Sickness absenteeism among public servants in the Amazon region during the COVID-19 pandemic: a longitudinal study (2019-2022).

Knowledge on the profile of sickness absenteeism among civil servants reveals their health and working conditions and provides valuable information for the creation of policies aimed at surveillance of servants' health. To investigate sickness absenteeism in a federal public education institution. This was a cross-sectional, documentary, descriptive-exploratory study with a quantitative approach that dealt with the occurrence of sickness absenteeism among federal civil servants at National Institute of Northern Minas Gerais (Instituto Federal do Norte de Minas Gerais). In the study period, of the total of 1,339 servants, 112 were responsible for 150 episodes of sick leave, which represented a frequency of workers on medical license of 8.36% and a severity index of 3.21 days. Sickness absenteeism was more prevalent among servants aged from 31 to 40 years and among women. Education administrative technicians presented a greater number of leaves when compared to teachers. Mental and behavioral disorders were the most prevalent conditions. The results of this research may support the creation of more assertive occupational health policies and interventions.

Sickness Absence: The Scale of the problem: Sickness absence is a massive economic problem worldwide in both the public and private sectors. In the UK, according to the Federation of British Industries Report "Pulling Together: 2001 Absence and Labour Turnover Survey" (1), the gap in sickness absence performance between public and private sectors was at that time 10.2 days lost per employee against 7.2 days lost respectively. The gap in the sickness absence performance between private sectors of the economy has increased with transport and communication sectors being the highest, after the public sector, at 9.4 days lost per employee. The sectors with the lowest sickness absence rate are the professional services which include physicians. Who Manages Sickness Absence? In order to manage sickness absence it is important for employers to first know their levels of absence and how they compare with other companies in the same industry or business sector. Benchmarking sickness absence as a key performance indicator helps employers to identify gaps in performance between their organization and the average across a range of similar organizations. There is good evidence to suggest that an organization’s performance in the field of sickness absence management usually correlates well with a range of human resource management issues throughout the organization as a whole.The involvement of OH professionals was the favored approach in dealing with longer-term absences. This supports the earlier contention that non-medical factors may be significant in sickness absence and predominantly so in short-term rather than long-term absence.

Castleman Disease: A Population Based Study Using the National Cancer Institute's Surveillance, Epidemiology, and End Results Program

Background: Workplace absenteeism significantly impacts productivity and employee well-being. This study examines the relationship between sickness absence and healthcare utilization (general practitioners and specialists) among employees in Serbia, focusing on the public healthcare sector. Understanding these associations can inform policies to improve workplace health and reduce absenteeism. Methods and Objectives: This secondary data analysis from the 2019 Serbian National Health Survey involved 3746 employees aged 18-65. Participants were categorized based on sickness absence duration (≤ 30 days or > 30 days) and their visits to general practitioners and specialists. Descriptive analyses and Chi-square tests explored relationships between absenteeism and healthcare utilization. Results: In total, 1319 (49.6%) participants did not visit a general practitioner; 39.4% had no sickness absence, 8.8% had ≤ 30 days and 1.4% had > 30 days. Those who did visit were primarily in the public healthcare sector (34.4%), with higher proportions experiencing prolonged sickness absence: 44.5% had no sickness absence, 8.3% had ≤ 30 days, and 2.7% had > 30 days. Similarly, 829 (50.0%) did not visit a specialist; 36.7% had no sickness absence, 8.1% had ≤ 30 days and 3.4% had > 30 days. The majority who visited a specialist were in the public sector (40.3%): 28.8% had no sickness absence, 8.1% had ≤ 30 days, and 3.4% had > 30 days. Significant statistical differences were found for visits to general practitioners (p = 0.003) and specialists (p = 0.005). Conclusions: This study highlights that increased healthcare utilization is associated with prolonged sickness absence. Targeted healthcare interventions could help address absenteeism and improve health outcomes for employees in Serbia. This study provides a foundation for future research and policy-making to enhance workplace health and reduce absenteeism.

The Future of Public Service: A Search for a New Balance

[corrected] This paper focuses on the relationship between sickness absence rates certified as attributable to common (non-work-related) diseases and occupational categories. A cohort of 2,909 workers from an urban bus company was analysed from 1994 to 1996 (8,388.7 person-years), who reported 2,893 non-work-related sickness absence spells of three days or more (78% of all sickness absence spells in the period). Rates of sickness absence were calculated and an extension of the Andersen-Gill approach to proportional hazards modelling was used to adjust covariables. Assistant staff (35.63 per 100 person-years), and bus drivers (37.23) had the highest incidence rates. After adjusting for demographic variables, employment duration and health status, it was found that all occupational categories had a significant rate ratio (RR) in comparison to the manager category. The strongest positive associations were found among bus drivers (RR = 2.45; 95% CI: 1.52-3.97), assistant staff (RR = 2.57; 1.67-3.94), and technical staff (RR = 2.42; 1.57-3.74). Occupational category was an important predictor of sickness absence incidence certified as attributable to common disease. Further research on the associations between working conditions and sickness absence needs to take into account the distinction between sickness absences due to work-related diseases and those due to common diseases.

To investigate relationship of mental ill health to absence from work in different occupational classifications. Examined sickness absence, mental health (GHQ-12), physical health, job characteristics, and personal characteristics in 18 waves of the British Household Panel Survey. Overall sickness absence rate was 1.68%. Increased absence was associated with age greater than 45 years, female gender, lower occupational classification, and public-sector employers. Decreased absence was associated with part-time working. Scoring 4 or more on the General Health Questionnaire 12-item version (GHQ-12 caseness) was strongly associated with sickness absence. Public-sector employers had highest rates of sickness absence. GHQ-12 caseness had largest impact on absence in the public and nonprofit sectors, whereas physical health problems impacted more in the private sector. GHQ-12 caseness is strongly associated with increased absence in all classifications of occupations. Differences between sectors require further investigation.

The need for novel strategies to analyze the dynamic pattern of worker’s health over time and the consequences for sustained employability

Background Mental health problems among young adults is a public health problem as well as increasing work incapacity in terms of sickness absence (SA) due to mental diagnoses in Sweden. SA levels among those working in the public sector tend to exceed those working in the private sector. Knowledge is however lacking on whether experiencing mental health problems in young years can contribute to the association. The aim was to examine the association between type of employment and sickness absence among women and men, also adjusting for previously experienced depression and anxiety as well as for familial factors. Methods The study included data on 25,496 twins born in Sweden 1959-1990. Information on depression and anxiety as well as on type of employment was obtained from a web-based survey conducted in 2005. Twins were followed prospectively until 2013 through national register data on SA. Logistic regression analyses were run with adjustment for familial factors, such as common genetics and environmental factors related to the family. Odds ratios (OR) with 95% Confidence Intervals (CI) are presented. Results Higher rates of SA in the public (59%) as compared to the private (50%) sector were observed among women. There were 29% and 30% of men in public and private sectors, respectively, that have had at least one SA spell during the follow-up. Preliminary results showed that working in the private sector was associated with a higher risk for future SA among women (OR 1.63, 95% CI 1.14-2.33) but not among men (OR 0.66, 95% CI 0.41-1.07). The results changed only slightly after adjusting for previous depression or anxiety as well as for familial factors. Conclusions Higher rates of SA among women working in the public as compared to the private sector were not explained by previously experienced depression or anxiety disorders. Key messages Higher rates of SA were observed among women working in public as compared to private sector. Previously experienced depression and anxiety were of similar importance for future SA in both public and private sectors.

Short- and long-term sickness absence (SA) vary in their determinants. We examined short- and long-term SA contemporaneously as two interconnected phenomena to characterize their temporal development, and to identify employees with increasing SA at an early stage. We extracted 46- to 55-year-old employed women from the Helsinki Health Study occupational cohort during 2000-17 (N = 3206) and examined the development of short- (1-14 days) and long-term (>14 days) SA using group-based dual trajectory modelling. In addition, we investigated the associations of social-, work- and health-related factors with trajectory group membership. For short-term SA, we selected a three-group solution: 'no short-term SA' (50%), 'low frequency short-term SA' (40%), and 'high frequency short-term SA' (10%) (7 spells/year). For long-term SA, we also selected three trajectory groups: 'no long-term SA' (65%), 'low long-term SA' (27%), and 'high long-term SA' (8%). No SA in the short-term SA model, indicated a high probability of no SA in the long-term model and vice versa. The developmental pattern was far less certain if participant was assigned to a trajectory of high SA in either one of the models (short- or long-term SA model). Low occupational class and poor health behaviours were associated with the trajectory groups with more SA. SA does not increase with age among most employees. If either SA rate was high, the developmental patterns were heterogenous. Employers' attention to health behaviours might aid in reducing both short- and long-term SA.

Long sickness absence is more common among low socioeconomic status (SES) groups than high SES groups. This study aimed to evaluate whether work and family characteristics contribute to SES and sex differences in long sickness absence (7 days or more). The participants were 3080 civil servants working for a local Japanese government. In both sexes, low-grade employees were likely to take long sickness absence, with a statistically significant association for men (age-adjusted OR of lowest-grade employees for long sickness absence: 2.30 (95% Confidence Interval (CI): 1.32–4.02)). After adjusting for all variables, SES differences in long sickness absence in men decreased to OR 1.98 (CI 1.10–3.55) but remained significant; in men, being without a spouse was significantly associated with long sickness absence. Employees working long hours had lower OR for long sickness absence after adjusting for all variables in both sexes. Conversely, poor sleep quality and longstanding illness significantly increased OR for long sickness absence. In conclusion, SES differences in sickness absence were explained partly by work and family characteristics, longstanding illness, and poor sleep quality; however, other factors that were not evaluated in this study may also be associated with SES differences.

Several studies showed that sickness absenteeism has considerable impact on civil service given the increasing number of sick leaves granted to and days off work among employees. Health care workers, especially those in hospitals, represent one of the occupational groups at higher risk of absenteeism. Therefore, establishing the pattern of sickness absenteeism in this group is crucial to implement efficacious preventive measures against work-related diseases. To characterize sickness absenteeism among the nursing and medical staff of a public hospital in São Paulo, Brazil, in the period from 2011 through 2013. Cross-sectional descriptive study conducted in a large hospital in which we analyzed data from January 2011 to December 2013. Employees missed a total of 71,460 days of work along the analyzed period; 3,323 sick leave benefits were granted to 1,533 workers. Nursing assistants and workers in the adult emergency department accounted for the largest number of days off work to a total of 11,460. The most common reasons for sick leaves among emergency department employees were musculoskeletal diseases and mental and behavioral disorders. We detected changes in the morbidity profile of the nursing and medical staff along the analyzed period, characterized by conditions which demand longer time for recovery and return to work.

Sickness absence mesmerizes many researchers, given the numerous publications on risk factors for sickness absence. A large variety of risk factors have been identified, including work-related risk factors such as physical work demands and psychosocial work factors (1,2), unhealthy behaviors such as lack pf physical activity and smoking (3,4), and chronic health problems (5). A logical next step seems to be the development of a prediction model, whereby an individual’s profile on risk factors is converted into a probability on future sickness absence. In the past few years, several prediction models have been developed and validated. It is in intriguing question how to use these models in occupational health practice to identify workers at risk for prolonged sickness absence reliably and to act on this. The recent prediction models differ with respect to target populations, number, and type of predictors, and definition of sickness absence to be predicted. A large Finnish study among public sector employees with a 12-year follow-up used 17 factors in a prediction model for short-term sickness absence (≥10 days) and 14 predictors for long-term sickness absence (≥90 days). The performance of the model, evaluated by the C-index (also known as the area under the curve), for short-term sickness absence was 0.65 and for long-term sickness absence 0.74, representing poor-to-moderate model performance. For workers with higher risk levels, the positive predictive values were 42 days for mental health complaints during the 2-year follow-up (9). Shiri and colleagues developed a model with seven self-reported predictors for disability retirement due to musculoskeletal disorders during an 11-year follow-up in Finland. Although the overall performance of the model was promising with a C-index of 0.82, the rare occurrence of the outcome of interest resulted in a positive predictive value of about 14%, illustrating that most workers who entered disability retirement due to musculoskeletal disorders were not identified (10). Are prediction models for sickness absence the holy grail in occupational health ? In theory, such models hold the promise to target individuals at risk and improve management of sickness absence by addressing risk factors. However, so far the models have shown disappointing results. Nevertheless, several authors seem optimistic that their prediction models are useful for prevention strategies. Even with a C-index well below 0.70, it is stated that “individual’s risk can be estimated” (6), that “the model discriminated significantly between workers with frequent sickness absence and those without”(7), or that “the model’s risk predictions were adequate” (8). Apart from the challenges to develop a valid prediction model, it appears that recent publications discuss too little whether the aim of the decision model should be to detect predictors and their relative importance or to deliver predictions for individuals at risk. The important distinction between focus on predictors or prediction links directly to the potential usefulness of a prediction model in prevention strategies. The performance of recently developed models is certainly not good enough for selective prevention, whereby groups of individuals at risk of prolonged sickness absence or disability benefits can be identified and targeted for subsequent preventive actions. At best, these models illustrate the relative importance of various individual and work-related risk factors for sickness absence that may be targeted in prevention strategies. In essence, current models are much more informative for identifying predictors than for making predictions. This conclusion raises the question whether prediction models for sickness absence can guide in the classical choice between selective prevention or universal prevention. It is almost 35 years since Geoffrey Rose eloquently phrased the notion that it is important to distinguish between individuals who are sick and sick populations. He argued that the choice for the best prevention strategy should be determined by the distribution of risk in the population: with an extreme distribution, the ‘high-risk’ approach ‒which seeks to protect susceptible individuals ‒ is more beneficial, whereas, with a continuous distribution of risk, the population approach ‒ which seeks to control the causes of disease ‒ is more appropriate (11). The recent prediction models on sickness absence have identified several risk factors that contribute to future sickness absence such as high physical work demands, and shift work. These risk factors are commonly present in occupational populations. Therefore, in line with Rose’s notion, our main concern should be to discover and control the causes of sickness absence in the workforce.

Sickness absence (SA) is affected by societal factors. Increasing socioeconomic stress may cause or worsen mental health disorders, which are among the most frequent causes of SA. Employees may also be more cautious about being absent, for example in times of poor economy. To monitor the incidence of SA due to mental health disorders in the Netherlands from 2001 to 2010. Descriptive observational study of long-term (> 3 weeks) SA available from an occupational health service register. The incidence of both total and mental health long-term SA in each year was calculated and evaluated alongside the changes in SA compensation policies, gross national product and national unemployment statistics. The incidence of mental health SA was stratified based on the economic (agricultural, industrial, private, public) sector. The incidence of both total and mental health SA decreased gradually since 2004, and fell during the economic recession in 2009 in all economic sectors, particularly the agricultural and industrial sectors. The incidence of mental health SA increased with preliminary economic recovery in 2010 in the private and public sectors, but not in the agricultural and industrial sectors. Long-term SA due to mental health disorders has decreased since 2004, but further studies across countries are required to confirm and explain this trend.

Sickness absence is increasingly affecting society at different levels. This study explores how work, family, and personal health-related characteristics will contribute to socioeconomic status (SES) differences in future long sickness absence (7 days or more) with respect to sex differences. A total of 1562 civil servants worked for the Local Japanese government and were considered from 2003 to 2014 for this study. Logistic regression analyses were performed to examine whether there were employment-grade differences in long sickness absence after 11 years and whether such SES differences were associated with work, family, and personal health-related characteristics or sexes. Male low-grade employees had a significantly higher odds ratio (OR) for long sickness absence (age-adjusted OR=1.75, 95% confidence interval (CI) [1.04-2.95]). However, after adjustment for work characteristics, the association of significance disappeared (OR=1.65, [0.96-2.84]). Female low-grade employees had a significantly lower OR for long sickness absence after 11 years (OR=0.26, [0.08-0.86]). Male employees working long hours and male and female employees in high job demand take less sickness absence. Meanwhile, male employees working short hours take longer sickness absence. This study showed that male employees at low grades take longer sickness absence than those at high grades; however, this was alleviated by work characteristics. Female employees at low grades take less sickness absence than those at high grades. Japanese female employees with low grades, male employees with long working hours, and both sexes with high job demands take less sickness absence, although they may be unhealthy because of work stress.