26 research outputs found

    Derivation and external validation of a clinical prognostic model identifying children at risk of death following presentation for diarrheal care

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    Diarrhea continues to be a leading cause of death for children under-five. Amongst children treated for acute diarrhea, mortality risk remains elevated during and after acute medical management. Identification of those at highest risk would enable better targeting of interventions, but available prognostic tools lack validation. We used clinical and demographic data from the Global Enteric Multicenter Study (GEMS) to build clinical prognostic models (CPMs) to predict death (in-treatment, after discharge, or either) in children aged ≤59 months presenting with moderate-to-severe diarrhea (MSD), in Africa and Asia. We screened variables using random forests, and assessed predictive performance with random forest regression and logistic regression using repeated cross-validation. We used data from the Kilifi Health and Demographic Surveillance System (KHDSS) and Kilifi County Hospital (KCH) in Kenya to externally validate our GEMS-derived CPM. Of 8060 MSD cases, 43 (0.5%) children died in treatment and 122 (1.5% of remaining) died after discharge. MUAC at presentation, respiratory rate, age, temperature, number of days with diarrhea at presentation, number of people living in household, number of children <60 months old living in household, and how much the child had been offered to drink since diarrhea started were predictive of death both in treatment and after discharge. Using a parsimonious 2-variable prediction model, we achieved an area under the ROC curve (AUC) of 0.84 (95% CI: 0.82, 0.86) in the derivation dataset, and an AUC = 0.74 (95% CI 0.71, 0.77) in the external dataset. Our findings suggest it is possible to identify children most likely to die after presenting to care for acute diarrhea. This could represent a novel and cost-effective way to target resources for the prevention of childhood mortality

    A Chronic Opioid Therapy Dose Reduction Policy in Primary Care

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    Background: High-dose opioids prescribed for the treatment of chronic pain have been associated with increased risk of opioid overdose. Health systems and states have responded by developing opioid dose limitation policies. Little is known about how these policies affect prescribing practices or characteristics of patients who respond best to opioid tapers from high-dose opioids. Methods: We conducted a retrospective cohort study to evaluate change in total opioid dose after the implementation of a provider education intervention and a 120 mg morphine equivalents per day (MED) opioid dose limitation policy in one academic primary care clinic. We compared opioid prescriptions 1 year before and 1 year after the intervention. We used univariate and multivariate logistic regression to assess which patient characteristics predicted opioid dose reduction from high opioid dose. Results: Out of a total of 516 patients prescribed chronic opioid therapy, 116 patients (22%) were prescribed high-dose opioid therapy (\u3e120 mg MED). After policy adoption, the average daily dose of opioids declined by 64 mg MED (95% confidence interval [CI]: 32–96; P \u3c .001) and 41 patients (37%) on high-dose opioids tapered their doses below 120 mg MED (Tapered to Safer Dose group). In multivariate analyses, female sex was the only significant association with dose taper; female patients were less likely to taper to a safer dose (adjusted odds ratio [aOR] = 0.28, 95% CI: 0.11–0.70). Conclusions: A combined intervention of education and a practice policy that limits opioid doses for patients prescribed chronic opioid therapy may be an important component of system-level strategies to reduce opioid misuse and overdose; it may also help identify patients suitable for medication-assisted treatment for opioid use disorder. Specific strategies may be needed to assist women with opioid dose tapers

    Monthly proportion of a) norovirus cases and b) norovirus outbreaks by season-year in northern hemisphere.

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    <p>New strain years highlighted (Blue = 2002-03; Green = 2006-07). Dotted line indicates studies where monthly datasets were reported as averages over several years.</p

    Flow chart of search strategy.

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    <p>Note that final numbers refer to total number of data series included in analyses, whereas other numbers refer to numbers of articles reviewed.</p

    A Systematic Review and Meta-Analysis of the Global Seasonality of Norovirus

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    <div><p>Background</p><p>Noroviruses are the most common cause of acute gastroenteritis across all ages worldwide. These pathogens are generally understood to exhibit a wintertime seasonality, though a systematic assessment of seasonal patterns has not been conducted in the era of modern diagnostics.</p><p>Methods</p><p>We conducted a systematic review of the Pubmed Medline database for articles published between 1997 and 2011 to identify and extract data from articles reporting on monthly counts of norovirus. We conducted a descriptive analysis to document seasonal patterns of norovirus disease, and we also constructed multivariate linear models to identify factors associated with the strength of norovirus seasonality.</p><p>Results</p><p>The searched identified 293 unique articles, yielding 38 case and 29 outbreak data series. Within these data series, 52.7% of cases and 41.2% of outbreaks occurred in winter months, and 78.9% of cases and 71.0% of outbreaks occurred in cool months. Both case and outbreak studies showed an earlier peak in season-year 2002-03, but not in season-year 2006-07, years when new genogroup II type 4 variants emerged. For outbreaks, norovirus season strength was positively associated with average rainfall in the wettest month, and inversely associated with crude birth rate in both bivariate and multivariate analyses. For cases, none of the covariates examined was associated with season strength. When case and outbreaks were combined, average rainfall in the wettest month was positively associated with season strength.</p><p>Conclusions</p><p>Norovirus is a wintertime phenomenon, at least in the temperate northern hemisphere where most data are available. Our results point to possible associations of season strength with rain in the wettest month and crude birth rate.</p></div
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