Evaluation of physical and mental health conditions related to employees' absenteeism

Background

Employees' health conditions are issues for not only employees themselves but also companies and society to keep medical costs low and productivity high.

Data and methods

In this analysis, 15,574 observations from 2,319 employees at four operational sites of a large corporation were used. The dataset contained physical and mental health conditions obtained from annual mandatory medical checkups, the Brief Job Stress Questionnaire (BJSQ), and work record information. Health and other factors related to long-term absenteeism (over three days in a quarter) were analyzed. Data were collected between February 2021 and January 2022, and we converted into quarterly observations. A logit (logistic regression) model was used in the analysis.

Results

Age and gender were identified as important basic characteristics. The estimates for these variables were positive and negative and significant at the 1% level. Among the variables obtained from the medical checkups, the estimates for diastolic blood pressure, HbA1c, anamnesis, heart disease history, smoking, increased weight, and frequency of alcohol consumption were positive and significant at the 1% level, further those for taking antihypertensive medications and kidney disease history were positive and significant at the 5% level. In contrast, the estimates for systolic blood pressure and amount of alcohol consumption were negative and significant at the 1% level. The estimate for taking antihyperglycemic medications and health guidelines were negative and significant at the 5% level. Among the variables obtained from the BJSQ, the estimates for amount of work felt, fatigue and support from family and friends were positive and significant at the 1%, and the estimate for irritation was positive and significant at the 5% level. The estimates for controlling job and physical complaints were negative and significant at the 1% level, and those for usage of employee's ability to work and suitability of the work were negative and significant at the 5% level. As all four operational sites were located in the northeastern region of Japan (cold and snowy in winter), the seasonal effects were significant at the 1% level. The effect of year was also significant and significant differences were observed among the sites at the 1% level.

Conclusion

Some physical and mental health conditions were strongly associated with long-term absenteeism. By improving these conditions, corporations could reduce the number of employee absence days. As absenteeism was costly for corporations due to replacement employees and their training costs to maintain operations, employers must be concerned about rising healthcare (direct and indirect) costs and implement investments to improve employees' health conditions.

Limitations

This study's results were based on only one corporation and the dataset was observatory. The employees were primarily operators working inside the building and most of them are healthy. Therefore, the sample selection biases might exist, and the results cannot be generalized to other types of jobs, working conditions, or companies. As medical checkups and the BJSQ are mandatory for most companies in Japan, the framework of this study can be applied to other companies. Although we used the BJSQ results, better mental measures might exist. Similar analyses for different corporations are necessary.

Prevalence of undiagnosed hypertension and associated factors among adults in Durame town, Southern Ethiopia: a cross-sectional study

Background

Undiagnosed hypertension is a major public health problem causing severe cardiovascular disorders that are responsible for a high proportion of morbidities and mortalities, especially among adults living in low-income countries. However, there is a paucity of information that describes its epidemiology in Ethiopia. This study aimed to assess the prevalence of undiagnosed hypertension and associated factors among adults in Durame town, southern Ethiopia.

Methods

A community-based cross-sectional study was conducted from July to September 2022. Data were collected from 526 randomly selected adults aged ≥18 years using a pre-tested questionnaire. The binary logistic regression models were used to identify factors associated with undiagnosed hypertension.

Results

The prevalence of undiagnosed hypertension among adults in Durame town, southern Ethiopia, was found to be 14.0% (95% CI: 11.2-17.1). Family history of hypertension [AOR = 6.9, 95% CI: (3.62, 13.27)], drinking too much alcohol [AOR = 5.7, 95% CI: (2.97, 10.75)], physical inactivity [AOR = 2.5, 95% CI: (1.34, 4.73)], consuming street foods regularly [AOR = 2.8, 95% CI: (1.28, 6.01)], and seeking healthcare for hypertensive symptoms without serious illness [AOR = 2.4, 95% CI: (1.28, 4.56)] were significantly associated with developing undiagnosed hypertension.

Conclusion

The study has revealed that one in seven adults had undiagnosed hypertension in the study area. Thus, interventions to prevent hypertension should target increasing awareness among people with a family history of hypertension, controlling excessive alcohol consumption, promoting physical exercise, regulating street food markets, and improving the health-seeking behavior of adults in urban settings.

Fairness of the Distribution of Public Medical and Health Resources

The fairness of health services is an important indicator of the World Health Organization's performance evaluation of health services, and the fairness of health resource allocation is the prerequisite for the fairness of health services. The research in this article aims to explore how to use health and medical resources fairly and effectively to allocate health resources in different fields, populations and projects, in order to achieve the maximization of social and economic benefits of health and medical resources. In the study of the distribution and equity of public health and medical resources, we comprehensively apply Gini coefficient, Theil index, Lorentz curve and difference index, based on the theory of health resource allocation and the theory of health equity, the province's health service resources have been researched and evaluated, combined with regional health planning theories and public health theories, a variety of scientific methods were used to analyze community health service resources at all levels across the country. At the same time, we reviewed the journal literature about the treatment of patients and children, and analyzed the patients admitted to medical institutions in various regions. The research in this paper found that from 2016 to 2020, the Gini coefficient of the province's health institutions according to population distribution has been fluctuating between 0.14 and 0.17. During this 5-year period, the Gini coefficient of the distribution of medical and health expenditures by population shows a downward trend year by year. From 2019, reach below 0.1, this shows that the fairness of the allocation of health resources according to population has a clear trend of improvement.

Bridging the Gap Between AI and Healthcare Sides: Towards Developing Clinically Relevant AI-Powered Diagnosis Systems

Despite the success of Convolutional Neural Network-based Computer-Aided Diagnosis research, its clinical applications remain challenging. Accordingly, developing medical Artificial Intelligence (AI) fitting into a clinical environment requires identifying/bridging the gap between AI and Healthcare sides. Since the biggest problem in Medical Imaging lies in data paucity, confirming the clinical relevance for diagnosis of research-proven image augmentation techniques is essential. Therefore, we hold a clinically valuable AI-envisioning workshop among Japanese Medical Imaging experts, physicians, and generalists in Healthcare/Informatics. Then, a questionnaire survey for physicians evaluates our pathology-aware Generative Adversarial Network (GAN)-based image augmentation projects in terms of Data Augmentation and physician training. The workshop reveals the intrinsic gap between AI/Healthcare sides and solutions on Why (i.e., clinical significance/interpretation) and How (i.e., data acquisition, commercial deployment, and safety/feeling safe). This analysis confirms our pathology-aware GANs’ clinical relevance as a clinical decision support system and non-expert physician training tool. Our findings would play a key role in connecting inter-disciplinary research and clinical applications, not limited to the Japanese medical context and pathology-aware GANs.