

The distribution and abundance of Phlebotomus papatasi, the primary vector of zoonotic cutaneous leishmaniasis in most semi-/arid countries, is a major public health challenge. This study compares several approaches to model the spatial distribution of the species in an endemic region of the disease in Golestan province, northeast of Iran. The intent is to assist decision makers for targeted interventions. We developed a geo-database of the collected Phlebotominae sand flies from different parts of the study region. Sticky paper traps coated with castor oil were used to collect sand flies. In 44 out of 142 sampling sites, Ph. papatasi was present. We also gathered and prepared data on related environmental factors including topography, weather variables, distance to main rivers and remotely sensed data such as normalized difference vegetation cover and land surface temperature (LST) in a GIS framework. Applicability of three classifiers: (vanilla) logistic regression, random forest and support vector machine (SVM) were compared for predicting presence/absence of the vector. Predictive performances were compared using an independent dataset to generate area under the ROC curve (AUC) and Kappa statistics. All three models successfully predicted the presence/absence of the vector, however, the SVM classifier (Accuracy = 0.906, AUC = 0.974, Kappa = 0.876) outperformed the other classifiers on predicting accuracy. Moreover, this classifier was the most sensitive (85%), and the most specific (93%) model. Sensitivity analysis of the most accurate model (i.e. SVM) revealed that slope, nighttime LST in October and mean temperature of the wettest quarter were among the most important predictors. The findings suggest that machine learning techniques, especially the SVM classifier, when coupled with GIS and remote sensing data can be a useful and cost-effective way for identifying habitat suitability of the species.

A Plate Waste Evaluation of the Farm to School Program.

OBJECTIVE: To investigate the impacts of the Farm to School (FTS) Program on the selection and consumption of fruits and vegetables. DESIGN: Plate waste data were recorded using the visual inspection method before and after implementation of the program. SETTING: Six elementary schools in Florida: 3 treatment and 3 control schools. PARTICIPANTS: A total of 11,262 meal observations of National School Lunch Program (NSLP) participants in grades 1-5. INTERVENTION: The FTS Program, specifically local procurement of NSLP offerings, began in treatment schools in November, 2015 after the researchers collected preintervention data. MAIN OUTCOME MEASURES: The NSLP participants' selection and consumption of fruits and vegetables. ANALYSIS: Data were analyzed using Mann-Whitney U and proportions tests and difference-in-difference regressions. RESULTS: The NSLP participants at the treatment schools consumed, on average, 0.061 (P = .002) more servings of vegetables and 0.055 (P = .05) more servings of fruit after implementation of the FTS Program. When school-level fixed effects are included, ordinary least squares and tobit regression results indicated that NSLP participants at the treatment schools respectively consumed 0.107 (P < .001) and 0.086 (P < .001) more servings of vegetables, on average, after implementation of the FTS Program. CONCLUSIONS AND IMPLICATIONS: Local procurement positively affected healthy eating.