Purpose: Occupational health nurses are considered specialists of health management for the workers in their companies, but they are working with many kinds of difficulties.

Nevertheless there are little studies of influences owing to the numbers of workers in the companies, therefore, we tried to make it clear that there exist the component pattern of the difficulties which effects on occupational health nursing activities and the differences depend on the number of workers, between less than one thousand and more.

Object: A questionnaire survey was conducted for the occupational health nurses who were on the active services and attended the seminar held by the Occupational Health Promotion Center in the Tokai area. 28 items of experiences felt difficult in the business, were used for exploratory factor analysis, and t-test of the factor scores were conducted between the groups composed the numbers of workers. 4 items of experiences felt difficult in the business, and learning environment such as possibility study of in/out office were compared using Mann-Whitney’s U test

Result: The response rate was 75.4% and number of valid respondents was 240 (73.8%). According to the exploratory factor analysis of difficulties in business, 4 factors were obtained, and called Factor1 co-operation and using information with related workers, Factor2 Activity and evaluation of the occupational health activities, Factor 3 activity one’s company and Factor 4 treatment of mental health. No differences on the numbers of the companies were seen the means of factor score by using t-test.

Conclusion: We found it clear that there are four component factors which effect the difficulty business of the occupational health nurses. About the difference of difficulty in the activity to be able to put according to a scale, the significant difference was not recognized.

Objectives: We analyzed the association between wet bulb globe temperature (WBGT) and mortality, and compared WBGT with mean temperature for evaluating mortality risk in 47 Japanese prefectures using data from 1972-2012.

Methods: We first calculated the prefecture-specific effect of WBGT on mortality using a time series regression model combined with a distributed lag non-linear model, and then used the same modelling strategy to examine the mean temperature. Second, we compared the minimum mortality WBGT (MMW) and minimum mortality (mean) temperature (MMT) for all the prefectures.

Results: We found that the curves of WBGT- and mean temperature-mortality were similar and an inverse J-shape was identified among most prefectures. In northern areas, the heat effect was comparable to the cold effect, and in southern areas, the cold effects were more pronounced. In general, MMW and MMT increased from north to south, except for some prefectures. The MMW and MMT percentile values ranged between the 80th and 90th percentile for most prefectures. We compared MMW and MMT for all the prefectures and found that Kochi is an outlier. We conducted an identical analysis for Kochi except for setting shorter lags (seven days from the original 21 days), because the heat effect usually has a short lag. After this modification, Kochi became a non-outlier. This implies that Kochi appeared to be an outlier because the model had an extraneous long lag.

Conclusions: We found mean temperature and WBGT were highly correlated in the evaluation of mortality in most prefectures. Therefore, in cases where WBGT data are not available for heat warnings, mean temperature is a good substitute.