Sleep disruption is a common precursor to deterioration and relapse in people living with psychotic disorders. Understanding the temporal relationship between sleep and psychopathology is important for identifying and developing interventions which target key variables that contribute to relapse.

We used a purpose-built digital platform to sample self-reported sleep and psychopathology variables over 1 year, in 36 individuals with schizophrenia. Once-daily measures of sleep duration and sleep quality, and fluctuations in psychopathology (positive and negative affect, cognition and psychotic symptoms) were captured. We examined the temporal relationship between these variables using the Differential Time-Varying Effect (DTVEM) hybrid exploratory-confirmatory model.

Poorer sleep quality and shorter sleep duration maximally predicted deterioration in psychosis symptoms over the subsequent 1–8 and 1–12 days, respectively. These relationships were also mediated by negative affect and cognitive symptoms. Psychopathology variables also predicted sleep quality, but not sleep duration, and the effect sizes were smaller and of shorter lag duration.

Reduced sleep duration and poorer sleep quality anticipate the exacerbation of psychotic symptoms by approximately 1–2 weeks, and negative affect and cognitive symptoms mediate this relationship. We also observed a reciprocal relationship that was of shorter duration and smaller magnitude. Sleep disturbance may play a causal role in symptom exacerbation and relapse, and represents an important and tractable target for intervention. It warrants greater attention as an early warning sign of deterioration, and low-burden, user-friendly digital tools may play a role in its early detection.

To assess the effect of a multimodal intervention on hand hygiene compliance (HHC) in nursing homes.

HHC was evaluated using direct, unobtrusive observation in a cluster randomized controlled trial at publicly funded nursing homes in the Netherlands. In total, 103 nursing home organizations were invited to participate; 18 organizations comprising 33 nursing homes (n = 66 nursing home units) participated in the study. Nursing homes were randomized into a control group (no intervention, n = 30) or an intervention group (multimodal intervention, n = 36). The primary outcome measure was HHC of nurses. HHC was appraised at baseline and at 4, 7, and 12 months after baseline. Observers and nurses were blinded.

Audits regarding hand hygiene (HH) materials and personal hygiene rules, 3 live lessons, an e-learning program, posters, and a photo contest. We used a new method to teach the nurses the WHO-defined 5 moments of HH: Room In, Room Out, Before Clean, and After Dirty.

HHC increased in both arms. The increase after 12 months was larger for units in the intervention arm (from 12% to 36%) than for control units (from 13% to 21%) (odds ratio [OR], 2.10; confidence interval [CI], 1.35–3.28). The intervention arm exhibited a statistically significant increase in HHC at 4 of the 5 WHO-defined HH moments. At follow-up, HHC in the intervention arm remained statistically significantly higher (OR, 1.93; 95% CI, 1.59–2.34) for indications after an activity (from 37% to 39%) than for indications before an activity (from 14% to 27%).

The HANDSOME intervention is successful in improving HHC in nursing homes.

Nosocomial outbreaks due to multidrug-resistant microorganisms in rehabilitation centers have rarely been reported. We report an outbreak of extended-spectrum beta-lactamase (ESBL)–producing Klebsiella pneumoniae (ESBL-K. pneumoniae) on a single ward in a rehabilitation center in Rotterdam, The Netherlands.

Outbreak description.

A 40-bed ward of a rehabilitation center in the Netherlands.

In October 2016, 2 patients were found to be colonized by genetically indistinguishable ESBL-K. pneumoniae isolates. Therefore, an outbreak management team was installed, by whom a contact tracing plan was made. In addition to general outbreak measures, specific measures were formulated to allow continuation of the rehabilitation process. Also, environmental cultures were taken. Multiple-locus variable-number tandem-repeat analysis and amplification fragment-length polymorphism were used to determine strain relatedness. Selected isolates were subjected to whole-genome multilocus sequence typing.

The outbreak lasted 8 weeks. In total, 14 patients were colonized with an ESBL-K. pneumoniae, of whom 11 patients had an isolate belonging to sequence type 307. Overall, 163 environmental cultures were taken. Several sites of a household washing machine were repeatedly found to be contaminated with the outbreak strain. This machine was used to wash lifting slings and patient clothing contaminated with feces. The outbreak was contained after taking the machine temporarily out of service and implementing a reinforced and adapted protocol on the use of this machine.

We conclude that in this outbreak, the route of transmission of the outbreak strain via the household washing machine played a major role.

Obesity is considered a risk factor for surgical site infection (SSI). We quantified impact of body mass index (BMI) on the risk of SSI for a variety of surgical procedures.

We included 2012–2017 data from the Dutch national surveillance network PREZIES on a selection of frequently performed surgical procedures across different specialties. Patients were stratified into 5 categories: underweight (BMI, <18.5 kg/m2), normal weight (BMI, 18.5–25), overweight (BMI, 25–30), obese (BMI, 30–40) and morbidly obese (BMI, ≥40). Multilevel log binomial regression analyses were performed to assess the effect of BMI category on the risk of superficial, deep (including organ-space) and total SSI.

Of the 387,919 included patients (ranging from 2,616 for laparoscopic appendectomy to 119,834 for total hip prosthesis), 3,676 (1%) were underweight, 116,778 (30%) had normal weight, 154,339 (40%) were overweight, 104,288 (27%) had obesity, and 8,838 (2%) were morbidly obese. A trend of increasing risk of SSI when BMI increased from normal to morbidly obese was observed for almost all surgery types. The increase was most profound in surgeries with clean wounds, with relative risks for morbidly obese patients ranging up to 7.8 (95% CI, 6.0–10.2) for deep SSI in total hip prosthesis. In chest and abdominal surgeries, the impact was larger for superficial SSI than for deep SSI.

The results of our research provide evidence for the need of preventive programs targeting SSI in overweight and obese patients, as well as for the prevention of obesity in the general population.

The mechanisms underlying both depressive and anxiety disorders remain poorly understood. One of the reasons for this is the lack of a valid, evidence-based system to classify persons into specific subtypes based on their depressive and/or anxiety symptomatology. In order to do this without a priori assumptions, non-parametric statistical methods seem the optimal choice. Moreover, to define subtypes according to their symptom profiles and inter-relations between symptoms, network models may be very useful. This study aimed to evaluate the potential usefulness of this approach.

A large community sample from the Canadian general population (N = 254 443) was divided into data-driven clusters using non-parametric k-means clustering. Participants were clustered according to their (co)variation around the grand mean on each item of the Kessler Psychological Distress Scale (K10). Next, to evaluate cluster differences, semi-parametric network models were fitted in each cluster and node centrality indices and network density measures were compared.

A five-cluster model was obtained from the cluster analyses. Network density varied across clusters, and was highest for the cluster of people with the lowest K10 severity ratings. In three cluster networks, depressive symptoms (e.g. feeling depressed, restless, hopeless) had the highest centrality. In the remaining two clusters, symptom networks were characterised by a higher prominence of somatic symptoms (e.g. restlessness, nervousness).

Finding data-driven subtypes based on psychological distress using non-parametric methods can be a fruitful approach, yielding clusters of persons that differ in illness severity as well as in the structure and strengths of inter-symptom relationships.

To investigate the effects of friendly competition on hand hygiene compliance as part of a multimodal intervention program.

Prospective observational study in which the primary outcome was hand hygiene compliance. Differences were analyzed using the Pearson χ2 test. Odds ratios (ORs) with 95% confidence interval were calculated using multilevel logistic regression.

Observations were performed in 9 public hospitals and 1 rehabilitation center in Rotterdam, Netherlands.

From 2014 to 2016, at 5 time points (at 6-month intervals) in 120 hospital wards, 20,286 hand hygiene opportunities were observed among physicians, nurses, and other healthcare workers (HCWs).

The multimodal, friendly competition intervention consisted of mandatory interventions: monitoring and feedback of hand hygiene compliance and optional interventions (ie, e-learning, kick-off workshop, observer training, and team training). Hand hygiene opportunities, as formulated by the World Health Organization (WHO), were unobtrusively observed at 5 time points by trained observers. Compliance data were presented to the healthcare organizations as a ranking.

The overall mean hand hygiene compliance at time point 1 was 42.9% (95% confidence interval [CI], 41.4–44.4), which increased to 51.4% (95% CI, 49.8–53.0) at time point 5 (P<.001). Nurses showed a significant improvement between time points 1 and 5 (P<.001), whereas the compliance of physicians and other HCWs remained unchanged. In the multilevel logistic regressions, time points, type of ward, and type of HCW showed a significant association with compliance.

Between the start and the end of the multimodal intervention program in a friendly competition setting, overall hand hygiene compliance increased significantly.

Despite significant research examining mental health in conflict-affected populations we do not yet have a comprehensive epidemiological model of how mental disorders are distributed, or which factors influence the epidemiology in these populations. We aim to derive prevalence estimates specific for region, age and sex of major depression, and PTSD in the general populations of areas exposed to conflict, whilst controlling for an extensive range of covariates.

A systematic review was conducted to identify epidemiological estimates of depression and PTSD in conflict-affected populations and potential predictors. We analyse data using Bayesian meta-regression techniques.

We identified 83 studies and a list of 34 potential predictors. The age-standardised pooled prevalence of PTSD was 12.9% (95% UI 6.9–22.9), and major depression 7.6% (95% UI 5.1–10.9) – markedly lower than estimated in previous research but over two-times higher than the mean prevalence estimated by the Global Burden of Disease Study [3.7% (95% UI 3.0–4.5) and 3.5% (95% UI 2.9–4.2) for anxiety disorders and MDD, respectively]. The age-patterns reveal sharp prevalence inclines in the childhood years. A number of ecological variables demonstrated associations with prevalence of both disorders. Symptom scales were shown to significantly overestimate prevalence of both disorders. Finding suggests higher prevalence of both disorders in females.

This study provides, for the first time, age-specific estimates of PTSD and depression prevalence adjusted for an extensive range of covariates and is a significant advancement on our current understanding of the epidemiology in conflict-affected populations.

Manual surveillance of healthcare-associated infections is cumbersome and vulnerable to subjective interpretation. Automated systems are under development to improve efficiency and reliability of surveillance, for example by selecting high-risk patients requiring manual chart review. In this study, we aimed to validate a previously developed multivariable prediction modeling approach for detecting drain-related meningitis (DRM) in neurosurgical patients and to assess its merits compared to conventional methods of automated surveillance.

Prospective cohort study in 3 hospitals assessing the accuracy and efficiency of 2 automated surveillance methods for detecting DRM, the multivariable prediction model and a classification algorithm, using manual chart review as the reference standard. All 3 methods of surveillance were performed independently. Patients receiving cerebrospinal fluid drains were included (2012–2013), except children, and patients deceased within 24 hours or with pre-existing meningitis. Data required by automated surveillance methods were extracted from routine care clinical data warehouses.

In total, DRM occurred in 37 of 366 external cerebrospinal fluid drainage episodes (12.3/1000 drain days at risk). The multivariable prediction model had good discriminatory power (area under the ROC curve 0.91–1.00 by hospital), had adequate overall calibration, and could identify high-risk patients requiring manual confirmation with 97.3% sensitivity and 52.2% positive predictive value, decreasing the workload for manual surveillance by 81%. The multivariable approach was more efficient than classification algorithms in 2 of 3 hospitals.

Automated surveillance of DRM using a multivariable prediction model in multiple hospitals considerably reduced the burden for manual chart review at near-perfect sensitivity.

Infect Control Hosp Epidemiol 2015;36(1): 65–75

Mental and substance use disorders are common and often persistent, with many emerging in early life. Compared to adult mental and substance use disorders, the global burden attributable to these disorders in children and youth has received relatively little attention.

Data from the Global Burden of Disease Study 2010 was used to investigate the burden of mental and substance disorders in children and youth aged 0–24 years. Burden was estimated in terms of disability-adjusted life years (DALYs), derived from the sum of years lived with disability (YLDs) and years of life lost (YLLs).

Globally, mental and substance use disorders are the leading cause of disability in children and youth, accounting for a quarter of all YLDs (54.2 million). In terms of DALYs, they ranked 6th with 55.5 million DALYs (5.7%) and rose to 5th when mortality burden of suicide was reattributed. While mental and substance use disorders were the leading cause of DALYs in high-income countries (HICs), they ranked 7th in low- and middle-income countries (LMICs) due to mortality attributable to infectious diseases.

Mental and substance use disorders are significant contributors to disease burden in children and youth across the globe. As reproductive health and the management of infectious diseases improves in LMICs, the proportion of disease burden in children and youth attributable to mental and substance use disorders will increase, necessitating a realignment of health services in these countries.

Mortality-associated burden of disease estimates from the Global Burden of Disease 2010 (GBD 2010) may erroneously lead to the interpretation that premature death in people with mental, neurological and substance use disorders (MNSDs) is inconsequential when evidence shows that people with MNSDs experience a significant reduction in life expectancy. We explore differences between cause-specific and excess mortality of MNSDs estimated by GBD 2010.

GBD 2010 cause-specific death estimates were produced using the International Classification of Diseases death-coding system. Excess mortality (all-cause) was estimated using natural history models. Additional mortality attributed to MNSDs as underlying causes but not captured through GBD 2010 methodology is quantified in the comparative risk assessments.

In GBD 2010, MNSDs were estimated to be directly responsible for 840 000 deaths compared with more than 13 million excess deaths using natural history models.

Numbers of excess deaths and attributable deaths clearly demonstrate the high degree of mortality associated with these disorders. There is substantial evidence pointing to potential causal pathways for this premature mortality with evidence-based interventions available to address this mortality. The life expectancy gap between persons with MNSDs and the general population is high and should be a focus for health systems reform.

Autism spectrum disorders (ASDs) are persistent disabling neurodevelopmental disorders clinically evident from early childhood. For the first time, the burden of ASDs has been estimated for the Global Burden of Disease Study 2010 (GBD 2010). The aims of this study were to develop global and regional prevalence models and estimate the global burden of disease of ASDs.

A systematic review was conducted for epidemiological data (prevalence, incidence, remission and mortality risk) of autistic disorder and other ASDs. Data were pooled using a Bayesian meta-regression approach while adjusting for between-study variance to derive prevalence models. Burden was calculated in terms of years lived with disability (YLDs) and disability-adjusted life-years (DALYs), which are reported here by world region for 1990 and 2010.

In 2010 there were an estimated 52 million cases of ASDs, equating to a prevalence of 7.6 per 1000 or one in 132 persons. After accounting for methodological variations, there was no clear evidence of a change in prevalence for autistic disorder or other ASDs between 1990 and 2010. Worldwide, there was little regional variation in the prevalence of ASDs. Globally, autistic disorders accounted for more than 58 DALYs per 100 000 population and other ASDs accounted for 53 DALYs per 100 000.

ASDs account for substantial health loss across the lifespan. Understanding the burden of ASDs is essential for effective policy making. An accurate epidemiological description of ASDs is needed to inform public health policy and to plan for education, housing and financial support services.

Despite their high prevalence, the global burden of anxiety disorders has never been calculated comprehensively. The new Global Burden of Disease (GBD) study has estimated burden due to morbidity and mortality caused by any anxiety disorder.

Prevalence was estimated using Bayesian meta-regression informed by data identified in a systematic review. Years of life lived with disability (YLDs) were calculated by multiplying prevalent cases by an average disability weight based on severity proportions (mild, moderate and severe). Disability-adjusted life years (DALYs) were then calculated and age standardized using global standard population figures. Estimates were also made for additional suicide mortality attributable to anxiety disorders. Findings are presented for YLDs, DALYs and attributable burden due to suicide for 21 world regions in 1990 and 2010.

Anxiety disorders were the sixth leading cause of disability, in terms of YLDs, in both high-income (HI) and low- and middle-income (LMI) countries. Globally, anxiety disorders accounted for 390 DALYs per 100 000 persons [95% uncertainty interval (UI) 191–371 DALYs per 100 000] in 2010, with no discernible change observed over time. Females accounted for about 65% of the DALYs caused by anxiety disorders, with the highest burden in both males and females experienced by those aged between 15 and 34 years. Although there was regional variation in prevalence, the overlap between uncertainty estimates means that substantive differences in burden between populations could not be identified.

Anxiety disorders are chronic, disabling conditions that are distributed across the globe. Future estimates of burden could be further improved by obtaining more representative data on severity state proportions.