Honey bee colony loss linked to parasites, pesticides and extreme weather across the United States.

Honey bee (Apis mellifera) colony loss is a widespread phenomenon with important economic and biological implications, whose drivers are still an open matter of investigation. We contribute to this line of research through a large-scale, multi-variable study combining multiple publicly accessible data sources. Specifically, we analyzed quarterly data covering the contiguous United States for the years 2015-2021, and combined open data on honey bee colony status and stressors, weather data, and land use. The different spatio-temporal resolutions of these data are addressed through an up-scaling approach that generates additional statistical features which capture more complex distributional characteristics and significantly improve modeling performance. Treating this expanded feature set with state-of-the-art feature selection methods, we obtained findings that, nation-wide, are in line with the current knowledge on the aggravating roles of Varroa destructor and pesticides in colony loss. Moreover, we found that extreme temperature and precipitation events, even when controlling for other factors, significantly impact colony loss. Overall, our results reveal the complexity of biotic and abiotic factors affecting managed honey bee colonies across the United States.

Summer weather conditions influence winter survival of honey bees (Apis mellifera) in the northeastern United States.

Honey bees are crucial pollinators for agricultural and natural ecosystems, but are experiencing heavy mortality in North America and Europe due to a complex suite of factors. Understanding the relative importance of each factor would enable beekeepers to make more informed decisions and improve assessment of local and regional habitat suitability. We used 3 years of Pennsylvania beekeepers' survey data to assess the importance of weather, topography, land use, and management factors on overwintering mortality at both apiary and colony levels, and to predict survival given current weather conditions and projected climate changes. Random Forest, a tree-based machine learning approach suited to describing complex nonlinear relationships among factors, was used. A Random Forest model predicted overwintering survival with 73.3% accuracy for colonies and 65.7% for apiaries where Varroa mite populations were managed. Growing degree days and precipitation of the warmest quarter of the preceding year were the most important predictors at both levels. A weather-only model was used to predict colony survival probability, and to create a composite map of survival for 1981-2019. Although 3 years data were likely not enough to adequately capture the range of possible climatic conditions, the model performed well within its constraints.

Using a GIS to support the spatial reorganization of outpatient care services delivery in Italy.

BACKGROUND: Studying and measuring accessibility to care services has become a major concern for health care management, particularly since the global financial collapse. This study focuses on Tuscany, an Italian region, which is re-organizing its inpatient and outpatient systems in line with new government regulations. The principal aim of the paper is to illustrate the application of GIS methods with real-world scenarios to provide support to evidence-based planning and resource allocation in healthcare. METHODS: Spatial statistics and geographical analyses were used to provide health care policy makers with a real scenario of accessibility to outpatient clinics. Measures for a geographical potential spatial accessibility index using the two-step floating catchment area method for outpatient services in 2015 were calculated and used to simulate the rationalization and reorganization of outpatient services. Parameters including the distance to outpatient clinics and volumes of activity were taken into account. RESULTS: The spatial accessibility index and the simulation of reorganization in outpatient care delivery are presented through three cases, which highlight three different managerial strategies. The results revealed the municipalities where health policy makers could consider a new spatial location, a shutdown or combining selected outpatient clinics while ensuring equitable access to services. CONCLUSIONS: A GIS-based approach was designed to provide support to healthcare management and policy makers in defining evidence-based actions to guide the reorganization of a regional health care delivery system. The analysis provides an example of how GIS methods can be applied to an integrated framework of administrative health care and geographical data as a valuable instrument to improve the efficiency of healthcare service delivery, in relation to the population's needs.

Proximity and waiting times in choice models for outpatient cardiological visits in Italy.

We apply mixed logit regression to investigate patients' choice of non-emergency outpatient cardiovascular specialists in Tuscany, Italy. We focused on the effects of travel time and waiting time. Results reveal that patients prefer clinics nearby and with shorter waiting times. Differences in patient choice depend on age and socioeconomic conditions, thus confirming equity concerns in the access of non-acute services. Our results could be used to optimize the allocation of resources, reduce inequities and increase the efficiency and responsiveness of outpatient systems considering patient preferences.