Nurse Staffing Level, Length of Work Experience, and Risk of Health Care-Associated Infections Among Hospital Patients: A Prospective Record Linkage Study.

BACKGROUND: Nurse understaffing may have several adverse consequences for patients in hospitals, such as health care-associated infections (HAIs), but there is little longitudinal evidence available on staffing levels and HAIs with consideration of incubation times to confirm this. Using daily longitudinal data, we analyzed temporal associations between nurse understaffing and limited work experience, and the risk of HAIs. METHODS: The study was based on administrative data of 40 units and 261,067 inpatient periods for a hospital district in Finland in 2013-2019. Survival analyses with moving time windows were used to examine the association of nurse understaffing and limited work experience with the risk of an HAI 2 days after exposure, adjusting for individual risk factors. We reported hazard ratios (HRs) with 95% CIs. RESULTS: Neither nurse understaffing nor limited work experience were associated with the overall risk of HAIs. The results were inconsistent across staffing measures and types of HAIs, and many of the associations were weak. Regarding specific HAI types, 1-day exposure to low proportion of nurses with >3 years of in-hospital experience and low proportion of nurses more than 25 years old were associated with increased risk of bloodstream infections (HR=1.30; 95% CI: 1.04-1.62 and HR=1.40; 95% CI: 1.07-1.83). Two-day exposure to low nursing hours relative to target hours was associated with an increased risk of surgical-site infections (HR=2.64, 95% CI: 1.66-4.20). CONCLUSIONS: Data from time-varying analyses suggest that nursing staff shortages and limited work experience do not always increase the risk of HAI among patients.

Sequence analysis of the combinations of work shifts and absences in health care - comparison of two years of administrative data.

BACKGROUND: In health care, the shift work is arranged as irregular work shifts to provide operational hours for 24/7 care. We aimed to investigate working hour trends and turnover in health care via identification of time-related sequences of work shifts and absences among health care employees. The transitions between the work shifts (i.e., morning, day, evening, and night shifts), and absences (days off and other leaves) over time were analyzed and the predictors of change in irregular shift work were quantified. METHODS: A longitudinal cohort study was conducted using employer-owned payroll-based register data of objective and day-to-day working hours and absences of one hospital district in Finland from 2014 to 2019 (n = 4931 employees). The working hour data included start and end of work shifts, any kind of absence from work (days off, sickness absence, parental leave), and employee's age, and sex. Daily work shifts and absences in 2014 and 2019 were used in sequence analysis. Generalized linear model was used to estimate how each identified sequence cluster was associated with sex and age. RESULTS: We identified four sequence clusters: "Morning" (60% in 2014 and 56% in 2019), "Varying shift types" (22% both in 2014 and 2019), "Employee turnover" (13% in 2014 and 3% in 2019), and "Unstable employment (5% in 2014 and 19% in 2019). The analysis of transitions from one cluster to another between 2014 and 2019 indicated that most employees stayed in the same clusters, and most often in the "Varying shift types" (60%) and "Morning" (72%) clusters. The majority of those who moved, moved to the cluster "Morning" in 2019 from "Employee turnover" (43%), "Unstable employment" (46%) or "Varying shift types" (21%). Women were more often than men in the clusters "Employee turnover" and "Unstable employment", whereas older employees were more often in "Morning" and less often in the other cluster groups. CONCLUSION: Four clusters with different combinations of work shifts and absences were identified. The transition rates between work shifts and absences with five years in between indicated that most employees stayed in the same clusters. The likelihood of a working hour pattern characterized by "Morning" seems to increase with age.

Association of nurse understaffing and limited nursing work experience with in-hospital mortality among patients: A longitudinal register-based study.

BACKGROUND: Although nurse understaffing and limited nursing work experience may affect hospital patients' risk of mortality, relatively little longitudinal patient-level evidence on these associations is available. Hospital administrative data could provide important information about the level of staffing, nurses' work experience and patient mortality over time. OBJECTIVE: To examine whether daily exposure to nurse understaffing and limited nursing work experience is associated with patient mortality, using patient-level data with different exposure time windows and accounting for several patient-related characteristics. METHODS: This longitudinal register-based study combined administrative data on patients (clinical database Auria) and employees (Titania® shift-scheduling) from one hospital district in Finland in 2013-2019, covering a total of 254,446 hospital stays in 40 units. We quantified nurse understaffing as the number of days with low nursing hours in relation to target hours (<90 % of the annual unit median), and limited work experience as the number of days with a low proportion of nurses with >3 years of in-hospital experience, and those aged over 25 (<90 % of the annual unit median). We used two survival model designs to analyze the associations between nurse understaffing and limited nursing work experience and the in-hospital mortality of the patients: we considered these exposures during the first days in hospital and as a cumulative proportion of days with suboptimal staffing during the first 30 days. RESULTS: In total, 1.5 % (N = 3937) of the hospital stays ended in death. A 20 % increase in the proportion of days with nurse understaffing was associated with an increased, 1.05-fold mortality risk at the patient level (95 % confidence interval, 1.01-1.10). The cumulative proportion of days with limited nursing work experience, or the combination of nurse understaffing and limited work experience were not associated with increased risk of death among all patients. However, both indicators of limited nursing work experience were associated with an increased mortality risk among patients with comorbidities (HR 1.05, 95 % CI 1.02-1.08 and HR 1.05, 95 % CI 1.00-1.10, respectively). CONCLUSIONS: Nurse understaffing was associated with a slight, but a potentially critical increase in patient in-hospital mortality. Limited nursing work experience was associated with increased in-hospital mortality in a subgroup of patients with comorbidities. Increased use of administrative data on planned and realized working hours could be a routine tool for reducing avoidable in-hospital mortality.

Work unit level personnel working hours and the patients' length of in-hospital stay-An administrative data approach.

Administrative data accumulating daily from hospitals would provide new possibilities to assess work shifts and patient care. We aimed to investigate associations of work unit level average work shift length and length of patient in-hospital stay, and to examine the role of nurse-patient-ratio, year, night work, age, work units and working hours at the work units for these estimations. The data for this study were based on combined administrative day-to-day patient and pay-roll based objective working hour data of employees of one hospital district in Finland for 2013-2019. Three patient measures were calculated: the overall length of in-hospital stay, the length of in-hospital stay before a medical procedure and the length of in-hospital stay after a medical procedure. A Generalized Linear Mixed Model (GLMM) with multivariate normal random effects was used with Penalized Quasi-Likelihood for relative risk ratios (RR) with 95% confidence intervals (CI). The results showed that compared to <8 hours work shifts, 8-10 hours work shifts were associated with an increased likelihood of overall length of in-hospital stay (RR 1.16, 95%CI 1.15, 1.16), and the length of in-hospital stay after a medical procedure (RR 1.28, 95%CI 1.27, 1.30). The >10 hours work shifts were associated with a decreased likelihood of the overall length of in-hospital stay (RR 0.94, 95% CI 0.94, 0.95) and length of in-hospital stay after a medical procedure among all occupations (RR 0.94, 95% CI 0.92, 0.97). These associations retained the magnitude and direction in the models additionally adjusted for work, employee, and patient characteristics, and the associations were weaker for nurses than among all occupations. To conclude, compared with the standard work shifts, 8-10 hours work shifts seem to be associated with longer, and >10 hours work shifts with shorter length of in-hospital stay. Administrative data provides feasible possibilities to investigate working hours and length of in-hospital stay.

A Longitudinal Study on Trajectories of Night Work and Sickness Absence among Hospital Employees.

This study aimed to investigate trajectories of night shift work in irregular shift work across a 12-year follow-up among hospital employees with and without sickness absence (SA). The payroll-based register data of one hospital district in Finland included objective working hours and SA from 2008 to 2019. The number of night shifts per year was used in group-based trajectory modeling (GBTM). The results indicate that, among those who had any sickness absence episodes, the amount of night work decreased prior to the first SA. In general, trajectories of night shift work varied from stably high to low-but-increasing trajectories in terms of the number of shifts. However, a group with decreasing pattern of night work was identified only among those with sickness absence episodes but not among those without such episodes. To conclude, the identified trajectories of night work with or without sickness absences may indicate that, among those with sickness absence episodes, night work was reduced due to increasing health problems. Hence, the hospital employees working night shifts are likely a selected population because the employees who work at night are supposed to be healthier than those not opting for night work.

Concurrent trajectories of self-rated health and working hour patterns in health care shift workers: A longitudinal analysis with 8-year follow-up.

Background: The association between health and working hours is hypothesized to be reciprocal, but few longitudinal studies have examined changes in both health and working hour patterns over time. We examined combined trajectories of self-related health and two working hour patterns (working <35 h/week and working night shifts) and the extent to which these trajectories were predicted by employees' lifestyle and mental health. Methods: Participants of this cohort study with a 8-year follow-up were 5,947 health care shift workers. We linked self-reports of health from three repeated surveys with objective pay-roll based data on working hours. Using group-based multi-trajectory analysis we identified concurrent trajectories for self-rated health and working hour patterns. We examined their associations with baseline lifestyle-related factors (smoking, at-risk alcohol use, obesity, and physical inactivity) and mental health (sleep problems and psychological distress) using multinomial regression analysis. Results: Three combined trajectories of self-rated health and working <35 h/week and four combined trajectories of self-rated health and night work were identified. Unhealthy lifestyle and poor mental health were associated with trajectories of moderate and declining health. Sleep problems were linked with working <35 h/week. Younger age and good mental health were associated with a combined trajectory of good health and continued night shift work. Conclusion: Trajectories of suboptimal and declining health are associated with trajectories of reducing working hours and leaving night work, and are more common in employees with unhealthy lifestyle, sleep problems, and psychological distress.