Access to evidence-based care: a systematic review of trauma and surgical literature costs across resource settings

Background Evidence-based medicine has become the foundation for surgeons around the world to provide the most effective surgical care. However, the article processing charges (APCs) and subscription fees for surgical journals may be a barrier, particularly for those in low-income and middle-income countries (LMICs).Objectives The objective of this study was to define the current options for producers and consumers of surgical literature, inclusive of trauma, across resource settings.Data sources The Web of Science Core Collection database.Study appraisal and synthesis methods A complete list of journals publishing surgical content between 2019 and 2020 was compiled. The most frequently indexed journals were reviewed using the individual journal websites to extract the type of access (ie, open, closed, hybrid), impact factors, publication languages, APCs, subscription pricing, and any discounts listed.Results The literature search revealed 4759 unique journals. The 500 most frequently indexed were reviewed. The mean APC for a fully open access surgical journal was US$1574 and for a hybrid surgical journal was US$3338. The average costs for a 1-year subscription in a hybrid surgical journal were US$434 and US$1878 for an individual and institution, respectively. When considering purchasing power parity, APCs and subscription costs ranged from 2 to 15 times more expensive in LMICs when compared with those in the USA.Limitations Primary search term was in English only, and only peer-reviewed journal articles were reviewed.Conclusions or implications of key findings Although initiatives exist to support peer-reviewed journals in LMICs, there is an exorbitant cost for authors in these countries, as well as those in high-income countries that are not affiliated with a large institution, to either publish in, or access, a majority of surgical journals. Efforts to lower the overall cost of publishing must be made to provide greater access to medical literature.PROSPERO registration number CRD4202140227.Level of evidence Level IV

Infant-level and child-level predictors of mortality in low-resource settings : the WHO Child Mortality Risk Stratification Multi-Country Pooled Cohort

Background : Despite impressive reductions in overall global child mortality, the rate of decline has slowed during the past decade. Current guidelines for the care of paediatric patients in low-resource settings mostly focus on broad clinical syndromes or undernutrition rather than children's individual contextualised risk. We aimed to identify readily assessable child-level characteristics that can predict mortality risk in a range of community and health-care settings in high-burden settings.Methods : The WHO Child Mortality Risk Stratification Multi-Country Pooled Cohort (WHO-CMRS) included pooled data from individual children enrolled in observational or randomised controlled trials in low-income and middle-income countries. The criteria for inclusion of a dataset were documentation of age, weight, vital status, and date of death, and at least two observations per participant younger than 60 months. To calculate odds ratios, we built generalised linear mixed effects regression (glmer) models with each child and each study as random intercepts and time interval as the offset. In all analyses, the outcome was defined as death within the respective observation period of the child. From the glmer models, we predicted absolute risk of death per child-month associated with risk exposures separately and combined with anthropometry according to the following age groups: 0–5 months, 6–11 months, 12–23 months, and 24–59 months. Studies were grouped according to population types studied: the general population, populations selected based on anthropometric criteria, and populations selected based on the presence of illness.Findings : We analysed pooled data from WHO-CMRS, including 75 287 children from 33 studies done in 17 countries between Jan 1, 2001, and Dec 31, 2021. During a total of 69 085 child-years of follow-up, 2805 (3·7%) children died. Age younger than 24 months, low anthropometry, preterm birth, low birthweight, and absence of breastfeeding (either was breastfeeding not offered or an underlying illness interfered with breastfeeding practices) were each associated with increased mortality: risks declined with increasing age. The highest absolute mortality risk was among the youngest children (age 0−5 months), with a weight-for-age Z score of less than −3 (ie, a predicted absolute risk of 11·0 [95% CI 6·2−19·5] per 1000 child-months in general population studies). Risks were additive: underlying risk exposures such as low birthweight and preterm birth added to the mortality risks in children with anthropometric deficit. For example, children aged 0−5 months with a weight-for-age Z score of less than −3 and a history of preterm birth had a predicted absolute mortality risk of 40·1 (95% CI 22·0−72·1). However, overall mortality and the association between child-level characteristics and mortality differed according to the type of study population and child age.Interpretation : Risk assessments combining individual child-level characteristics including anthropometry can enable programmes to identify children at high and lower risk of mortality and, thereafter, differentiate care accordingly. Such a strategy could reduce mortality and optimise health system efficiency and effectiveness.Background : Despite impressive reductions in overall global child mortality, the rate of decline has slowed during the past decade. Current guidelines for the care of paediatric patients in low-resource settings mostly focus on broad clinical syndromes or undernutrition rather than children's individual contextualised risk. We aimed to identify readily assessable child-level characteristics that can predict mortality risk in a range of community and health-care settings in high-burden settings.Methods : The WHO Child Mortality Risk Stratification Multi-Country Pooled Cohort (WHO-CMRS) included pooled data from individual children enrolled in observational or randomised controlled trials in low-income and middle-income countries. The criteria for inclusion of a dataset were documentation of age, weight, vital status, and date of death, and at least two observations per participant younger than 60 months. To calculate odds ratios, we built generalised linear mixed effects regression (glmer) models with each child and each study as random intercepts and time interval as the offset. In all analyses, the outcome was defined as death within the respective observation period of the child. From the glmer models, we predicted absolute risk of death per child-month associated with risk exposures separately and combined with anthropometry according to the following age groups: 0–5 months, 6–11 months, 12–23 months, and 24–59 months. Studies were grouped according to population types studied: the general population, populations selected based on anthropometric criteria, and populations selected based on the presence of illness.Findings : We analysed pooled data from WHO-CMRS, including 75 287 children from 33 studies done in 17 countries between Jan 1, 2001, and Dec 31, 2021. During a total of 69 085 child-years of follow-up, 2805 (3·7%) children died. Age younger than 24 months, low anthropometry, preterm birth, low birthweight, and absence of breastfeeding (either was breastfeeding not offered or an underlying illness interfered with breastfeeding practices) were each associated with increased mortality: risks declined with increasing age. The highest absolute mortality risk was among the youngest children (age 0−5 months), with a weight-for-age Z score of less than −3 (ie, a predicted absolute risk of 11·0 [95% CI 6·2−19·5] per 1000 child-months in general population studies). Risks were additive: underlying risk exposures such as low birthweight and preterm birth added to the mortality risks in children with anthropometric deficit. For example, children aged 0−5 months with a weight-for-age Z score of less than −3 and a history of preterm birth had a predicted absolute mortality risk of 40·1 (95% CI 22·0−72·1). However, overall mortality and the association between child-level characteristics and mortality differed according to the type of study population and child age.Interpretation : Risk assessments combining individual child-level characteristics including anthropometry can enable programmes to identify children at high and lower risk of mortality and, thereafter, differentiate care accordingly. Such a strategy could reduce mortality and optimise health system efficiency and effectiveness.A