Profiling the U.S. Sick Leave Landscape: Presenteeism among Females.
Ontology highlight
ABSTRACT: Objective
To profile the sick leave landscape in the United States.Data sources
The 2011 Leave Supplement of the American Time Use Survey.Study design
Bivariate and multivariate analyses to identify (i) employees without sick pay coverage and (ii) employees who attend work sick.Principal findings
Sixty-five percent of full-time employees have sick pay coverage. Coverage rates are below 20 percent for employees with hourly wages below $10, part-time employees, and employees in the hospitality and leisure industry.Conclusion
Each week, up to 3 million U.S. employees go to work sick. Females, low-income earners, and those aged 25 to 34 years have a significantly elevated risk of presenteeism behavior.
SUBMITTER: Susser P
PROVIDER: S-EPMC5134145 | biostudies-literature | 2016 Dec
REPOSITORIES: biostudies-literature
Publications
Profiling the U.S. Sick Leave Landscape: Presenteeism among Females.
Susser Philip P Ziebarth Nicolas R NR
Health services research 20160307 6
<h4>Objective</h4>To profile the sick leave landscape in the United States.<h4>Data sources</h4>The 2011 Leave Supplement of the American Time Use Survey.<h4>Study design</h4>Bivariate and multivariate analyses to identify (i) employees without sick pay coverage and (ii) employees who attend work sick.<h4>Principal findings</h4>Sixty-five percent of full-time employees have sick pay coverage. Coverage rates are below 20 percent for employees with hourly wages below $10, part-time employees, and ...[more]
Similar Datasets
Project description:Many survivors after trauma suffer from long-term morbidity. The aim of this observational cohort study was to develop a prognostic prediction tool for early assessment of full-time sick leave one year after trauma. Potential predictors were assessed combining individuals from a trauma register with national health registers. Two models were developed using logistic regression and stepwise backward elimination. 4458 individuals were included out of which 488 were on sick leave full-time 12 months after the trauma. One comprehensive and one simplified model were developed including nine and seven predictors respectively. Both models showed excellent discrimination (AUC 0.81). The comprehensive model had very good calibration, and the simplified model good calibration. Prediction models can be used to assess post-trauma sick leave using injury-related variables as well as factors not related to the trauma per se. Among included variables, pre-injury sick leave was the single most important predictor for full-time sick leave one year after trauma. These models could facilitate a more efficient use of resources, targeting groups for follow-up interventions to improve outcome. External validation is necessary in order to evaluate generalizability.
| S-EPMC6329751 | biostudies-literature
Project description:BackgroundThe COVID-19 pandemic has caused difficulties and changes in many aspects of people's health and lives. Although infection affected work capacity, during the first wave policies for sick leave due to COVID-19 were unclear. The aim of this study was to investigate the impact of sick leave diagnoses in the year before the COVID-19 diagnosis on sick leave duration due to COVID-19 in a nationwide non-hospitalised population.MethodsData from three Swedish registries were analysed for sick leave commencing between 1 March and 31 August 2020, with a follow-up period of 4 months. Sick leave due to COVID-19 was considered the number of days that sickness benefits were used and included at least one registered COVID-19 diagnosis. Sick leave in the year before COVID-19 diagnosis were categorised into five diagnostic groups and one reference group (participants without prior sick leave).ResultsThe study comprised 8935 individuals who received sickness benefits due to COVID-19 in Sweden during the first pandemic wave (mean age 46.7 years, 67% females, and 24% had diagnoses for sick leave in the year before COVID-19 diagnosis). The duration of sick leave due to COVID-19 was significantly higher in the groups with prior sick leave owing to musculoskeletal system diseases (odds ratio [OR]: 1.08, 95% confidence interval [CI]: 1.01-1.15); respiratory system diseases (OR: 1.22, 95% CI: 1.14-1.31); all other isolated diagnoses (OR: 1.08, 95% CI: 1.03-1.14); and multiple diagnoses (OR: 1.32, 95% CI: 1.21-1.43).ConclusionsThe results of this nationwide registry-based study indicate that individuals with premorbid conditions are more prone to longer sick leave durations due to COVID-19. Prediction of sick leave duration during the first wave of the COVID-19 pandemic is complex and several factors played a role.
| S-EPMC9884157 | biostudies-literature
Project description:ObjectiveTo compare a structuralized sick-leave programme with usual care sick-leave management in patients after an acute myocardial infarction. We hypothesize that a structured sick-leave programme will yield a faster return to work without negatively affecting quality of life.MethodsPatients admitted to Oslo University Hospital due to an acute myocardial infarction were included in the study. Patients were randomized into an intervention group or a conventional care group. Patients randomized to the intervention group were provided with a standard programme with full-time sick leave for 2 weeks after discharge and then encouraged to return to work. The sick leave of the conventional group was mainly managed by their general practitioner.ResultsA total of 143 patients were included in the study. The conventional care group had a mean of 20.4 days absent from work, while that of the intervention group was significantly lower, with a mean of 17.2 days (p < 0.001) absent. There was no significant change in quality of life between the groups.ConclusionThese findings strengthen the case for structuralized follow-up of patients with acute myocardial infarction, as this will have positive economic consequences for the patient and society as a whole, without making quality of life worse. Further investigation, with a larger study population, is warranted to determine the extent of health benefits conferred by early return to work.
| S-EPMC10405811 | biostudies-literature
Project description:ObjectivesThe aim was to study whether a workplace-registered frequent short-term sick leave spell pattern was an early indicator of future disability pension or future long-term sick leave among municipal eldercare workers.SettingThe municipal healthcare sector in the city of Aarhus, which is the second largest city in Denmark.ParticipantsAll elder care employees who worked the entire year of 2004 in the municipality of Aarhus, Denmark (N=2774). The employees' sick leave days during 2004 were categorised into: 0-2 and 3-17 short (1-7 days) spells, 2-13 mixed short and long (8+ days) spells and long spells only. Student workers (n=180), employees who were absent due to maternal/paternal leave (n=536) and employees who did not work the entire year of 2004 (n=1218) were not included.Primary outcomeDisability pension and long-term sick leave (≥8 weeks) were subsequently identified in a National register. The cumulative incidence proportion as a function of follow-up weeks was estimated using the Kaplan-Meier curve. The relative cumulative incidence (RR) of experiencing events within 352 weeks was analysed in a generalised linear regression model using the pseudo values method adjusted for age, occupation, unfavourable work factors and sick leave length.ResultsA frequent short-term and a mixed sick leave pattern showed RRs of being granted a disability pension of 2.08 (95% CI 1.00 to 4.35) and 2.61 (95% CI 1.33 to 5.12) compared with 0-2 short spells. The risk of long-term sick leave was significantly increased for all sick leave patterns compared with 0-2 short spells. Adding sick leave length to the models attenuated all RRs and they became non-significant.ConclusionsSick leave length was a better indicator of future workability than spell frequency. Preventive actions should target employees engaged in homecare. The more sick leave days the greater the preventive potential seems, irrespective of spell frequency.
| S-EPMC3918999 | biostudies-literature
Project description:I analyze how general practitioners (GPs) indirectly affect their patients' employment outcomes by deciding the length of sick leaves. I use an instrumental variables framework where spell durations are identified through supply-side certification measures. I find that a day of sick leave certified only because the worker's GP has a high propensity to certify sick leaves decreases the employment probability persistently by 0.45-0.69 percentage points, but increases the risk of becoming unemployed by 0.28-0.44 percentage points. These effects are mostly driven by workers with low job tenure. Several robustness checks show that endogenous matching between patients and GPs does not impair identification. My results bear important implications for doctors: Whenever medically justifiable, certifying shorter sick leaves to protect the employment status of the patient may be beneficial.
| S-EPMC5947550 | biostudies-literature
Project description:BackgroundStudies on sociodemographic differences in sick leave after coronavirus disease 2019 (COVID-19) are limited and research on COVID-19 long-term health consequences has mainly addressed hospitalized individuals. The aim of this study was to investigate the social patterning of sick leave and determinants of longer sick leave after COVID-19 among mild and severe cases.MethodsThe study population, from the Swedish multi-register observational study SCIFI-PEARL, included individuals aged 18-64 years in the Swedish population, gainfully employed, with a first positive polymerase chain reaction (PCR) test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from 1 January 2020 until 31 August 2021 (n = 661 780). Using logistic regression models, analyses were adjusted for sociodemographic factors, vaccination, prior sick leave, comorbidities and stratified by hospitalization.ResultsIn total, 37 420 (5.7%) individuals were on sick leave due to COVID-19 in connection with their first positive COVID-19 test. Individuals on sick leave were more often women, older, had lower income and/or were born outside Sweden. These differences were similar across COVID-19 pandemic phases. The highest proportion of sick leave was seen in the oldest age group (10.3%) with an odds ratio of 4.32 (95% confidence interval 4.18-4.47) compared with the youngest individuals. Among individuals hospitalized due to COVID-19, the sociodemographic pattern was less pronounced, and in some models, even reversed. The intersectional analysis revealed considerable variability in sick leave between sociodemographic groups (range: 1.5-17.0%).ConclusionIn the entire Swedish population of gainfully employed individuals, our findings demonstrated evident sociodemographic differences in sick leave due to COVID-19. In the hospitalized group, the social patterning was different and less pronounced.
| S-EPMC10843940 | biostudies-literature
Project description:IntroductionPrevious studies suggest an association between paid sick leave (PSL) and better population health, including fewer infectious and nosocomial gastrointestinal disease outbreaks. Yet few studies examine whether laws requiring employers to offer PSL demonstrate a similar association. This mixed-methods study examined whether laws requiring employers to provide PSL are associated with decreased foodborne illness rates, particularly laws that are more supportive of employees taking leave.MethodsThe four earliest PSL laws were classified by whether they were more or less supportive of employees taking leave. Jurisdictions with PSL were matched to comparison jurisdictions by population size and density. Using difference-in-differences, monthly foodborne illness rates (2000-2014) in implementation and comparison jurisdictions before and after the laws were effective were compared, stratifying by how supportive the laws were of employees taking leave, and then by disease. The empirical analysis was conducted from 2015-2017.ResultsFoodborne illness rates declined after implementation of the PSL law in jurisdictions with laws more supportive of employees taking leave, but increased in jurisdictions with laws that are less supportive. In adjusted analyses, PSL laws that were more supportive of employees taking sick leave were associated with an adjusted 22% decrease in foodborne illness rates (p=0.005). These results are driven by campylobacteriosis.ConclusionsAlthough the results suggest an association between more supportive PSL laws and decreased foodborne illness rates, they should be interpreted cautiously because the trend is driven by campylobacteriosis, which has low person-to-person transmission.
| S-EPMC5677603 | biostudies-literature
Project description:BackgroundBy the end of 2022, nearly 20 million workers in the United States have gained paid-sick-leave coverage from mandates that require employers to provide benefits to qualified workers, including paid time off for the use of preventive services. Although the lack of paid-sick-leave coverage may hinder access to preventive care, current evidence is insufficient to draw meaningful conclusions about its relationship to cancer screening.MethodsWe examined the association between paid-sick-leave mandates and screening for breast and colorectal cancers by comparing changes in 12- and 24-month rates of colorectal-cancer screening and mammography between workers residing in metropolitan statistical areas (MSAs) that have been affected by paid-sick-leave mandates (exposed MSAs) and workers residing in unexposed MSAs. The comparisons were conducted with the use of administrative medical-claims data for approximately 2 million private-sector employees from 2012 through 2019.ResultsPaid-sick-leave mandates were present in 61 MSAs in our sample. Screening rates were similar in the exposed and unexposed MSAs before mandate adoption. In the adjusted analysis, cancer-screening rates were higher among workers residing in exposed MSAs than among those in unexposed MSAs by 1.31 percentage points (95% confidence interval [CI], 0.28 to 2.34) for 12-month colorectal cancer screening, 1.56 percentage points (95% CI, 0.33 to 2.79) for 24-month colorectal cancer screening, 1.22 percentage points (95% CI, -0.20 to 2.64) for 12-month mammography, and 2.07 percentage points (95% CI, 0.15 to 3.99) for 24-month mammography.ConclusionsIn a sample of private-sector workers in the United States, cancer-screening rates were higher among those residing in MSAs exposed to paid-sick-leave mandates than among those residing in unexposed MSAs. Our results suggest that a lack of paid-sick-leave coverage presents a barrier to cancer screening. (Funded by the National Cancer Institute.).
| S-EPMC10084522 | biostudies-literature
Project description:BackgroundWorkplace absenteeism increases significantly during influenza epidemics. Sick leave records may facilitate more timely detection of influenza outbreaks, as trends in increased sick leave may precede alerts issued by sentinel surveillance systems by days or weeks. Sick leave data have not been comprehensively evaluated in comparison to traditional surveillance methods. The aim of this paper is to study the performance and the feasibility of using a detection system based on sick leave data to detect influenza outbreaks.MethodsSick leave records were extracted from private French health insurance data, covering on average 209,932 companies per year across a wide range of sizes and sectors. We used linear regression to estimate the weekly number of new sick leave spells between 2016 and 2017 in 12 French regions, adjusting for trend, seasonality and worker leaves on historical data from 2010 to 2015. Outbreaks were detected using a 95%-prediction interval. This method was compared to results from the French Sentinelles network, a gold-standard primary care surveillance system currently in place.ResultsUsing sick leave data, we detected 92% of reported influenza outbreaks between 2016 and 2017, on average 5.88 weeks prior to outbreak peaks. Compared to the existing Sentinelles model, our method had high sensitivity (89%) and positive predictive value (86%), and detected outbreaks on average 2.5 weeks earlier.ConclusionSick leave surveillance could be a sensitive, specific and timely tool for detection of influenza outbreaks.
| S-EPMC7799403 | biostudies-literature
Project description:Shift work is associated with a number of negative health outcomes, although it is not known whether it is associated with sick leave. This systematic review therefore aimed to determine whether an association exists between shift work and sick leave. A systematic literature search was conducted in six databases on observational studies. Two reviewers independently selected relevant articles and appraised methodological quality. Data extraction was performed independently by review couples. Articles were categorised according to shift work characteristics and summarised using a levels of evidence synthesis. In total, the search strategy yielded 1207 references, of which 24 studies met the inclusion criteria. Nine studies were appraised as high quality and used in the levels of evidence synthesis. Two high quality longitudinal studies found a positive association between fixed evening shifts and longer sick leave for female healthcare workers. The evidence was assessed as strong. Evidence was inconclusive for rotating shifts, shift work including nights, for fixed night work, and for 8-hour and 12-hour shifts. The association found between evening work and sick leave in female healthcare workers implies that the association between shift work and sick leave might be schedule and population specific. To study the association further, more high quality studies are necessary that assess and adjust for detailed shift work exposure.
| S-EPMC3597215 | biostudies-literature