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Background: Low back pain is highly prevalent and the main cause of years lived with disability (YLDs). We present the most up-to-date global, regional, and national data on prevalence and YLDs for low back pain from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021. Methods: Population-based studies from 1980 to 2019 identified in a systematic review, international surveys, US medical claims data, and dataset contributions by collaborators were used to estimate the prevalence and YLDs for low back pain from 1990 to 2020, for 204 countries and territories. Low back pain was defined as pain between the 12th ribs and the gluteal folds that lasted a day or more; input data using alternative definitions were adjusted in a network meta-regression analysis. Nested Bayesian meta-regression models were used to estimate prevalence and YLDs by age, sex, year, and location. Prevalence was projected to 2050 by running a regression on prevalence rates using Socio-demographic Index as a predictor, then multiplying them by projected population estimates. Findings: In 2020, low back pain affected 619 million (95% uncertainty interval 554–694) people globally, with a projection of 843 million (759–933) prevalent cases by 2050. In 2020, the global age-standardised rate of YLDs was 832 per 100 000 (578–1070). Between 1990 and 2020, age-standardised rates of prevalence and YLDs decreased by 10·4% (10·9–10·0) and 10·5% (11·1–10·0), respectively. A total of 38·8% (28·7–47·0) of YLDs were attributed to occupational factors, smoking, and high BMI. Interpretation: Low back pain remains the leading cause of YLDs globally, and in 2020, there were more than half a billion prevalent cases of low back pain worldwide. While age-standardised rates have decreased modestly over the past three decades, it is projected that globally in 2050, more than 800 million people will have low back pain. Challenges persist in obtaining primary country-level data on low back pain, and there is an urgent need for more high-quality, primary, country-level data on both prevalence and severity distributions to improve accuracy and monitor change. Funding: Bill and Melinda Gates Foundation.

The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD Statement is explained in detail and accompanied by published examples of good reporting. The document also provides a valuable reference of issues to consider when designing, conducting, and analyzing prediction model studies. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, it is recommended that authors include a completed checklist in their submission. The TRIPOD checklist can also be downloaded from www.tripod-statement.org. For members of the TRIPOD Group, see the Appendix. This article is the translation in to Russian by Dr. Ruslan Saygitov (ORCID: 0000-0002-8915-6153) from the original published in [Ann Intern Med. 2015; 162:W1-W73. doi: 10.7326/M14-0698 ].

OBJECTIVES: In biomedical research, spin is the overinterpretation of findings, and it is a growing concern. To date, the presence of spin has not been evaluated in prognostic model research in oncology, including studies developing and validating models for individualized risk prediction. STUDY DESIGN AND SETTING: We conducted a systematic review, searching MEDLINE and EMBASE for oncology-related studies that developed and validated a prognostic model using machine learning published between 1st January, 2019, and 5th September, 2019. We used existing spin frameworks and described areas of highly suggestive spin practices. RESULTS: We included 62 publications (including 152 developed models; 37 validated models). Reporting was inconsistent between methods and the results in 27% of studies due to additional analysis and selective reporting. Thirty-two studies (out of 36 applicable studies) reported comparisons between developed models in their discussion and predominantly used discrimination measures to support their claims (78%). Thirty-five studies (56%) used an overly strong or leading word in their title, abstract, results, discussion, or conclusion. CONCLUSION: The potential for spin needs to be considered when reading, interpreting, and using studies that developed and validated prognostic models in oncology. Researchers should carefully report their prognostic model research using words that reflect their actual results and strength of evidence. ispartof: JOURNAL OF CLINICAL EPIDEMIOLOGY vol:157 pages:120-133 ispartof: location:United States status: published

Aims Multinomial logistic regression models allow one to predict the risk of a categorical outcome with > 2 categories. When developing such a model, researchers should ensure the number of participants ([Formula: see text]) is appropriate relative to the number of events ([Formula: see text]) and the number of predictor parameters ([Formula: see text]) for each category k. We propose three criteria to determine the minimum n required in light of existing criteria developed for binary outcomes. Proposed criteria The first criterion aims to minimise the model overfitting. The second aims to minimise the difference between the observed and adjusted [Formula: see text] Nagelkerke. The third criterion aims to ensure the overall risk is estimated precisely. For criterion (i), we show the sample size must be based on the anticipated Cox-snell [Formula: see text] of distinct ‘one-to-one’ logistic regression models corresponding to the sub-models of the multinomial logistic regression, rather than on the overall Cox-snell [Formula: see text] of the multinomial logistic regression. Evaluation of criteria We tested the performance of the proposed criteria (i) through a simulation study and found that it resulted in the desired level of overfitting. Criterion (ii) and (iii) were natural extensions from previously proposed criteria for binary outcomes and did not require evaluation through simulation. Summary We illustrated how to implement the sample size criteria through a worked example considering the development of a multinomial risk prediction model for tumour type when presented with an ovarian mass. Code is provided for the simulation and worked example. We will embed our proposed criteria within the pmsampsize R library and Stata modules.

Nuclear relaxation caused by diffusion of 111In/Cd probe atoms was measured in four phases having the tetragonal FeGa3 structure (tP16) using perturbed angular correlation spectroscopy (PAC) and used to gain insight into diffusion processes in phases having more than one diffusion sublattice. The three indide phases studied in this work have two inequivalent and interpenetrating In-sublattices, labeled In1 and In2, and nuclear quadrupole interactions were resolved for probes on each sublattice. The phases are line-compounds with narrow field-widths. Diffusional relaxations, fitted using an exponential damping ansatz, were measured at the two opposing boundary compositions as a function of temperature. “High” and “low” relaxation regimes were observed that are attributed to In-poorer and In-richer compositions, under the reasonable assumption that the atomic motion occurs via an indium-vacancy diffusion mechanism. Relaxation was observed to be greater for tracer atoms starting on In2 sites in the indides immediately following decay of 111In into 111Cd, which is attributed to a preference of daughter Cd-tracer atoms and/or indium vacancies to occupy In1 sites. Activation enthalpies for relaxation are compared with enthalpies for self-diffusion in indium metal.

ObjectiveThis study reported the burden of gout and its attributable risk factors in the Middle East and North Africa (MENA) region between 1990 and 2019 by age, sex, and sociodemographic index (SDI).MethodsData on the prevalence, incidence, and years lived with disability (YLD) due to gout were obtained from the Global Burden of Disease 2019 study for the 21 countries in the MENA region, from 1990 to 2019.ResultsIn 2019, the regional age-standardized point prevalence and annual incidence rates of gout were 509.1 and 97.7 per 100,000 population, which represent a 12% and 11.1% increase since 1990, respectively. Moreover, in 2019 the regional age-standardized YLD rate was 15.8 per 100,000 population, an 11.7% increase since 1990. In 2019, Qatar and Afghanistan had the highest and lowest age-standardized YLD rates, respectively. Regionally, the age-standardized point prevalence of gout increased with age up to the oldest age group, and it was more prevalent among males in all age groups. In addition, there was an overall positive association between SDI and the burden of gout between 1990 and 2019. In 2019, high BMI (46.1%) was the largest contributor to the burden of gout in the MENA region.ConclusionThere were large intercountry variations in the burden of gout, but in general, it has increased in MENA over the last 3 decades. This increase is in line with the global trends of gout. However, the age-standardized YLD rate change was higher in MENA than at the global level.

Objective: This study aimed to describe the methodologies used to develop and evaluate models that useartificial intelligence (AI) to analyse lung images in order todetect, segment (outline borders of), or classify pulmonary nodules as benign or malignant. Methods: In October 2019, we systematically searched the literature for original studies publishedbetween 2018 and 2019 that described prediction models using AI toevaluate human pulmonary nodules on diagnostic chest images. Two evaluators independently extracted information from studies,such as study aims, sample size, AI type, patient characteristics, and performance. We summarised data descriptively. Results: The review included 153 studies:136 (89%) development-only studies, 12 (8%) development andvalidation, and 5 (3%) validation-only. Computed tomography scans were the most common type of image type used (83%), often acquired from public databases (58%). Eight studies (5%) compared modeloutputs with biopsy results. Forty-one studies (26.8%) reported patient characteristics. The models were based on different units of analysis, such as patients, images, nodules, or image slices or patches. Conclusions: The methods used to develop and evaluate prediction models using AI to detect, segment, or classify pulmonary nodules in medical imaging vary, are poorly reported, andtherefore difficult to evaluate.Transparent and complete reporting of methods, results and code would fill the gaps in information we observed in thestudy publications. Advances in knowledge We reviewed the methodology of AI models detecting nodules on lung images and found that the models were poorly reported and had no description of patient characteristics, with just a few comparing models’ outputs with biopsies results. When lung biopsy is not available, Lung-RADS could help standardise the comparisons between the human radiologist and the machine. · The field of radiology should not give up principles from the diagnostic accuracy studies, such as the choice for the correct ground truth, just because AI is used. Clear and complete reporting of the reference standard used would help radiologists trust in the performance that AI models claim to have. · This review presents clear recommendations about the essential methodological aspects of diagnostic models that should be incorporated in studies using artificial intelligence (AI) to help detect or segmentate lung nodules. The manuscript also reinforces the need for more complete and transparent reporting, which can be helped using the recommended reporting guidelines

Background Patients with fractures are a common emergency presentation and may be misdiagnosed at radiologic imaging. An increasing number of studies apply artificial intelligence (AI) techniques to fracture detection as an adjunct to clinician diagnosis. Purpose To perform a systematic review and meta-analysis comparing the diagnostic performance in fracture detection between AI and clinicians in peer-reviewed publications and the gray literature (ie, articles published on preprint repositories). Materials and Methods A search of multiple electronic databases between January 2018 and July 2020 (updated June 2021) was performed that included any primary research studies that developed and/or validated AI for the purposes of fracture detection at any imaging modality and excluded studies that evaluated image segmentation algorithms. Meta-analysis with a hierarchical model to calculate pooled sensitivity and specificity was used. Risk of bias was assessed by using a modified Prediction Model Study Risk of Bias Assessment Tool, or PROBAST, checklist. Results Included for analysis were 42 studies, with 115 contingency tables extracted from 32 studies (55 061 images). Thirty-seven studies identified fractures on radiographs and five studies identified fractures on CT images. For internal validation test sets, the pooled sensitivity was 92% (95% CI: 88, 93) for AI and 91% (95% CI: 85, 95) for clinicians, and the pooled specificity was 91% (95% CI: 88, 93) for AI and 92% (95% CI: 89, 92) for clinicians. For external validation test sets, the pooled sensitivity was 91% (95% CI: 84, 95) for AI and 94% (95% CI: 90, 96) for clinicians, and the pooled specificity was 91% (95% CI: 81, 95) for AI and 94% (95% CI: 91, 95) for clinicians. There were no statistically significant differences between clinician and AI performance. There were 22 of 42 (52%) studies that were judged to have high risk of bias. Meta-regression identified multiple sources of heterogeneity in the data, including risk of bias and fracture type. Conclusion Artificial intelligence (AI) and clinicians had comparable reported diagnostic performance in fracture detection, suggesting that AI technology holds promise as a diagnostic adjunct in future clinical practice. Clinical trial registration no. CRD42020186641 © RSNA, 2022

ObjectiveTo investigate the extent and qualitatively synthesize open science practices within research published in the top five sports medicine journals from 01 May 2022 and 01 October 2022.DesignMeta-research systematic reviewData SourcesMEDLINEEligibility CriteriaStudies were included if they were published in one of the identified top five sports medicine journals as ranked by Clarivate. Studies were excluded if they were systematic reviews, qualitative research, grey literature, or animal or cadaver models.Results243 studies were included. The median number of open science practices met per study was 2, out of a maximum of 12 (Range: 0-8; IQR: 2). 234 studies (96%, 95% CI: 94-99) provided an author conflict of interest statement and 163 (67%, 95% CI: 62-73) reported funding. 21 studies (9%, 95% CI: 5-12) provided open access data. 54 studies (22%, 95% CI: 17-included a data availability statement and 3 (1%, 95% CI: 0-3) made code available. 76 studies (32%, 95% CI: 25-37) had transparent materials and 30 (12%, 95% CI: 8-16) included a reporting guideline. 28 studies (12%, 95% CI: 8-16) were pre-registered. 6 studies (3%, 95% CI: 1-4) published a protocol. 4 studies (2%, 95% CI: 0-3) reported the availability of an analysis plan. 7 studies (3%, 95% CI: 1-5) reported patient and public involvement.ConclusionSports medicine open science practices are extremely limited. The least followed practices were sharing code, data, and analysis plans. Without implementing open practices, barriers concerning the ability to aggregate findings and create cumulative science will continue to exist.What is already knownOpen science practices provide a mechanism for evaluating and improving the quality and reproducibility of research in a transparent manner, thereby enhancing the benefits to patient outcomes and society at large.Understanding the current open science practices in sport medicine research can assist in identifying where and how sports medicine leadership can raise awareness, and develop strategies for improvement.What are the new findingsNo study published in the top five sports medicine journals met all open science practicesStudies often only met a small number of open science practicesOpen science practices that were least met included providing open access code, data sharing, and the availability of an analysis plan.

Introduction: Dozens of multivariable prediction models for atrial fibrillation after cardiac surgery (AFACS) have been published, but none have been incorporated into regular clinical practice. One of the reasons for this lack of adoption is poor model performance due to methodological weaknesses in model development. In addition, there has been little external validation of these existing models to evaluate their reproducibility and transportability. The aim of this systematic review is to critically appraise the methodology and risk of bias of papers presenting the development and/or validation of models for AFACS.Methods: We will identify studies that present the development and/or validation of a multivariable prediction model for AFACS through searches of PubMed, Embase and Web of Science from inception to 31 December 2021. Pairs of reviewers will independently extract model performance measures, assess methodological quality and assess risk of bias of included studies using extraction forms adapted from a combination of the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist and the Prediction Model Risk of Bias Assessment Tool. Extracted information will be reported by narrative synthesis and descriptive statistics.Ethics and dissemination: This systemic review will only include published aggregate data, so no protected health information will be used. Study findings will be disseminated through peer-reviewed publications and scientific conference presentations. Further, this review will identify weaknesses in past AFACS prediction model development and validation methodology so that subsequent studies can improve upon prior practices and produce a clinically useful risk estimation tool. Introduction Dozens of multivariable prediction models for atrial fibrillation after cardiac surgery (AFACS) have been published, but none have been incorporated into regular clinical practice. One of the reasons for this lack of adoption is poor model performance due to methodological weaknesses in model development. In addition, there has been little external validation of these existing models to evaluate their reproducibility and transportability. The aim of this systematic review is to critically appraise the methodology and risk of bias of papers presenting the development and/or validation of models for AFACS. Methods We will identify studies that present the development and/or validation of a multivariable prediction model for AFACS through searches of PubMed, Embase and Web of Science from inception to 31 December 2021. Pairs of reviewers will independently extract model performance measures, assess methodological quality and assess risk of bias of included studies using extraction forms adapted from a combination of the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist and the Prediction Model Risk of Bias Assessment Tool. Extracted information will be reported by narrative synthesis and descriptive statistics. Ethics and dissemination This systemic review will only include published aggregate data, so no protected health information will be used. Study findings will be disseminated through peer-reviewed publications and scientific conference presentations. Further, this review will identify weaknesses in past AFACS prediction model development and validation methodology so that subsequent studies can improve upon prior practices and produce a clinically useful risk estimation tool. PROSPERO registration number CRD42019127329.

BackgroundAlzheimer’s disease (AD) is the most common cause of dementia and this progressive neurological disorder is associated with substantial mortality and morbidity. We aimed to report the burden of AD and other types of dementia in the Middle East and North Africa (MENA) region, by age, sex and sociodemographic index (SDI), for the period 1990–2019.Methodspublicly accessible data on the prevalence, death and disability-adjusted life years (DALYs) because of AD, and other types of dementia, were retrieved from the global burden of disease 2019 project for all MENA countries from 1990 to 2019.Resultsin 2019, the age-standardised point prevalence of dementia was 777.6 per 100,000 populations in MENA, which was 3.0% higher than in 1990. The age-standardised death and DALY rates of dementia were 25.5 and 387.0 per 100,000, respectively. In 2019, the highest DALY rate was observed in Afghanistan and the lowest rate was in Egypt. That same year, the age-standardised point prevalence, death and DALY rates increased with advancing age and were higher for females of all age groups. From 1990 to 2019, the DALY rate of dementia decreased with increasing SDI up to 0.4, then slightly increased up to an SDI of 0.75, followed by a decrease for the remaining SDI levels.Conclusionsthe point prevalence of AD and other types of dementia has increased over the past three decades, and in 2019, the corresponding regional burden was higher than the global average.

Objective: To provide approximations to recover the full regression equation across different scenarios of incompletely reported prediction models that were developed from binary logistic regression. Study design and setting: In a case study, we considered four common scenarios and illustrated their corresponding approximations: (A) Missing: the intercept, Available: the regression coefficients of predictors, overall frequency of the outcome and descriptive statistics of the predictors; (B) Missing: regression coefficients and the intercept, Available: a simplified score; (C) Missing: regression coefficients and the intercept, Available: a nomogram; (D) Missing: regression coefficients and the intercept, Available: a web calculator. Results: In the scenario A, a simplified approach based on the predicted probability corresponding to the average linear predictor was inaccurate. An approximation based on the overall outcome frequency and an approximation of the linear predictor distribution was more accurate, however, the appropriateness of the underlying assumptions cannot be verified in practice. In the scenario B, the recovered equation was inaccurate due to rounding and categorization of risk scores. In the scenarios C and D, the full regression equation could be recovered with minimal error. Conclusion: The accuracy of the approximations in recovering the regression equation varied depending on the available information. © 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons. org/licenses/by-nc-nd/4.0/)

Background Understanding global trends in the point prevalence, deaths and disability adjusted life years (DALYs) for asthma will facilitate evidence-based decision making. Research Question What is the global, regional and national burden of asthma in 204 countries and territories between 1990 and 2019, by age, sex and socio-demographic index (SDI)? Study Design and Methods Publicly available data from the Global Burden of Disease study were used over the period 1990 to 2019. All estimates were presented as counts and age-standardised rates per 100,000, along with their associated uncertainty intervals (UIs). Results In 2019, the global age-standardised point prevalence and death rates for asthma were 3415.5 and 5.8 per 100,000, which represent a 24% and 51.3% decrease since 1990, respectively. Moreover, in 2019 the global age-standardised DALY rate was 273.6 and the global point prevalence of asthma was highest in the 5-9 age group. Also in 2019, the USA (10399.3) had the highest age-standardised point prevalence rate of asthma. Generally, the burden of asthma decreased with increasing socio-demographic index (SDI). Globally, high body mass index (16.9%), smoking (9.9%) and occupational asthmagens (8.8%) contributed to the 2019 asthma DALYs. Interpretation Asthma remains an important public health issue, particularly in regions with low socio-economic development. Future research is needed to thoroughly examine the associations asthma has with its risk factors and the factors impeding optimal self-management. Further research is also needed to understand and better implement the interventions that have reduced the burden of asthma.

The aim of this study was to report the level and trends of 13 cancers that are attributable to excess body weight (EBW) for 204 countries and territories from 1990 to 2019.Using publicly available data, the burden of cancers attributable to EBW was reported from 1990 to 2019 based on the comparative risk assessment approach used in the Global Burden of Disease study 2019. [Correction added on 27 January 2022, after first online publication: 'Using publicly available data,' has been added before the first sentence and 'estimated' was corrected to 'reported'.] RESULTS: In 2019, EBW caused 11.2 million disability-adjusted life-years (DALYs), or 4.4% of all cancer-related DALYs. Between 1990 and 2019, the global EBW-attributable age-standardized cancer DALY rates (per 100,000) increased from 109.9 to 133.9, a relative increase of 21.9%. The age-standardized DALY rates (per 100,000) of cancers attributable to EBW in 2019 were highest and lowest in Mongolia (611.8) and Bangladesh (30.2), respectively. The 60- to 64-year age group had the highest number of DALYs attributable to EBW, whereas there were no large sex differences in the cancer-related burden attributable to EBW. Furthermore, the association between the age-standardized DALY rates and the sociodemographic index was generally positive.Overall, the EBW-attributable burden of cancers has increased in the past three decades. Public health efforts should focus on identifying appropriate preventive interventions at the population and individual levels, especially in the regions and countries with the highest burden.

The increasing availability of large combined datasets (or big data), such as those from electronic health records and from individual participant data meta-analyses, provides new opportunities and challenges for researchers developing and validating (including updating) prediction models. These datasets typically include individuals from multiple clusters (such as multiple centres, geographical locations, or different studies). Accounting for clustering is important to avoid misleading conclusions and enables researchers to explore heterogeneity in prediction model performance across multiple centres, regions, or countries, to better tailor or match them to these different clusters, and thus to develop prediction models that are more generalisable. However, this requires prediction model researchers to adopt more specific design, analysis, and reporting methods than standard prediction model studies that do not have any inherent substantial clustering. Therefore, prediction model studies based on clustered data need to be reported differently so that readers can appraise the study methods and findings, further increasing the use and implementation of such prediction models developed or validated from clustered datasets. ispartof: BMJ-BRITISH MEDICAL JOURNAL vol:380 ispartof: location:England status: published

The TRIPOD-Cluster (transparent reporting of multivariable prediction models developed or validated using clustered data) statement comprises a 19 item checklist, which aims to improve the reporting of studies developing or validating a prediction model in clustered data, such as individual participant data meta-analyses (clustering by study) and electronic health records (clustering by practice or hospital). This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD-Cluster statement is explained in detail and accompanied by published examples of good reporting. The document also serves as a reference of factors to consider when designing, conducting, and analysing prediction model development or validation studies in clustered data. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, authors are recommended to include a completed checklist in their submission. ispartof: BMJ-BRITISH MEDICAL JOURNAL vol:380 ispartof: location:England status: published

Background Low back pain (LBP) is the most common musculoskeletal disorder globally. Providing region- and national-specific information on the burden of low back pain is critical for local healthcare policy makers. The present study aimed to report, compare, and contextualize the prevalence, incidence and years lived with disability (YLDs) of low back pain in the Middle East and North Africa (MENA) region by age, sex and sociodemographic index (SDI), from 1990 to 2019. Methods Publicly available data were obtained from the Global Burden of Disease (GBD) study 2019. The burden of LBP was reported for the 21 countries located in the MENA region, from 1990 to 2019. All estimates were reported as counts and age-standardised rates per 100,000 population, together with their corresponding 95% uncertainty intervals (UIs). Results In 2019, the age-standardised point prevalence and incidence rate per 100,000 in MENA were 7668.2 (95% UI 6798.0 to 8363.3) and 3215.9 (95%CI 2838.8 to 3638.3), which were 5.8% (4.3 to 7.4) and 4.4% (3.4 to 5.5) lower than in 1990, respectively. Furthermore, the regional age-standardised YLD rate in 2019 was 862.0 (605.5 to 1153.3) per 100,000, which was 6.0% (4.2 to 7.7) lower than in 1990. In 2019, Turkey [953.6 (671.3 to 1283.5)] and Lebanon [727.2 (511.5 to 966.0)] had the highest and lowest age-standardised YLD rates, respectively. There was no country in the MENA region that showed increases in the age-standardised prevalence, incidence or YLD rates of LBP over the measurement period. Furthermore, in 2019 the number of prevalent cases were highest in the 35–39 age group, with males having a higher number of cases in all age groups. In addition, the age-standardised YLD rates for males in the MENA region were higher than the global estimates in almost all age groups, in both 1990 and 2019. Furthermore, the burden of LBP was not associated with the level of socio-economic development during the measurement period. Conclusion The burden attributable to LBP in the MENA region decreased slightly from 1990 to 2019. Furthermore, the burden among males was higher than the global average. Consequently, more integrated healthcare interventions are needed to more effectively alleviate the burden of low back pain in this region.

BackgroundLower respiratory infections (LRIs) cause a substantial mortality, morbidity and economic burden. The present study reported the global, regional and national burden of LRIs and their attributable risk factors in 204 countries and territories, between 1990 and 2019, by age, sex, etiology, and Socio-demographic Index (SDI).MethodsUsing publicly available data from the Global Burden of Disease (GBD) study 2019, we reported the incidence, deaths and disability-adjusted life-years (DALYs), due to LRIs. Estimates were presented as counts and age-standardized rates per 100,000 population with their associated uncertainty intervals (UIs).ResultsGlobally, in 2019 there were 488.9 million (95% UI: 457.6 to 522.6) incident cases and 2.4 million (2.3–2.7) deaths due to LRIs. The global age-standardized incidence and death rates for LRIs were 6,295 (5,887.4–6,737.3) and 34.3 (31.1–37.9) per 100,000 in 2019, which represents a 23.9% (22.5–25.4) and 48.5% (42.9–54.0) decrease, respectively since 1990. In 2019, Guinea [12,390.4 (11,495.5–13,332.8)], Chad [12,208.1 (11,289.3–13,202.5)] and India [11,862.1 (11,087.0–12,749.0)] had the three highest age-standardized incidence rates of LRI. Equatorial Guinea [−52.7% (95% UI: −55.8 to −49.3)], Chile [−50.2% (95% UI: −53.4 to −47.0)] and Albania [−48.6% (95% UI: −51.7 to −45.3)] showed the largest decreases from 1990 to 2019. In 2019, a decrease in the incidence rate of LRI was observed at the global level up to the 25–29 age group, then the incidence rates increased with age. The burden of LRIs decreased with increasing SDI at both the regional and national levels. Globally, child wasting (33.1%), household air pollution from solid fuels (24.9%) and a lack of access to handwashing facilities (14.4%) made the largest contributions to the LRI burden in 2019.ConclusionsAlthough the burden of LRIs decreased over the period 1990–2019, LRIs still contribute to a large number of incident cases, deaths and DALYs. Preventative programs with a focus on reducing exposure to attributable risk factors should be implemented, especially in less developed countries.

Objective: To review systematically and assess the accuracy of prediction models for bronchopulmonary dysplasia (BPD) at 36 weeks of postmenstrual age. Study design: Searches were conducted in MEDLINE and EMBASE. Studies published between 1990 and 2022 were included if they developed or validated a prediction model for BPD or the combined outcome death/BPD at 36 weeks in the first 14 days of life in infants born preterm. Data were extracted independently by 2 authors following the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (ie, CHARMS) and PRISMA guidelines. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (ie, PROBAST). Results: Sixty-five studies were reviewed, including 158 development and 108 externally validated models. Median c-statistic of 0.84 (range 0.43-1.00) was reported at model development, and 0.77 (range 0.41-0.97) at external validation. All models were rated at high risk of bias, due to limitations in the analysis part. Meta-analysis of the validated models revealed increased c-statistics after the first week of life for both the BPD and death/BPD outcome. Conclusions: Although BPD prediction models perform satisfactorily, they were all at high risk of bias. Methodologic improvement and complete reporting are needed before they can be considered for use in clinical practice. Future research should aim to validate and update existing models.

Background Journal articles describing randomized controlled trials (RCTs) and systematic reviews with meta-analysis of RCTs are not optimally reported and often miss crucial details. This poor reporting makes assessing these studies’ risk of bias or reproducing their results difficult. However, the reporting quality of diet- and nutrition-related RCTs and meta-analyses has not been explored. Objective We aimed to assess the reporting completeness and identify the main reporting limitations of diet- and nutrition-related RCTs and meta-analyses of RCTs, estimate the frequency of reproducible research practices among these RCTs, and estimate the frequency of distorted presentation or spin among these meta-analyses. Methods Two independent meta-research studies will be conducted using articles published in PubMed-indexed journals. The first will include a sample of diet- and nutrition-related RCTs; the second will include a sample of systematic reviews with meta-analysis of diet- and nutrition-related RCTs. A validated search strategy will be used to identify RCTs of nutritional interventions and an adapted strategy to identify meta-analyses in PubMed. We will search for RCTs and meta-analyses indexed in 1 calendar year and randomly select 100 RCTs (June 2021 to June 2022) and 100 meta-analyses (July 2021 to July 2022). Two reviewers will independently screen the titles and abstracts of records yielded by the searches, then read the full texts to confirm their eligibility. The general features of these published RCTs and meta-analyses will be extracted into a research electronic data capture database (REDCap; Vanderbilt University). The completeness of reporting of each RCT will be assessed using the items in the CONSORT (Consolidated Standards of Reporting Trials), its extensions, and the TIDieR (Template for Intervention Description and Replication) statements. Information about practices that promote research transparency and reproducibility, such as the publication of protocols and statistical analysis plans will be collected. There will be an assessment of the completeness of reporting of each meta-analysis using the items in the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) statement and collection of information about spin in the abstracts and full-texts. The results will be presented as descriptive statistics in diagrams or tables. These 2 meta-research studies are registered in the Open Science Framework. Results The literature search for the first meta-research retrieved 20,030 records and 2182 were potentially eligible. The literature search for the second meta-research retrieved 10,918 records and 850 were potentially eligible. Among them, random samples of 100 RCTs and 100 meta-analyses were selected for data extraction. Data extraction is currently in progress, and completion is expected by the beginning of 2023. Conclusions Our meta-research studies will summarize the main limitation on reporting completeness of nutrition- or diet-related RCTs and meta-analyses and provide comprehensive information regarding the particularities in the reporting of intervention studies in the nutrition field. International Registered Report Identifier (IRRID) DERR1-10.2196/43537

Background: Over the past decade, research has attempted to elucidate the cause of throwing-related injuries in the baseball athlete. However, when considering the entire kinetic chain, full body mechanics, and pitching cycle sequencing, there are hundreds of variables that could influence throwing arm health, and there is a lack of quality investigations evaluating the relationship and influence of multiple variables on arm stress. Purpose: To identify which variables have the most influence on elbow valgus torque and shoulder distraction force using a statistical model and a machine learning approach. Study Design: Cross-sectional study; Level of evidence, 3. Methods: A retrospective review was performed on baseball pitchers who underwent biomechanical evaluation at the university biomechanics laboratory. Regression models and 4 machine learning models were created for both elbow valgus torque and shoulder distraction force. All models utilized the same predictor variables, which included pitch velocity and 17 pitching mechanics. Results: The analysis included a total of 168 high school and collegiate pitchers with a mean age of 16.7 years (SD, 3.2 years) and BMI of 24.4 (SD, 1.2). For both elbow valgus torque and shoulder distraction force, the gradient boosting machine models demonstrated the smallest root mean square errors and the most precise calibrations compared with all other models. The gradient boosting model for elbow valgus torque reported the highest influence for pitch velocity (relative influence, 28.4), with 5 mechanical variables also having significant influence. The gradient boosting model for shoulder distraction force reported the highest influence for pitch velocity (relative influence, 20.4), with 6 mechanical variables also having significant influence. Conclusion: The gradient boosting machine learning model demonstrated the best overall predictive performance for both elbow valgus torque and shoulder distraction force. Pitch velocity was the most influential variable in both models. However, both models also revealed that pitching mechanics, including maximum humeral rotation velocity, shoulder abduction at foot strike, and maximum shoulder external rotation, significantly influenced both elbow and shoulder stress. Clinical Relevance: The results of this study can be used to inform players, coaches, and clinicians on specific mechanical variables that may be optimized to mitigate elbow or shoulder stress that could lead to throwing-related injury.

Apart from high-risk scenarios such as the presence of highly penetrant genetic mutations, breast screening typically comprises mammography or tomosynthesis strategies defined by age. However, age-based screening ignores the range of breast cancer risks that individual women may possess and is antithetical to the ambitions of personalised early detection. Whilst screening mammography reduces breast cancer mortality, this is at the risk of potentially significant harms including overdiagnosis with overtreatment, and psychological morbidity associated with false positives. In risk-stratified screening, individualised risk assessment may inform screening intensity/interval, starting age, imaging modality used, or even decisions not to screen. However, clear evidence for its benefits and harms needs to be established. In this scoping review, the authors summarise the established and emerging evidence regarding several critical dependencies for successful risk-stratified breast screening: risk prediction model performance, epidemiological studies, retrospective clinical evaluations, health economic evaluations and qualitative research on feasibility and acceptability. Family history, breast density or reproductive factors are not on their own suitable for precisely estimating risk and risk prediction models increasingly incorporate combinations of demographic, clinical, genetic and imaging-related parameters. Clinical evaluations of risk-stratified screening are currently limited. Epidemiological evidence is sparse, and randomised trials only began in recent years.

Participating in sport carries inherent risk of injury. Clinicians execute high-level clinical reasoning and decision making to support athletes to achieve the best outcomes. Accurately diagnosing a problem, estimating prognosis, or selecting the most suitable intervention for each athlete is challenging. Clinical prediction models are tools to assist clinicians in estimating the risk or probability of a health outcome for an individual by using data from multiple predictors. Although common in general medical literature, clinical prediction models are rare in sports medicine. The purpose of this article was to (1) describe the steps required to develop and validate (ie, evaluate) a clinical prediction model for clinical researchers, and (2) help sports medicine clinicians understand and interpret clinical prediction model studies. Using a case study to illustrate how to implement clinical prediction models in practice, we address the following issues in developing and validating a clinical prediction model: study design and data, sample size, missing data, selecting predictors, handling continuous predictors, model fitting, internal and external validation, performance measures, reporting, and model presentation. Our work builds on initiatives to improve diagnostic and prognostic clinical research, including the PROGnosis RESearch Strategy (PROGRESS) series of papers and textbook and the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement.

Recent minimum sample size formula (Riley et al.) for developing clinical prediction models help ensure that development datasets are of sufficient size to minimise overfitting. While these criteria are known to avoid excessive overfitting on average, the extent of variability in overfitting at recommended sample sizes is unknown. We investigated this through a simulation study and empirical example to develop logistic regression clinical prediction models using unpenalised maximum likelihood estimation, and various post-estimation shrinkage or penalisation methods. While the mean calibration slope was close to the ideal value of one for all methods, penalisation further reduced the level of overfitting, on average, compared to unpenalised methods. This came at the cost of higher variability in predictive performance for penalisation methods in external data. We recommend that penalisation methods are used in data that meet, or surpass, minimum sample size requirements to further mitigate overfitting, and that the variability in predictive performance and any tuning parameters should always be examined as part of the model development process, since this provides additional information over average (optimism-adjusted) performance alone. Lower variability would give reassurance that the developed clinical prediction model will perform well in new individuals from the same population as was used for model development.

BackgroundThe link between ethnicity and healthcare inequity, and the urgency for better data is well-recognised. This study describes ethnicity data in nation-wide electronic health records in England, UK.MethodsWe conducted a retrospective cohort study using de-identified person-level records for the England population available in the National Health Service (NHS) Digital trusted research environment. Primary care records (GDPPR) were linked to hospital and national mortality records. We assessed completeness, consistency, and granularity of ethnicity records using all available SNOMED-CT concepts for ethnicity and NHS ethnicity categories.FindingsFrom 61.8 million individuals registered with a primary care practice in England, 51.5 (83.3%) had at least one ethnicity record in GDPPR, increasing to 93·9% when linked with hospital records. Approximately 12·0% had at least two conflicting ethnicity codes in primary care records. Women were more likely to have ethnicity recorded than men. Ethnicity was missing most frequently in individuals from 18 to 39 years old and in the southern regions of England. Individuals with an ethnicity record had more comorbidities recorded than those without. Of 489 SNOMED-CT ethnicity concepts available, 255 were used in primary care records. Discrepancies between SNOMED-CT and NHS ethnicity categories were observed, specifically within “Other-” ethnicity groups.InterpretationMore than 250 ethnicity sub-groups may be found in health records for the English population, although commonly categorised into “White”, “Black”, “Asian”, “Mixed”, and “Other”. One in ten individuals do not have ethnicity information recorded in primary care or hospital records. SNOMED-CT codes represent more diversity in ethnicity groups than the NHS ethnicity classification. Improved recording of self-reported ethnicity at first point-of-care and consistency in ethnicity classification across healthcare settings can potentially improve the accuracy of ethnicity in research and ultimately care for all ethnicities.FundingBritish Heart Foundation Data Science Centre led by Health Data Research UK.Research in contextEvidence before this studyEthnicity has been highlighted as a significant factor in the disproportionate impact of SARS-CoV-2 infection and mortality. Better knowledge of ethnicity data recorded in real clinical practice is required to improve health research and ultimately healthcare. We searched PubMed from database inception to 14thJuly 2022 for publications using the search terms “ethnicity” and “electronic health records” or “EHR,” without language restrictions. 228 publications in 2019, before the COVID-19 pandemic, and 304 publications between 2020 and 2022 were identified. However, none of these publications used or reported any of over 400 available SNOMED-CT concepts for ethnicity to account for more granularity and diversity than captured by traditional high-level classification limited to 5 to 9 ethnicity groups.Added value of this studyWe provide a comprehensive study of the largest collection of ethnicity records from a national-level electronic health records trusted research environment, exploring completeness, consistency, and granularity. This work can serve as a data resource profile of ethnicity from routinely-collected EHR in England.Implications of all the available evidenceTo achieve equity in healthcare, we need to understand the differences between individuals, as well as the influence of ethnicity both on health status and on health interventions, including variation in the behaviour of tests and therapies. Thus, there is a need for measurements, thresholds, and risk estimates to be tailored to different ethnic groups. This study presents the different medical concepts describing ethnicity in routinely collected data that are readily available to researchers and highlights key elements for improving their accuracy in research. We aim to encourage researchers to use more granular ethnicity than the than typical approaches which aggregate ethnicity into a limited number of categories, failing to reflect the diversity of underlying populations. Accurate ethnicity data will lead to a better understanding of individual diversity, which will help to address disparities and influence policy recommendations that can translate into better, fairer health for all.

Background Despite endorsement by medical journals, reporting guidelines have only modestly affected reporting quality. We aimed to identify facilitators and barriers that researchers might encounter when using reporting guidelines. Methods We searched MEDLINE, Embase, PsychINFO, AMED, WHO Global Index Medicus, SciELO, Chinese Biomedical Literature Database, China National Knowledge Infrastructure, Wanfang Data, VIP Chinese Medical Journal Database, OSF, and MiRoR for research that collected qualitative data to explore researchers’ experiences of using reporting guidelines, published after 1996 in English, Chinese, Spanish, or Portuguese. We appraised studies using CASP-Qual. For thematic synthesis, we applied descriptive codes to all sentences that reported qualitative findings, then aggregated codes inductively into descriptive themes that captured the codes’ meaning. We interpreted and contextualised possible facilitators and barriers from these descriptive themes to create analytic themes. Results From 18 eligible studies, we developed 12 analytic themes. 1) Researchers may not understand guidance as intended. 2) Researchers report many reasons for using reporting guidelines, and that some are more important than others. 3) Researchers use reporting guidelines for different tasks and want guidance to be delivered in ways that fit their needs. 4) Using reporting guidelines has costs which researchers may feel outweigh benefits. 5) Reporting guidelines may need to be revised and updated for different reasons. 6) Researchers report feeling uncertain or worried if they are unable to report all items. 7) Awareness and accessibility may limit reporting guideline usage. 8) Reporting guidelines may be most beneficial to less experienced researchers, but these researchers may find them harder to use. 9) Researchers seek design advice but reporting guidelines may not be the right place to find it. 10) A reporting guideline's scope must be well-balanced and well-defined. 11) Researchers may have to use multiple reporting guidelines, multiplying complexity and costs. 12) Researchers may use checklists but never read the full guidance. Discussion Few reporting guidelines have been evaluated qualitatively. Many studies used surveys which resulted in thin data and were subject to recall bias. Nevertheless, we identified many potential barriers and facilitators. Our findings will help guideline developers and advocates improve reporting guidance dissemination.

BACKGROUND Journal articles describing randomized controlled trials (RCTs) and systematic reviews with meta-analysis of RCTs are not optimally reported and often miss crucial details. This poor reporting makes assessing these studies’ risk of bias or reproducing their results difficult. However, the reporting quality of diet- and nutrition-related RCTs and meta-analyses has not been explored. OBJECTIVE We aimed to assess the reporting completeness and identify the main reporting limitations of diet- and nutrition-related RCTs and meta-analyses of RCTs, estimate the frequency of reproducible research practices among these RCTs, and estimate the frequency of distorted presentation or spin among these meta-analyses. METHODS Two independent meta-research studies will be conducted using articles published in PubMed-indexed journals. The first will include a sample of diet- and nutrition-related RCTs; the second will include a sample of systematic reviews with meta-analysis of diet- and nutrition-related RCTs. A validated search strategy will be used to identify RCTs of nutritional interventions and an adapted strategy to identify meta-analyses in PubMed. We will search for RCTs and meta-analyses indexed in 1 calendar year and randomly select 100 RCTs (June 2021 to June 2022) and 100 meta-analyses (July 2021 to July 2022). Two reviewers will independently screen the titles and abstracts of records yielded by the searches, then read the full texts to confirm their eligibility. The general features of these published RCTs and meta-analyses will be extracted into a research electronic data capture database (REDCap; Vanderbilt University). The completeness of reporting of each RCT will be assessed using the items in the CONSORT (Consolidated Standards of Reporting Trials), its extensions, and the TIDieR (Template for Intervention Description and Replication) statements. Information about practices that promote research transparency and reproducibility, such as the publication of protocols and statistical analysis plans will be collected. There will be an assessment of the completeness of reporting of each meta-analysis using the items in the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) statement and collection of information about spin in the abstracts and full-texts. The results will be presented as descriptive statistics in diagrams or tables. These 2 meta-research studies are registered in the Open Science Framework. RESULTS The literature search for the first meta-research retrieved 20,030 records and 2182 were potentially eligible. The literature search for the second meta-research retrieved 10,918 records and 850 were potentially eligible. Among them, random samples of 100 RCTs and 100 meta-analyses were selected for data extraction. Data extraction is currently in progress, and completion is expected by the beginning of 2023. CONCLUSIONS Our meta-research studies will summarize the main limitation on reporting completeness of nutrition- or diet-related RCTs and meta-analyses and provide comprehensive information regarding the particularities in the reporting of intervention studies in the nutrition field. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/43537

We aimed to report the burden of bulimia nervosa (BN) in the Middle East and North Africa (MENA) region by age, sex, and sociodemographic index (SDI), for the period 1990-2019.Estimates of the prevalence, incidence, and disability-adjusted life-years (DALYs) attributable to BN were retrieved from the Global Burden of Disease study 2019, between 1990 and 2019, for the 21 countries in the MENA region. The counts and age-standardized rates (per 100,000) were presented, along with their corresponding 95% uncertainty intervals.In 2019, the estimated regional age-standardized point prevalence and incidence rates of BN were 168.3 (115.0-229.6) and 178.6 (117.0-255.6) per 100,000, which represented 22.0% (17.5-27.2) and 10.4% (7.1-14.7) increases, respectively, since 1990. Moreover, in 2019 the regional age-standardized DALY rate was 35.5 (20.6-55.5) per 100,000, which was 22.2% (16.7-28.2) higher than in 1990. In 2019, Qatar (58.6 [34.3-92.5]) and Afghanistan (18.4 [10.6-29.2]) had the highest and lowest age-standardized DALY rates, respectively. Regionally, the age-standardized point prevalence of BN peaked in the 30-34 age group and was more prevalent among women. In addition, there was a generally positive association between SDI and the burden of BN across the measurement period.In the MENA region, the burden of BN has increased over the last three decades. Cost-effective preventive measures are needed in the region, especially in the high SDI countries.This study reports the estimated burden of BN in the MENA region and shows that its burden has increased over the last three decades.

OBJECTIVE: To assess improvement in the completeness of reporting COVID-19 prediction models after the peer review process. STUDY DESIGN AND SETTING: Studies included in a living systematic review of COVID-19 prediction models, with both pre-print and peer-reviewed published versions available, were assessed. The primary outcome was the change in percentage adherence to the TRIPOD reporting guidelines between pre-print and published manuscripts. RESULTS: 19 studies were identified including seven (37%) model development studies, two external validations of existing models (11%), and 10 (53%) papers reporting on both development and external validation of the same model. Median percentage adherence amongst pre-print versions was 33% (min-max: 10 to 68%). The percentage adherence of TRIPOD components increased from pre-print to publication in 11/19 studies (58%), with adherence unchanged in the remaining eight studies. The median change in adherence was just 3 percentage points (pp, min-max: 0-14pp) across all studies. No association was observed between the change in percentage adherence and pre-print score, journal impact factor, or time between journal submission and acceptance. CONCLUSIONS: Pre-print reporting quality of COVID-19 prediction modelling studies is poor and did not improve much after peer review, suggesting peer review had a trivial effect on the completeness of reporting during the pandemic.

Background Baseball has specific sport and positional demands that may modify joint pain compared with other sports. Persistent joint pain reduces function and is an underlying reason for seeking medical care. The pain and functional status of players after they stop competitive play are unknown. Such knowledge can assist clinicians in creating personalized physical examinations and interventions for baseball players as they transition to retirement. The purpose of this study was to (1) evaluate persistent joint pain and arm function in former baseball players and (2) determine whether playing position is associated with increased odds of joint pain and reduced arm function in former baseball players. Methods A cross-sectional survey was performed. Eligibility criteria consisted of (1) played ≥1 collegiate baseball season, (2) aged ≥18 years, and (3) formerly played baseball (currently retired). Outcomes assessed included persistent joint pain and Single Assessment Numeric Evaluation (SANE). Explanatory variables included playing position (position, two-way, or pitcher). Multivariable logistic and linear regressions were performed. Models were adjusted for age, body mass index, arm dominance, playing standard, years played baseball, and injury and surgery history. Results A total of 117 former baseball players participated (age: 36.8 [13.7] years). The mean dominant SANE score was 70.2 (standard deviation 24.1), and the mean nondominant SANE score was 85.2 (standard deviation 19.4). There was no difference in dominant arm SANE scores when stratified by arm injury history (4.6 [95% confidence interval: −14.9, 5.8]) or arm surgery history (−3.8 [95% confidence interval: 13.4, 5.8]). The shoulders had the greatest persistent joint pain prevalence (28% of all participants) and elbows (21% of all participants). There was no relationship between dominant arm pain or function and playing position. Conclusion This is the first study to demonstrate an increase in dominant arm disability in former baseball players. The high prevalence of persistent arm pain and poor arm function among former baseball players is concerning considering participants were younger than 40 years of age. No differences were observed in arm function when stratifying by arm history, surgery, or position demonstrating the potential relationship between baseball participation and arm disability after cessation of play. Clinicians should consider working with baseball players to develop long-term strategies to maintain joint health, especially in the throwing arm, when baseball players are transitioning to retirement. Future research is needed to understand the long-term effectiveness of clinical treatments and the implications of specific arm injuries such as ulnar collateral ligament tears on persistent arm pain and function.

Objective: To characterize the trend of opioid use (number of users, dispensations and oral morphine milligram equivalents) in Catalonia (Spain).Design, setting, and participants: This population-based cohort study included all individuals aged 18 years or older, registered in the Information System for Research in Primary Care (SIDIAP), which covers >75% of the population in Catalonia, Spain, from 1 January 2007, to 31 December 2019.Main exposure and outcomes: The exposures were all commercialized opioids and their combinations (ATC-codes): codeine, tramadol, oxycodone, tapentadol, fentanyl, morphine, and other opioids (dihydrocodeine, hydromorphone, dextropropoxyphene, buprenorphine, pethidine, pentazocine). The main outcomes were the annual figures per 1,000 individuals of 1) opioid users, 2) dispensations, and 3) oral morphine milligram equivalents (MME). Results were stratified separately by opioid types, age (5-year age groups), sex (male or female), living area (rural or urban), and socioeconomic status (from least, U1, to most deprived, U5). The overall trends were quantified using the percentage change (PC) between 2007 and 2019.Results: Among 4,656,197 and 4,798,114 residents from 2007 to 2019, the number of opioid users, dispensations and morphine milligram equivalents per 1,000 individuals increased 12% (percentage change: 95% confidence interval (CI) 11.9–12.3%), 105% (95% confidence interval 83%–126%) and 339% (95% CI 289%–390%) respectively. Tramadol represented the majority of opioid use in 2019 (61, 59, and 54% of opioid users, dispensations, and total MME, respectively). Individuals aged 80 years or over reported the sharpest increase regarding opioid users (PC: 162%), dispensations (PC: 424%), and MME (PC: 830%). Strong opioids were increasingly prescribed for non-cancer pains over the years.Conclusion: Despite the modest increase of opioid users, opioid dispensations and MME increased substantially, particularly in the older population. In addition, strong opioids were incrementally indicated for non-cancer pains over the years. These findings suggest a transition of opioid prescriptions from intermittent to chronic and weak to strong and call for more rigorous opioid stewardship.

ObjectiveTo report the global, regional, and national burden of chronic obstructive pulmonary disease (COPD) and its attributable risk factors between 1990 and 2019, by age, sex, and sociodemographic index.DesignSystematic analysis.Data sourceGlobal Burden of Disease Study 2019.Main outcome measuresData on the prevalence, deaths, and disability adjusted life years (DALYs) of COPD, and its attributable risk factors, were retrieved from the Global Burden of Disease 2019 project for 204 countries and territories, between 1990 and 2019. The counts and rates per 100 000 population, along with 95% uncertainty intervals, were presented for each estimate.ResultsIn 2019, 212.3 million prevalent cases of COPD were reported globally, with COPD accounting for 3.3 million deaths and 74.4 million DALYs. The global age standardised point prevalence, death, and DALY rates for COPD were 2638.2 (95% uncertainty intervals 2492.2 to 2796.1), 42.5 (37.6 to 46.3), and 926.1 (848.8 to 997.7) per 100 000 population, which were 8.7%, 41.7%, and 39.8% lower than in 1990, respectively. In 2019, Denmark (4299.5), Myanmar (3963.7), and Belgium (3927.7) had the highest age standardised point prevalence of COPD. Egypt (62.0%), Georgia (54.9%), and Nicaragua (51.6%) showed the largest increases in age standardised point prevalence across the study period. In 2019, Nepal (182.5) and Japan (7.4) had the highest and lowest age standardised death rates per 100 000, respectively, and Nepal (3318.4) and Barbados (177.7) had the highest and lowest age standardised DALY rates per 100 000, respectively. In men, the global DALY rate of COPD increased up to age 85-89 years and then decreased with advancing age, whereas for women the rate increased up to the oldest age group (≥95 years). Regionally, an overall reversed V shaped association was found between sociodemographic index and the age standardised DALY rate of COPD. Factors contributing most to the DALYs rates for COPD were smoking (46.0%), pollution from ambient particulate matter (20.7%), and occupational exposure to particulate matter, gases, and fumes (15.6%).ConclusionsDespite the decreasing burden of COPD, this disease remains a major public health problem, especially in countries with a low sociodemographic index. Preventive programmes should focus on smoking cessation, improving air quality, and reducing occupational exposures to further reduce the burden of COPD.

The purpose of this study was to compare basketball performance markers one year prior to initial severe lower extremity injury, including ankle, knee, and hip injuries, to one- and two-years following injury during the regular NBA season. Publicly available data were extracted through a reproducible extraction computed programmed process. Eligible participants were NBA players with at least three seasons played between 2008 and 2019, with a time-loss injury reported during the study period. Basketball performance was evaluated for season minutes, points, and rebounds. Prevalence of return to performance and linear regressions were calculated. 285 athletes sustained a severe lower extremity injury. 196 (69%) played one year and 130 (45%) played two years following the injury. Time to return to sport was similar between groin/hip/thigh [227 (88)], knee [260 (160)], or ankle [260 (77)] (P = 0.289). 58 (30%) players participated in a similar number of games and 57 (29%) scored similar points one year following injury. 48 (37%) participated in a similar number of games and 55 (42%) scored a similar number of points two years following injury. Less than half of basketball players that suffered a severe lower extremity injury were participating at the NBA level two years following injury, with similar findings for groin/hip/thigh, knee, and ankle injuries. Less than half of players were performing at previous pre-injury levels two years following injury. Suffering a severe lower extremity injury may be a prognostic factor that can assist sports medicine professionals to educate and set performance expectations for NBA players.

Background: Classification systems for orthopaedic infection include patient health status, but there is no consensus about which comorbidities affect prognosis. Modifiable factors including substance use, glycaemic control, malnutrition and obesity may predict post-operative recovery from infection. Aim: This systematic review aimed (1) to critically appraise clinical prediction models for individual prognosis following surgical treatment for orthopaedic infection where an implant is not retained; (2) to understand the usefulness of modifiable prognostic factors for predicting treatment success. Methods: EMBASE and MEDLINE databases were searched for clinical prediction and prognostic studies in adults with orthopaedic infections. Infection recurrence or re-infection after at least 6 months was the primary outcome. The estimated odds ratios for the primary outcome in participants with modifiable prognostic factors were extracted and the direction of the effect reported. Results: Thirty-five retrospective prognostic cohort studies of 92 693 patients were included, of which two reported clinical prediction models. No studies were at low risk of bias, and no externally validated prediction models were identified. Most focused on prosthetic joint infection. A positive association was reported between body mass index and infection recurrence in 19 of 22 studies, similarly in 8 of 14 studies reporting smoking history and 3 of 4 studies reporting alcohol intake. Glycaemic control and malnutrition were rarely considered. Conclusion: Modifiable aspects of patient health appear to predict outcomes after surgery for orthopaedic infection. There is a need to understand which factors may have a causal effect. Development and validation of clinical prediction models that include participant health status will facilitate treatment decisions for orthopaedic infections., Journal of Bone and Joint Infection, 6 (7)

Covariate adjustment is integral to the validity of observational studies assessing causal effects. It is common practice to adjust for as many variables as possible in observational studies in the hopes of reducing confounding by other variables. However, indiscriminate adjustment for variables using standard regression models may actually lead to biased estimates. In this paper, we differentiate between confounders, mediators, colliders, and effect modifiers. We will discuss that while confounders should be adjusted for in the analysis, one should be wary of adjusting for colliders. Mediators should not be adjusted for when examining the total effect of an exposure on an outcome. Automated statistical programs should not be used to decide which variables to include in causal models. Using a case scenario in cardiology, we will demonstrate how to identify confounders, colliders, mediators and effect modifiers and the implications of adjustment or non-adjustment for each of them.

Clinical prediction models must be appropriately validated before they can be used. While validation studies are sometimes carefully designed to match an intended population/setting of the model, it is common for validation studies to take place with arbitrary datasets, chosen for convenience rather than relevance. We call estimating how well a model performs within the intended population/setting “targeted validation”. Use of this term sharpens the focus on the intended use of a model, which may increase the applicability of developed models, avoid misleading conclusions, and reduce research waste. It also exposes that external validation may not be required when the intended population for the model matches the population used to develop the model; here, a robust internal validation may be sufficient, especially if the development dataset was large.

There is growing interest in the role of predictive analytics in sport, where such extensive data collection provides an exciting opportunity for the development and utilisation of prediction models for medical and performance purposes. Clinical prediction models have traditionally been developed using regression-based approaches, although newer machine learning methods are becoming increasingly popular. Machine learning models are considered 'black box'. In parallel with the increase in machine learning, there is also an emergence of proprietary prediction models that have been developed by researchers with the aim of becoming commercially available. Consequently, because of the profitable nature of proprietary systems, developers are often reluctant to transparently report (or make freely available) the development and validation of their prediction algorithms; the term 'black box' also applies to these systems. The lack of transparency and unavailability of algorithms to allow implementation by others of 'black box' approaches is concerning as it prevents independent evaluation of model performance, interpretability, utility, and generalisability prior to implementation within a sports medicine and performance environment. Therefore, in this Current Opinion article, we: (1) critically examine the use of black box prediction methodology and discuss its limited applicability in sport, and (2) argue that black box methods may pose a threat to delivery and development of effective athlete care and, instead, highlight why transparency and collaboration in prediction research and product development are essential to improve the integration of prediction models into sports medicine and performance.