We aim to quantitatively synthesise available epidemiological evidence on the prevalence rates of workplace violence (WPV) by patients and visitors against healthcare workers. We systematically searched PubMed, Embase and Web of Science from their inception to October 2018, as well as the reference lists of all included studies. Two authors independently assessed studies for inclusion. Data were double-extracted and discrepancies were resolved by discussion. The overall percentage of healthcare worker encounters resulting in the experience of WPV was estimated using random-effects meta-analysis. The heterogeneity was assessed using theI²statistic. Differences by study-level characteristics were estimated using subgroup analysis and meta-regression. We included 253 eligible studies (with a total of 331 544 participants). Of these participants, 61.9% (95% CI 56.1% to 67.6%) reported exposure to any form of WPV, 42.5% (95% CI 38.9% to 46.0%) reported exposure to non-physical violence, and 24.4% (95% CI 22.4% to 26.4%) reported experiencing physical violence in the past year. Verbal abuse (57.6%; 95% CI 51.8% to 63.4%) was the most common form of non-physical violence, followed by threats (33.2%; 95% CI 27.5% to 38.9%) and sexual harassment (12.4%; 95% CI 10.6% to 14.2%). The proportion of WPV exposure differed greatly across countries, study location, practice settings, work schedules and occupation. In this systematic review, the prevalence of WPV against healthcare workers is high, especially in Asian and North American countries, psychiatric and emergency department settings, and among nurses and physicians. There is a need for governments, policymakers and health institutions to take actions to address WPV towards healthcare professionals globally.

Bibliometric analysis based on literature in the Web of Science (WOS) has become an increasingly popular method for visualizing the structure of scientific fields. Keywords Plus and Author Keywords are commonly selected as units of analysis, despite the limited research evidence demonstrating the effectiveness of Keywords Plus. This study was conceived to evaluate the efficacy of Keywords Plus as a parameter for capturing the content and scientific concepts presented in articles. Using scientific papers about patient adherence that were retrieved from WOS, a comparative assessment of Keywords Plus and Author Keywords was performed at the scientific field level and the document level, respectively. Our search yielded more Keywords Plus terms than Author Keywords, and the Keywords Plus terms were more broadly descriptive. Keywords Plus is as effective as Author Keywords in terms of bibliometric analysis investigating the knowledge structure of scientific fields, but it is less comprehensive in representing an article's content.

Observational studies suggest that insomnia might be associated with an increased risk of depression with inconsistent results. This study aimed at conducting a meta-analysis of prospective cohort studies to evaluate the association between insomnia and the risk of depression. Relevant cohort studies were comprehensively searched from the PubMed, Embase, Web of Science, and China National Knowledge Infrastructure databases (up to October 2014) and from the reference lists of retrieved articles. A random-effects model was used to calculate the pooled risk estimates and 95 % confidence intervals (CIs). The I 2 statistic was used to assess the heterogeneity and potential sources of heterogeneity were assessed with meta-regression. The potential publication bias was explored by using funnel plots, Egger’s test, and Duval and Tweedie trim-and-fill methods. Thirty-four cohort studies involving 172,077 participants were included in this meta-analysis with an average follow-up period of 60.4 months (ranging from 3.5 to 408). Statistical analysis suggested a positive relationship between insomnia and depression, the pooled RR was 2.27 (95 % CI: 1.89–2.71), and a high heterogeneity was observed (I 2 = 92.6 %, P < 0.001). Visual inspection of the funnel plot revealed some asymmetry. The Egger’s test identified evidence of substantial publication bias (P <0.05), but correction for this bias using trim-and-fill method did not alter the combined risk estimates. This meta-analysis indicates that insomnia is significantly associated with an increased risk of depression, which has implications for the prevention of depression in non-depressed individuals with insomnia symptoms.

Several systematic reviews and meta-analyses demonstrated the association between depression and the risk of coronary heart disease (CHD), but the previous reviews had some limitations. Moreover, a number of additional studies have been published since the publication of these reviews. We conducted an updated meta-analysis of prospective studies to assess the association between depression and the risk of CHD. Relevant prospective studies investigating the association between depression and CHD were retrieved from the PubMed, Embase, Web of Science search (up to April 2014) and from reviewing reference lists of obtained articles. Either a random-effects model or fixed-effects model was used to compute the pooled risk estimates when appropriate. Thirty prospective cohort studies with 40 independent reports met the inclusion criteria. These groups included 893,850 participants (59,062 CHD cases) during a follow-up duration ranging from 2 to 37 years. The pooled relative risks (RRs) were 1.30 (95% CI, 1.22-1.40) for CHD and 1.30 (95% CI, 1.18-1.44) for myocardial infarction (MI). In the subgroup analysis by follow-up duration, the RR of CHD was 1.36 (95% CI, 1.24-1.49) for less than 15 years follow-up, and 1.09 (95% CI, 0.96-1.23) for equal to or more than 15 years follow-up. Potential publication bias may exist, but correction for this bias using trim-and-fill method did not alter the combined risk estimate substantially. The results of our meta-analysis suggest that depression is independently associated with a significantly increased risk of CHD and MI, which may have implications for CHD etiological research and psychological medicine.

Epidemiological evidence on the association between sedentary behaviors and the risk of depression is inconsistent. We conducted a meta-analysis of prospective studies to identify the impact of sedentary behaviors on the risk of depression. We systematically searched in the PubMed and Embase databases to June 2019 for prospective cohort studies investigating sedentary behaviors in relation to the risk of depression. The pooled relative risks (RRs) and 95% confidence intervals (CIs) were calculated with random-effect meta-analysis. In addition, meta-regression analyses, subgroup analyses, and sensitivity analyses were performed to explore the potential sources of heterogeneity. Twelve prospective studies involving 128,553 participants were identified. A significantly positive association between sedentary behavior and the risk of depression was observed (RR = 1.10, 95% CI 1.03–1.19, I² = 60.6%, P < 0.01). Subgroup analyses revealed that watching television was positively associated with the risk of depression (RR = 1.18, 95% CI 1.07–1.30), whereas using a computer was not (RR = 0.99, 95% CI 0.79–1.23). Mentally passive sedentary behaviors could increase the risk of depression (RR = 1.17, 95% CI 1.08–1.27), whereas the effect of mentally active sedentary behaviors were non-significant (RR = 0.98, 95% CI 0.83–1.15). Sedentary behaviors were positively related to depression defined by clinical diagnosis (RR = 1.08, 95% CI 1.03, 1.14), whereas the associations were statistically non-significant when depression was evaluated by the CES-D and the Prime-MD screening. The present study suggests that mentally passive sedentary behaviors, such as watching television, could increase the risk of depression. Interventions that reduce mentally passive sedentary behaviors may prevent depression.

An increasing number of original studies suggest that exposure to shift work could be associated with the risk of overweight and obesity, but the results remain conflicted and inconclusive. This study aimed to quantitatively synthesize available epidemiological evidence on the association between shift work and the risk of overweight and obesity by a meta-analysis. The authors searched PubMed, Embase and the reference lists of all included studies up to April 2017, with a verification search in December 2017. Inclusion criteria were original studies that reported odds ratios, relative risks or hazard ratios (ORs, RRs or HRs, respectively) of at least one outcome of overweight or obesity. Summary risk estimates were calculated by random-effect models. Twenty-six studies (7 cohort studies, 18 cross-sectional studies and 1 case–control study) involving 311 334 participants were identified. Among these studies, the cut-off points of overweight and obesity varied greatly, so the heterogeneity was substantial; however, the results were stable. Shift work was found to be positively associated with the risk of overweight [RR: 1.25; 95% confidence interval (95% CI): 1.08–1.44] and obesity (RR: 1.17; 95% CI: 1.12–1.22). Individuals involved in shift work are more likely to become overweight or obese. Appropriate preventive interventions in the organization of shift schedules according to ergonomic criteria would allow shift workers to avoid potential health impairment.