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Year 0 was the year before the hospitals reported as a single unit; year 1 was the first year the hospitals reported as a unit; year 2 was the second year, and so on. The registration data reflected whether the hospitals were officially merged on January 1 of the given year. For several of the hospitals, the official merger date fell in year 0.

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Years within the study period from to , prior to year 0 and after year 5, were defined as non-merger years and were used as the reference group. All mergers implied engagement of a common chief executive. However, the mergers differed, particularly regarding the extent of centralization of acute services, and the amount of change activities. Further, several hospitals were also involved in more than one merger during the study period. In some cases, the merger was between a small and large hospital. In those cases where a hospital was five or more times larger than the other hospital, we found that the merger was unlikely to affect the larger hospital.

However, we expected to find an effect on the smaller hospital, so we defined it as a merger for the small hospital only. It is rare to observe a reduction in employee numbers; however, in some cases the smallest hospitals experienced a transfer of employees to larger hospitals. This was observed in the case of Aker, which merged with Oslo University Hospitals; Aker was gradually closed down as approached.

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Long-term sickness absence requires physician certification; this has been shown to be a better measure of ill health, rather than shorter spells [ 46 ]. Thus, our preferred measure of sickness absence was less prone to bias caused by non-health-related absence than short-term sickness absence. In addition, because sickness absence pay is covered by the state from day 17 of absence, the registry for absences of this length was complete, with no missing data.

The year-specific variable was considered to be a linear variable for time, to control for the general increase in long-term sickness absence over the study period. This variable also controlled for any linear increase in absence as the participants grew older. Reforms during to the Norwegian sick pay scheme placed stricter requirements on patients, doctors, and employers. Immediately after the implementation of these reforms, sickness absence rates dropped by 23 per cent [ 46 ].

A legislation-specific variable was a dummy variable for every year after , and was included to control for this legislative change. Variation in long-term sickness absence was explained through a fixed effects multivariate regression analysis, with years since merger as the independent variable. An important advantage of this method is that it allowed us to use the longitudinal data to isolate within-employee effects, i.

This method was useful for controlling for all of the differences that stayed constant over time between individuals and hospitals; it inherently controls for known differences such as gender and education , but also unknown differences between employees that could have a larger effect. Examples of scenarios that could lead to inflated or underestimated effects when comparing between-individuals include: 1 some hospitals experience a greater number of mergers and have more unhealthy employees; 2 unhealthy employees leave more frequently at the beginning of a merger, and therefore experience fewer merger years.

A drawback of this fixed effects method is that because we focused solely on changes within employees, we lost information i.

The loss of this information may have caused increased confidence intervals and standard errors. We believe that this loss of information is justified because the lost data was more likely to have been confounded by between-individual differences. Another drawback of the statistical analyses is that while the method controlled for between-individual differences, the method did not provide estimates of their relative importance. To analyze the effect of gender, education and so forth, other methods, such as random effects, may have been more suitable.

We used logistic fixed effects regression to analyze the odds of entering long-term sickness absence each year. We chose logistic regression, rather than Poisson regression i. The results from the fixed effects analysis of the hospital merger effect shows an increase in the odds of long-term absence in year 0; this was the final period when the hospitals were still separate entities, but also represents the first period of the merger.

The odds returned to normal after the first year and increased in years 2, 3 and 4. For the analyses separated by gender, the results are replicated for female employees, with significantly higher long-term sickness absence in year 0, 2, 3 and 4. For men, the odds of entering long-term sickness absence were only significantly higher in year 4. The odds ratio for women can be interpreted as between a 4.


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How much these impact the absolute figures for hospitals depends on the existing sickness absence levels at the hospital. However, for a female employee with an average likelihood of entering long-term sickness absence of Our analysis shows that hospital mergers have a significant effect on long-term sickness absence, supporting a negative health effect from mergers on employees.

The results indicate that mergers have a particularly significant effect on sickness absences during the initial phase of the merger. However, the effect was not significant after a year; the workers seemed to adapt to their new work situations very quickly. This effect may be explained by the internal changes that take place after mergers. Other studies have shown that merging hospitals have a higher degree of internal organizational change compared with stable hospitals [ 47 ], and that the main intention of mergers is to trigger such change [ 48 , 49 ].

It is likely that we are observing an indirect effect of mergers. It has been reported that sick leave is higher in larger organizations. Voss, Floderus, and Diderichsen [ 50 ] found that employees in work places with more than 50 employees had a moderately higher risk of sick leave than employees in smaller work places. However, the fact that most of the work places in our study employed more than 50 people before the merger took place weakens this interpretation. When the analyses were divided by gender, the results showed that hospital mergers have a significant effect on female employee long-term sickness absence for year 0, and years 2 to 4 after the merger; this supports a negative health effect of mergers on female employees.

The analysis for male employees showed significantly higher sickness absence in year 4 only, which partially supports a negative effect on men.


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  • It is plausible that females may react more adversely to mergers than men. In Norway, females have higher rates of self-certified and medically certified absence, even after adjusting for pregnancy-related absenteeism [ 10 , 11 ]. A literature review of the relationship between gender and sickness absence, reveals that the psychosocial work environment might influence sickness absence of women differently than men; women might react differently to stressors, use different resources, and use absence as a coping mechanism to a greater extent [ 12 ]. However, with clearly fewer men, and less variation in the dependent variable due to less absence among men , we cannot exclude a similar effect on men using the current findings.

    Our results are consistent with studies that have shown negative health consequences from the announcement of, and during, a merger [ 19 , 28 , 30 ].

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    Our results are not congruent with the findings of Westerlund, Ferrie, Hagberg, Jeding, Oxenstierna and Theorell [ 31 ] who found no significant effect of mergers on long-term sickness absence after merger. Therefore, the timing of the measure might be crucial in relation to the ability to detect effects.

    Future studies should attempt to include even longer time spans than used in the present study, to investigate this assumption. Our results indicate that mergers and the quest for higher productivity may come at a price, i. Mergers are initiated by regional health authorities to improve the production, distribution and organization of healthcare services. If the effects of mergers and subsequent internal changes include increased long-term sickness absence, then mergers should be seen as counterproductive. However, the seriousness of the output variable implies that this effect should be interpreted as being meaningful and relevant.

    For hospitals and the state, long-term sickness absence represents a large monetary loss, and a loss of workforce capacity. Employees are compensated with full pay for up to a year [ 51 ]. The long-term sickness absence of an individual employee is generally a symptom of an increased risk of serious health impairments [ 7 ]. Additionally, long-term absence from work may have negative consequences, including: alienation, feelings of guilt, reduced well-being and enthusiasm for work, hindered career and salary development, and increased risk of exiting the workforce [ 53 — 56 ].

    Mergers can be very disruptive for the work environment because they are often combined with heavy layoffs. However, the mergers we analyzed did not result in layoffs. For this reason, our findings are especially interesting. However, we still do not know exactly what merger characteristics are stressful and exhausting and lead to higher sickness absence. We believe the answer might be found by looking closely at the factors related to changes in the psychosocial working conditions and perceived threats to current job tasks.

    Although this was a unique dataset, it limited our ability to gain detailed knowledge about specific change processes. We do not know precisely when the mergers occurred. We do not know the consequences that the different events had on working conditions at the different hospitals, or how they were experienced by employees. Mergers have different approaches and impacts; the findings described here are an average.

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    Although we did not have the official merger dates, the fact that people generally reacted to such changes prior to implementation makes the official date less important [ 15 , 57 ]. The reference group the non-merging hospitals consisted of years before the merger and six years or later after the merger. It could be argued that the effect of the merger could still be present after year five.

    However, it was important to include some time after the merger in the reference group, since we was interested in the effect of the change process due to the merger. In a longer time span there would be a risk that the effect is due to changes other than the merger.

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