IntroductionChildhood malnutrition is widespread in low-income and middle-income countries (LMICs) and increases the frequency and severity of infections such as pneumonia. We aimed to identify ...studies investigating pneumonia deaths in malnourished children and estimate mortality risk by malnutrition severity.MethodsWe conducted a systematic review of MEDLINE, EMBASE and Global Health databases to identify relevant studies. We used a network meta-analysis to derive ORs of death from pneumonia for moderately and severely underweight children using low weight-for-age, the most reported measure of malnutrition. We compared meta-estimates of studies conducted before and after 2000 to assess changes in mortality risk over time. We estimated the prevalence of underweight hospitalised children from hospital-based cohort studies and calculated the population attributable fraction of in-hospital pneumonia deaths from being underweight using our results.ResultsOur network meta-analysis included 33 544 underweight children from 23 studies. The estimated OR of death from pneumonia was 2.0 (95% CI 1.6 to 2.6) and 4.6 (95% CI 3.7 to 5.9) for children moderately and severely underweight, respectively. The OR of death from pneumonia for those severely underweight was 5.3 (95% CI 3.9 to 7.4) pre-2000 and remained high post-2000 at 4.1 (95% CI 3.0 to 6.0). Prevalence of underweight children hospitalised with pneumonia varied (median 40.2%, range 19.6–66.3) but was high across many LMIC settings. We estimated a median 18.3% (range 10.8–34.6) and 40.9% (range 14.7–69.9) of in-hospital pneumonia deaths were attributable to being moderately and severely underweight, respectively.ConclusionsThe risk of death from childhood pneumonia dramatically increases with malnutrition severity. This risk has remained high in recent years with an estimated over half of in-hospital pneumonia deaths attributable to child malnutrition. Prevention and treatment of all child malnutrition must be prioritised to maintain progress on reducing pneumonia deaths.
Abstract
Background
Bronchiolitis is the leading cause of hospital admission for respiratory disease among infants aged <1 year. Clinical practice guidelines can benefit patients by reducing the ...performance of unnecessary tests, hospital admissions, and treatment with lack of a supportive evidence base. This review aimed to identify current clinical practice guidelines worldwide, appraise their methodological quality, and discuss variability across guidelines for the diagnosis and management of bronchiolitis.
Methods
A systematic literature review of electronic databases EMBASE, Global Health, and Medline was performed. Manual searches of the gray literature, national pediatric society websites, and guideline-focused databases were performed, and select international experts were contacted to identify additional guidelines. The Appraisal of Guidelines for Research and Evaluation assessment tool was used by 2 independent reviewers to appraise each guideline.
Results
Thirty-two clinical practice guidelines met the selection criteria. Quality assessment revealed significant shortcomings in a number of guidelines, including lack of systematic processes in formulating guidelines, failure to state conflicts of interest, and lack of consultation with families of affected children. There was widespread agreement about a number of aspects, such as avoidance of the use of unnecessary diagnostic tests, risk factors for severe disease, indicators for hospital admission, discharge criteria, and nosocomial infection control. However, there was variability, even within areas of consensus, over specific recommendations, such as variable thresholds for oxygen therapy. Guidelines showed significant variability in recommendations for the pharmacological management of bronchiolitis, with conflicting recommendations over whether use of nebulized epinephrine, hypertonic saline, or bronchodilators should be routinely trialled.
Conclusions
Future guidelines should aim to be compliant with international standards for clinical guidelines to improve their quality and clarity and to promote their adoption into practice. Variable recommendations between guidelines may reflect the evolving evidence base for bronchiolitis management, and platforms should be created to understand this variability and promote evidence-based recommendations.
To determine how the intrinsic severity of successively dominant SARS-CoV-2 variants changed over the course of the pandemic.
A retrospective cohort analysis in the NHS Greater Glasgow and Clyde (NHS ...GGC) Health Board. All sequenced non-nosocomial adult COVID-19 cases in NHS GGC with relevant SARS-CoV-2 lineages (B.1.177/Alpha, Alpha/Delta, AY.4.2 Delta/non-AY.4.2 Delta, non-AY.4.2 Delta/Omicron, and BA.1 Omicron/BA.2 Omicron) during analysis periods were included. Outcome measures were hospital admission, ICU admission, or death within 28 days of positive COVID-19 test. We report the cumulative odds ratio; the ratio of the odds that an individual experiences a severity event of a given level vs all lower severity levels for the resident and the replacement variant after adjustment.
After adjustment for covariates, the cumulative odds ratio was 1.51 (95% CI: 1.08–2.11) for Alpha versus B.1.177, 2.09 (95% CI: 1.42–3.08) for Delta versus Alpha, 0.99 (95% CI: 0.76–1.27) for AY.4.2 Delta versus non-AY.4.2 Delta, 0.49 (95% CI: 0.22–1.06) for Omicron versus non-AY.4.2 Delta, and 0.86 (95% CI: 0.68–1.09) for BA.2 Omicron versus BA.1 Omicron.
The direction of change in intrinsic severity between successively emerging SARS-CoV-2 variants was inconsistent, reminding us that the intrinsic severity of future SARS-CoV-2 variants remains uncertain.
•Dominant SARS-CoV-2 variants showed higher and lower severity than their precursors.•Conclusions are unchanged when a more stringent severity classifications are used.•The historical trend suggests more intrinsically severe variants arising is plausible.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Objectives The SARS-CoV-2 Alpha variant was associated with increased transmission relative to other variants present at the time of its emergence and several studies have shown an association ...between Alpha variant infection and increased hospitalisation and 28-day mortality. However, none have addressed the impact on maximum severity of illness in the general population classified by the level of respiratory support required, or death. We aimed to do this. Methods In this retrospective multi-centre clinical cohort sub-study of the COG-UK consortium, 1475 samples from Scottish hospitalised and community cases collected between 1st November 2020 and 30th January 2021 were sequenced. We matched sequence data to clinical outcomes as the Alpha variant became dominant in Scotland and modelled the association between Alpha variant infection and severe disease using a 4-point scale of maximum severity by 28 days: 1. no respiratory support, 2. supplemental oxygen, 3. ventilation and 4. death. Results Our cumulative generalised linear mixed model analyses found evidence (cumulative odds ratio: 1.40, 95% CI: 1.02, 1.93) of a positive association between increased clinical severity and lineage (Alpha variant versus pre-Alpha variants). Conclusions The Alpha variant was associated with more severe clinical disease in the Scottish population than co-circulating lineages.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Throughout the global coronavirus pandemic, we have seen an unprecedented volume of COVID-19 researchpublications. This vast body of evidence continues to grow, making it difficult for research users ...to keep up with the pace of evolving research findings. To enable the synthesis of this evidence for timely use by researchers, policymakers, and other stakeholders, we developed an automated workflow to collect, categorise, and visualise the evidence from primary COVID-19 research studies. We trained a crowd of volunteer reviewers to annotate studies by relevance to COVID-19, study objectives, and methodological approaches. Using these human decisions, we are training machine learning classifiers and applying text-mining tools to continually categorise the findings and evaluate the quality of COVID-19 evidence.
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