This study highlights the significant economic burden of presenteeism caused by common health issues such as low back pain, neck pain, and mental illnesses among Japanese workers. Workplace interventions that address these health problems, considering age and working style, are crucial for improving work productivity and reducing economic losses.
Keywords: health problem, presenteeism, productivity, cost, worker
Abstract
Objectives
This study investigated the common health conditions leading to presenteeism and the economic impact among Japanese workers in the post-COVID-19 era.
Methods
Between February and March 2023, 10,000 workers responded to a web-based, cross-sectional survey on health problems that interfered with their work, degree of presenteeism, and teleworking status.
Results
Approximately 35.6% of workers faced health problems that impacted their work. The primary health problems leading to presenteeism were low back pain, neck pain/stiff shoulders, and mental illness, which varied slightly with age and work style. The estimated annual costs due to presenteeism per 1000 employees were $488,210 for low back pain, $346,308 for neck pain/stiff shoulders, and $327,137 for mental illness.
Conclusions
Our findings suggest that presenteeism measures, primarily for musculoskeletal pain and mental health problems, are required to increase work productivity.
LEARNING OUTCOMES
To identify health problems leading to presenteeism according to age and working style in the post-COVID-19 era.
To estimate the indirect cost of presenteeism by health problems.
Recognizing the importance of health management in the workplace to improve work productivity and reduce economic burden.
Health conditions in workers can affect individuals’ quality of life, as well as work productivity and sustainable employment in a company.1 With Japan’s declining birth rate and aging population, there is an urgent need to create measures that enable individuals to work productively while maintaining their health.
Health-related productivity loss at work includes absence from work (called absenteeism) and reduced productivity due to health problems while at work (called presenteeism). Several studies have indicated that health-related costs are much larger indirect costs (due to productivity loss) than direct costs (medical/pharmaceutical expenses).2,3 Additionally, the economic impact of health problems is greater for presenteeism than for absenteeism.2,4 Thus, understanding presenteeism is essential for increasing work productivity and reducing economic impacts.
Many studies have investigated health problems associated with presenteeism. Musculoskeletal pain, psychological problems, headaches, and sleep problems were the most common health problems that contribute to presenteeism, leading to the highest estimated costs due to productivity loss.4–6 We conducted a large-scale survey on health conditions and presenteeism prior to the coronavirus disease 2019 (COVID-19) pandemic in 2019.7 This survey found that mental illness, low back pain, and neck pain were the primary health issues that interfered with work, similar to the findings of other studies.4–6 Since the COVID-19 pandemic began in December 2019, the lifestyles and socioeconomic dynamics of most populations have changed, with the effects of the pandemic still partially ongoing. One significant change has been the widespread adoption of telework, which has become a prominent feature of the modern work environment. Teleworking has influenced how employees manage health conditions and maintain productivity while working remotely, which may impact presenteeism. The increase in telework has raised questions about how different work environments, particularly remote settings, affect presenteeism and productivity. For instance, certain health problems, such as mental health issues and musculoskeletal or eye problems, may be exacerbated or alleviated depending on the remote or on-site work environment. Therefore, the relationship between telework, specific health conditions, and presenteeism warrants further exploration. Presenteeism may have become more important with changing lifestyles or workstyles after COVID-19.8
Previous studies on presenteeism since the beginning of the COVID-19 pandemic are scarce. However, certain studies have focused on specific issues, such as depression9 and disrupted care,10 or specific industries, such as medical professionals.11 To our knowledge, no study has investigated the status and economic burden of presenteeism on a wide variety of health issues in Japan since the COVID-19 pandemic. Health problems that cause presenteeism are presumed to vary with age. Younger adults may have more mental health problems than older adults,12,13 while more people tend to have chronic diseases, such as musculoskeletal disorders, that increase with age.14 The health problems that interfere with work should be recognized and stratified by age group, to develop strategies for improving work productivity.
Understanding the impact of worker health issues on the organization from an economic perspective may help us rethink investment in the health of our workforce. This study investigated the health problems that lead to presenteeism and the economic impact of health-related presenteeism during the post-COVID-19 pandemic era in Japan.
METHODS
Study Population
We conducted a cross-sectional, Internet-based study of health-related presenteeism in Japanese workers aged 20–69 years between February 1, 2023, and March 6, 2023. Participants were recruited from an Internet research agency (hamon Co., Ltd., Kanagawa, Japan), with approximately 4.2 million panelists registered in 2023.
An email containing a URL link to the survey questionnaire was distributed to 440,787 randomly selected panelists. Upon clicking the link, panelists were directed to a webpage displaying the survey protocol, which included the study’s purpose, design, and consent for participation form. The questionnaire screen was displayed to the panelists who provided consent to participate in the study. The inclusion criteria were as follows: 1) aged 20–69 years, 2) workers living in Japan, including individuals engaged in part-time employment, temporary positions, and freelance work, as well as those with regular employment. Individuals who were not employed, retirees, unemployed students, or full-time homemakers were excluded from the study. We had determined, in advance, a sample of 10,000 individuals as the target number of respondents for comparison with previous studies.4,7 The survey was terminated when the target number of respondents was reached in a manner that matched the population distribution in Japan stratified by sex and age (20s, 30s, 40s, 50s, and 60s). Surveys with missing data or incomplete responses were automatically rejected. Each participant received online points for Internet shopping as an incentive. This study protocol was approved by the Showa University Research Ethics Review Board (no. 22-213-A).
Measurements
Sample Characteristics
The questionnaire included questions on sex, age, height, weight, marital status, educational attainment, employment type, industry type, company size, teleworking, and annual income. Marital status was selected from the following options: married, never married (single), never married (living with family or relatives), and divorced/widowed. For educational attainment, respondents chose from the following options: junior high school, high school, vocational school, junior or technical college, university, graduate school, or other; these responses were then categorized as either “no university” or “university or higher.” For industry type, respondents selected one primary industry category from 19 options, based on the Japanese Standard Industrial Classification.15 The responses were then classified into three categories: primary (agriculture, forestry, and fishery), secondary (manufacturing, mining, and construction), and tertiary industries (other than those mentioned above). Companies were categorized by size as small (<100 employees), medium (100–999 employees), and large (1000 or more employees) based on the response options (1–9, 10–29, 30–99, 100–299, 300–499, 500–999, 1000–4999, 5000 or more). The status of teleworking implementation was selected from the following options: I have been teleworking since the COVID-19 pandemic, I have been teleworking since before the COVID-19 pandemic, and I am not currently teleworking. Telework was considered practiced if it was conducted at least once every one to 2 months.
Health Status, Presenteeism, and Cost
We assessed health conditions that interfered with their work and the degree of presenteeism caused by health problems using previously reported methods.4,7 First, we inquired whether participants had experienced any health issues in the last 4 weeks. Multiple answers were allowed from a list of 14 health conditions that were selected based on their high prevalence as symptoms in a previous study16 and were used in our previous study.7 The health conditions were 1) allergies, 2) skin disease/itchiness, 3) infectious diseases, 4) gastrointestinal disorders, 5) painful or disabling arm and leg joints, 6) low back pain, 7) neck pain or stiff shoulders, 8) headaches, 9) tooth troubles, 10) mental illnesses, 11) sleep-related problems, 12) weariness or fatigue, 13) eye problems, and 14) others. Second, if the participants indicated the presence of one or more health problems, we asked which of the health problems they answered in the previous question most interfered with their work. If the participants had no health problems that interfered with their work, we estimated no loss of productivity. Third, we asked participants about the number of days they had experienced the symptoms that most affected their work in the last 4 weeks. Fourth, we enquired about the level of work quantity and quality that participants were able to perform while experiencing their symptoms, as compared to when they were not affected by those conditions or symptoms (they had no health problems). Responses were rated on an 11-point numerical scale (0 = no productivity, 10 = no productivity loss). The degree of presenteeism was evaluated using the following formula: 1-quantity (0–10) × quality (0–10)/100. Higher scores indicated greater productivity loss. The construct validity of the method used to assess the degree of productivity loss has been previously confirmed.17
Additionally, we estimated the cost of lost productivity due to presenteeism caused by health conditions using the degree of presenteeism and the annual income to which participants responded. Regarding income, we determined the median value of the questionnaire choices (JPY) as follows: income less than 2, 2–4, 4–6, 6–8, 8–10, and 10 million JPY or more were correspondingly transformed into JPY 2, 3, 5, 7, 9, and 10 million. Subsequently, we calculate the daily wage by dividing the median by 240 (considering 12 months with 20 working days each). We then estimated the monetary value of productivity loss attributed to presenteeism per 1000 employees (referred to herein as per 1000 capita) utilizing the following formula: income (daily wage) × (degree of presenteeism) × (days with health problems during the last 4 weeks) × 20/28 × 12 (to convert into yearly costs) × (the number of workers with symptoms interfering with work)/10,000 × 1000. The calculated costs were converted from yen (JPY) to US dollars (USD), based on the average exchange rate (1 USD = 132.76 JPY) at the time of the study.
Statistical Analyses
Data were presented as numbers and percentages or medians (25th and 75th percentiles). The characteristics of the participants with or without symptoms were compared using the chi-squared test for categorical variables or the Wilcoxon rank-sum test for continuous variables. The degree of presenteeism was compared using the Wilcoxon rank-sum/Kruskal-Wallis test. A two-tailed significance level of 0.05 was used for all statistical tests. All data analyses were conducted using JMP 17 (SAS Institute Inc., Cary, NC). The reporting of this study conformed to the STrengthening the Reporting of OBservational studies in Epidemiology statement (Supplementary Digital Content, http://links.lww.com/JOM/B821).
RESULTS
The participants’ characteristics are listed in Table 1. The median age was 47 years, and 50.4% of the participants were men. Most participants (77.0%) were engaged in tertiary industries. Among the three company size categories, the proportion of workers in small companies was the highest.
TABLE 1.
Characteristics of the Study Participants (N = 10,000)
| Characteristics | n | % |
|---|---|---|
| Sex | ||
| Men | 5,042 | 50.4 |
| Women | 4,958 | 49.6 |
| Age, yr | 47 (36, 57) | |
| Body mass index, kg/m2 | 21.5 (19.5, 24.0) | |
| Marital status | ||
| Married | 5,036 | 50.4 |
| Never married (single) | 1,925 | 19.3 |
| Never married (with family or relatives) | 2,143 | 21.4 |
| Divorced or widowed | 896 | 9.0 |
| Education level | ||
| No university | 4,423 | 44.2 |
| University or higher | 5,577 | 55.8 |
| Employment type | ||
| Regular employee | 5,241 | 52.4 |
| Nonregular employee | 4,759 | 47.6 |
| Industry type | ||
| Primary industry | 107 | 1.1 |
| Secondary industry | 2,197 | 22.0 |
| Tertiary industry | 7,696 | 77.0 |
| Size of company | ||
| Small (1–99 employees) | 4,945 | 49.5 |
| Medium (100–999 employees) | 2,654 | 26.5 |
| Large (1,000 or more employees) | 2,401 | 24.0 |
| Telework | ||
| Starting from COVID-19 pandemic | 1,449 | 14.5 |
| No teleworking/start prior to COVID-19 pandemic | 8,551 | 85.5 |
| Annual income (JPY) | ||
| Less than 2 million | 3,241 | 32.4 |
| 2–4 million | 3,202 | 32.0 |
| 4–6 million | 2,003 | 20.0 |
| 6–8 million | 852 | 8.5 |
| 8–10 million | 390 | 3.9 |
| 10 million or more | 312 | 3.1 |
Data are presented as numbers and percentages, or median (25th, 75th percentiles).
Of the participants, 35.6% had experienced health problems that interfered with their work during the past 4 weeks. The demographic information of the participants was compared according to the presence or absence of health problems (Table 2). Women were more likely than men to have symptoms that interfered with their work. Workers in nonregular employment were found to experience a higher prevalence of health issues affecting their work than those in regular employment. A higher percentage of workers who began teleworking after the COVID-19 pandemic had symptoms that interfered with their work than those who had been teleworking before the pandemic or had not teleworked. The degree of presenteeism tended to be higher among younger workers than older workers.
TABLE 2.
Comparison of the Characteristics of Workers With or Without Symptoms Affecting One’s Work, and the Degree of Work Productivity (Presenteeism)
| No Symptoms | With Symptoms | With Symptoms | |||||
|---|---|---|---|---|---|---|---|
| (n = 6,442) | (n = 3,558) | ||||||
| n | % | n | % | P* | Degree of presenteeism | P† | |
| Sex | |||||||
| Men | 3,404 | 67.5 | 1,638 | 32.5 | <0.001 | 0.51 (0.28, 0.75) | 0.583 |
| Women | 3,038 | 61.3 | 1,920 | 38.7 | 0.51 (0.20, 0.75) | ||
| Age, yr | |||||||
| 20–29 | 1,022 | 64.3 | 567 | 35.7 | <0.001 | 0.60 (0.36, 0.76) | <0.001 |
| 30–39 | 1,145 | 64.2 | 640 | 35.9 | 0.58 (0.36, 0.79) | ||
| 40–49 | 1,443 | 61.4 | 906 | 38.6 | 0.51 (0.30, 0.75) | ||
| 50–59 | 1,434 | 63.7 | 816 | 36.3 | 0.44 (0.20, 0.75) | ||
| 60–69 | 1,398 | 69.0 | 629 | 31.0 | 0.36 (0.19, 0.62) | ||
| Employment type | |||||||
| Regular | 3,491 | 66.6 | 1,750 | 33.4 | <0.001 | 0.51 (0.28, 0.75) | 0.329 |
| Nonregular | 2,951 | 62.0 | 1,808 | 38.0 | 0.52 (0.20, 0.75) | ||
| Industrial classification | |||||||
| Primary industry | 60 | 56.1 | 47 | 43.9 | 0.023 | 0.44 (0.19, 0.75) | 0.785 |
| Secondary industry | 1,458 | 66.4 | 739 | 33.6 | 0.51 (0.28, 0.75) | ||
| Tertiary industry | 4,924 | 64.0 | 2,772 | 36.0 | 0.51 (0.28, 0.75) | ||
| Company size | |||||||
| Small | 3,172 | 64.2 | 1,773 | 35.8 | 0.491 | 0.51 (0.28, 0.75) | 0.471 |
| Medium | 1,699 | 64.0 | 955 | 36.0 | 0.51 (0.28, 0.75) | ||
| Large | 1,571 | 65.4 | 830 | 34.6 | 0.51 (0.28, 0.75) | ||
| Telework | |||||||
| Start from COVID-19 pandemic | 874 | 60.3 | 575 | 39.7 | <0.001 | 0.52 (0.36, 0.75) | 0.007 |
| No teleworking/start prior to COVID-19 pandemic | 5,568 | 65.1 | 2,983 | 34.9 | 0.51 (0.28, 0.75) | ||
Data are presented as numbers and percentages, or median (25th, 75th percentiles).
*Chi-squared test.
†Wilcoxon rank-sum test.
Table 3 shows the number of workers and degree of presenteeism for each health problem that interfered the most with work. Overall, the primary health problem causing interference with work was low back pain, followed by neck pain or stiff shoulders, headaches, and mental illnesses. The top three health problems among men were low back pain, neck pain or stiff shoulders, and sleep-related problems, whereas those among women were neck pain or stiff shoulders, low back pain, and headaches. The degree of presenteeism (lost productivity) was highest for infectious diseases, followed by mental illnesses.
TABLE 3.
Health Conditions that Interfered Most With Work and the Degree of Presenteeism
| Health Conditions | Total | Men | Women | Degree of Presenteeism | |
|---|---|---|---|---|---|
| n | (%) | n | n | Median(25th, 75th percentiles) | |
| Low back pain | 666 | 6.66 | 384 | 282 | 0.44 (0.20, 0.68) |
| Neck pain or stiff shoulders | 479 | 4.79 | 177 | 302 | 0.40 (0.19, 0.65) |
| Headaches | 286 | 2.86 | 96 | 190 | 0.64 (0.44, 0.84) |
| Mental illnesses | 285 | 2.85 | 122 | 163 | 0.75 (0.51, 0.88) |
| Eye problems | 262 | 2.62 | 109 | 153 | 0.37 (0.20, 0.65) |
| Sleep-related problems | 234 | 2.34 | 123 | 111 | 0.58 (0.36, 0.76) |
| Allergies | 200 | 2 | 96 | 104 | 0.37 (0.19, 0.75) |
| Skin diseases/itchiness | 197 | 1.97 | 104 | 93 | 0.36 (0.10, 0.58) |
| Painful or disabling arm/leg joints | 197 | 1.97 | 70 | 127 | 0.36 (0.19, 0.58) |
| Weariness or fatigue | 197 | 1.97 | 92 | 105 | 0.58 (0.36, 0.80) |
| Gastrointestinal disorders | 186 | 1.86 | 89 | 97 | 0.51 (0.28, 0.72) |
| Infectious diseases | 170 | 1.7 | 69 | 101 | 0.85 (0.51, 1.00) |
| Others | 124 | 1.24 | 68 | 56 | 0.70 (0.36, 0.99) |
| Tooth troubles | 75 | 0.75 | 39 | 36 | 0.51 (0.19, 0.70) |
The top five health problems that interfered most with work, stratified by age and teleworking implementation status, are shown in Table 4. The most frequent health problem that interfered with work among young workers (aged 20–29 years) was mental illness. Low back pain was the most common health problem that interfered with work among workers in their 40s and older, followed by neck pain or stiff shoulders. Among the top five health problems, headaches ranked for workers in their 20–40s, while painful or disabling arm/leg joints were included for workers in their 50–60s. Low back pain and neck pain/stiff shoulders topped the list regardless of the status of telework implementation, and eye problems were third for those who started teleworking during the COVID-19 pandemic.
TABLE 4.
Top 5 Health Conditions With the Most Impact on One’s Work Stratified by Age and Style of Work
| (a) by Industrial Classification | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Aged 20–29 | Aged 30–39 | Aged 40–49 | |||||||
| n | % | n | % | n | % | ||||
| 1 | Mental illnesses | 90 | 5.7% | Neck pain or stiff shoulders | 100 | 5.6% | Low back pain | 164 | 7.0% |
| 2 | Low back pain | 74 | 4.7% | Low back pain | 82 | 4.6% | Neck pain or stiff shoulders | 131 | 5.6% |
| 3 | Neck pain or stiff shoulders | 69 | 4.3% | Headaches | 69 | 3.9% | Headaches | 92 | 3.9% |
| 4 | Headaches | 54 | 3.4% | Mental illnesses | 67 | 3.8% | Eye problems | 69 | 2.9% |
| 5 | Allergies | 42 | 2.6% | Sleep-related problems | 53 | 3.0% | Mental illnesses | 67 | 2.9% |
| Aged 50–59 | Aged 60–69 | ||||||||
| n | % | n | % | ||||||
| 1 | Low back pain | 183 | 8.1% | Low back pain | 163 | 8.0% | |||
| 2 | Neck pain or stiff shoulders | 104 | 4.6% | Neck pain or stiff shoulders | 75 | 3.7% | |||
| 3 | Eye problems | 73 | 3.2% | Painful or disabling arm/leg joints | 65 | 3.2% | |||
| 4 | Painful or disabling arm/leg joints | 67 | 3.0% | Eye problems | 63 | 3.1% | |||
| 5 | Weariness or fatigue | 57 | 2.5% | Allergies | 39 | 1.9% | |||
| (b) by Status of Telework Implementation | |||||||||
| No Teleworking/Implemented Prior to COVID-19 Pandemic | Started from COVID-19 Pandemic | ||||||||
| n | % | n | % | ||||||
| 1 | Low back pain | 575 | 6.7% | Low back pain | 91 | 6.3% | |||
| 2 | Neck pain or stiff shoulders | 400 | 4.7% | Neck pain or stiff shoulders | 79 | 5.5% | |||
| 3 | Headaches | 240 | 2.8% | Eye problems | 57 | 3.9% | |||
| 4 | Mental illnesses | 238 | 2.8% | Mental illnesses | 47 | 3.2% | |||
| 5 | Eye problems | 205 | 2.4% | Headaches | 46 | 3.2% | |||
Figure 1 presents the estimated annual cost of each health-related productivity loss. The estimated costs per 1000 employees were 488,210 USD for low back pain, 346,308 USD for neck pain or stiff shoulders, and 327,137 USD for mental illness.
FIGURE 1.
Estimated annual cost of health-related productivity loss per 1000 employees.
DISCUSSION
We investigated health problems that interfered with work and the estimated cost of productivity loss due to presenteeism caused by health problems in Japanese workers. Musculoskeletal pain, headaches, and mental illness were the leading symptoms that impeded work. This study also found that the top-ranked health problems differed according to age and work style. The economic burden of health-related productivity loss was considerably high, especially for musculoskeletal and mental conditions. Our findings indicate the current landscape (including the economic impact) of health issues hindering workplace productivity across Japan in the aftermath of the COVID-19 pandemic.
We found that the most common health problem that interfered with work was low back pain, followed by neck pain or stiff shoulders. In our previous study conducted in 2019 (before the COVID-19 pandemic), the primary health problems that interfered most with work were neck pain or stiff shoulders, low back pain, and mental illness.7 The spread of COVID-19 may have affected the growing number of workers who reported low back pain as the most disabling aspect of their work. The COVID-19 pandemic could have led to a decrease in physical activity to avoid social contact and an increase in sedentary lifestyles,18,19 which have been considered important risk factors for low back pain.20 During the COVID-19 pandemic, we investigated the pain condition among workers with physical pain and found that workers who experienced a decrease in physical activity had a higher likelihood of worsening pain.21 In addition, various increased psychological stressors associated with the spread of COVID-19 may have influenced our results, such as changes in lifestyle, anxiety about one’s health, and financial concerns, although their impacts may be smaller than those in the early COVID-19 pandemic. Many studies have indicated the link between psychological stress and low back pain.22,23 Our study indicated that musculoskeletal pain was the leading cause of presenteeism, which is consistent with the findings of previous studies.
In the analysis stratified by age, we found that mental illness was the most common health problem among young workers. A previous report indicated that young workers had a higher rate of mental distress than other age groups.12 Psychosocial workplace exposure, such as low job control and poor psychosocial job quality, may affect the mental health of young workers.24,25 Mental problems that interfere with work are issues that need to be addressed, as they can lead to burnout or retirement, particularly among young workers. We also found that, among workers in their 50s and 60s, painful or disabling arm/leg joints ranked among the top five health problems. Joint dysfunctions, such as osteoarthritis, increase with age.14 With declining birth rates and accelerating population aging, the number of older workers is expected to increase with the introduction of systems of extended retirement ages in the near future. To enable older workers to continue working actively for a long time, it is necessary to understand the age-specific health problems that lead to presenteeism. We believe that countermeasures against joint disorders are important to improve work productivity and individual health.
The COVID-19 pandemic has changed working styles, and teleworking is still being introduced in some companies. We compared the main health problems that interfered with work between the group that started and continued teleworking after the COVID-19 pandemic and the group that did not. In the former group, eye problems were the third most common health problem disturbing work. This may be partly due to deskwork with inadequate environmental conditions, such as laptops, tablets, or lighting.26 Mental illness ranked high in both groups. In this regard, teleworkers face unique challenges, such as a lack of communication, workplace isolation, and work-family conflicts.27,28 Thus, factors contributing to mental illness may have differed between the two groups, although we did not assess these factors.
The annual cost of health-related productivity loss per 1000 employees was approximately 490,000 USD for low back pain, 350,000 USD for neck pain or stiff shoulders, and 330,000 USD for mental illnesses. These results indicated that the economic burden of productivity loss caused by musculoskeletal pain or mental problems was enormous, similar to the findings of other studies.4,6 Unlike absenteeism, which is relatively easy to track as sick leave, the health problems that cause presenteeism are mainly invisible. However, they are important issues that should not be ignored, including their economic impact. Previous studies have indicated that the health-related costs of lost productivity were greater than medical/drug expenditures.2,3 Understanding employees’ health issues from the perspective of presenteeism could provide a different perspective on situations in which workers face difficulties not directly related to diagnostic diseases, such as cancer or depression, and could lead to initiatives that improve employee productivity. Measures to reduce presenteeism are essential for employers to manage the impact of poor health on their businesses successfully.
Our study identified health problems that lead to presenteeism, stratified by age and telework status in the post-COVID-19 era. Our findings may help in developing age-specific health measures to improve productivity. However, our study has some limitations that should be acknowledged. First, we investigated the single health problem in each worker that most interfered with work; some workers were assumed to have multiple health problems that were not assessed in our study. Further studies are required to determine the combined effects of health problems on work productivity. Second, our study incorporated the perspective of the impact of telework on presenteeism in the post-pandemic context. However, other potentially influential factors, such as workplace safety and work-life balance, were not evaluated in our survey. These factors, along with mental health impacts, may also play significant roles in presenteeism and productivity outcomes. Future research should consider these additional factors to provide a more comprehensive understanding of the influences on presenteeism in the post-COVID-19 landscape. Third, we could not infer causal relationships because of the cross-sectional nature of the study design. Fourth, our study was conducted among registered panelists of an Internet research company, which may have caused selection bias, although the participants were selected to align with the age and sex distribution of the Japanese population. For instance, those familiar with Internet use may have been more likely to participate in our study. These issues may have affected the accuracy and generalizability of our findings.
CONCLUSIONS
Our study identified health problems as the leading cause of presenteeism among Japanese workers and estimated the economic burden of presenteeism in the post-COVID-19 era. The major health problems that interfered with work varied slightly with age and work style. Our findings may provide insights for promoting targeted health management programs in the workplace to improve individuals’ health and productivity in their organizations.
ACKNOWLEDGMENTS
The author thank Editage (www.editage.jp) for English language editing.
Footnotes
Funding sources: This study was supported by a grant from the Ministry of Health, Labour and Welfare (No. 22FG1002). The funders played no role in the study design, data collection and analysis, decision to publish, or manuscript preparation. SK received a Grant-in-Aid for Scientific Research (C) from the Japan Society for the Promotion of Science (Grant numbers: 20K07755, 24K13083), which had no involvement in this study and did not support it.
Conflict of interest: None declared.
Authors’ contributions: K.M. contributed to the conception of this study. T.Y. designed this study and the data collection. T.Y. and H.O. conducted the data analysis. T.Y. drafted the manuscript. K.M., S.K., A.K., and S.I. contributed the interpretation of the findings. All authors contributed critical revisions of the manuscript, and approved the final version of the manuscript.
Data Availability: The datasets used in the current study are available from the corresponding author on reasonable request.
EQUATER Network Reporting Guidelines: Reporting of this study followed the STROBE guidelines for cross-sectional studies. We have uploaded the STROBE checklist for cross-sectional studies via Editorial Manager.
AI detailed statements: AI was not utilized at any stage during research development & design, data collection, manuscript preparation etc.
Ethical Considerations & Disclosure: This study protocol was approved by the Showa University Research Ethics Review Board (No. 22-213-A).
Supplemental digital contents are available for this article. Direct URL citation appears in the printed text and is provided in the HTML and PDF versions of this article on the journal’s Web site (www.joem.org).
Contributor Information
Ko Matsudaira, Email: kohart801@gmail.com.
Hiroyuki Oka, Email: taigis54@yahoo.co.jp.
Satoshi Kasahara, Email: namahagenator@gmail.com.
Akatsuki Kokaze, Email: akokaze@med.showa-u.ac.jp.
Shinsuke Inoue, Email: sinoue0626@gmail.com.
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