Traumatic Brain Injury-Related Pediatric Mortality and Morbidity in Low- and Middle-Income Countries: A Systematic Review.

BACKGROUND: The burden of pediatric traumatic brain injury (pTBI) in low- and middle-income countries (LMICs) is unknown. To fill this gap, we conducted a review that aimed to characterize the causes of pTBI in LMICs, and their reported associated mortality and morbidity. METHODS: A systematic review was conducted. MEDLINE, Embase, Global Health, and Global Index Medicus were searched from January 2000 to May 2020. Observational or experimental studies on pTBI of individuals aged between 0 and 16 years in LMICs were included. The causes of pTBI and morbidity data were descriptively analyzed, and case fatality rates were calculated. PROSPERO ID: CRD42020171276. RESULTS: A total of 136 studies were included. Fifty-seven studies were at high risk of bias. Of the remaining studies, 170,224 cases of pTBI were reported in 32 LMICs. The odds of having a pTBI were 1.8 times higher (95% confidence interval, 1.6-2.0) in males. The odds of a pTBI being mild were 4.4 times higher (95% confidence interval, 1.9-6.8) than a pTBI being moderate or severe. Road traffic accidents were the most common cause (n = 16,275/41,979; 39%) of pTBIs. On discharge, 24% of patients (n = 4385/17,930) had a reduction in their normal mental or physical function. The median case fatality rate was 7.3 (interquartile range, 2.1-7.7). CONCLUSIONS: Less than a quarter (n = 32) of all LMICs have published high-quality data on the volume and burden of pTBI. From the limited data available, young male children are at a high risk of pTBIs in LMICs, particularly after road traffic accidents.

Association of Clinician Diagnostic Performance With Machine Learning-Based Decision Support Systems: A Systematic Review.

Importance: An increasing number of machine learning (ML)-based clinical decision support systems (CDSSs) are described in the medical literature, but this research focuses almost entirely on comparing CDSS directly with clinicians (human vs computer). Little is known about the outcomes of these systems when used as adjuncts to human decision-making (human vs human with computer). Objectives: To conduct a systematic review to investigate the association between the interactive use of ML-based diagnostic CDSSs and clinician performance and to examine the extent of the CDSSs' human factors evaluation. Evidence Review: A search of MEDLINE, Embase, PsycINFO, and grey literature was conducted for the period between January 1, 2010, and May 31, 2019. Peer-reviewed studies published in English comparing human clinician performance with and without interactive use of an ML-based diagnostic CDSSs were included. All metrics used to assess human performance were considered as outcomes. The risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Risk of Bias in Non-Randomised Studies-Intervention (ROBINS-I). Narrative summaries were produced for the main outcomes. Given the heterogeneity of medical conditions, outcomes of interest, and evaluation metrics, no meta-analysis was performed. Findings: A total of 8112 studies were initially retrieved and 5154 abstracts were screened; of these, 37 studies met the inclusion criteria. The median number of participating clinicians was 4 (interquartile range, 3-8). Of the 107 results that reported statistical significance, 54 (50%) were increased by the use of CDSSs, 4 (4%) were decreased, and 49 (46%) showed no change or an unclear change. In the subgroup of studies carried out in representative clinical settings, no association between the use of ML-based diagnostic CDSSs and improved clinician performance could be observed. Interobserver agreement was the commonly reported outcome whose change was the most strongly associated with CDSS use. Four studies (11%) reported on user feedback, and, in all but 1 case, clinicians decided to override at least some of the algorithms' recommendations. Twenty-eight studies (76%) were rated as having a high risk of bias in at least 1 of the 4 QUADAS-2 core domains, and 6 studies (16%) were considered to be at serious or critical risk of bias using ROBINS-I. Conclusions and Relevance: This systematic review found only sparse evidence that the use of ML-based CDSSs is associated with improved clinician diagnostic performance. Most studies had a low number of participants, were at high or unclear risk of bias, and showed little or no consideration for human factors. Caution should be exercised when estimating the current potential of ML to improve human diagnostic performance, and more comprehensive evaluation should be conducted before deploying ML-based CDSSs in clinical settings. The results highlight the importance of considering supported human decisions as end points rather than merely the stand-alone CDSSs outputs.

Infection and mortality of healthcare workers worldwide from COVID-19: a systematic review.

OBJECTIVES: To estimate COVID-19 infections and deaths in healthcare workers (HCWs) from a global perspective during the early phases of the pandemic. DESIGN: Systematic review. METHODS: Two parallel searches of academic bibliographic databases and grey literature were undertaken until 8 May 2020. Governments were also contacted for further information where possible. There were no restrictions on language, information sources used, publication status and types of sources of evidence. The AACODS checklist or the National Institutes of Health study quality assessment tools were used to appraise each source of evidence. OUTCOME MEASURES: Publication characteristics, country-specific data points, COVID-19-specific data, demographics of affected HCWs and public health measures employed. RESULTS: A total of 152 888 infections and 1413 deaths were reported. Infections were mainly in women (71.6%, n=14 058) and nurses (38.6%, n=10 706), but deaths were mainly in men (70.8%, n=550) and doctors (51.4%, n=525). Limited data suggested that general practitioners and mental health nurses were the highest risk specialities for deaths. There were 37.2 deaths reported per 100 infections for HCWs aged over 70 years. Europe had the highest absolute numbers of reported infections (119 628) and deaths (712), but the Eastern Mediterranean region had the highest number of reported deaths per 100 infections (5.7). CONCLUSIONS: COVID-19 infections and deaths among HCWs follow that of the general population around the world. The reasons for gender and specialty differences require further exploration, as do the low rates reported in Africa and India. Although physicians working in certain specialities may be considered high risk due to exposure to oronasal secretions, the risk to other specialities must not be underestimated. Elderly HCWs may require assigning to less risky settings such as telemedicine or administrative positions. Our pragmatic approach provides general trends, and highlights the need for universal guidelines for testing and reporting of infections in HCWs.