Bridging the Gap between Field Experiments and Machine Learning: The EC H2020 B-GOOD Project as a Case Study towards Automated Predictive Health Monitoring of Honey Bee Colonies

Honey bee colonies have great societal and economic importance. The main challenge that beekeepers face is keeping bee colonies healthy under ever-changing environmental conditions. In the past two decades, beekeepers that manage colonies of Western honey bees (Apis mellifera) have become increasingly concerned by the presence of parasites and pathogens affecting the bees, the reduction in pollen and nectar availability, and the colonies' exposure to pesticides, among others. Hence, beekeepers need to know the health condition of their colonies and how to keep them alive and thriving, which creates a need for a new holistic data collection method to harmonize the flow of information from various sources that can be linked at the colony level for different health determinants, such as bee colony, environmental, socioeconomic, and genetic statuses. For this purpose, we have developed and implemented the B-GOOD (Giving Beekeeping Guidance by computational-assisted Decision Making) project as a case study to categorize the colony's health condition and find a Health Status Index (HSI). Using a 3-tier setup guided by work plans and standardized protocols, we have collected data from inside the colonies (amount of brood, disease load, honey harvest, etc.) and from their environment (floral resource availability). Most of the project's data was automatically collected by the BEEP Base Sensor System. This continuous stream of data served as the basis to determine and validate an algorithm to calculate the HSI using machine learning. In this article, we share our insights on this holistic methodology and also highlight the importance of using a standardized data language to increase the compatibility between different current and future studies. We argue that the combined management of big data will be an essential building block in the development of targeted guidance for beekeepers and for the future of sustainable beekeeping.

Trajectories of sickness absence and disability pension by type of occupation in multiple sclerosis

Background

Multiple sclerosis (MS) can impact working life, sickness absence (SA), and disability pension (DP). Different types of occupations involve different demands, which may be associated with trajectories of SA and DP among people with MS (PwMS). We aim to explore if annual levels of SA and DP differ according to type of occupation among PwMS and references. Further, we aim to gain knowledge of how trajectories of SA and DP are associated with type of occupation among PwMS.

Methods

A nationwide Swedish register-based prospective cohort study with six-year follow-up was conducted, including 6,100 individuals with prevalent MS and 38,641 matched population references. Mean annual SA and DP net days during follow-up years were calculated and stratified by type of occupation. Trajectories of SA and DP were identified with group-based trajectory modelling. Multinomial logistic regressions were estimated for associations between identified trajectories and different types of occupations.

Results

An increase of SA and DP over time in all types of occupations was observed in both PwMS and references, with higher levels of SA and DP among PwMS. Managers had the lowest levels of SA and DP in both groups. Three SA and DP trajectory groups were identified: Persistently Low (55.2%), Moderate Increasing (31.9%), and High Increasing (12.8%). Managers (Odds Ratio [OR] 0.37, 95%CI 0.26-0.52) and those working in Science & Technology (OR 0.64, 95% CI 0.50-0.82) had less probability of belonging to the Moderate Increasing group. Similarly, Managers (OR 0.52, 95%CI 0.30-0.89) and Science & Technology (OR 0.58, 95%CI 0.39-0.88) had also less probability of belonging to the High Increasing group.

Conclusions

Our findings suggest that the type of occupation plays a role in the level and course of SA and DP among PwMS.

Key messages

Over time SA and DP levels increased among PwMS regardless of type of occupation. PwMS in Managers or Science and Technology had less probability of belonging to the increasing trajectories.

A search strategy for occupational health intervention studies

BACKGROUND

As a result of low numbers and diversity in study type, occupational health intervention studies are not easy to locate in electronic literature databases.

AIM

To develop a search strategy that facilitates finding occupational health intervention studies in Medline, both for researchers and practitioners.

METHODS

A gold standard of articles was created by going through two whole volumes of 19 biomedical journals, both occupational health specialty and non-occupational health journals. Criteria for occupational health intervention studies were: evaluating an intervention with an occupational health outcome and a study design with a control group. Each journal was searched independently by two of the authors. Search terms were developed by asking specialists and counting word frequencies in gold standard articles.

RESULTS

Out of 11 022 articles published we found 149 occupational health intervention studies. The most sensitive single terms were work*[tw] (sensitivity 71%, specificity 88%) and effect*[tw] (sensitivity 75%, specificity 63%). The most sensitive string was (effect*[tw] OR control*[tw] OR evaluation*[tw] OR program*[tw]) AND (work*[tw] OR occupation*[tw] OR prevention*[tw] OR protect*[tw]) (sensitivity 89%, specificity 78%). The most specific single terms were "occupational health"[tw] (sensitivity 22%, specificity 98%) and effectiveness[tw] (sensitivity 22%, specificity 98%). The most specific string was (program[tw] OR "prevention and control"[sh]) AND (occupational[tw] OR worker*[tw]) (sensitivity 47%, specificity 98%).

CONCLUSION

No single search terms are available that can locate occupational health intervention studies sufficiently. The authors' search strings have acceptable sensitivity and specificity to be used by researchers and practitioners respectively. Redefinition and elaboration of keywords in Medline could greatly facilitate the location of occupational health intervention studies.