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Abstract Kidney cancer, a type of urogenital cancer, imposes a high burden on patients. Despite this, no recent research has evaluated the burden of this type of cancer in the Middle East and North Africa (MENA) region. This study explored the burden of kidney cancer from 1990 to 2019 according to age, sex and socio-demographic index (SDI). The Global Burden of Disease (GBD) 2019 data was utilized to estimate the incidence, death, and disability-adjusted life-years (DALYs) caused by kidney cancer. These estimates were reported as counts and as age-standardised rates with 95% uncertainty intervals (UIs). The estimated age-standardised incidence, mortality, and DALY rates of kidney cancer in 2019 were 3.2 (2.8–3.6), 1.4 (1.2–1.6), and 37.2 (32.0–42.6) per 100,000, respectively. Over the period from 1990 to 2019, these rates have increased by 98.0%, 48.9%, and 37.7%, respectively. In 2019, the United Arab Emirates, Qatar, and Lebanon had the largest age-standardised incidence, mortality, and DALY rates. The smallest age-standardised incidence rates were seen in Yemen, Afghanistan, and the Syrian Arab Republic. Additionally, the smallest age-standardised mortality and DALY rates were observed in the Syrian Arab Republic, Yemen, and Morocco. The highest incidence rates were found among individuals aged 75–79 in both males and females. In 2019, the MENA/Global DALY ratio exceeded one for females aged 5–19 age and males aged 5–14, compared to 1990age groups in males. The burden of kidney cancer consistently rose with increasing SDI levels from 1990 to 2019. The increasing burden of kidney cancer highlights the urgent need for interventions aimed at improving early diagnosis and treatment in the region.

# Background Athlete injury risk assessment and management is an important, yet challenging task for sport and exercise medicine professionals. A common approach to injury risk screening is to stratify athletes into risk groups based on their performance on a test relative to a cut-off threshold. However, one potential reason for ineffective injury prevention efforts is the over-reliance on identifying these ‘at-risk’ groups using arbitrary cut-offs for these tests and measures. The purpose of this commentary is to discuss the conceptual and technical issues related to the use of a cut-off in both research and clinical practice. # Clinical Question How can we better assess and interpret clinical tests or measures to enable a more effective injury risk assessment in athletes? # Key Results Cut-offs typically lack strong biologic plausibility to support them; and are typically derived in a data-driven manner and thus not generalizable to other samples. When a cut-off is used in analyses, information is lost, leading to potentially misleading results and less accurate injury risk prediction. Dichotomizing a continuous variable using a cut-off should be avoided. Using continuous variables on its original scale is advantageous because information is not discarded, outcome prediction accuracy is not lost, and personalized medicine can be facilitated. # Clinical Application Researchers and clinicians are encouraged to analyze and interpret the results of tests and measures using continuous variables and avoid relying on singular cut-offs to guide decisions. Injury risk can be predicted more accurately when using continuous variables in their natural form. A more accurate risk prediction will facilitate personalized approaches to injury risk mitigation and may lead to a decline in injury rates. # Level of Evidence 5

Abstract Background Psychological readiness is an important consideration for athletes and clinicians when making return to sport decisions following anterior cruciate ligament reconstruction (ACLR). To improve our understanding of the extent of deficits in psychological readiness, a systematic review is necessary. Objective To investigate psychological readiness (measured via the Anterior Cruciate Ligament-Return to Sport after Injury scale (ACL-RSI)) over time after ACL tear and understand if time between injury and surgery, age, and sex are associated with ACL-RSI scores. Methods Seven databases were searched from the earliest date available to March 22, 2022. Articles reporting ACL-RSI scores after ACL tear were included. Risk of bias was assessed using the ROBINS-I, RoB-2, and RoBANS tools based on the study design. Evidence certainty was assessed for each analysis. Random-effects meta-analyses pooled ACL-RSI scores, stratified by time post-injury and based on treatment approach (i.e., early ACLR, delayed ACLR, and unclear approach). Results A total of 83 studies were included in this review (78% high risk of bias). Evidence certainty was ‘weak’ or ‘limited’ for all analyses. Overall, ACL-RSI scores were higher at 3 to 6 months post-ACLR (mean = 61.5 [95% confidence interval (CI) 58.6, 64.4], I2 = 94%) compared to pre-ACLR (mean = 44.4 [95% CI 38.2, 50.7], I2 = 98%), remained relatively stable, until they reached the highest point 2 to 5 years after ACLR (mean = 70.7 [95% CI 63.0, 78.5], I2 = 98%). Meta-regression suggests shorter time from injury to surgery, male sex, and older age were associated with higher ACL-RSI scores only 3 to 6 months post-ACLR (heterogeneity explained R2 = 47.6%), and this reduced 1–2 years after ACLR (heterogeneity explained R2 = 27.0%). Conclusion Psychological readiness to return to sport appears to improve early after ACL injury, with little subsequent improvement until ≥ 2-years after ACLR. Longer time from injury to surgery, female sex and older age might be negatively related to ACL-RSI scores 12–24 months after ACLR. Due to the weak evidence quality rating and the considerable importance of psychological readiness for long-term outcomes after ACL injury, there is an urgent need for well-designed studies that maximize internal validity and identify additional prognostic factors for psychological readiness at times critical for return to sport decisions. Registration: Open Science Framework (OSF), https://osf.io/2tezs/ .

Abstract Schizophrenia ranks as the third-most common cause of disability among mental disorders globally. This study presents findings on the prevalence, incidence and years lived with disability (YLDs) as a result of schizophrenia in the Middle East and North Africa (MENA), stratified by age, sex and sociodemographic index (SDI). We collected publicly accessible data from the Global Burden of Disease (GBD) study 2019. This study reports the burden of schizophrenia, from 1990 to 2019, for the 21 countries that comprise MENA. In 2019, MENA exhibited an age-standardised point prevalence of 248.2, an incidence rate of 14.7 and an YLD rate of 158.7 per 100,000, which have not changed substantially between 1990 and 2019. In 2019, the age-standardised YLD rate was highest in Qatar and lowest in Afghanistan. No MENA countries demonstrated noteworthy changes in the burden of schizophrenia from 1990 to 2019. Furthermore, in 2019, the highest number of prevalent cases and the point prevalence were observed among those aged 35–39, with a higher prevalence among males in almost all age categories. Additionally, in 2019, the age-standardised YLD rates in MENA were below the worldwide average. Finally, there was a positive correlation between the burden of schizophrenia and the SDI from 1990 to 2019. The disease burden of schizophrenia has remained relatively stable over the past thirty years. Nevertheless, as the regional life-expectancy continues to increase, the burden of schizophrenia is also expected to rise. Therefore, early planning for the increase in the burden of the disease is urgently needed in the region.

Abstract Intersectional social determinants including ethnicity are vital in health research. We curated a population-wide data resource of self-identified ethnicity data from over 60 million individuals in England primary care, linking it to hospital records. We assessed ethnicity data in terms of completeness, consistency, and granularity and found one in ten individuals do not have ethnicity information recorded in primary care. By linking to hospital records, ethnicity data were completed for 94% of individuals. By reconciling SNOMED-CT concepts and census-level categories into a consistent hierarchy, we organised more than 250 ethnicity sub-groups including and beyond “White”, “Black”, “Asian”, “Mixed” and “Other, and found them to be distributed in proportions similar to the general population. This large observational dataset presents an algorithmic hierarchy to represent self-identified ethnicity data collected across heterogeneous healthcare settings. Accurate and easily accessible ethnicity data can lead to a better understanding of population diversity, which is important to address disparities and influence policy recommendations that can translate into better, fairer health for all.

Abstract The Consolidated Standards of Reporting Trials extension for Artificial Intelligence interventions (CONSORT-AI) was published in September 2020. Since its publication, several randomised controlled trials (RCTs) of AI interventions have been published but their completeness and transparency of reporting is unknown. This systematic review assesses the completeness of reporting of AI RCTs following publication of CONSORT-AI and provides a comprehensive summary of RCTs published in recent years. 65 RCTs were identified, mostly conducted in China (37%) and USA (18%). Median concordance with CONSORT-AI reporting was 90% (IQR 77–94%), although only 10 RCTs explicitly reported its use. Several items were consistently under-reported, including algorithm version, accessibility of the AI intervention or code, and references to a study protocol. Only 3 of 52 included journals explicitly endorsed or mandated CONSORT-AI. Despite a generally high concordance amongst recent AI RCTs, some AI-specific considerations remain systematically poorly reported. Further encouragement of CONSORT-AI adoption by journals and funders may enable more complete adoption of the full CONSORT-AI guidelines.

Abstract Background Each year, thousands of clinical prediction models are developed to make predictions (e.g. estimated risk) to inform individual diagnosis and prognosis in healthcare. However, most are not reliable for use in clinical practice. Main body We discuss how the creation of a prediction model (e.g. using regression or machine learning methods) is dependent on the sample and size of data used to develop it—were a different sample of the same size used from the same overarching population, the developed model could be very different even when the same model development methods are used. In other words, for each model created, there exists a multiverse of other potential models for that sample size and, crucially, an individual’s predicted value (e.g. estimated risk) may vary greatly across this multiverse. The more an individual’s prediction varies across the multiverse, the greater the instability. We show how small development datasets lead to more different models in the multiverse, often with vastly unstable individual predictions, and explain how this can be exposed by using bootstrapping and presenting instability plots. We recommend healthcare researchers seek to use large model development datasets to reduce instability concerns. This is especially important to ensure reliability across subgroups and improve model fairness in practice. Conclusions Instability is concerning as an individual’s predicted value is used to guide their counselling, resource prioritisation, and clinical decision making. If different samples lead to different models with very different predictions for the same individual, then this should cast doubt into using a particular model for that individual. Therefore, visualising, quantifying and reporting the instability in individual-level predictions is essential when proposing a new model.

Summary: Background: Diagnosis is a cornerstone of medical practice. Worldwide, there is increased demand for diagnostic services, exacerbating workforce shortages. Artificial intelligence (AI) technologies may improve diagnostic efficiency, accuracy, and access. Understanding stakeholder perspectives is key to informing implementation of complex interventions. We systematically reviewed the literature on stakeholder perspectives on diagnostic AI, including all English-language peer-reviewed primary qualitative or mixed-methods research. Methods: We searched PubMed, Ovid MEDLINE/Embase, Scopus, CINAHL and Web of Science (22/2/2023 and updated 8/2/2024). The Critical Appraisal Skills Programme Checklist informed critical appraisal. We used a ‘best-fit’ framework approach for analysis, using the Non-adoption, Abandonment, Scale-up, Spread, Sustainability (NASSS) framework. This study was pre-registered (PROSPERO CRD42022313782). Findings: We screened 16,577 articles and included 44. 689 participants were interviewed, and 402 participated in focus groups. Four stakeholder groups were described: patients, clinicians, researchers and healthcare leaders. We found an under-representation of patients, researchers and leaders across articles. We summarise the differences and relationships between each group in a conceptual model, hinging on the establishment of trust, engagement and collaboration. We present a modification of the NASSS framework, tailored to diagnostic AI. Interpretation: We provide guidance for future research and implementation of diagnostic AI, highlighting the importance of representing all stakeholder groups. We suggest that implementation strategies consider how any proposed software fits within the extended NASSS-AI framework, and how stakeholder priorities and concerns have been addressed. Funding: RK is supported by an NIHR Doctoral Research Fellowship grant (NIHR302562), which funded patient and public involvement activities, and access to Covidence.

Abstract Background The aim of this study was to forecast future patient demand for shoulder replacement surgery in England and investigate any geographic and socioeconomic inequalities in service provision and patient outcomes. Methods For this cohort study, all elective shoulder replacements carried out by NHS hospitals and NHS-funded care in England from 1999 to 2020 were identified using Hospital Episode Statistics data. Eligible patients were aged 18 years and older. Shoulder replacements for malignancy or acute trauma were excluded. Population estimates and projections were obtained from the Office for National Statistics. Standardised incidence rates and the risks of serious adverse events (SAEs) and revision surgery were calculated and stratified by geographical region, socioeconomic deprivation, sex, and age band. Hospital costs for each admission were calculated using Healthcare Resource Group codes and NHS Reference Costs based on the National Reimbursement System. Projected rates and hospital costs were predicted until the year 2050 for two scenarios of future growth. Results A total of 77,613 elective primary and 5847 revision shoulder replacements were available for analysis. Between 1999 and 2020, the standardised incidence of primary shoulder replacements in England quadrupled from 2.6 to 10.4 per 100,000 population, increasing predominantly in patients aged over 65 years. As many as 1 in 6 patients needed to travel to a different region for their surgery indicating inequality of service provision. A temporal increase in SAEs was observed: the 30-day risk increased from 1.3 to 4.8% and the 90-day risk increased from 2.4 to 6.0%. Patients from the more deprived socioeconomic groups appeared to have a higher risk of SAEs and revision surgery. Shoulder replacements are forecast to increase by up to 234% by 2050 in England, reaching 20,912 procedures per year with an associated annual cost to hospitals of £235 million. Conclusions This study reports a rising incidence of shoulder replacements, regional disparities in service provision, and an overall increasing risk of SAEs, especially in more deprived socioeconomic groups. These findings highlight the need for better healthcare planning to match local population demand, while more research is needed to understand and prevent the increase observed in SAEs.

Background: Risk profiling and education are strategies implemented to help reduce injury risk; however, currently. there is little evidence on the effect of these interventions on injury incidence. The purpose of this study was to evaluate the influence of risk profiling and education on upper extremity injury incidence in minor league (MiLB) pitchers and to stratify by injury severity. Methods: A prospective natural experiment study was conducted from 2013 to 2019 on MiLB pitchers. Beginning in the 2015 season, pitchers were examined and risk profiled for upper extremity injury. Shoulder external, internal, total range of motion, horizontal adduction, and humeral torsion were measured. Organizational risk profiling and education was implemented starting in 2015, based on preseason assessments. Chi-squared test was performed to investigate potential differences between shoulder range of motion risk categories between 2013-2014 (pre) and 2015-2019 (post) seasons. Interrupted time series analyses were performed to assess the association between organizational risk profiling and education on arm injury in MiLB pitchers and were repeated for 7-27 and 28+ day injury severity. Results: 297 pitchers were included (pre: 119, post: 178). Upper extremity injury incidence was 1.5 injuries per 1000 athletic exposures. Pitchers in the 2015-2019 seasons demonstrated increased preseason shoulder injury risk for internal (P = .003) and external (P = .007), while the 2013-2014 seasons demonstrated greater horizontal adduction risk (P = .04). There were no differences between seasons for total range of motion risk (P =.76). Risk profiling and education resulted in an adjusted time loss upper extremity injury reduction for the 2015-2019 seasons (0.68 (95% CI: 0.47, 0.99)), which impacted 7-27 days (0.62 (95% CI: 0.42, 0.93)) but not for 28+ days (0.71 (95% CI: 0.47, 1.06)) time loss. There was no reduction in combined trunk and lower extremity injuries for the 2015-2019 seasons (1.55 (95% CI: 0.79, 3.01)). Conclusions: Organizational risk profiling and education appear to reduce professional pitching overall and 7-27-day upper extremity injury risk by 33%-38%. There was no difference in trunk and lower extremity injuries over the period, strengthening the reduction in upper extremity injury risk results. This suggests that while injury risk increased over time, organizational risk profiling mitigated the expected increase in upper extremity injury rates. Risk profiling and education can be used as a clinical screening and intervention tool to help decrease upper extremity injuries in professional baseball populations.

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. This article is the translation in to Russian by Dr. Ruslan Saygitov (ORCID: https://orcid.org/0000-0002-8915-6153) from the original published in [Ann Intern Med. 2015;162:W1-W73. doi: https://doi.org/10.7326/M14-0698].

Background: Neck pain is a common cause of disability across the world. The objective of the present study was to present a thorough investigation of the burden caused by neck pain in the Middle East and North Africa (MENA) region, by country, sex, age group and socio-demographic index (SDI). Methods: The data on the burden of neck pain, encompassing its prevalence, incidence and years lived with disability (YLDs), were extracted from the Global Burden of Disease (GBD) 2019 study. These findings are reported as age-standardised numbers and rates (per 100,000), accompanied by 95 % uncertainty intervals (UIs). Results: The age-standardised point prevalence of neck pain in 2019 was 3066.7 (95 % UI: 2407.8 to 3894.3) per 100,000, with an age-standardised incidence rate of 649.2 (509.2–829.2) in the MENA region, neither of which have changed since 1990. The age-standardised YLD rate of neck pain was 303.0 (201.5–438.8) per 100,000 population in 2019. The highest YLD rate of neck pain was found in Iran [423.5 (280.3–609.8)] and the lowest in Kuwait [215.0 (141.0–314.1)]. The highest number of prevalent cases were seen in the 45–49 age-group for both sexes in 2019, but overall females had a higher point prevalence than males. Furthermore, over the study period (1990–2019) there was no clear and consistent relationship between the SDI and the burden of neck pain. Conclusion: Although the burden of neck pain has largely remained stable over the past three decades, the prevalence and morbidity in the MENA region remains high. Preventive and rehabilitative programs should be implemented that firstly target middle-aged females and males.

Summary: Background: Interventional trials that evaluate treatment effects using surrogate endpoints have become increasingly common. This paper describes four linked empirical studies and the development of a framework for defining, interpreting and reporting surrogate endpoints in trials. Methods: As part of developing the CONSORT (Consolidated Standards of Reporting Trials) and SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) extensions for randomised trials reporting surrogate endpoints, we undertook a scoping review, e-Delphi study, consensus meeting, and a web survey to examine current definitions and stakeholder (including clinicians, trial investigators, patients and public partners, journal editors, and health technology experts) interpretations of surrogate endpoints as primary outcome measures in trials. Findings: Current surrogate endpoint definitional frameworks are inconsistent and unclear. Surrogate endpoints are used in trials as a substitute of the treatment effects of an intervention on the target outcome(s) of ultimate interest, events measuring how patients feel, function, or survive. Traditionally the consideration of surrogate endpoints in trials has focused on biomarkers (e.g., HDL cholesterol, blood pressure, tumour response), especially in the medical product regulatory setting. Nevertheless, the concept of surrogacy in trials is potentially broader. Intermediate outcomes that include a measure of function or symptoms (e.g., angina frequency, exercise tolerance) can also be used as substitute for target outcomes (e.g., all-cause mortality)—thereby acting as surrogate endpoints. However, we found a lack of consensus among stakeholders on accepting and interpreting intermediate outcomes in trials as surrogate endpoints or target outcomes. In our assessment, patients and health technology assessment experts appeared more likely to consider intermediate outcomes to be surrogate endpoints than clinicians and regulators. Interpretation: There is an urgent need for better understanding and reporting on the use of surrogate endpoints, especially in the setting of interventional trials. We provide a framework for the definition of surrogate endpoints (biomarkers and intermediate outcomes) and target outcomes in trials to improve future reporting and aid stakeholders' interpretation and use of trial surrogate endpoint evidence. Funding: SPIRIT-SURROGATE/CONSORT-SURROGATE project is Medical Research Council Better Research Better Health (MR/V038400/1) funded.

Abstract 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.

Abstract 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.

Abstract Background Anorexia nervosa (AN) is a complex and heritable psychiatric disorder, which imposes significant mortality and morbidity on sufferers globally. We aimed to report the prevalence, incidence and disability-adjusted life-years (DALYs) attributable to AN in the Middle East and North Africa (MENA) region by age, sex and socio-demographic index (SDI), between 1990 and 2019. Methods The disease burden attributable to AN was obtained for the 21 countries located in the MENA region between 1990 and 2019 using publicly available data from the Global Burden of Disease (GBD) 2019 study. All estimates were provided as counts and age-standardized rates per 100,000 population, along with 95% uncertainty intervals (UIs). Results In 2019, the estimated age-standardised point prevalence and incidence rate of AN (per 100,000) in MENA were 49.3 (95% UI: 34.6–70.4) and 16.0 (11.3–22.0), which were 11.4% (7.3–15.4) and 5.9% (2.6–9.1) higher than in 1990, respectively. Furthermore, the regional age-standardised DALY rate was 10.6 (6.3–17.0) per 100,000 in 2019, which was 11.8% (5.2–19.1) higher than in 1990. In 2019, Kuwait [17.3 (10.3-27.9)] and Afghanistan [6.3 (3.7-10.3)] had the highest and lowest age-standardised DALY rates, respectively. In addition, Iran showed the largest increases in the age-standardised point prevalence [30.0% (24.1–36.2)], incidence [24.6% (18.6–30.4)] and DALY [30.5% (22.6–38.9)] rates between 1990 and 2019. In 2019, the number of prevalent cases and prevalence estimates peaked in the 15–19 age group for males and the 20–24 age group for females, with females having a higher number of cases and prevalence in all age groups. In 2019, the age-standardised DALY rates in MENA were higher than the global rates among males aged 10–34 years, but were lower than the global estimates among females in almost all age groups. In addition, the burden of AN was positively associated with the level of socio-economic development during the measurement period. Conclusions The burden of AN in the MENA region increased between 1990 and 2019, which indicates that it is likely to become a more serious public health issue in the future. Up-to-date information about the epidemiological trends in the region would allow health policymakers to make informed and appropriate decisions to help address this issue. Plain English summary The findings of the present study showed that the point prevalence and incidence rate of anorexia nervosa have increased in the Middle East and North Africa region between 1990 and 2019. The highest burden in 2019 was found in Kuwait, while Afghanistan had the lowest attributable burden. In addition, between 1990 and 2019 Iran had the largest increase in the point prevalence of anorexia nervosa. Also in 2019, anorexia nervosa was more prevalent in females and peaked in the 15–19 age group for males and the 20–24 age group for females. Furthermore, as the level of socioeconomic development increased, so did the burden attributable to anorexia nervosa.

Abstract Background Chronic obstructive pulmonary disease (COPD) is a preventable and treatable disease that is characterised by persistent respiratory symptoms and airflow limitation. The present study reported the burden of COPD, and its attributable risk factors, in the Middle East and North Africa (MENA) region between 1990 and 2019, by age, sex and socio-demographic index (SDI). Methods Data from the Global Burden of Disease (GBD) 2019 study were used to report the burden of COPD in the MENA countries. The prevalence, deaths, and disability-adjusted life-years (DALYs) were presented as counts and age-standardised rates per 100,000 population, with their associated 95% uncertainty intervals (UIs). Results In 2019, the regional age-standardised point prevalence and rates of death due to COPD were 2333.9 (2230.1, 2443.6) and 26.1 (22.2, 29.5) per 100,000, which represent a 30.6% (28.2%, 33.0%) increase and an 18.0% (2.8%, 30.9%) decrease, respectively, since 1990. The regional age-standardised DALY rate in 2019 was 649.1 (574.6, 717.7) per 100,000, which had decreased by 11.8% (0.9%, 21.1%) since 1990. Turkey had the highest age-standardised point prevalence in 2019 [3287.1 (3187.4, 3380.3)], while Afghanistan had the highest age-standardised death [40.4 (24.2, 52.6)] and DALY [964.5 (681.8, 1203.2)] rates. The regional age-standardised point prevalence, death and DALY rates in 2019 increased with advancing age and were higher in males in almost all age groups. There was a U-shaped association between SDI and the burden of COPD over the period 1990 to 2019. Moreover, in 2019 smoking (43.7%), ambient particulate matter pollution (22.8%) and occupational particulate matter (11.4%) had the largest proportion of attributable DALYs for both sexes. Conclusions COPD is one of the leading causes of death and disability in the MENA region, although the age-standardised burden has decreased over the last 30 years. Nevertheless, COPD accounted for a substantial number of deaths and DALYs, especially among the elderly. Programs targeting risk factors, like smoking, should be taken into consideration.

Abstract Alcohol consumption is associated with a number of diseases and injuries, including cardiovascular diseases, cancers, mental and neurological disorders, as well as transport-related injuries. This article reports the alcohol-attributable burden of diseases and injuries at the regional and national levels in the Middle East and North Africa (MENA) region between 1990 and 2019, by sex, age, underlying cause, and Socio-demographic Index (SDI). The regional deaths and disability-adjusted life-years (DALYs) attributable to alcohol consumption were reported for the MENA region, between 1990 and 2019, using the methodological framework and analytical strategies adopted by the Global Burden of Disease (GBD) study 2019. The estimates were all reported as counts, population-attributable fractions, and age-standardised rates per 100,000 population, along with their corresponding 95% uncertainty intervals (UIs). Also, the average annual percentage changes were used to represent the trends of age-standardised rates. In 2019, there were an estimated 22.0 thousand deaths (95% UI: 16.1–29.4) and 1.1 million DALYs (0.8–1.3) attributable to alcohol consumption in the MENA region. The number of DALYs attributable to alcohol consumption were much higher in men (878.0 thousand, 691.4–1104.8) than among women (181.8, 138.6–232.0). The overall age-standardised death and DALY rates attributable to alcohol consumption decreased by 34.5% (13.2–48.3) and 31.9% (16.9–42.5), respectively, over the study period. Egypt (10.1 [5.7–16.6]) and Kuwait (1.1 [0.8–1.5]) had the highest and lowest age-standardised death rates attributable to alcohol consumption, respectively. In 2019, the number of deaths and DALYs in the MENA region were highest in those aged 60–64 and 50–54 years, respectively. A negative association was observed between a country’s SDI and their corresponding age-standardised DALY rates over the period 1990 to 2019. Digestive diseases were the main contributor to the alcohol-attributable burden. Over 1990–2019, the regional deaths and DALYs of diseases and injuries attributable to alcohol consumption decreased with AAPC of − 1.45 (− 1.78 to − 1.12) and − 1.31 (− 1.46 to − 1.15), respectively. The death and DALY rates attributable to alcohol consumption in the MENA region have decreased over the past three decades. Further decreases can be facilitated by implementing country-level policies and increasing public awareness.

Abstract Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease. The present study reported the burden of RA in the Middle East and North Africa (MENA) region from 1990 to 2019 by age, sex, and socio-demographic index (SDI). Publicly available data from the Global Burden of Disease (GBD) 2019 study was used to report the modelled point prevalence, annual incidence, and disability-adjusted life-years (DALYs) of RA, as counts and age-standardised rates with their corresponding 95% uncertainty intervals (UIs). In 2019, RA had an age-standardised point prevalence of 120.6 per 100,000 population (107.0–135.7) and an annual incidence rate of 5.9 (5.2–6.6) in MENA, which have increased 28.3% and 25.2%, respectively, since 1990. In 2019, the number of DALYs due to RA in the region was 103.6 thousand (74.2–136.7), with an age-standardised rate of 19.0 (13.9–24.9) DALYs per 100,000 population, which has increased by 18.6% since 1990 (6.7–28.2). The highest point prevalence was found in females aged 50–54, and in males aged 45–49. The highest number of DALYs was observed in the 50–54 age group. The MENA DALY rate was lower than the global rate (19.0 vs. 39.6 per 100,000), but the rate was higher in all age groups in 2019, when compared with 1990. In addition, from 1990 to 2019 an increased burden from RA was associated with an increase in SDI. In line with global trends, the burden of RA in the MENA region showed a steady increase from 1990 to 2019. This highlights the increasing need for updating the available health data to design more accurate guidelines to enable the early detection and treatment of RA in the MENA countries.

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 ].

Abstract Background Cancers are leading causes of mortality and morbidity, with smoking being recognized as a significant risk factor for many types of cancer. We aimed to report the cancer burden attributable to tobacco smoking by sex, age, socio‐demographic index (SDI), and cancer type in 204 countries and territories from 1990 to 2019. Methods The burden of cancers attributable to smoking was reported between 1990 and 2019, based upon the Comparative Risk Assessment approach used in the Global Burden of Disease (GBD) study 2019. Results Globally, in 2019 there were an estimated 2.5 million cancer‐related deaths (95% UI: 2.3 to 2.7) and 56.4 million DALYs (51.3 to 61.7) attributable to smoking. The global age‐standardized death and DALY rates of cancers attributable to smoking per 100,000 decreased by 23.0% (−29.5 to −15.8) and 28.6% (−35.1 to −21.5), respectively, over the period 1990–2019. Central Europe (50.4 [44.4 to 57.6]) and Western Sub‐Saharan Africa (6.7 [5.7 to 8.0]) had the highest and lowest age‐standardized death rates, respectively, for cancers attributable to smoking. In 2019, the age‐standardized DALY rate of cancers attributable to smoking was highest in Greenland (2224.0 [1804.5 to 2678.8]) and lowest in Ethiopia (72.2 [51.2 to 98.0]). Also in 2019, the global number of DALYs was highest in the 65–69 age group and there was a positive association between SDI and the age‐standardized DALY rate. Conclusions The results of this study clearly illustrate that renewed efforts are required to increase utilization of evidence‐based smoking cessation support in order to reduce the burden of smoking‐related diseases.

Abstract Background Prognostic models are used widely in the oncology domain to guide medical decision-making. Little is known about the risk of bias of prognostic models developed using machine learning and the barriers to their clinical uptake in the oncology domain. Methods We conducted a systematic review and searched MEDLINE and EMBASE databases for oncology-related studies developing a prognostic model using machine learning methods published between 01/01/2019 and 05/09/2019. The primary outcome was risk of bias, judged using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). We described risk of bias overall and for each domain, by development and validation analyses separately. Results We included 62 publications (48 development-only; 14 development with validation). 152 models were developed across all publications and 37 models were validated. 84% (95% CI: 77 to 89) of developed models and 51% (95% CI: 35 to 67) of validated models were at overall high risk of bias. Bias introduced in the analysis was the largest contributor to the overall risk of bias judgement for model development and validation. 123 (81%, 95% CI: 73.8 to 86.4) developed models and 19 (51%, 95% CI: 35.1 to 67.3) validated models were at high risk of bias due to their analysis, mostly due to shortcomings in the analysis including insufficient sample size and split-sample internal validation. Conclusions The quality of machine learning based prognostic models in the oncology domain is poor and most models have a high risk of bias, contraindicating their use in clinical practice. Adherence to better standards is urgently needed, with a focus on sample size estimation and analysis methods, to improve the quality of these models.

Abstract Introduction Tension-type headache (TTH) is the most prevalent neurological disorder. As there is a gap in the literature regarding the disease burden attributable to TTH in the Middle East and North Africa (MENA) region, the aim of the present study was to report the epidemiological indicators of TTH in MENA, from 1990 to 2019, by sex, age and socio-demographic index (SDI). Methods Publicly available data on the point prevalence, annual incidence and years lived with disability (YLDs) were retrieved from the global burden of disease (GBD) 2019 study for the 21 countries and territories in MENA, between 1990 and 2019. The results were presented with numbers and age-standardised rates per 100000 population, along with their corresponding 95% uncertainty intervals (UIs). Results In 2019, the age-standardised point prevalence and annual incidence rates for TTH in the MENA region were 24504.5 and 8680.1 per 100000, respectively, which represents a 2.0% and a 0.9% increase over 1990-2019, respectively. The age-standardised YLD rate of TTH in this region in 2019 was estimated to be 68.1 per 100000 population, which has increased 1.0% since 1990. Iran [29640.4] had the highest age-standardised point prevalence rate for TTH, while Turkey [21726.3] had the lowest. In 2019, the regional point prevalence of TTH was highest in the 35-39 and 70-74 age groups, for males and females, respectively. Furthermore, the number of prevalent cases was estimated to be highest in those aged 35-39 and 25-29 years, in both males and females, respectively. Moreover, the burden of TTH was not observed to have a clear association with SDI. Conclusions While the prevalence of TTH in the MENA region increased from 1990 to 2019, the incidence rate did not change. In addition, the burden of TTH in MENA was higher than at the global level for both sexes and all age groups. Therefore, prevention of TTH would help alleviate the attributable burden imposed on the hundreds of millions of people suffering from TTH around the region.

Abstract Background Describe and evaluate the methodological conduct of prognostic prediction models developed using machine learning methods in oncology. Methods We conducted a systematic review in MEDLINE and Embase between 01/01/2019 and 05/09/2019, for studies developing a prognostic prediction model using machine learning methods in oncology. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, Prediction model Risk Of Bias ASsessment Tool (PROBAST) and CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) to assess the methodological conduct of included publications. Results were summarised by modelling type: regression-, non-regression-based and ensemble machine learning models. Results Sixty-two publications met inclusion criteria developing 152 models across all publications. Forty-two models were regression-based, 71 were non-regression-based and 39 were ensemble models. A median of 647 individuals (IQR: 203 to 4059) and 195 events (IQR: 38 to 1269) were used for model development, and 553 individuals (IQR: 69 to 3069) and 50 events (IQR: 17.5 to 326.5) for model validation. A higher number of events per predictor was used for developing regression-based models (median: 8, IQR: 7.1 to 23.5), compared to alternative machine learning (median: 3.4, IQR: 1.1 to 19.1) and ensemble models (median: 1.7, IQR: 1.1 to 6). Sample size was rarely justified (n = 5/62; 8%). Some or all continuous predictors were categorised before modelling in 24 studies (39%). 46% (n = 24/62) of models reporting predictor selection before modelling used univariable analyses, and common method across all modelling types. Ten out of 24 models for time-to-event outcomes accounted for censoring (42%). A split sample approach was the most popular method for internal validation (n = 25/62, 40%). Calibration was reported in 11 studies. Less than half of models were reported or made available. Conclusions The methodological conduct of machine learning based clinical prediction models is poor. Guidance is urgently needed, with increased awareness and education of minimum prediction modelling standards. Particular focus is needed on sample size estimation, development and validation analysis methods, and ensuring the model is available for independent validation, to improve quality of machine learning based clinical prediction models.

Abstract Stroke is one of the leading causes of mortality and morbidity across the globe. Providing comprehensive data on the burden of stroke in the Middle East and North Africa (MENA) could be useful for health policy makers in the region. Therefore, this article reported the burden of stroke and its attributable risk factors between 1990 and 2019 by age, sex, type of stroke, and socio-demographic index. Data on the point prevalence, death, and disability-adjusted life-years (DALYs), due to stroke, were retrieved from the Global Burden of Disease study 2019 for the 21 countries located in the MENA region from 1990 to 2019. The counts and age-standardised rates (per 100,000) were presented, along with their corresponding 95% uncertainty intervals (UIs). In 2019, the regional age-standardised point prevalence and death rates of stroke were 1537.5 (95% UI: 1421.9–1659.9) and 87.7 (78.2–97.6) per 100,000, which represent a 0.5% (− 2.3 to 1.1) and 27.8% (− 35.4 to − 16) decrease since 1990, respectively. Moreover, the regional age-standardised DALY rate in 2019 was 1826.2 (1635.3–2026.2) per 100,000, a 32.0% (− 39.1 to − 23.3) decrease since 1990. In 2019, Afghanistan [3498.2 (2508.8–4500.4)] and Lebanon [752.9 (593.3–935.9)] had the highest and lowest age-standardised DALY rates, respectively. Regionally, the total number of stroke cases were highest in the 60–64 age group and was more prevalent in women in all age groups. In addition, there was a general negative association between SDI and the burden of stoke from 1990 to 2019. Also, in 2019, high systolic blood pressure [53.5%], high body mass index [39.4%] and ambient particulate air pollution [27.1%] made the three largest contributions to the burden of stroke in the MENA region. The stroke burden has decreased in the MENA region over the last three decades, although there are large inter-country differences. Preventive programs should be implemented which focus on metabolic risk factors, especially among older females in low SDI countries.

Abstract Background While many studies have consistently found incomplete reporting of regression-based prediction model studies, evidence is lacking for machine learning-based prediction model studies. We aim to systematically review the adherence of Machine Learning (ML)-based prediction model studies to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement. Methods We included articles reporting on development or external validation of a multivariable prediction model (either diagnostic or prognostic) developed using supervised ML for individualized predictions across all medical fields. We searched PubMed from 1 January 2018 to 31 December 2019. Data extraction was performed using the 22-item checklist for reporting of prediction model studies ( www.TRIPOD-statement.org ). We measured the overall adherence per article and per TRIPOD item. Results Our search identified 24,814 articles, of which 152 articles were included: 94 (61.8%) prognostic and 58 (38.2%) diagnostic prediction model studies. Overall, articles adhered to a median of 38.7% (IQR 31.0–46.4%) of TRIPOD items. No article fully adhered to complete reporting of the abstract and very few reported the flow of participants (3.9%, 95% CI 1.8 to 8.3), appropriate title (4.6%, 95% CI 2.2 to 9.2), blinding of predictors (4.6%, 95% CI 2.2 to 9.2), model specification (5.2%, 95% CI 2.4 to 10.8), and model’s predictive performance (5.9%, 95% CI 3.1 to 10.9). There was often complete reporting of source of data (98.0%, 95% CI 94.4 to 99.3) and interpretation of the results (94.7%, 95% CI 90.0 to 97.3). Conclusion Similar to prediction model studies developed using conventional regression-based techniques, the completeness of reporting is poor. Essential information to decide to use the model (i.e. model specification and its performance) is rarely reported. However, some items and sub-items of TRIPOD might be less suitable for ML-based prediction model studies and thus, TRIPOD requires extensions. Overall, there is an urgent need to improve the reporting quality and usability of research to avoid research waste. Systematic review registration PROSPERO, CRD42019161764.

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.

Abstract Background Anemia is a common disease which affects around 40% of children and 30% of reproductive age women and can have major health consequences. The present study reports the global, regional and national burden of anemia and its underlying causes between 1990 and 2019, by age, sex and socio-demographic index (SDI). Methods Publicly available data on the point prevalence and years lived with disability (YLDs) were retrieved from the global burden of disease (GBD) 2019 study for 204 countries and territories between 1990 and 2019. The point prevalence, YLD counts and rates per 100,000 population were presented, along with their corresponding 95% uncertainty intervals. Results In 2019, the global age-standardized point prevalence and YLD rates for anemia were 23,176.2 (22,943.5–23,418.6) and 672.4 (447.2–981.5) per 100,000 population, respectively. Moreover, the global age-standardized point prevalence and YLD rate decreased by 13.4% (12.1–14.5%) and 18.8% (16.9–20.8%), respectively, over the period 1990–2019. The highest national point prevalences of anemia were found in Zambia [49327.1 (95% UI: 46,838.5–51,700.1)], Mali [46890.1 (95% UI: 44,301.1–49,389.8)], and Burkina Faso [46117.2 (95% UI: 43,640.7–48,319.2)]. In 2019, the global point prevalence of anemia was highest in the 15–19 and 95+ age groups in females and males, respectively. Also, the burden of anemia was lower in regions with higher socio-economic development. Globally, most of the prevalent cases were attributable to dietary iron deficiency, as well as hemoglobinopathies and hemolytic anemias. Conclusions Anemia remains a major health problem, especially among females in less developed countries. The implementation of preventive programs with a focus on improving access to iron supplements, early diagnosis and the treatment of hemoglobinopathies should be taken into consideration.

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.

BackgroundTo report the burden of cancers attributable to high fasting plasma glucose (HFPG) by sex, age, location, cancer type and Socio-demographic Index (SDI) over the period 1990 to 2019 for 204 countries and territories.MethodsUsing the Comparative Risk Assessment approach of Global Burden of Disease (GBD) study 2019, the burden of cancers attributable to HFPG was reported in 1990 and 2019.ResultsGlobally, in 2019 there were an estimated 419.3 thousand cancer deaths (95% UI: 115.7 to 848.5) and 8.6 million cancer DALYs (2.4 to 17.6) attributable to HFPG. By sex, 4.6 (1.1 to 9.9) and 4.0 (1.1 to 8.4) million global cancer DALYs were attributable to HFPG in men and women, respectively. The global age-standardized death and DALY rates of cancers attributable to HFPG (per 100,000) have increased by 27.8% (20.5 to 38.7%) and 24.5% (16.4 to 35.6%), respectively, since 1990. High-income North America (9.5 [2.7 to 18.8]) and Eastern Sub-Saharan Africa (2.0 [0.5 to 4.2]) had the highest and lowest regional age-standardized death rates, respectively, for cancers attributable to HFPG. In 2019, the global number of attributable cancer DALYs were highest in 65-69 age group. Moreover, there was an overall positive association between SDI and the regional age-standardized DALY rate for HFPG-attributable cancers.ConclusionsHFPG was associated with more burden in 2019. Preventive programs for diabetes and screening of individuals with diabetes for cancers, especially in older males living in developed countries, are required to arrest the large increases in HFPG-attributable cancers.

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.

ObjectiveWe aimed to report the most current data on the prevalence, incidence, and years lived with disability (YLDs) associated with osteoarthritis (OA) for the 21 countries and territories located in the Middle East and North Africa (MENA) region from 1990 to 2019 by age, sex, cause, and sociodemographic index (SDI).MethodsPublicly available data from the Global Burden of Disease 2019 study were used to report the OA-related burden. Estimates are reported as counts and age-standardized rates, along with their corresponding 95% uncertainty intervals (UIs).ResultsIn 2019, the age-standardized prevalence of OA in MENA was 5,342.8 per 100,000 (95% UI: 4,815.9–5,907.8), which is 9.3% higher than in 1990 (8.1–10.5%). Similarly, the age-standardized annual incidence of OA per 100,000 was 430.4 (382.2–481.9), demonstrating a 9.4% increase since 1990 (8.3–10.5). OA was the cause of 185.4 (92.8–370.2) age-standardized YLDs per 100,000 in 2019, which was 10% higher than in 1990 (8.7–11.4). Saudi Arabia, Kuwait, and Iran had the highest OA burden in MENA, while Yemen, Afghanistan, and Sudan had the lowest burden. In all MENA countries, OA affected more women than men, had an increasing burden with increased age, and had the highest impact on the knee, hip, and hand joints, respectively. OA was also positively associated with the SDI.ConclusionThe burden of OA increased over 1990–2019 in the MENA region. The study emphasizes the importance of early preventative approaches in order to control any future health, economic, and quality of life crises imposed by OA in this region.

Abstract Clinical prediction models (CPMs) have become fundamental for risk stratification across healthcare. The CPM pipeline (development, validation, deployment, and impact assessment) is commonly viewed as a one-time activity, with model updating rarely considered and done in a somewhat ad hoc manner. This fails to address the fact that the performance of a CPM worsens over time as natural changes in populations and care pathways occur. CPMs need constant surveillance to maintain adequate predictive performance. Rather than reactively updating a developed CPM once evidence of deteriorated performance accumulates, it is possible to proactively adapt CPMs whenever new data becomes available. Approaches for validation then need to be changed accordingly, making validation a continuous rather than a discrete effort. As such, “living” (dynamic) CPMs represent a paradigm shift, where the analytical methods dynamically generate updated versions of a model through time; one then needs to validate the system rather than each subsequent model revision.

Introduction Breast cancer is the most common cancer and the leading cause of cancer-related death in women worldwide. Risk prediction models may be useful to guide risk-reducing interventions (such as pharmacological agents) in women at increased risk or inform screening strategies for early detection methods such as screening.Methods and analysis The study will use data for women aged 20–90 years between 2000 and 2020 from QResearch linked at the individual level to hospital episodes, cancer registry and death registry data. It will evaluate a set of modelling approaches to predict the risk of developing breast cancer within the next 10 years, the ‘combined’ risk of developing a breast cancer and then dying from it within 10 years, and the risk of breast cancer mortality within 10 years of diagnosis. Cox proportional hazards, competing risks, random survival forest, deep learning and XGBoost models will be explored. Models will be developed on the entire dataset, with ‘apparent’ performance reported, and internal-external cross-validation used to assess performance and geographical and temporal transportability (two 10-year time periods). Random effects meta-analysis will pool discrimination and calibration metric estimates from individual geographical units obtained from internal-external cross-validation. We will then externally validate the models in an independent dataset. Evaluation of performance heterogeneity will be conducted throughout, such as exploring performance across ethnic groups.Ethics and dissemination Ethics approval was granted by the QResearch scientific committee (reference number REC 18/EM/0400: OX129). The results will be written up for submission to peer-reviewed journals.

Abstract Background Heart failure (HF) is a chronic and common condition with a rising prevalence, especially in the elderly. Morbidity and mortality rates in people with HF are similar to those with common forms of cancer. Clinical guidelines highlight the need for more detailed prognostic information to optimise treatment and care planning for people with HF. Besides proven prognostic biomarkers and numerous newly developed prognostic models for HF clinical outcomes, no risk stratification models have been adequately established. Through a number of linked systematic reviews, we aim to assess the quality of the existing models with biomarkers in HF and summarise the evidence they present. Methods We will search MEDLINE, EMBASE, Web of Science Core Collection, and the prognostic studies database maintained by the Cochrane Prognosis Methods Group combining sensitive published search filters, with no language restriction, from 1990 onwards. Independent pairs of reviewers will screen and extract data. Eligible studies will be those developing, validating, or updating any prognostic model with biomarkers for clinical outcomes in adults with any type of HF. Data will be extracted using a piloted form that combines published good practice guidelines for critical appraisal, data extraction, and risk of bias assessment of prediction modelling studies. Missing information on predictive performance measures will be sought by contacting authors or estimated from available information when possible. If sufficient high quality and homogeneous data are available, we will meta-analyse the predictive performance of identified models. Sources of between-study heterogeneity will be explored through meta-regression using pre-defined study-level covariates. Results will be reported narratively if study quality is deemed to be low or if the between-study heterogeneity is high. Sensitivity analyses for risk of bias impact will be performed. Discussion This project aims to appraise and summarise the methodological conduct and predictive performance of existing clinically homogeneous HF prognostic models in separate systematic reviews. Registration: PROSPERO registration number CRD42019086990

Abstract Background Sports participants are faced with the decision to continue or cease play when injured. The implications of playing sport while injured on joint health and health-related quality of life (HRQoL) has not been investigated. The purpose of this study was to investigate the relationship between having played sport while injured and HRQoL, osteoarthritis, and persistent joint pain; and compare findings in elite and recreational cricketers. Methods The Cricket Health and Wellbeing Study cohort was used for this study. Inclusion criteria were: age ≥ 18 years, played ≥1 cricket season. Questionnaire data collected included a history of playing sport injured, SF-8 (physical (PCS) and mental (MCS) component scores), physician-diagnosed osteoarthritis, and persistent joint pain (most days of the last month). Multivariable linear regressions and logistic regressions were performed. Continuous covariates were handled using fractional polynomials. Models were adjusted for age, sex, cricket-seasons played, playing status, joint injury, and orthopaedic surgery. All participants (n = 2233) were included in HRQoL analyses, only participants aged ≥30 years (n = 2071) were included in osteoarthritis/pain analyses. Results Of the 2233 current and former cricketers (mean age: 51.7 SD 14.7, played 30 IQR 24 cricket seasons, 60% were current cricketers, 62% played recreationally; median PCS: 51.4 IQR 9.0; MCS: 54.3 IQR 8.6) 1719 (77%) had played sport while injured. People who had played sport injured reported worse adjusted PCS (Effect(95% CI): − 1.78(− 2.62, − 0.93) and MCS (− 1.40(− 2.25, − 0.54), had greater odds of osteoarthritis (adjusted OR(95% CI): 1.86(1.39, 2.51) and persistent joint pain (2.34(1.85, 2.96)), compared to people who had not played sport injured. Similar relationships were observed regarding PCS, osteoarthritis and pain in elite and recreational subgroups. Playing injured was only related to worse MCS scores for elite cricketers (− 2.07(− 3.52, − 0.63)); no relationship was observed in recreational cricketers (− 0.70(− 1.79, 0.39)). Conclusion Cricketers that had played sport injured had impaired HRQoL, increased odds of osteoarthritis and persistent joint pain, compared to those who had not played sport injured. Playing sport injured was only related to impaired mental-components of HRQoL in elite cricketers. The long-term impact of playing while injured on musculoskeletal health, should be considered when advising athletes on their ability to compete following injury.

Abstract Background Health related quality of life (HRQoL) and flourishing are constructs that encompasses a holistic representation of physical, psychological, and social health. The underlying psychological factors that can affect HRQoL and flourishing in sports participants is poorly understood. The purpose of this study was to i) evaluate HRQoL (physical and mental-components) and flourishing in recreational and elite and current and former cricketers; ii) determine the effect of resilience, playing-standard, and playing status on HRQoL and flourishing in cricketers. Methods The Cricket Health and Wellbeing Study (n = 2598 current and former cricketers, aged ≥18 years) collected cross-sectional questionnaire data including the Flourishing Scale, Short Form-8 (physical (PCS) and mental (MCS) component scores), resilience (European Social Survey), highest standard-of-play, and playing status. Multivariable linear regressions with fractional polynomials were utilised, adjusted for age, gender, total cricket-seasons, comorbidity, ≥ 4-week time-loss injury, and orthopaedic surgery. Results Two thousand two hundred eighty individuals (aged (mean (SD)) 51.7(14.7) years, 61% played recreationally, 37% former cricketers) were included in analyses. The median (IQR) PCS was 51.4(46.9–55.9), MCS was 54.3(50.0–58.6), and Flourishing Scale score was 48 [ (1–7)] .Greater resilience was associated with better PCS (effect (95% CI) 1.41(0.70–2.11)), MCS (4.78(4.09–5.48)), and flourishing (2.07(2.55–3.59)) compared to less resilience. Playing standard was not associated with HRQoL. Playing at an elite standard was associated with greater flourishing (1.21(0.68, 1.73)), compared with playing recreationally. Current cricket participation was associated with better PCS (3.61(2.92–4.30)) and flourishing scores (0.53(0.02–1.04)), compared to former cricket participation. Conclusion Cricketers reported high levels of mental-components of HRQoL and flourishing, and this was similar in recreational, elite, current and former cricketers. Current cricket participation and a higher standard-of-play was associated with greater flourishing. Current cricket participation was also associated with better PCS, however playing-status was not related to MCS. Further research is needed to understand if cricket participation may have psychological benefits that persist beyond cricket retirement.

The Al4Ba crystal structure is the most common structure among binary intermetallic compounds. It is well suited for accommodating large atoms of group II elements and is often the intermediate phase closest to the terminal phase. It is, therefore, of interest to characterize diffusion properties of compounds with this tetragonal crystal structure. In the present study, 111In perturbed angular correlation spectroscopy was used to study solute site occupation and atom movement in In4Ba, Al4Ba, Al4Eu, Al4Sr, and Ga4Sr. The indium tracer and its daughter cadmium were found to occupy only the two Al-type sublattices in these compounds through detection of nuclear quadrupole interactions with axially symmetric EFGs. Measurements with increasing temperature revealed merging of signals due to dynamical averaging of these interactions as Cd atoms jumped at increasing rates between alternating sublattices. The jump rates were estimated to be between 8 kHz and 2 MHz at about 350 °C for Al4Eu and at about 450 °C for In4Ba and Al4Ba. Fits of spectra using Blume’s stochastic model allowed determination of activation enthalpies for average Cd jump rates between alternating Al sublattices in Al4Sr and Ga4Sr to be 1.16(3) eV and 1.47(3) eV, respectively. This result was used to estimate transverse diffusivities of Cd.

Abstract Background As complete reporting is essential to judge the validity and applicability of multivariable prediction models, a guideline for the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) was introduced. We assessed the completeness of reporting of prediction model studies published just before the introduction of the TRIPOD statement, to refine and tailor its implementation strategy. Methods Within each of 37 clinical domains, 10 journals with the highest journal impact factor were selected. A PubMed search was performed to identify prediction model studies published before the launch of TRIPOD in these journals (May 2014). Eligible publications reported on the development or external validation of a multivariable prediction model (either diagnostic or prognostic) or on the incremental value of adding a predictor to an existing model. Results We included 146 publications (84% prognostic), from which we assessed 170 models: 73 (43%) on model development, 43 (25%) on external validation, 33 (19%) on incremental value, and 21 (12%) on combined development and external validation of the same model. Overall, publications adhered to a median of 44% (25th–75th percentile 35–52%) of TRIPOD items, with 44% (35–53%) for prognostic and 41% (34–48%) for diagnostic models. TRIPOD items that were completely reported for less than 25% of the models concerned abstract (2%), title (5%), blinding of predictor assessment (6%), comparison of development and validation data (11%), model updating (14%), model performance (14%), model specification (17%), characteristics of participants (21%), model performance measures (methods) (21%), and model-building procedures (24%). Most often reported were TRIPOD items regarding overall interpretation (96%), source of data (95%), and risk groups (90%). Conclusions More than half of the items considered essential for transparent reporting were not fully addressed in publications of multivariable prediction model studies. Essential information for using a model in individual risk prediction, i.e. model specifications and model performance, was incomplete for more than 80% of the models. Items that require improved reporting are title, abstract, and model-building procedures, as they are crucial for identification and external validation of prediction models.

Abstract Background Treatment intensity scores can predict mortality and estimate resource use. They may therefore be of interest for essential neonatal care in low resource settings where neonatal mortality remains high. We sought to systematically review neonatal treatment intensity scores to (1) assess the level of evidence on predictive performance in predicting clinical outcomes and estimating resource utilisation and (2) assess the applicability of the identified models to decision making for neonatal care in low resource settings. Methods We conducted a systematic search of PubMed, EMBASE (OVID), CINAHL, Global Health Library (Global index, WHO) and Google Scholar to identify studies published up until 21 December 2016. Included were all articles that used treatments as predictors in neonatal models. Individual studies were appraised using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). In addition, Grading of Recommendations Assessment, Development, and Evaluation (GRADE) was used as a guiding framework to assess certainty in the evidence for predicting outcomes across studies. Results Three thousand two hundred forty-nine articles were screened, of which ten articles were included in the review. All of the studies were conducted in neonatal intensive care units with sample sizes ranging from 22 to 9978, with a median of 163. Two articles reported model development, while eight reported external application of existing models to new populations. Meta-analysis was not possible due heterogeneity in the conduct and reporting of the identified studies. Discrimination as assessed by area under receiver operating characteristic curve was reported for in-hospital mortality, median 0.84 (range 0.75–0.96, three studies), early adverse outcome and late adverse outcome (0.78 and 0.59, respectively, one study). Conclusion Existing neonatal treatment intensity models show promise in predicting mortality and morbidity. There is however low certainty in the evidence on their performance in essential neonatal care in low resource settings as all studies had methodological limitations and were conducted in intensive care. The approach may however be developed further for low resource settings like Kenya because treatment data may be easier to obtain compared to measures of physiological status. Systematic review registration PROSPERO CRD42016034205

Abstract Background Temporal changes in the placebo arm of randomized controlled trials (RCTs) have not been thoroughly investigated, despite the fact that results of RCTs depend on the comparison between arms. Methods In this update of our earlier systematic review and meta-analysis, we set out to investigate the effect of assessment time and number of visits on the magnitude of change from baseline in the placebo arm of these trials. We used linear mixed-effects models to account for within-trial correlations. Results Across all 47 trials the magnitude of response in the placebo arm did not change with time (β = -0.0070, 95% CI -0.024, 0.010) or visit (β = -0.033, 95% CI -0.082, 0.017) and remained significantly different from baseline for at least 12 months or seven follow-up visits. Change in the placebo arm in trials with subjective outcomes was large (β0 = 0.68, 95% CI 0.53, 0.82) and relatively constant across time (β = -0.0042, 95% CI -0.024, 0.016) and visit (β = -0.029, 95% CI -0.089, 0.031), whereas in trials with objective outcomes the response was smaller (β0 = 0.28, 95% CI 0.11, 0.46) and diminished with time (β = -0.030, 95% CI -0.050, -0.010), but not with visit (β = -0.099, 95% CI -0.30, 0.11). For trials with assessed outcomes, there was no significant effect of time (β = -0.0071, 95% CI -0.026, 0.011) or visit (β = -0.032, 95% CI -0.33, 0.26); however, these results should be interpreted with caution due to the small number of studies, and high clinical heterogeneity between studies. In trials with pain as an outcome, the improvement was significant (β0 = 0.91, 95% CI 0.75, 1.07), but there was no effect of time (β = -0.013, 95% CI -0.06, 0.03) or visit (β = -0.045, 95% CI -0.16, 0.069), and pain ratings remained significantly different from baseline for 12 months or seven visits. Conclusions These results are consistent with our previous findings. In trials with subjective outcomes response in the placebo arm remains large and relatively constant for at least a year, which is interesting considering that this is an effect of a single application of an invasive procedure. The lack of effect of time and visit number on subjective outcomes raises further questions regarding whether the observed response is the result of placebo effect or the result of bias.

Abstract Background Double-adjustment can be used to remove confounding if imbalance exists after propensity score (PS) matching. However, it is not always possible to include all covariates in adjustment. We aimed to find the optimal imbalance threshold for entering covariates into regression. Methods We conducted a series of Monte Carlo simulations on virtual populations of 5,000 subjects. We performed PS 1:1 nearest-neighbor matching on each sample. We calculated standardized mean differences across groups to detect any remaining imbalance in the matched samples. We examined 25 thresholds (from 0.01 to 0.25, stepwise 0.01) for considering residual imbalance. The treatment effect was estimated using logistic regression that contained only those covariates considered to be unbalanced by these thresholds. Results We showed that regression adjustment could dramatically remove residual confounding bias when it included all of the covariates with a standardized difference greater than 0.10. The additional benefit was negligible when we also adjusted for covariates with less imbalance. We found that the mean squared error of the estimates was minimized under the same conditions. Conclusion If covariate balance is not achieved, we recommend reiterating PS modeling until standardized differences below 0.10 are achieved on most covariates. In case of remaining imbalance, a double adjustment might be worth considering.

Background: Clinical outcomes data are a crucial component of efforts to improve health systems globally. Strengthening of these health systems is essential if the Sustainable Development Goals (SDG) are to be achieved. Target 3.2 of SDG Goal 3 is to end preventable deaths and reduce neonatal mortality to 12 per 1,000 or lower by 2030. There is a paucity of data on neonatal in-hospital mortality in Kenya that is poorly captured in the existing health information system. Better measurement of neonatal mortality in facilities may help promote improvements in the quality of health care that will be important to achieving SDG 3 in countries such as Kenya. Methods: This was a cohort study using routinely collected data from a large urban neonatal unit in Nairobi, Kenya. All the patients admitted to the unit between April 2014 to December 2015 were included. Clinical characteristics are summarised descriptively, while the competing risk method was used to estimate the probability of in-hospital mortality considering discharge alive as the competing risk. Results: A total of 9,115 patients were included. Most were males (966/9115, 55%) and the majority (6287/9115, 69%) had normal birthweight (2.5 to 4 kg). Median length of stay was 2 days (range, 0 to 98 days) while crude mortality was 9.2% (839/9115). The probability of in-hospital death was higher than discharge alive for birthweight less than 1.5 kg with the transition to higher probability of discharge alive observed after the first week in birthweight 1.5 to

Abstract Background Ten events per variable (EPV) is a widely advocated minimal criterion for sample size considerations in logistic regression analysis. Of three previous simulation studies that examined this minimal EPV criterion only one supports the use of a minimum of 10 EPV. In this paper, we examine the reasons for substantial differences between these extensive simulation studies. Methods The current study uses Monte Carlo simulations to evaluate small sample bias, coverage of confidence intervals and mean square error of logit coefficients. Logistic regression models fitted by maximum likelihood and a modified estimation procedure, known as Firth’s correction, are compared. Results The results show that besides EPV, the problems associated with low EPV depend on other factors such as the total sample size. It is also demonstrated that simulation results can be dominated by even a few simulated data sets for which the prediction of the outcome by the covariates is perfect (‘separation’). We reveal that different approaches for identifying and handling separation leads to substantially different simulation results. We further show that Firth’s correction can be used to improve the accuracy of regression coefficients and alleviate the problems associated with separation. Conclusions The current evidence supporting EPV rules for binary logistic regression is weak. Given our findings, there is an urgent need for new research to provide guidance for supporting sample size considerations for binary logistic regression analysis.

Background The objective was to summarize the methodology used to reach consensus for recommended minimum data elements that should be collected and reported when conducting injury surveillance research in military settings. This paper summarizes the methodology used to develop the international Minimum Data Elements for surveillance and Reporting of Musculoskeletal Injuries in the MILitary (ROMMIL) statement. Methods A Delphi methodology was employed to reach consensus for minimum reporting elements. Preliminary steps included conducting a literature review and surveying a convenience sample of military stakeholders to 1) identify barriers and facilitators of military musculoskeletal injury (MSKI) prevention programs, 2) identify relevant knowledge/information gaps and 3) establish future research priorities. The team then led a sequential three-round Delphi consensus survey, including relevant stakeholders from militaries around the world, and then conducted asynchronous mixed knowledge user meeting to explore level of agreement among subject matter experts. Knowledge users, including former and current military service members, civil servant practitioners, and global-wide subject matter experts having experience with policy, execution, or clinical investigation of MSKI mitigation programs, MSKI diagnoses, and MSKI risk factors in military settings. For each round, participants scored each question on a Likert scale of 1-5. Scores ranged from No Importance (1) to Strong Importance (5). Results Literature review and surveys helped informed the scope of potential variables to vote on. Three rounds were necessary to reach minimum consensus. Ninety-five, 65 and 42 respondents participated in the first, second and third rounds of the Delphi consensus, respectively. Ultimately, consensus recommendations emerged consisting of one data principle and 33 minimum data elements. Conclusions Achieving consensus across relevant stakeholders representing military organizations globally can be challenging. This paper details the methodology employed to reach consensus for a core minimum data elements checklist for conducting MSKI research in military settings and improve data harmonization and scalability efforts. These methods can be used as a resource to assist in future consensus endeavors of similar nature.