Natural disasters are increasing in frequency and severity. They cause widespread hardship and are associated with detrimental effects on mental health.

Our aim is to provide the best estimate of the effects of natural disasters on mental health through a systematic review and meta-analysis of the rates of psychological distress and psychiatric disorder after natural disasters.

This systematic review and meta-analysis is limited to studies that met predetermined quality criteria. We required included studies to make comparisons with pre-disaster or non-disaster exposed controls, and sample representative populations. Key studies were identified through a comprehensive search of PubMed, EMBASE and PsycINFO from 1980 to 3 March 2017. Random effects meta-analyses were performed for studies that reported key outcomes with appropriate statistics.

Forty-one studies were identified by the literature search, of which 27 contributed to the meta-analyses. Continuous measures of psychological distress were increased after natural disasters (combined standardised mean difference 0.63, 95% CI 0.27–0.98, P = 0.005). Psychiatric disorders were also increased (combined odds ratio 1.84, 95% CI 1.43–2.38, P < 0.001). Rates of post-traumatic stress disorder and depression were significantly increased after disasters. Findings for anxiety and alcohol misuse/dependence were not significant. High rates of heterogeneity suggest that disaster-specific factors and, to a lesser degree, methodological factors contribute to the variance between studies.

Increased rates of psychological distress and psychiatric disorders follow natural disasters. High levels of heterogeneity between studies suggest that disaster variables and post-disaster response have the potential to mitigate adverse effects.

None.

Estimating the risk of a complicated course of Clostridium difficile infection (CDI) might help doctors guide treatment. We aimed to validate 3 published prediction models: Hensgens (2014), Na (2015), and Welfare (2011).

The validation cohort comprised 148 patients diagnosed with CDI between May 2013 and March 2014. During this period, 70 endemic cases of CDI occurred as well as 78 cases of CDI related to an outbreak of C. difficile ribotype 027. Model calibration and discrimination were assessed for the 3 prediction rules.

A complicated course (ie, death, colectomy, or ICU admission due to CDI) was observed in 31 patients (21%), and 23 patients (16%) died within 30 days of CDI diagnosis. The performance of all 3 prediction models was poor when applied to the total validation cohort with an estimated area under the curve (AUC) of 0.68 for the Hensgens model, 0.54 for the Na model, and 0.61 for the Welfare model. For those patients diagnosed with CDI due to non-outbreak strains, the prediction model developed by Hensgens performed the best, with an AUC of 0.78.

All 3 prediction models performed poorly when using our total cohort, which included CDI cases from an outbreak as well as endemic cases. The prediction model of Hensgens performed relatively well for patients diagnosed with CDI due to non-outbreak strains, and this model may be useful in endemic settings.

Infect Control Hosp Epidemiol 2017;38:897–905