Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 88 appraisals
Current pain and headache reports
Chronic Pain and the Risk of Dementia: A Systematic Review and Meta-Analysis
BACKGROUND: The relationship between chronic pain and dementia risk has been extensively studied, but findings remain inconclusive. We conducted an updated meta-analysis to synthesize evidence from recent large-scale cohort studies. METHODS: Two authors independently and systematically searched PubMed, Web of Science, Embase, Cochrane Library, and Chinese National Knowledge Infrastructure for cohort studies published through April 2025, with a minimum follow-up of one year. Hazard ratios (HRs) were considered equivalent to risk ratios (RRs) assuming low event rates. Random-effect models pooled risk ratios (RR) with 95% confidence intervals (CI). Subgroup analyses and meta-regression explored heterogeneity. RESULTS: Of 3,823 publications, 30 studies met the inclusion criteria. After excluding five studies with overlapping populations, 25 studies involving 2,091,835 participants were pooled in the primary analysis. Individuals with chronic pain had a 28% higher dementia risk (RR = 1.28, 95% CI = 1.18-1.38). Heterogeneity was high (I²=91%). Subgroup analyses showed migraine, headaches, arthritis-related pain, and widespread pain were associated with increased risk. The association was significant in Asia but not in America or Europe, and when using ICD (but not DSM) criteria. Chronic pain increased the risk of Alzheimer's disease and non-vascular dementia, but not vascular dementia. Study quality, region, measurement methods, and pain types influenced the relationship. Pain type was a source of heterogeneity. CONCLUSIONS: In this meta-analysis of cohort studies, chronic pain was associated with an increased risk of dementia. Due to the observational nature of the included studies and high heterogeneity, this association should not be interpreted as causal. Further research is needed to determine whether pain management can mitigate dementia risk.
23 July 2026
Read appraisal →Reviews on environmental health
Air quality and public health co-benefits of national transport policies
INTRODUCTION: Transport is a major contributor to urban air pollution and premature mortality worldwide. This systematic review evaluates the air-quality and public health co-benefits of national and regional transport policies aimed at reducing population exposure to air pollution. CONTENT: Following PRISMA 2020 guidelines, this review synthesized evidence published between 2011 and 2024 on the health and environmental impacts of national or regional transport policies. Ten eligible studies were reviewed, covering diesel vehicle phase-outs, inspection and maintenance programs, electric vehicle incentives, fuel taxation, and promotion of public or active transport. SUMMARY: Across multiple settings, these policies were associated with reductions in PM2.5 concentrations and with avoided premature mortality, respiratory and cardiovascular hospitalizations, and healthcare costs. The largest and most consistent health benefits were observed for integrated policy packages, particularly those combining regulatory and technological measures such as fuel quality improvements, vehicle electrification and diesel phase-out, complemented by behavioral or fiscal instruments. Most available evidence originates from high-income countries, while studies from low- and middle-income regions remain scarce. The limited number of eligible studies and their concentration in specific regions and modeling approaches constrain generalizability, especially to settings with different transport systems, resource availability, and air-quality profiles. OUTLOOK: Embedding health-impact assessment within transport planning and expanding long-term evaluations in low- and middle-income countries are essential to ensure equitable and sustained air-quality and health improvements. Future policy frameworks should prioritize source-oriented regulatory and technological measures such as vehicle electrification, fuel-quality regulation, and diesel phase-out implemented within integrated policy packages to maximize public-health co-benefits.
25 June 2026
Read appraisal →Journal of affective disorders
Prediction models for suicide reattempts by lasso regression through machine learning models: Single versus multiple suicide attempters.
The best predictor of a suicide attempt is a previous attempt, apart from psychiatric diagnoses also associated. Some studies found other indicators of great risk for suicide reattempts. Machine Learning algorithms offer the potential for systematic detection of features that carry greater risk for an event. This study sought to develop a classification algorithm distinguishing between Single Suicide Attempters (SSA) and Multiple Suicide Attempters (MSA) in a Spanish multicentre national cohort to explore prediction of subsequent attempts in suicidal patients. Two models including the same sociodemographic and clinical variables grouped in more specific (Model I) or broad (Model II) categories were developed to explore risk factors for suicide reattempts. A Least Absolute Shrinkage and Regression Operator logistic regression with a 10-fold cross-validation was adopted. 1443 adult patients from the SURVIVE cohort were included (582 SSA and 861 MSA). Both Model I (AUC = 0.696; BAC = 0.644) and Model II (AUC = 0.678; BAC = 0.621) outperformed naïve majority-class classification for SSA and MSA. Bipolar disorder type II, binge-eating disorder, and schizophrenia variables weighted heavier on Model I for suicide reattempt-related; while eating disorder diagnosis, Africa as birthplace, affective disorder diagnosis, being employed, schizophrenia-spectrum disorder and substance use disorder diagnoses were the most important suicide reattempt-related of Model II. Affective disorders, eating disorders and schizophrenia-spectrum disorders emerged as the most important variables in predicting reattempts. Both models showed similar sensitivity and specificity when discriminating between SSA and MSA. Identifying specific risk factors for reattempts could have a significant impact on tailoring prevention strategies and interventions.
16 June 2026
Read appraisal →Environmental research
Air pollution and adverse birth outcomes: A systematic review of fine critical windows
Epidemiological studies have reported associations between prenatal exposure to outdoor air pollution and adverse pregnancy outcomes. However, most analyses assessed exposure over the entire pregnancy or by trimester, potentially missing specific critical windows of vulnerability that recent statistical methods can identify. The objective of this study was to summarise studies examining critical windows of exposure to outdoor air pollution in relation to adverse pregnancy outcomes. This systematic review (PROSPERO-CRD42024523345) followed PRISMA recommendations using PubMed, Embase and CENTRAL databases. The search was performed on April 2024 and udpated on February 2025. Eligible studies analysed critical windows of association between air pollutants (PM2.5, PM10, NO2, O3) and the following outcomes: prematurity, low birth weight, intrahepatic cholestasis of pregnancy, gestational diabetes, pre-eclampsia, eclampsia, gestational hypertension and stillbirth. Two independent reviewers screened titles, abstracts and full texts. Results were synthesised narratively. Out of 3631 articles, 20 studies were included. All were conducted in China or the United States and relied almost exclusively on distributed lag models (DLM or DLNM). Heterogeneity was observed across pathologies and between studies addressing the same outcome. The most consistent finding concerned preterm birth, for which the end of the second trimester (weeks 17-28) was often identified as a critical window. No studies addressed intrahepatic cholestasis of pregnancy or stillbirth. Conclusions. The variability in results reflects distinct pathophysiological mechanisms, differences in exposure levels, or individual characteristics. Precisely identifying critical windows of environmental exposure during pregnancy appears essential to improve perinatal environmental prevention and guide public health action.
15 June 2026
Read appraisal →JMIR public health and surveillance
Digital Health Center App for Community and National Malaria Surveillance in Cambodia: Implementation Case Study
BACKGROUND: By 2015, the emergence and dissemination of multidrug-resistant Plasmodium falciparum in the Greater Mekong Subregion threatened regional and global malaria control efforts. In response, Greater Mekong Subregion countries committed to malaria elimination by 2030, with strengthened surveillance as a strategic pillar. In 2017, Cambodia introduced an elimination-oriented digital Malaria Information System (MIS). Its health center app enables real-time, geo-located, case-based malaria reporting across primary health centers, and is fully integrated with the MIS. OBJECTIVE: This study aimed to evaluate the real-world national implementation of Cambodia's Android-based health center app, considering coverage, fidelity, timeliness, and data use, and their effects on malaria surveillance performance, case management, programmatic response, and public health outcomes. METHODS: System performance and public health use were assessed using system-generated metadata, national surveillance data, and user surveys. Operational indicators included technical performance, data completeness, and reporting timeliness, alongside surveillance outcomes such as case notification, classification, reactive case detection, and foci investigation. Nationwide user experience was measured via a survey of 761 health centers across 21 provinces, with in-depth structured surveys at 9 health centers in 3 provinces. Descriptive analyses evaluated system functionality, contribution to malaria surveillance and response, and usability among frontline health workers. RESULTS: The health center app demonstrated strong technical performance, with rapid loading and resilient data transmission under low-bandwidth conditions, supporting reliable reporting in resource-constrained settings. Integrated real-time dashboards provided analytics for case management, surveillance monitoring, risk stratification, and targeted public health interventions. Data completeness remained high (99%, 89/90 fields in 2024), demonstrating consistent routine use even as case incidence declined. Between January 1, 2025, and July 31, 2025, 69 malaria cases were reported nationally (23 locally acquired, 7 domestically imported, and 39 internationally imported). Of these, 95.7% (66/69) were notified and classified within 1 day. Reactive case detection was completed within 3 days for all 21 eligible cases, and 16 of 19 eligible foci received a response within 7 days, indicating strong operational responsiveness. User surveys showed 96.3% (733/761) of health centers were satisfied or very satisfied, 90.1% (686/761) reported rare or no technical issues, and 91.7% (698/761) found the app easy to navigate. Operational challenges included limited internet connectivity, transport to remote areas, and electricity interruptions. In-depth surveys confirmed high uptake, confidence in reporting, and routine use of surveillance data, although gaps in local analytical capacity were identified. CONCLUSIONS: Developed and managed locally to enhance sustainability, the MIS drove significant reductions in malaria case incidence, with the health center app contributing timely, complete, structured reporting at the point of care. Public health responses were facilitated by real-time analysis, targeted interventions, and decentralized decision-making. User engagement was sustained as malaria cases declined, and further enhancements are planned to ensure seamless transition to postelimination surveillance, reducing the risk of malaria reestablishment in Cambodia.
13 June 2026
Read appraisal →Journal of epidemiology and community health
Role of socioeconomic status in modifying the relationship between alcohol use and injury events: evidence from the UK Biobank
BACKGROUND: Alcohol consumption is a well-established risk factor for injuries, yet the role of socioeconomic status (SES) in modifying this relationship remains underexplored. This study examines how SES influences the association between alcohol use and injury events, including both fatal and non-fatal injuries as well as the risk of repeated injuries requiring hospitalisation. METHODS: Using data from the UK Biobank, we applied Cox proportional hazards models to assess whether SES modifies the relationship between different dimensions of alcohol use, average level and heavy episodic drinking (HED), and injury events. For injuries requiring hospitalisation, we fitted negative binomial regression models to estimate incidence rate ratios (IRRs) for the total number of unique injury admissions per individual. RESULTS: Among 482 078 participants, 48 045 injuries were recorded during follow-up. A statistically significant interaction was observed between alcohol use and low education, with individuals in lower education groups showing elevated injury risk compared with the high education group, particularly among those consuming 20-40 g/day with HED (HR: 1.17, 95% CI 1.06 to 1.28) and those consuming over 40 g/day (HR: 1.13, 95% CI 1.04 to 1.23). For non-fatal injuries, similar statistically significant interactions were found (IRR 1.17, 95% CI 1.06 to 1.28; and 1.18, 95% CI 1.08 to 1.29, respectively). CONCLUSION: Our findings highlight that individuals with lower SES face higher risks of alcohol-related injuries. Future research should investigate specific mechanisms underlying these disparities to inform more targeted policies. Population-based pricing policies could be combined with community-based programmes and tailored health messaging to address SES-related vulnerabilities.
13 June 2026
Read appraisal →Journal of epidemiology and community health
Incidence of physical non-communicable disease in people who have experienced imprisonment compared with the general population in high-income countries: a systematic review and meta-analysis
BACKGROUND: Non-communicable diseases (NCDs) represent a significant cause of morbidity and mortality globally. Despite a strong evidence base outlining increased risk of communicable diseases and poor mental health in people who have experienced imprisonment, the risk of NCDs has not been quantified in previous reviews. This study aimed to describe the incidence of morbidity and mortality of physical NCDs in people who have experienced imprisonment compared with the general population in high-income countries. METHODS: We searched MEDLINE, Embase, CINAHL and SocINDEX (PROSPERO CRD42024540173) for comparative studies of morbidity or mortality from NCDs. Articles were assessed against predefined criteria to select studies that included people with experience of imprisonment; measured NCD incidence; compared incidence to a general population; and were conducted in a high-income country. Two reviewers conducted independent risk of bias assessment using an adapted version of the Newcastle-Ottawa Scale. Data were synthesised using random-effects meta-analyses and vote counting based on effect direction. Meta-regression and subgroup analysis investigated heterogeneity. RESULTS: Of 3085 articles screened, 32 were included providing 341 datapoints. Meta-analysis showed a higher risk of mortality from several diseases, including respiratory disease (risk ratio (RR) 2.38 (95% CI 1.18 to 4.80), I2=97%), cardiovascular disease (RR 1.80 (95% CI 1.32 to 2.46), I2=99%), liver disease and cirrhosis (RR 2.50 (95% CI 1.08 to 5.77), I2=99%), digestive disease (RR 2.92 (95% CI 1.09 to 7.78), I2=99%), neurological disease (RR 1.94 (95% CI 1.09 to 3.44), I2=92%), head and neck cancer (RR 21.31 (95% CI 4.32 to 105.14), I2=97%), liver cancer (RR 4.07 (95% CI 2.34 to 7.08), I2=94%), cervical cancer (RR 3.95 (95% CI 3.11 to 5.01), I2=0%) and lung cancer (RR 1.95 (95% CI 1.34 to 2.85), I2=0%). Synthesis without meta-analysis also indicated imprisonment was associated with increased morbidity from multiple NCDs. CONCLUSIONS: Several NCDs, including multiple cancers, cardiovascular disease, respiratory disease and liver disease, are substantially more common in people who have experienced imprisonment compared with the general population, highlighting a need for primary prevention and NCD management.
13 June 2026
Read appraisal →International journal of biometeorology
Integrating google trends and hybrid statistical-machine learning models for dengue surveillance in an inland vietnamese province: a 94-month evaluation (2013-2021) with search-signal anomaly screening
Dengue transmission in inland Southeast Asia shows strong seasonality and short-term surges that challenge timely public-health response. We assessed whether province-level Google search activity can support short-horizon dengue monitoring in Dong Nai, Vietnam. Monthly reported dengue cases from July 2013 to April 2021 were aligned with the Google Trends index (GTI) for "Sốt xuất huyết". Models were evaluated using leakage-free rolling-origin expanding-window cross-validation with 3-month validation blocks. We compared a negative binomial (NB) autoregressive baseline, NB models incorporating GTI, tree-based machine-learning models using the same covariates, and a simple NB-random-forest ensemble. Predictive performance was assessed using root mean squared error (RMSE), mean absolute error (MAE), R2, and discrimination for 95th-percentile outbreak exceedance. Cross-correlation peaked at lag 0, indicating that GTI functioned primarily as a contemporaneous nowcasting signal rather than a long-lead predictor. NB models incorporating GTI showed competitive calibration, while boosted tree models achieved the lowest point-estimate errors; XGBoost achieved RMSE ≈ 40.95 and R2 ≈ 0.861 compared with RMSE ≈ 45.23 and R2 ≈ 0.823 for NB with GTI. Adding GTI to the autoregressive NB baseline reduced RMSE by 11.77%, but the 95% confidence interval crossed zero, indicating modest and statistically non-significant incremental gain. Search-signal anomaly screening suggested that GTI spikes largely coincided with epidemic peaks and did not clearly inflate out-of-sample errors. Overall, GTI provides operational value for dengue nowcasting and alert triage in inland provinces, while future systems should integrate climate, vector, mobility, and media-monitoring data.
9 June 2026
Read appraisal →PloS one
Reinforcement learning for policymaking in epidemic control: A scoping review
BACKGROUND: Managing an epidemic demands policies that respond at the pace of the outbreak. Conventional rule‑based interventions struggle to keep up, prompting interest in reinforcement learning (RL) for designing non‑pharmaceutical interventions (NPIs). However, current evidence is fragmented across diverse models and reporting styles. OBJECTIVES: To systematically map how RL is applied for epidemic NPI design, describe modeling choices, algorithm architectures, evaluation practices, and identify trends and research gaps. METHODS: Peer-reviewed studies (2014-2025, English) that applied deep RL to select NPIs were retrieved from IEEE Xplore, ACM Digital Library, ScienceDirect, and Scopus, searched on December 23, 2025. Reference list scanning supplemented database results. Predefined data items (bibliographic details, epidemic and RL model characteristics, experiments, validation methods, outcomes) were charted and summarized descriptively. RESULTS: Of 512 retrieved records, 10 met the inclusion criteria, and three additional studies were identified via reference-list scanning, yielding 13. Five employed value‑based methods, four policy‑gradient, and four hybrid; one study additionally incorporated model-based planning. Six simulations relied on compartmental models, six on agent‑based models, and one on a hybrid model. Action spaces were predominantly discrete restriction levels. Five studies incorporated sequence-modeling techniques to include temporal context into a state space. Eleven studies designed reward functions as a trade-off between pandemic severity and socio-economic cost. According to the reviewed studies, RL policies across various settings outperform heuristic, rule-based, and historical baselines in reducing infections, deaths, or lockdown duration while limiting economic loss. CONCLUSIONS: RL shows promise for adaptive epidemic control. Comparison is hampered by simplified economic costs, inconsistent calibration rigor, varied evaluation metrics, and limited uncertainty or policy robustness analysis. Future work should establish common benchmark environments and reporting standards, incorporate empirically grounded economic and behavioral models, adopt uncertainty-aware and probabilistic RL, develop more sophisticated control spaces, investigate more advanced algorithms, and validate learned policies prospectively to enable real-world deployment.
9 June 2026
Read appraisal →Annals of medicine
A body roundness index (BRI)-based predictive model for metabolic syndrome in perimenopausal and postmenopausal women-from a cross-sectional machine learning study to a longitudinal dynamic assessment
BACKGROUND AND AIMS: Metabolic syndrome (MetS) is highly prevalent among perimenopausal and postmenopausal women and poses a major public health challenge because of its association with cardiovascular disease, type 2 diabetes, and premature mortality. However, prediction tools for this population remain limited. Therefore, this study aimed to develop a Body Roundness Index (BRI)-based prediction model for MetS by integrating cross-sectional machine learning and longitudinal assessment. METHODS AND RESULTS: Cross-sectional models were trained using NHANES 2007-2020 and validated in the Affiliated Hospital of Dalian University (2023-2024). Sixteen predictors were selected via LASSO and Boruta, and eight models were evaluated using AUC, calibration, and decision curve analysis. SHAP ranked high-contribution factors. Longitudinal analysis used a 10-year cohort. Annualized change rates and cumulative exposure metrics of five key predictors were combined with baseline values to build Cox models, compared by C-index and time-dependent ROC.The artificial neural network (ANN) demonstrated optimal cross-sectional performance (internal AUC: 0.854; external AUC: 0.878) with good calibration and clinical benefit. SHAP identified BRI, WBC, ALT, MCV, and AST as top contributors, with BRI showing the strongest impact. Longitudinal analysis revealed that integrating annual change rates and annual cumulative exposure of these five predictors achieved optimal discriminative ability (C-index: 0.847), with time-dependent AUCs of 0.853, 0.859, and 0.847 at 1, 3, and 5 years, respectively. CONCLUSION: BRI is significantly associated with MetS in perimenopausal and postmenopausal women. The ANN model provides an efficient cross-sectional screening tool, while incorporating longitudinal trajectories of BRI and key laboratory indicators enhances long-term MetS risk prediction.
7 June 2026
Read appraisal →Critical care explorations
Linking the Virtual Pediatric System and Pediatric Hospital Information System Databases to Create Novel PICU Research Datasets
BACKGROUND: Lack of a robust, national PICU database hinders epidemiologic and quasi-experimental research of PICU care. Combining the virtual pediatric system (VPS) and pediatric health information system (PHIS) databases would overcome this data gap. However, limits on the use of variables containing protected health information (PHI) have prevented prior large-scale VPS-PHIS linkage. We sought to link VPS and PHIS records from 31 hospitals without using PHI. METHODS: We performed a database linkage study utilizing a sequential direct + probabilistic linkage approach with records from 31 hospitals contributing to VPS and PHIS for patients discharged between January 1, 2017 and December 31, 2022. Using directly linked records as a reference, we assessed the accuracy of probabilistically linked pairs with data from four hospitals by describing the percent of false-negative and false-positive links. We also compared demographic and clinical variables between linked and unlinked records using Cohen's Effect size (CES). RESULTS: We obtained 337,836 VPS and 3,515,595 PHIS records without PHI. In total, we linked 319,955 of 337,836 (94.7%) VPS records. Direct linkage contributed 274,345 of 319,955 (85.7%) of all record linkages. Probabilistic linkage contributed 45,610 of 319,955 (14.3%) of all record linkages. Assessment of the probabilistic linkage found 0.21% false-negative links and no false-positive links. We found negligible imbalance by CES for gender (% female in linked vs. unlinked: 44.8% vs. 43.6%, CES 0.024), age in years (median [interquartile range (IQR)]: 5.1 [1.21-13.22] vs. 5.53 [1.53-13.38], CES 0.054), Pediatric Risk of Mortality 3 score (median [IQR]: 0.44 [0.3-0.9] vs. 0.37 [0.29-1.02], CES 0.096), or days in PICU (median [IQR]: 1.55 [0.8-3.47] vs. 1.33 [0.79-3.45], CES 0.031). CONCLUSIONS: Using a combined direct and probabilistic linkage approach, we created a highly accurate linkage VPS and PHIS database records from 31 hospitals without using PHI. This combined database can be used to create novel datasets for PICU research.
31 May 2026
Read appraisal →Medicine
Burden of Helicobacter pylori infection in children and adults in Vietnam: A systematic review and meta-analysis of prevalence
BACKGROUND: Although Helicobacter pylori (H. pylori) prevalence has declined in many high-income countries, data from Vietnam remain limited and fragmented. This systematic review and meta-analysis aimed to estimate the pooled prevalence of H. pylori infection in Vietnamese adults and children. METHODS: A systematic search of PubMed, Embase, Scopus, and Web of Science was conducted for observational studies published between January 2000 and September 2025. Eligible studies reported H. pylori prevalence in Vietnamese populations using validated diagnostic methods. Random-effects meta-analyses were used to calculate pooled prevalence estimates with 95% confidence intervals (CIs). Subgroup analyses were conducted by age group, geographic area, and study setting. RESULTS: Twenty-five studies met inclusion criteria, comprising 8204 adults and 4641 children. The pooled prevalence of H. pylori infection was 58.7% (95% CI: 49.9%-67.2%) in adults and 77.2% (95% CI: 67.1%-86.0%) in children, with high heterogeneity across studies (I2 > 98%, P < .001). Among adults, prevalence was 56.4% in those aged <50 years and 65.8% in those ≥50 years (P = .455). Prevalence was lower in rural areas (46.8%) compared to urban areas (60.7%) (P = .045), with no significant difference between community- and hospital-based settings (P = .723). Funnel plot assessment did not indicate evident publication bias. CONCLUSION: H. pylori infection remains common in the populations represented in this study, particularly among urban and healthcare-seeking groups in Vietnam. These findings underscore the need for expanded surveillance and monitoring to better characterize H. pylori infection patterns and inform appropriate response strategies.
31 May 2026
Read appraisal →Journal of global health
Global, regional, and national trends in blindness and vision loss, 1990-2021: a secondary ecological trend analysis based on modelled population estimates.
BACKGROUND: We aimed to provide global, regional, and national estimates of the burden of blindness and vision loss from 1990 to 2021, stratified by cause, age, and sociodemographic index (SDI). METHODS: This population-based study used data from the 2021 Global Burden of Diseases, Injuries, and Risk Factors Study 2021 (GBD) study, covering 1990 to 2021. It evaluates the burden of blindness and vision loss based on prevalence, age-standardised prevalence rate (ASPR), disability-adjusted life years (DALYs), age-standardised DALY rate (ASR of DALY), and average annual percent changes (AAPCs). RESULTS: Globally, the ASPR of blindness and vision loss increased from 12 453.52 (95% uncertainty interval (UI) = 10 287.58, 15 226.09) in 1990 to 15 784.33 (95% UI = 12 761.44, 19 502.32) in 2021, with an AAPC of 0.85 (95% confidence interval (CI) = 0.7, 1.11, P < 0.001). In contrast, the ASR of DALYs remained stable, changing from 342.01 (95% UI = 237.87, 482.17) in 1990 to 342.78 (95% UI = 224.21, 503.61) in 2021, with an AAPC of 0.05 (95% CI = -0.04, 0.2, P = 0.298). Notably, near vision loss and cataract showed consistent increases, with AAPCs of 1.14 (95% CI = 0.94, 1.51, P < 0.001) and 0.25 (95% CI = 0.16, 0.33, P < 0.001), respectively. Blindness and vision loss prevalence was significantly higher in low and low-middle SDI regions compared to other SDI regions. CONCLUSIONS: Global blindness and vision loss increased from 1990 to 2021, while DALYs stayed stable. Prevention should be tailored by region, country, gender, cause, and age.
31 May 2026
Read appraisal →PloS one
Determinants of maternal postnatal care utilization in Bangladesh: A machine learning and SHAP-based analysis of BDHS 2022 data
Postnatal care (PNC) plays a crucial role in minimizing maternal and neonatal morbidity and mortality, but the uptake of services in Bangladesh remains below the recommended level. Although logistic regression has been widely used, it may miss complex nonlinear interactions among social, economic, and healthcare factors. This study contributes to the body of knowledge by using machine learning (ML) to identify the most significant determinants of PNC and to enhance prediction accuracy. We compared logistic regression to several ML models, including Random Forest, XGBoost, CatBoost, Support Vector Machine, AdaBoost, and Gradient Boosting, using nationally representative data from the 2022 Bangladesh Demographic and Health Survey (BDHS) with ADASYN oversampling to correct class imbalance. Among all models, Random Forest achieved the highest AUC (0.9050), closely followed by XGBoost (0.9036) and CatBoost (0.9028), all of which substantially outperformed logistic regression (AUC = 0.8470). SHAP analysis of the Random Forest model indicated that delivery place, husband's occupation, rural residence, wealth index, and media exposure were the most influential predictors of PNC utilization, alongside maternal education, women's occupation, and age-related factors. The results indicate that ML is more effective than classical procedures for revealing latent patterns and making accurate predictions. Policy implications include encouraging facility-based deliveries, improving maternal education, reducing wealth disparities, and enhancing media coverage of health, particularly among rural and low-income groups. This paper not only identifies key drivers of PNC in Bangladesh but also demonstrates how ML can supplement traditional methods to reinforce maternal health policy and interventions.
28 May 2026
Read appraisal →Journal of chemical information and modeling
Machine Learning-Driven Simulations of the SARS-CoV-2 Fitness Landscape from Deep Mutational Scanning Experiments
Predicting protein variant effects is a key challenge in preparing for pathogenic viral strains, understanding mutation-linked diseases, and designing new proteins. Protein sequence-structure-function relationships are difficult to model due to complex allosteric and epistatic effects. To investigate efficient modeling strategies, we trained supervised machine learning (ML) models with deep mutational scanning (DMS) libraries of SARS-CoV-2 receptor binding domain (RBD) sequences labeled with angiotensin converting enzyme 2 (ACE2) binding affinity. These models demonstrate superior performance predicting combinatorial mutation effects compared to adding or averaging the effects of point mutations and exhibit strong extrapolative performance ranking omicron variants when training only near wild type (WT) variants. We characterize the RBD fitness landscape by combining ML with Markov Chain Monte Carlo simulations to predict evolutionary patterns from the WT sequence. These generate comparable sequence profiles to high-fitness sequences in DMS data and predict mutations in unseen omicron variants. These models provide insight into the relationship between RBD sequence elements and offer a new perspective on the use of DMS to predict emerging viral strains, which we anticipate will be applicable to other evolutionary prediction tasks. To facilitate application and future development of this strategy, we introduce Mavenets: https://github.com/SztainLab/mavenets.
27 May 2026
Read appraisal →Current obesity reports
How Have Researchers Estimated the Impact of Excess Weight on Mortality? A Systematic Review.
OBJECTIVE: The aim of this review is to describe and reflect on the methodology of the studies that estimate the attributable mortality (AM) to excess weight. The purpose was to provide a methodological framework and serve as a starting point to improve the estimation of AM to excess weight. This will lead to more comparable estimates over time and across countries. METHODS: A systematic review was conducted considering MEDLINE (Ovid), Embase and Web of Science databases. Studies that had estimated AM to excess weight published until December 2024 were included. Data extraction was performed in an ad-hoc table based on the STREAMS-P tool, and a descriptive analysis of the methodology employed was performed. RESULTS: The search strategy yielded 2,163 results. Twenty-three studies estimating AM in four continents were included. The majority estimated AM to all-cause mortality (n = 9) and for cardiovascular diseases (n = 9), followed by cancer without specifying which type of cancer (n = 7). Variation was observed in the body mass index (BMI) cut-off points used to define overweight, obesity and excess weight. Thirteen studies applied the population attributable fraction (PAF) formula denoted by PAF = [P (RR-1)] / [1 + P (RR-1)]. Most of the relative risks (RR) derived from meta-analysis (n = 14), and only a minority studies (n = 6) used RR from national studies based on the population for which AM was estimated. CONCLUSION: This review emphasizes methodological constraints on AM estimation to excess weight. Causes of death causally associated with excess weight should be prioritized rather than relying solely on all-cause mortality. The ideal scenario would be to consider lag times; RR derived from the population under study and to use WHO BMI cut-offs and measured data rather than self-reported weight and height. The absence of this data should not prevent AM estimation if limitations are acknowledged. Expanding available estimates worldwide is essential to improve understanding of the mortality burden associated with excess weight.
26 May 2026
Read appraisal →Lancet (London, England)
Updated trends in the global prevalence and burden of mental disorders, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023
BACKGROUND: The 2023 iteration of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) estimated prevalence, incidence, and health burden for 375 diseases and injuries, including 12 mental disorders. We assess past, current, and emerging trends in the prevalence and burden of mental disorders across sexes and age groups, for 21 regions, 204 countries and territories, and by Socio-demographic Index (SDI) quintile, from 1990 to 2023. METHODS: Mental disorders included in GBD 2023 were anxiety disorders, major depressive disorder, dysthymia, bipolar disorder, schizophrenia, autism spectrum disorders, conduct disorder, attention-deficit hyperactivity disorder, anorexia nervosa, bulimia nervosa, idiopathic developmental intellectual disability, and a residual category of other mental disorders. A literature review identified epidemiological data for each disorder. These were analysed via a Bayesian meta-regression to estimate prevalence by disorder, sex, age, location, and year. Disorder-specific prevalence was multiplied by disability weights representing the severity of health loss associated with each disorder to estimate years lived with disability (YLDs). Deaths due to anorexia nervosa were assessed with a Cause of Death Ensemble modelling strategy to estimate deaths by sex, age, location, and year, and then multiplied by the standard life expectancy at age of death to estimate years of life lost (YLLs). YLDs equalled disability-adjusted life-years (DALYs) for all mental disorders except anorexia nervosa (the only mental disorder considered as an underlying cause of death in GBD), for which DALYs represented the sum of YLDs and YLLs. We presented prevalence, deaths, YLDs, YLLs, and DALYs as counts, age-specific rates per 100 000 population, and age-standardised rates per 100 000 population. FINDINGS: We estimated 1·17 billion (95% uncertainty interval 1·06-1·31) prevalent cases of mental disorders globally in 2023, equivalent to an age-standardised prevalence rate of 14 210·7 cases (12 849·5-15 940·1) per 100 000 population. These estimates represented a 95·5% (75·0-121·2) increase in prevalent cases and 24·2% (11·4-41·4) increase in age-standardised prevalence rate between 1990 and 2023. All mental disorders showed increases in prevalent cases between 1990 and 2023, while notable increases were seen in age-standardised prevalence rates for anxiety disorders, major depressive disorder, dysthymia, anorexia nervosa, bulimia nervosa, schizophrenia, and conduct disorder. There were an estimated 171 million (127-228) DALYs due to mental disorders globally across sex and age in 2023, equivalent to an age-standardised DALY rate of 2070·5 DALYs (1519·1-2750·5) per 100 000 population. Mental disorders contributed to 6·1% (4·8-7·6) of all-cause DALYs in 2023, making them the fifth leading cause of global DALYs (up from 12th in 1990). DALYs were almost entirely composed of YLDs. Mental disorders were the leading cause of YLDs in 2023 (up from second in 1990), explaining 17·3% (14·8-20·6) of all-cause global YLDs. Leading causes of mental disorder DALYs were anxiety disorders (ranked 11th among the 304 diseases and injuries at Level 4 of the GBD cause hierarchy), major depressive disorder (15th), and schizophrenia (41st). Globally in 2023, mental disorder age-standardised DALY rates were higher among females (2239·6 [1643·7-3014·1] per 100 000) than among males (1900·2 [1399·8-2510·8] per 100 000), and peaked in the 15-19 years age group (2617·3 [1850·6-3696·8] per 100 000). All locations showed increased mental disorder DALY rates in 2023 compared with 1990, ranging across countries and territories from 1302·4 (952·7-1683·7) per 100 000 in Viet Nam to 3555·8 (2661·9-4715·0) per 100 000 in the Netherlands. Across SDI quintiles, DALY rates ranged from 1853·0 (1352·1-2469·3) per 100 000 for middle SDI to 2184·1 (1606·1-2890·3) per 100 000 for high SDI. INTERPRETATION: A significant health burden was imposed by mental disorders in all countries and territories in 2023, irrespective of the health resources available. In some instances, this burden has increased over time and is unevenly distributed across populations. Stronger surveillance systems, particularly in low-income and middle-income countries, are required. Additionally, we need more coordinated and inclusive policies to reduce the burden through early treatment and prevention, tailored to sex and age differences across locations. Responding to the mental health needs of our global population, especially those most vulnerable, is an obligation, not a choice. FUNDING: Gates Foundation, Queensland Health, and University of Queensland.
25 May 2026
Read appraisal →The Science of the total environment
Ambient and personal air monitoring using low-cost sensors to identify elevated air pollution in vulnerable communities
In the United States, regulatory air monitoring networks are designed to assess regional air quality but do not capture neighborhood-level variability in pollutant concentrations. Low-cost air monitors may be one approach to address these gaps. The objective of this study was to evaluate whether neighborhood-level and personal monitoring identified increased concentrations of fine particulate matter (PM2.5) exposures in the neighborhood of Lower Price Hill (LPH), Cincinnati, Ohio, compared with regional regulatory monitors. We deployed a network of 12 PurpleAir monitors across LPH from April-June 2022 and calibrated measurements to a nearby U.S. EPA regulatory monitor. PM2.5 concentrations and Air Quality Index (AQI) values measured by the PurpleAir sensors were compared with two EPA monitoring sites. Additionally, three residents conducted 7-day personal monitoring using wearable AirBeam devices. Calibrated PurpleAir data demonstrated significantly increased PM2.5 concentrations at four of the 12 PurpleAir sites and increased AQI at 11 of the 12 PurpleAir sites in LPH compared to EPA regulatory sites. PurpleAir data also found that LPH experienced up to six days where AQI exceeded healthy levels (>50) but the EPA regulatory monitor did not exceed 50. Personal monitoring revealed substantial variability, with two of three participants exceeding the EPA 24-hour outdoor PM2.5 standard (>35 μg/m3) on all study days, indicating exposures not measured by outdoor stationary monitoring. Overall, regulatory monitoring may underestimate neighborhood-level and personal PM2.5 exposures in LPH. Community-based and personal monitoring provides novel insights into localized exposure disparities and may improve risk communication and inform interventions in vulnerable communities.
25 May 2026
Read appraisal →Environmental monitoring and assessment
XGBoost-based analysis of short- and long-term COVID-19 air quality impacts in Tehran, Iran.
This study examines the air quality trends associated with COVID-19 lockdowns and the subsequent recovery on air quality in Tehran, employing an established XGBoost framework for de-weathering with daily data from 22 monitoring stations from 2019 to 2025. A comprehensive analysis was conducted on six principal pollutants (PM₂.₅, PM₁₀, NO₂, O₃, SO₂, and CO) across four distinct temporal phases: Pre-pandemic, Pandemic, Post-COVID, and After Termination. While the model effectively reduced meteorological bias, exhibiting strong efficacy for particulate matter (R = 0.78-0.88; IOA = 0.68-0.88; FAC2 > 0.95), we explicitly acknowledge less consistent outcomes for reactive gases such as O₃ (R < 0.75; NMGE > 0.30). The imposition of lockdown measures coincided with significant reductions in pollutants associated with vehicular traffic: NO₂ exhibited a decline of - 30 to - 36%, PM₂.₅ decreased by - 18 to - 22%, and CO diminished by - 20%. In contrast, O₃ experienced an increase of + 20-30%, indicative of diminished NO titration. Principal Component Analysis (PCA) corroborated these trends, with PC1 (NO₂, PM₂.₅, CO) accounting for approximately 60% of the variance throughout the pandemic. In subsequent phases, partial recoveries were noted: PM₂.₅ escalated by + 15-20%, NO₂ stabilized at around -10%, SO₂ consistently decreased (-30% by 2025), whereas O₃ surged by + 20% by 2025, clustering with PM₁₀ in PC2, thereby highlighting the impacts of dust and secondary formation processes. Lockdowns yielded transient enhancements in air quality, with restricted long-term advantages. Sustainable progress necessitates the adoption of comprehensive policies targeting traffic, industrial emissions, VOCs, and dust management.
25 May 2026
Read appraisal →Journal of medical systems
A Standardized Framework for Evaluating State Opioid Surveillance Dashboards: A Health Informatics Assessment
BACKGROUND: State public health agencies across the U.S. depend on opioid surveillance dashboards to monitor overdose trends and guide interventions. Despite their central role in decision-making, no standardized framework exists to evaluate how well these dashboards actually function. OBJECTIVE: We developed a health informatics framework to systematically assess state opioid surveillance dashboards and applied it across all 50 U.S. states. METHODS: Our framework evaluates dashboards across nine domains (including temporal analysis, geographic granularity, data disaggregation, visualization features, narrative support, simplification techniques, and interactive functionality) using 28 specific sub-variables. Sub-variables were selected based on public health informatics literature on effective dashboard design, including features identified in COVID-19 dashboard evaluations and usability frameworks. We scored each feature as either present or absent, creating composite scores with a theoretical maximum of 28 points. All 50 U.S. state-level dashboards were reviewed twice by the study team, with an external consistency check on a subset of 10 dashboards. We calculated the correlation coefficient between dashboard scores and state population estimates. RESULTS: Dashboard scores varied widely, ranging from 12 to 22 points (mean:19.18, standard deviation:2.20). Three states (Delaware, Indiana, and New York) achieved the highest score of 22 by offering comprehensive geographic detail, extensive data breakdowns, and full interactive capabilities. Interactive filtering was present in 92% of dashboards, but only 80% enabled switching between visualization modes. While nearly all dashboards showed time trends, only 6% provided zip code-level detail, and over a third lacked race and ethnicity data. Single external reviewer demonstrated strong correlation (r = 0.84, p = 0.002). Dashboard scores were not correlated with state population size (r = 0.187, p = 0.193). CONCLUSIONS: State opioid surveillance dashboards varied substantially in observable capabilities. This framework provides a standardized method to compare dashboard features and identify gaps that may inform future refinement.
25 May 2026
Read appraisal →Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco
Training a Smoking Status Probabilistic Model Using Cotinine Levels in a Large Claims Database
INTRODUCTION: Smoking status is an important confounder for many epidemiologic studies, yet it is not well documented in common sources of real-world data, including administrative claims. Probabilistic models can be used to create a proxy for smoking status, yet most published models have been trained using self-reported data. The objective of this study was to train a smoking status probabilistic model using cotinine values available in a large claims database. METHODS: Beneficiaries were included if they had at least one cotinine measurement and were categorized as a "current smoker" if their serum or plasma cotinine value was ≥5 ng/mL or urine cotinine value was ≥30 ng/mL. Predictors were collected across one year prior to the cotinine assessment date. The model was fit using logistic regression with stepwise forward selection. Model performance was assessed using discrimination and calibration. RESULTS: The final model yielded an area under the receiver operating characteristic curve of 0.77 (95%CI:0.75-0.78) and was well calibrated across most prediction deciles. The strongest predictors included diagnosis codes for smoking and drug abuse, and number of medications. The model was found to be highly specific, yet not sensitive at probability cutoffs ≥0.2. CONCLUSIONS: A smoking status model was developed and internally validated for application in claims data, using available cotinine values to define smoking status and found to have acceptable discrimination and calibration. The model is based on 26 predictors, fewer than other similar published smoking status models. External validation of the model should be a next step toward utilizing the model for epidemiological research. IMPLICATIONS: This study tests the utility of cotinine values to validate a smoking status probabilistic model, which has not been done in the literature to date. The results were robust to various cotinine levels used to define smoking status, per current guidance. The final model uses only 26 factors to predict smoking status, simplifying the application of the model in other claims databases.
25 May 2026
Read appraisal →Medicine
Tech-based evaluation of healthcare quality during the COVID-19 pandemic
The coronavirus disease 2019 pandemic profoundly disrupted global health systems, exacerbating preexisting inequalities in access to and quality of care. Disparities in service accessibility, continuity, coordination, and comprehensiveness highlight the need for a multidimensional, data-driven approach to healthcare evaluation. We conducted a retrospective analysis using 52,490 responses from the 2021 Global Burden of Disease COVID-19 Health Service Disruption Survey. We developed a 4-dimensional evaluation framework aligned with World Health Organization principles for people-centered care. Following variable selection, standardized preprocessing, and manual labeling, we trained and assessed 6 machine learning (ML) algorithms. We further optimized the 2 best-performing models - support vector machine (SVM) and random forest (RF) - using interpolation-based data augmentation, cross-validated hyperparameter tuning, and particle swarm optimization. SVM showed the highest baseline performance (accuracy 0.93), whereas RF achieved competitive macro-F1 scores. Interpolation-based data augmentation improved the generalizability of the SVM model, increasing its accuracy to 0.96. The RF model benefited most from particle swarm optimization-based hyperparameter optimization. Model predictions revealed persistent inequalities in healthcare service quality: respondents with lower income, lower educational attainment, and rural residence were disproportionately classified as receiving lower-quality services, whereas urban residence and higher income were associated with higher-quality care. Our scalable ML framework successfully assessed healthcare service quality during the pandemic, with optimized SVM and RF models demonstrating robust performance on high-dimensional, imbalanced survey data. Post-pandemic recovery strategies must prioritize socioeconomically disadvantaged populations. Furthermore, integrating ML tools can enhance real-time quality monitoring within health systems.
24 May 2026
Read appraisal →Medicine
Global, regional, and national burden of clavicle, scapula, or humerus fracture in 204 countries and territories, 1990 to 2021: A systematic analysis from the Global Burden of Disease Study 2021
Clavicle, scapula, or humerus fracture represent a major public health challenge, yet their epidemiology and health burden remain poorly characterized. Using the Global Burden of Disease 2021 framework, we quantified incidence, prevalence, and years lived with disability (YLDs) of clavicle, scapula, or humerus fractures across 204 countries from 1990 to 2021. Estimates were stratified by age, sex, region, and socio-demographic index (SDI), and reported with 95% uncertainty intervals (UIs) from Bayesian models. Globally, the age-standardized incidence rate (ASIR) declined from 276.68 (95% UI: 219.06-346.71) per 100,000 person-years in 1990 to 220.94 (95% UI: 172.60-280.99) per 100,000 person-years in 2021, with an estimated annual percentage change of -0.78% (95% UI: -0.82% to -0.73%). High SDI regions showed the greatest ASIR (300.30 per 100,000 person-years), age-standardized prevalence rate (112.03 per 100,000 person-years), and age-standardized YLDs rate (3.72 per 100,000 person-years). In 2021, Eastern Europe had the highest ASIR (465.06 per 100,000 person-years), while Australasia led in age-standardized prevalence rate (151.42 per 100,000 person-years) and age-standardized YLDs rate (5.04 per 100,000 person-years). China ranked first in absolute prevalence (1.21 million, 95% UI: 1.04-1.39 million) and YLDs (40,235, 95% UI: 24,471-61,196). Despite declines in age-standardized rates, absolute cases increased substantially, highlighting demographic and regional disparities. Priority populations for targeted prevention include women aged ≥60 years and residents of high-SDI regions, as these strata accounted for the highest burden across incidence/prevalence/YLD rates.
24 May 2026
Read appraisal →JMIR research protocols
A Large, Diverse, Urban Cardiovascular Health e-Cohort in Childhood and Adolescence: Protocol for the Young Hearts Study
BACKGROUND: Cardiovascular health (CVH), defined as a composite of 8 health factors and behaviors ranging from body composition to sleep duration, is strongly associated with the risk for future cardiovascular events in adults. However, there is little data on CVH among contemporary, diverse cohorts of children and adolescents. OBJECTIVE: This protocol describes the completed recruitment of a diverse pediatric e-cohort based in Chicago. We include details about ongoing longitudinal data collection for CVH behaviors, as well as neighborhood and environment-based socioeconomic factors, and linkage to electronic health records (EHRs) for direct capture of CVH factors. METHODS: Young Hearts (YH) uses a longitudinal cohort design, linking participant responses to annual electronic surveys to EHRs. Parents/guardians of children aged 0-17 years and adolescents aged 18 years are asked to complete surveys at 3 e-visits, completed annually in consecutive years. Adolescents aged 12-17 years are invited to complete surveys at each e-visit to provide self-reports of their data in addition to their parents/guardians. As adolescents reach the age of majority, they re-consent and complete their remaining e-visit data. Participants' clinical data are abstracted from EHRs at 6 health systems in Chicago and linked to their survey data. The main study outcome is the CVH score, adapted from the Life's Essential Eight framework for use in pediatric populations. We will also study component CVH behaviors and factors, each assessed using developmentally-appropriate measures, including body mass index, blood pressure, lipids, glycemic control, diet, physical activity, sleep, and smoking exposure. Our innovative study design will allow us to model trajectories of pediatric CVH from birth through 20 years of age. Funding for this study was provided by the National Heart, Lung, and Blood Institute (R01HL155864). RESULTS: The YH study, funded in March 2021, recruited, enrolled, and consented 7114 children and adolescents aged 0-18 years between April 2022 and February 2025. Baseline surveys have been completed by 6651 participants, of whom 3259 (49%) were female, and the mean age was 7.4 (SD 5.4) years. Data collection is still in progress, with completion anticipated in February 2027. We aim to publish results beginning in 2026. CONCLUSIONS: This study will define the epidemiology of cardiovascular risk factors in a diverse, contemporary cohort of US children and adolescents and identify behavioral and structural individual- and area-level economic, psychosocial, and lifestyle factors associated with identified disparities. We anticipate that the YH cohort will become an ongoing resource for pediatric CVH research.
23 May 2026
Read appraisal →Scientific reports
Analysis, control, and forecasting the dynamics of SIRD models with saturated treatment and nonlinear incidence
This study addresses the challenge of managing complex epidemic dynamics exhibited by a novel Susceptible-Infected-Recovered-Deceased (SIRD) model. The introduced model incorporates a biologically and behaviorally mediated nonlinear incidence rate alongside a saturated treatment function, realistically capturing both physical transmission risks and the human behavioral responses during an outbreak. Our comprehensive analysis establishes the mathematical rigor of the system through positivity, boundedness, and a detailed investigation of equilibrium points and bifurcations (transcritical, saddle-node, and Hopf), which reveal the potential for complex, oscillatory, and chaotic epidemic patterns. To mitigate the disease burden, we apply Pontryagin's Maximum Principle to derive optimal time-dependent prevention and treatment control strategies. Furthermore, we introduce a highly efficient machine learning framework based on a Logistic-Map Reservoir Computer (LMRC) for accurate, real-time forecasting of these complex dynamics. Numerical simulations validate the theoretical findings and reveal the model's rich dynamical behaviors, including stable limit cycles and chaotic regimes driven by parameter variations. The integrated analytical, control, and machine learning approach provides a robust toolkit for public health policymakers, offering data-driven insights crucial for designing adaptive, cost-effective interventions that directly support the achievement of Sustainable Development Goal 3 (Good Health and Well-being).
21 May 2026
Read appraisal →BMJ open
Measurement of quality of stroke care with national electronic health records: a prospective cohort study during and after the COVID-19 pandemic
OBJECTIVES: To evaluate the value of linked electronic health records (EHRs) for measuring stroke care quality in England before and after the COVID-19 pandemic, focusing on metrics not routinely captured: stroke incidence, dispensing of secondary prevention medications and a proxy of disability-time spent at home after stroke ('home-time'). DESIGN: Prospective cohort study using national linked datasets. SETTING: England-wide health data linkage including the Sentinel Stroke National Audit Programme (SSNAP), primary and secondary care, dispensed medications and mortality records, accessed via National Health Service (NHS) England's Secure Data Environment. PARTICIPANTS: 425 675 adults with a first stroke between 1 January 2020 and 31 December 2023; data were available for 304 210 in primary care, 279 825 in hospital admissions, 220 470 in SSNAP and 59 465 in death records. MAIN OUTCOME MEASURES: Annual stroke incidence; first-year medication dispensing rates for antiplatelets, anticoagulants, antihypertensives and lipid-lowering agents (with a 1-month washout period) and home-time at 180 days post stroke. RESULTS: Stroke ascertainment was highest when combining all sources, with 10.8% of non-fatal ischaemic strokes recorded exclusively in primary care and 19.4% of fatal strokes identified solely through death records. Standardised annual stroke incidence rose from 227.6 (95% CI 226.1 to 229.0) to 244.8 (95% CI 243.4 to 246.3) per 100 000 over the study period including the COVID-19 pandemic. During the COVID-19 lockdown, non-fatal stroke recordings decreased while stroke-related deaths rose, indicating that recording quality was sensitive to shifts in healthcare-seeking behaviour during the pandemic. Among people with ischaemic stroke, 89.1% received an antiplatelet or anticoagulant, 44.5% an antihypertensive and 80.5% a lipid-lowering therapy. For haemorrhagic stroke, these proportions were: for anticoagulants 13.5%, antiplatelets 13.2%, antihypertensives 46.6% and lipid lowering 41.1%. Medication dispensing for stroke prevention declined with increasing age and comorbidity, but varied little by ethnicity, region or pandemic period. Mean home-time within 180 days of stroke was 166.6 (95% CI 166.4 to 166) days, decreasing with greater age (141.4 days for 90 years or older (95% CI 140.7 to 142.1)), deprivation (166.4 days (95% CI 166.1 to 166.6) for most deprived quintile) and stroke severity (137.4 days for National Institutes of Health Stroke Scale (NIHSS) score on arrival over 22 (95% CI 135.8 to 139.1)) and increasing with years from the COVID-19 pandemic 2023 (169.3 days (95% CI 169.0 to 169.5) vs 2020 164.4 days (95% CI 164.1 to 164.7)). CONCLUSIONS: Standardised stroke incidence increased significantly over the study period, highlighting a growing public health burden that persisted despite disruptions due to the pandemic although variation in case ascertainment and stroke coding practices was observed. While secondary prevention coverage for antiplatelets and lipids was high, lower rates of dispensing of antihypertensives, particularly in older and comorbid populations, potentially signal a target for improvement. Home-time represents a sensitive, person-centred outcome that exposes disparities linked to socioeconomic deprivation and clinical severity that can be used to enhance routine stroke audits. These findings justify the expansion of linked EHR infrastructure and the modernisation of governance frameworks to enable the longitudinal evaluation of care quality beyond the COVID-19 era.
20 May 2026
Read appraisal →International journal of biometeorology
Machine learning and dose response effect models: an integrated approach to analyze the association between environmental variables and Cuneo Emergency Department admissions for Acute Otitis Media (2007-2015)
Acute otitis media (AOM) is a leading cause of pediatric Emergency Department visits, particularly among children under five years of age. Although its seasonal pattern is well established, the role of air pollution and meteorological factors remains unclear. This study aims to investigate their impact on daily AOM visits by integrating machine learning and epidemiological approaches. We conducted a retrospective analysis of pediatric AOM diagnoses (2007-2015) at S. Croce and Carle Hospital (Cuneo, Italy). Predictors included PM10, NO₂, O₃, and eleven meteorological variables. Ensemble machine learning models (Random Forest, XGBoost, and AdaBoost) were trained and validated using 10-fold cross-validation. Model interpretability was assessed through SHAP values. Distributed Lag Nonlinear Models (DLNM) were applied to estimate delayed exposure-response relationships over lag periods of 0-1, 0-3, 0-5, and 0-10 days, with results expressed as Relative Risks (RRs) and 95% Confidence Intervals (CIs). AdaBoost showed the best performance (R² = 0.974; MAE = 0.019 cases/day; cross-validated R² = 0.987). SHAP analysis identified mean temperature as the most influential predictor (44%), while PM10, NO₂, and O₃ each contributed approximately 10%. DLNM analysis confirmed a strong and consistent effect of temperature across all lag periods (RR > 1.20, CI > 1). Moderate associations were observed for NO₂ and PM10 (RR: 1.02-1.04). O₃ exhibited smaller but significant effects at shorter lags (RR = 1.01 at 0-1 days; RR = 1.02 at 0-3 days; CI > 1). Environmental factors, particularly temperature, play a significant role in pediatric AOM incidence. The integration of machine learning and DLNM enhances predictive accuracy and improves the understanding of exposure timing. These findings support the development of early warning systems and targeted preventive strategies under adverse environmental conditions. Further validation in larger urban settings is needed.
20 May 2026
Read appraisal →Environmental monitoring and assessment
Air pollution-related health impacts from domestic waste burning and associated interventions: the merits of a traditional versus machine learning scoping review methodology
Regularly updating repositories of air pollution impacts on health is essential for evidence-based policies, interventions, and progress monitoring. However, this is often time-consuming and labour-intensive. Automation can ensure up-to-date evidence; reduce retrieval, screening time, and costs; and make information accessible to non-academic stakeholders. This study investigated whether a machine learning approach can perform any stages of a traditional scoping review on the health impacts, policies, and interventions related to air pollution due to domestic waste burning. A traditional approach was conducted in parallel with machine learning methods enabling a comparison of the efficiency and quality of the partially automated approach against the manual review. Findings from the two approaches were compared and the final set of included articles were considered for (1) reported impacts of waste burning on health outcomes and (2) recommendations on solutions and interventions to prevent/reduce adverse effects on health from waste burning. We found a range of health impacts associated with waste burning, including low birth weight, hypertensive disorders of pregnancy, adverse respiratory outcomes like asthma and wheeze, cancer risk, and mortality. Few studies proposed solutions or evaluated the effectiveness of interventions. The ML approach showed a tendency towards false positives, which are preferable to false negatives (where relevant papers were excluded). Results showed that the model can conduct initial searches and decisions for the review. However, the articles included in the model should be screened manually for final acceptance. Therefore, we propose a hybrid approach be used until the automated model can be further refined.
19 May 2026
Read appraisal →Expert review of vaccines
Approaches to observational study designs and analytical options to evaluate the safety of multi-dose vaccines: a systematic review
INTRODUCTION: Observational studies require careful considerations when evaluating the safety of multidose vaccines. We reviewed design and analytical approaches in observational studies evaluating the safety of multidose vaccines in the post-licensure phase. METHODS: EMBASE, MEDLINE, Web of Science, and Scopus (2018-2022) were searched for hypothesis-testing studies evaluating the safety of multidose vaccines. Key features from frequently used designs were extracted. RESULTS: Among 123 eligible studies, cohort (46%) and self-controlled case series (SCCS)/self-controlled risk interval (SCRI) (40%) followed by case-control (12%) were the most common designs, and 15% of studies used multiple designs. Among cohort studies evaluating multiple doses, vaccination date (36%) and cohort entry with time-updated exposure status (32%) were frequent approaches used to define time zero. Twenty-eight percent of cohort studies did not report time zero; all but one evaluated COVID-19 vaccine effect on post-delivery and fertility-related outcomes. For SCCS/SCRI, 64% of studies accounted for event-dependent exposures, mainly by including pre-exposure periods (53%) and modified SCCS model (48%), while 20% employed multiple correction strategies. Among studies using multiple designs, 68% reached consistent conclusions. CONCLUSIONS: SCCS/SCRI and cohort designs dominate multidose vaccine safety studies. Clear reporting on time zero in pregnancy and fertility-related cohort studies, and on addressing event-dependent exposures in SCCS/SCRI studies is needed, along with guidance on interpreting results from multiple designs. Observational studies are important for assessing safety of vaccines especially for rare health outcomes that cannot be easily detected in randomized trials. Commonly used approaches include comparing these health outcomes between vaccinated and unvaccinated individuals or between different time points in relation to vaccination timing within individuals. However, both approaches have important design challenges. For example, comparison between vaccinated and unvaccinated individuals requires careful consideration of alignment of the time of starting to observe outcomes with the time when individuals become eligible and vaccination time for both comparison groups (time zero). Within-individual comparison requires that earlier occurrence of health outcomes does not influence whether someone receives later vaccine dose(s). These challenges become more complex to address when vaccines are given in multiple doses.In this review, we assessed how important design and analytical issues are handled and reported in published studies evaluating the safety of multidose vaccines and assessed agreement between different study designs when more than one is used.We found that 28% of studies comparing vaccinated and unvaccinated groups did not clearly report time alignment (time zero) mostly with studies on pregnancy and fertility outcomes. Similarly, about one-third of studies using within-individual comparison did not report checking and addressing the possibility that earlier outcomes could influence future vaccinations. When multiple designs were used, two-thirds of studies reached similar conclusions, although comparisons were mainly qualitative. Future work is needed to guide clear reporting of these issues to improve transparency in design and analysis and when comparing results across different designs.
19 May 2026
Read appraisal →Lifetime data analysis
Kolmogorov-Smirnov and Cramér-von Mises tests for the k-sample problem with left-truncated and right-censored data
k-Sample versions of the Kolmogorov-Smirnov and Cramér-von Mises tests are proposed for data subject to left truncation and right censoring. Their asymptotic behaviour is studied and a bootstrap resampling plan is proposed to approximate the null distribution of the new tests. The performance of the tests with finite sample sizes is investigated in a simulation study, where the classical log-rank test is also considered. An illustration with a real dataset regarding unemployment times is discussed.
18 May 2026
Read appraisal →Journal of global health
Unveiling the gap in heart failure: a Brazilian Unified Health System study.
BACKGROUND: Heart failure (HF) decompensation is the leading cause of hospitalisations in developed countries and the third most common cause in Brazil. Underdiagnosis or misdiagnosis thereof remains a critical challenge and with significant implications. At the patient level, it delays appropriate treatment and disease management. At the health system level, it leads to inefficient population health strategies, distorted epidemiological estimates, and suboptimal resource allocation. Addressing this hidden HF population is therefore essential for improving disease surveillance, optimising care pathways, and supporting more effective and equitable health system planning in Brazil. In this sense, we aimed to quantify underreporting of HF in individuals in Brazil and to estimate their mortality trends. METHODS: We identified potential HF patients using a guideline-based flowchart that combines ICD-10 codes (e.g. I21, I25, I42) and HF-related procedures (e.g. echocardiography, B-type Natriuretic Peptide (BNP) testing). Then, we developed a machine learning model for early prediction of HF using the Brazilian Department of Informatics of the Unified Health System (Departamento de Informática do Sistema Único de Saúde) mortality information system and ambulatory information system records, 2018-2022. A CatBoost algorithm was trained on a balanced cohort of patients (10 000 with HF and 9 995 without HF), restricting predictors to ≥12 months before the first I50 code to prevent leakage. Key predictors included age, chronic kidney disease, echocardiography, and lipoprotein disorders. We validated model performance in independent cohorts, including a low-prevalence cohort (~2% HF). Finally, we derived underreporting estimates via deterministic sensitivity analysis across scenarios from 0-100%. RESULTS: The proxy identified approximately 54 000 potential HF cases/y that were unreported in ambulatory data, with deterministic analysis suggesting 12-41% underestimation. Mortality analysis showed that around 200 000 deaths could be linked to unreported HF. The CatBoost model achieved an area under the curve (AUC) of 0.91 (balanced cohort; accuracy = 0.82, recall = 0.81, F1 = 0.82) and maintained strong discrimination in low-prevalence settings (AUC = 0.84, sensitivity = 0.82), with excellent calibration (Brier = 0.124-0.136; ECE = 0.01-0.03). CONCLUSIONS: We found that HF underreporting in Brazil is substantial and carries significant mortality implications. Our ML model demonstrates high accuracy in early risk stratification using routine administrative data, aligning with clinical pathways. Implementing it as a screening tool could optimise resource allocation, but ethical considerations around false positives warrant careful deployment. Future work should focus on clinical validation and cost-effectiveness analysis within the Brazilian Unified Health System. Furthermore, our findings highlight significant implications for public health and clinical HF management, emphasising the necessity of strategies that promote early detection of HF and precise case recording. Addressing underestimation is crucial to optimise healthcare resources and improve patient outcomes. The results underscore the importance of accurate diagnosis and comprehensive management approaches for better HF case tracking. Further research should explore the public health impact of these underestimations in Brazil, particularly regarding its health system's financial resources.
18 May 2026
Read appraisal →Journal of affective disorders
Biological age acceleration associated with mental and behavioural disorders: Evidence from the UK biobank cohort.
BACKGROUND: Mental and behavioural disorders may accelerate biological ageing, but comprehensive evidence integrating observational and causal approaches is lacking. METHODS: We performed observational and Mendelian randomisation (MR) analyses. First, 502,411 UK Biobank participants (78,674 with ICD-10 Mental and Behavioural Disorders) were propensity score matched (1:1). Biological Age Acceleration (BAA = biological age - chronological age) was regressed on mental and behavioural disorder status, and robustness checks were conducted using heteroscedasticity-robust and cluster-robust standard errors. Second, two-sample MR assessed causal effects of genetic liability to six mental and behavioural disorders on four ageing biomarkers, using strong genetic instruments (F > 10) and validated through sensitivity analyses including MR-Egger, Cochran's Q, and leave-one-out tests. RESULTS: Ever having a diagnosis of a mental and behavioural disorder was associated with higher BAA (β = 0.261, 95% CI: 0.229-0.293, P < 0.001). Strongest associations were observed for disorders of psychological development (β = 0.717), organic mental and behavioural disorders (β = 0.395), and mental and behavioural disorders due to psychoactive substance use (β = 0.338). BAA increased with comorbidity count (P-trend <0.001). MR indicated potential causal effects of depression (OR = 5.12 for frailty index), insomnia (OR = 2.04 for frailty index), and anxiety (OR = 1.06 for frailty index) on accelerated ageing, with no evidence of horizontal pleiotropy (MR-Egger intercept P > 0.05). Bipolar disorder was associated with slower epigenetic ageing (OR = 0.71 for GrimAge). Alcohol use disorder increased facial ageing (OR = 1.02). Schizophrenia showed no significant associations. CONCLUSIONS: Mental and behavioural disorders are observationally and robustly linked to accelerated biological ageing, with a dose-response by comorbidity. Genetic evidence supports potential causal roles for depression, insomnia, and anxiety in promoting ageing, while bipolar disorder may relate to slower epigenetic ageing. Biological ageing should be considered in the long-term management of mental health.
17 May 2026
Read appraisal →Computer methods and programs in biomedicine
Prompt-to-policy: Leveraging large language models to guide deep reinforcement learning in public health emergencies
Rapid and effective decision-making is critical in public health emergencies, where resource allocation must balance multiple objectives under uncertain conditions. Traditional optimization methods often struggle with computational complexity and real-time, heterogeneous data. To address these challenges, we propose a hybrid intelligent agent combining an enhanced Double Deep Q-Network (D2QN-JDA) with large language models (LLMs). The D2QN-JDA improves learning stability and adaptability through joint state-action inputs, dynamic exploration rate, and adaptive reward normalization. The LLM component uses Retrieval-Augmented Generation (RAG) to integrate structured and unstructured data for real-time decision support. Experiments based on data from the Hong Kong COVID-19 outbreak show that the D2QN-JDA outperforms dynamic programming, greedy algorithms, genetic algorithms, and Q-learning, achieving reductions in cost. The LLM component also outperforms manual and regex methods in both single- and multi-point data extraction, enhancing accuracy, recall, F1 score, cost, and time. Our framework effectively addresses complex, multi-objective resource allocation in public health crises.
17 May 2026
Read appraisal →European journal of preventive cardiology
Elevated blood pressure levels among 533 167 adults living in Sub-Saharan Africa: a systematic review and meta-analysis
AIMS: Synthesizing contemporary data from sub-Saharan African countries, we did a systematic review and meta-analysis of blood pressure (BP) levels and hypertension among adults living in the region. METHODS AND RESULTS: We searched PubMed and other databases to identify studies published from 1 January 2010 to 31 December 2021. We used a random-effects model to estimate the pooled-prevalence of hypertension and mean systolic/diastolic BP overall and on a sex- and age-specific basis. Heterogeneity (I²) was assessed via the χ² test on Cochran's Q statistic. We identified 170 high-quality studies (195 samples) comprising 533 167 adults living in 26 countries. The pooled prevalence of hypertension was 30.5% (95% CI 28.4-32.6%). Overall mean systolic/diastolic BP was 128 (95% CI 127-129)/80 (95% CI 79-80) mmHg, with males recording higher mean BP levels (3.10 [95% CI 2.30-3.90]/0.69 [95% CI 0.10-1.29] mmHg) compared with females. Reflecting increasingly higher BP levels with age, the pooled estimates of hypertension prevalence initially rose three-fold (from 10.6% [95% CI 8.2-13.0%] to 30.9% [95% CI 27.8-34.0%]) in those aged 21-30 to 41-50 years, and then two-fold to 66.4% (95% CI 64.2-68.7%) among those aged 71-80 years, respectively. Hypertension prevalence was lower in healthy weight [28.4% (95% CI 26.1-30.6%)] compared with overweight [35.8% (95% CI 31.4-40.1%)] adults. Regionally, prevalent hypertension was lowest in those living in Eastern Africa [27.2% (95% CI 24.8-29.7%)]. CONCLUSION: Our findings suggest a steep age-related pattern of increasing BP levels in the region that will adversely affect millions of people within the next 10-20 years without urgent intervention. A detailed review and analysis of 170 studies involving more than half a million adult men and women living in 26 sub-Saharan Africa countries, revealed that one in three have elevated blood pressure/hypertension.Elevated blood pressure levels, and the markedly increased risk of suffering a debilitating or fatal cardiovascular event at an early age, rose steeply from around one in ten affected at age 20–30 years to around one in three by the age of 50 years.Beyond the influence of age, the risk of having elevated blood pressure was different, given it increased markedly for overweight men and women, whilst those living in (mainly poorer) Eastern African countries had the lowest blood pressure levels.
16 May 2026
Read appraisal →European journal of preventive cardiology
Visit-to-visit blood pressure variability and cardiovascular outcomes: a systematic review and dose-response meta-analysis
AIMS: Visit-to-visit blood pressure variability (VVV BPV) is a recognized risk factor for cardiovascular disease (CVD) that is underutilized in clinical practice. The reliability of electronic health record (EHR) data in estimating BPV and predicting CVD remains uncertain. This study compared BPV estimation methodologies using EHR vs. non-EHR data and examined dose-response associations with CVD. METHODS AND RESULTS: A systematic review and meta-analysis was conducted across five databases (MEDLINE, Scopus, EMBASE, CINAHL, and Web of Science) for studies published from January 2012 to August 2024. Studies assessing VVV BPV in adults and its association with CVD outcomes (e.g. myocardial infarction, stroke, heart failure, and cardiovascular mortality) were included. A dose-response meta-analysis (DRMA) evaluated BPV thresholds linked to increased CVD risk using standard deviation (SD) and coefficient of variation (CV). A total of 4926 studies were screened, 49 of which met the inclusion criteria. No consensus has emerged on BPV estimation methodologies, although non-EHR studies have followed stricter protocols. The meta-analysis showed that VVV BPV predicted any CVD outcome. Effect sizes were comparable between EHR [the hazard ratio (HR): 1.17, 95% confidence interval (CI): 1.09-1.24] and non-EHR (HR: 1.14, 95% CI: 1.10-1.17) studies (P-value = 0.468). A BPV threshold of SD 6.72 mmHg or CV 9.05% was linked to a 10% higher CVD risk. CONCLUSION: The EHR data reliably estimate BPV, yielding effect sizes similar to those of non-EHR sources. A non-linear dose-response relationship suggests that a higher BPV increases CVD risk. Visit-to-visit blood pressure variability needs to be incorporated into clinical practice, and further research is required to identify strategies to implement and scale up into routine workflow. Visit-to-visit blood pressure variability (VVV BPV) is capable of predicting CVD development in 6 years. However, its clinical use remains limited, and the reliability of electronic health records (EHR) in measuring BPV is uncertain. Having a standard deviation of VVV BPV ≥6.72 mmHg or a coefficient of variation of VVV BPV ≥9.05% mmHg is linked to a 10% higher CVD risk. Blood pressure variability estimates from EHR data were comparable to those from non-EHR studies, suggesting that EHR-based BPV assessment is reliable for CVD risk evaluation.
16 May 2026
Read appraisal →Journal of affective disorders
Sex differences in cognitive function trajectories and influencing factors in older adults: A machine learning study based on CHARLS and HRS
BACKGROUND: Identify the influencing factors that respectively affect the differences in cognitive function trajectories between China and the United States, and explore the reasons for gender and transnational differences. METHODS: Five waves of data from the China Health and Retirement Longitudinal Study (CHARLS) and the Health and Retirement Study (HRS) were utilized, and Latent Class Growth Models (LCGMs) was employed to identify cognitive trajectories. The optimal machine learning (ML) model was adopted and the Boruta algorithm was used to screen the variables. The SHAP value is used to explain the ranking of feature importance. RESULTS: The XGBoost model performs best among the six ML models. Education was the top influencing factor across groups. Family factors mattered more in China, while U.S. medical and endowment insurance had stronger effects. Men were more affected by external social networks; women were influenced by family responsibilities and community environments. CONCLUSION: Differences were linked to public services, social roles, and traditional culture. These results support the development of tailored interventions targeting gender-specific and culture pathways at the individual, family, society and community levels, offering actionable insights for promoting cognitive health in aging populations globally.
16 May 2026
Read appraisal →Journal of affective disorders
Prediction models for longitudinal trajectories of depression and anxiety: a systematic review
BACKGROUND: Prediction of atypical health trajectories may enable early intervention. We systematically reviewed the existing literature on models for predicting longitudinal depression and/or anxiety trajectories. METHODS: MEDLINE, Embase and APA PsycINFO were searched (from inception to 31-Jan-2025). We included population-based studies of children and adults (aged 3-65 years). Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST-AI) tool. RESULTS: Seven of the nine included studies were in adult populations with a diagnosis of depression or anxiety at baseline; two focused on child and adolescent populations. Only one study included anxiety trajectories. Identified trajectories typically comprised three to four groups including: chronic/persistent-high, stable-low, increasing/worsening, and improved/remitted groups. Various supervised predictive modelling methods were used. The number of final predictors included in models ranged from three to 152. Family and own/personal psychiatric history were the most common predictors but were not always important for model performance. Models including more predictors did not always perform better. Overall risk of bias was high in all studies. No studies were externally validated and no studies assessed the clinical utility of models. CONCLUSION: This review highlights a need for robust, validated models that can forecast future risk of persistent or worsening anxiety and depression, especially in young people where early intervention is possible.
16 May 2026
Read appraisal →BMJ mental health
Emulated trial of artificial intelligence use and subsequent depressive outcomes in a survey of US adults
BACKGROUND: Generative artificial intelligence (AI) use has been suggested to have adverse mental health consequences but a causal relationship has not been examined. OBJECTIVE: To simulate a randomised controlled trial of AI use in a work, school or personal context by applying target trial emulation to multiple waves of data from a nationally representative survey. METHODS: We conducted a target trial emulation using non-probability survey data from three waves of a nationally representative survey conducted between 18 June 2024 and 8 January 2025. Participants aged ≥18 years reported generative AI use frequency at baseline. High-frequency use was defined as multiple times per week or more. The primary outcome was depressive symptom severity measured using the Patient Health Questionnaire 9-item (PHQ-9) at follow-up. Generalised causal forests assessed heterogeneity of treatment effects. FINDINGS: Among 19 099 participants assessed at baseline, 2862 (15.0%) reported AI use at least multiple times per week. A subset of 3109 (16.3%) returned for follow-up. In the primary weighted analysis, high-frequency use was not significantly associated with change in PHQ-9 score at follow-up (mean difference -0.18, 95% CI -0.94 to 0.59; p=0.65). Multiple sensitivity analyses using alternate outcome definitions also did not identify significant causal effects. Generalised causal forests yielded no significant evidence of heterogeneity of effect (p=0.81). CONCLUSIONS: In an emulated randomised trial among US adults, generative AI use was not associated with subsequent depressive symptoms. This result does not support the premise that AI use causes greater depressive symptoms, although adverse outcomes among vulnerable individuals cannot be excluded. CLINICAL IMPLICATIONS: AI use is unlikely to cause increased depressive symptoms among most US adults. Continued monitoring should clarify potential risks among vulnerable populations.
15 May 2026
Read appraisal →BMJ global health
Integrated mass drug administration for yaws eradication: evidence from a comparative observational study in Papua New Guinea and a systematic review with network meta-analysis
BACKGROUND: Yaws is a chronic infectious disease that disproportionately affects under-reached communities. In 2023, Papua New Guinea (PNG) reported 100 165 suspected yaws cases, accounting for 45% of cases reported worldwide. The WHO targets yaws eradication by 2030, primarily through mass drug administration (MDA) of azithromycin. This study evaluates the impact of an integrated MDA programme in PNG and reviews global evidence on optimal MDA strategies through a network meta-analysis (NMA). METHODS: A comparative observational study was undertaken analysing outpatient department (OPD) attendances between West New Britain province (WNB; intervention) and New Ireland province (NIP; comparator) from November 2023 to August 2024. The intervention in WNB involved a single-round, integrated four-drug MDA provided in December 2023, comprising azithromycin, ivermectin, diethylcarbamazine and albendazole. Temporal trends in OPD attendances were analysed using negative binomial regression. A Bayesian NMA of six studies identified through a systematic review compared the effectiveness of azithromycin-based regimens for yaws eradication. RESULTS: Between Q3 2023 and Q1 2024, yaws-related OPD attendances in WNB declined significantly, with a 41% (p<0.0001) reduction relative to the comparator province (NIP). This reduction was sustained for at least 6 months following MDA. Over the same period, a 33% decline in total skin-related outpatient attendances was observed. In the NMA, three rounds of azithromycin MDA were associated with lower odds of active yaws at follow-up than observation (OR 0.04, 95% CrI 0.005 to 0.24) and had the highest probability of being ranked most effective. Single-round azithromycin MDA was also associated with reduced odds of active yaws at follow-up (OR 0.15, 95% CrI 0.05 to 0.45). CONCLUSION: In a high-burden setting, a single round of integrated MDA was associated with substantial early reductions in yaws burden. Multi-round azithromycin strategies, particularly three-round regimens, were associated with larger and more sustained reductions in community prevalence. These findings inform considerations for yaws control efforts in settings with persistent transmission.
15 May 2026
Read appraisal →Biology of sex differences
Importance of integrating biological sex and age analyses in health research
Research findings in human, animal and cell populations may be influenced by biological sex and age. The traditional statistical approach to sex and/or age differences that are discovered in research data is to consider them as confounders that interfere with the ability to make accurate estimations and thus need to be controlled for. This article provides examples of how combining sexes or controlling for sex and/or age may lead to inaccurate findings and overlook important insights. Rather than treating these variables as confounding factors, they should be viewed as variables of importance to the research question. We contend that data should be analyzed according to sex/age as the primary analysis, while controlling for sex and age should be conducted as a secondary analysis. This topic is increasingly important as data are made publicly available and deep learning models/artificial intelligence are used to analyze large volumes of data. Mechanistic insights will continue to be lost if prompt action is not taken to report data according to sex and age and to analyze data by sex and age in health research.
14 May 2026
Read appraisal →Cancer metastasis reviews
Risk of cancer incidence and mortality in patients with chronic pain: A systematic review and meta-analysis
Evidence from experimental studies suggests that pain may influence cancer development. We conducted a systematic review (registration osf.io/ms437) of the epidemiological studies assessing whether chronic pain, with minimal or non-inflammatory features, increases the risk of cancer incidence or mortality. The search used Scopus and Web of Science up to 11/03/2025. Quality assessment used the Newcastle-Ottawa Scale. Study characteristics and outcomes were analysed using narrative synthesis, with random-effects meta-analysis and meta-regression conducted where appropriate. A total of 23 studies (12 prospective, 11 retrospective) met the inclusion criteria, of which six were rated as high quality. The outcome data synthesis indicates a small increase in cancer mortality rate ratios, and a potential increase in a subset of organs. A total of three body site-specific analyses suggest that chronic pain may be associated with higher incidence for breast (SIR 2.12-4.8) and prostate (SIR 1.49-5.59) cancers, and with increased mortality for lung cancer (MRR 3.09, 95% CI 1.45-6.62). Ten studies were included for meta-analysis, including four studies rated as high quality. Meta-analysis of hazard ratios showed no association between cancer-related mortality and pain (HR 1.00, 95% CI 0.97-1.04), with consistent estimates across pain subtypes in meta-regression. Our review and meta-analysis do not support an overall association between chronic pain and increased risk of cancer mortality or incidence and highlight multiple methodological and conceptual obstacles to exploring this relationship, including reverse causation and confounding factors such as pain and opioid use association. High-quality studies that rigorously address reverse causation and confounding are warranted.
14 May 2026
Read appraisal →PloS one
Burden and predisposing factors of physical inactivity among adults in Africa: Systematic review and Meta-analysis
INTRODUCTION: Physical inactivity is a primary driver of global mortality, data on its specific impact in Africa remains fragmented. We conducted this study to provide the first comprehensive pooled prevalence and identify predisposing factors contributing to physical inactivity among African adults. METHOD: We searched PubMed, Scopus, Embase, and Google Scholar for studies published between March 1, 2010, and March 31, 2025. A quality assessment of the studies was performed through the Newcastle-Ottawa Scale (NOS). The random-effect (DerSimonian) model was used to calculate the aggregated rates of physical inactivity among adults and their predisposing factors. Heterogeneity was assessed using the I2 statistic, along with subgroup and sensitivity analyses. Publication bias was evaluated using Egger's test, and all analyses were performed in STATA, version 17. RESULTS: This systematic review and meta-analysis included 34 studies with a total of 41,521 participants. The pooled prevalence of physical inactivity was 45% (95% CI: 35%-55%), and there was considerable heterogeneity (I2 = 98.4%). The highest prevalence of 57% (95% CI: 0.33, 0.81) I2 = 97.7 seen in 2013 and the lowest prevalence 28% (95% CI: 0.03, 0.53) I2 = 94.3%) observed in 2011. This study identified that, being female (OR = 1.87; 95% CI: 1.55, 2.25), Adults aged ≥ 60, (OR=2.18; 95% CI:1.98,4.77), being obese (OR=3.52; 95% CI: 2.26,5.49), urban dweller (OR=1.91, 95% CI:1.24,2.95), being overweight (OR=2.03; 95% CI: 1.61,2.68), depression symptom (OR= 1.34;95% CI: 1.07,1.68) and more drinking of alcohol (OR=1.73; 95% CI: 1.18,2.64), were significant predisposing factors associated with prevalence of physical inactivity. CONCLUSION: A high level of physical inactivity was observed amongst the African adults. Our findings indicate that female gender, older age, being overweight and obese, high levels of alcohol consumption and depression are significant predictors of physical inactivity in the African population. Promoting awareness about the positive impacts of regular exercise on health among women and an elderly age group could effectively lower the chances of hypokinetic diseases.
13 May 2026
Read appraisal →BMJ open respiratory research
Prevalence and determinants of drug-resistant pulmonary tuberculosis in the MENA region: a systematic review and meta-analysis
BACKGROUND: Multidrug-resistant tuberculosis (MDR-TB) is a constraining challenge for tuberculosis (TB) control worldwide. The Middle East and North African (MENA) region represents a significant part of the global MDR-TB burden. AIM: To estimate the pooled prevalence of MDR-TB and its determinants in the MENA region. METHODS: We searched for studies published in English and French on the subject up to 31 January 2026 on Web of Science, PubMed, Scopus and Cochrane, without time restriction. Original studies reporting data on the prevalence of MDR-TB in individuals living in the MENA region were selected. The meta-analysis was done using the random effects model considering the heterogeneity among the included studies and I 2 statistic was used to assess the heterogeneity. RESULTS: A total of 1239 articles were identified and 25 studies from 6 countries were included in this review. The prevalence of MDR-TB in the MENA region ranged from 0% (95% CI 0% to 4.1%) to 17.1% (95% CI 10.6% to 25.4%). The pooled prevalence was 3.54% (95% CI 2.18% to 5.72%) with a high heterogeneity, I2 =95.6%; 95% CI 94.4% to 96.5%. Previous exposition to TB treatment, HIV infection, smoking and the presence of comorbidities were the most reported associated factors. CONCLUSION: This review underscores the persistence of MDR-TB in the MENA region, suggesting insufficiency in TB control. Multisectoral interventions integrating strong prevention measures, standardised treatment protocols and measures to enhance treatment adherence should be implemented.
12 May 2026
Read appraisal →American journal of epidemiology
Building the mountain mama & baby cohort: study design, protocol, and early prenatal clinic-based recruitment outcomes
Prenatal e-cigarette and cannabis use are increasing. Though concerning, the risks associated with these emerging exposures are unclear due to methodological limitations of available research. To address these gaps, the Mountain Mama & Baby Study prospectively enrolled a cohort of pregnant women in their first trimester during their initial telehealth visit with a nurse navigator at West Virginia University Medicine obstetric clinics. The study's goals were to (1) demonstrate the feasibility of our recruitment methodology and the representativeness of the sample, (2) establish first and third trimester exposure rates and describe the epidemiology of prenatal e-cigarette and cannabis use, and (3) explore associations between prenatal e-cigarette and cannabis exposure and adverse maternal-infant outcomes. This report describes the rationale, study design, protocol, and the feasibility and generalizability of recruitment. We enrolled 417 of 920 eligible participants (45.3%; 95% CI, 42.1% to 48.6%), exceeding our 20% benchmark. Enrolled participants and those nonenrolled were similar across most sociodemographic characteristics (eg, age, race/ethnicity, marital status, rurality, area deprivation). The Mountain Mama & Baby Study will provide clinicians, pregnant women, and public health practitioners with critical information on the potential harms of prenatal e-cigarette and cannabis use, guiding the design of interventions and recommendations.
12 May 2026
Read appraisal →Journal of medical Internet research
The Digital Exposome: A Life Course Framework for Health in the Digital Age
Digital technologies are reshaping human behavior, health care delivery, and population health; however, their cumulative effects across the lifespan remain underexplored. This viewpoint argues that exposures arising from interactions with digital technologies should be formally integrated into exposome science as a distinct, measurable component of the human environment. Our aims are to (1) redefine the digital component of the exposome (the digital exposome) within the broader exposome framework, (2) examine its life course implications for health and equity, and (3) outline a research and policy agenda to enable its systematic measurement and integration into clinical and public health practice. Digital technology-related exposures can confer benefits such as enhanced health monitoring, personalized interventions, improved access to care, and the promotion of healthy behaviors. However, they may also introduce potential risks, including mental health challenges, cognitive and circadian disruptions, sedentary lifestyles, exposure to misinformation, and widening inequities among vulnerable populations. Despite their ubiquity, digital technology-related exposures remain poorly integrated into clinical medicine, epidemiology, or public and global health policies. Drawing on interdisciplinary evidence from exposure science, epidemiology, and digital phenotyping research, we propose a refined conceptual definition of the digital exposome grounded in the classical exposome domains. We propose redefining the digital exposome as the full spectrum of exposures resulting from interactions or proximities with digital technologies and their combined influence on health across the lifespan. This framework conceptualizes digital technology-related exposures as a dynamic set of environmental influences operating through sociotechnical, behavioral, and biological pathways over the life course. To operationalize this framework, we discuss practical approaches using validated behavioral instruments, objective device use logs, ecological momentary assessments, smartphone-based digital phenotyping, and wearable sensing technologies. Systematic measurement, large-scale longitudinal studies, and harmonized exposure metrics are needed to characterize the cumulative health impacts of digital environments more accurately. Emerging tools such as digital markers or biomarkers and digital phenotypes offer promising opportunities to link real-world technology use with physiological and biological outcomes, thereby supporting precision medicine and population health strategies. Ethical governance, privacy safeguards, and equity considerations must be embedded from the start, drawing on emerging exposomethics frameworks. Recognizing the digital exposome as a modifiable determinant of health offers a foundation for evidence-based guidance, prevention strategies, and policy interventions suited to increasingly digital societies. By integrating digital technology-related exposures into exposome science, clinical practice, and public health research, this viewpoint seeks to foster interdisciplinary dialogue, guide future empirical work, and support the development of safer and more equitable digital environments across the lifespan.
12 May 2026
Read appraisal →International health
Atopic dermatitis in Ethiopia: a systematic review and meta-analysis
Despite being a leading contributor to the global burden of skin disease there is no information on the national prevalence of atopic dermatitis in Ethiopia. Therefore, the purpose of this systematic review and meta-analysis was to estimate the pooled national prevalence of atopic dermatitis in Ethiopia. A computerized systematic search using multiple databases was performed in search of relevant English articles from the inception of the databases to 30 July 2023. It was done in accordance with the Preferred Reporting Items for Systematic review and Meta-Analysis (i.e. PRISMA) standard. R and R Studio software were used for statistical analysis. A random-effects model was used for calculating the pooled estimate of the prevalence of atopic dermatitis. Forest plots and tables were used to represent the data. We found 10 full-text observational studies with 25 920 participants suitable for the review after checking for inclusion and exclusion criteria. The overall pooled prevalence of atopic dermatitis in Ethiopia was 12.75% (95% CI 8.32 to 17.17%). The subgroup analysis found that the atopic dermatitis pooled prevalence was 7% (95% CI 3 to 10%), 17% (95% CI 4 to 30%) and 20% (95% CI 15 to 24%) in the Oromia region, Amhara region and Addis Ababa city administration, respectively. This study demonstrates that atopic dermatitis is highly prevalent in Ethiopia, affecting approximately one in eight individuals, with significant geographical variations. To effectively address this condition, it is crucial to implement awareness campaigns, prioritize specialized training for healthcare professionals, allocate resources to high-prevalence areas, establish a national registry for monitoring, develop community support programs and encourage further research, as there are currently few studies on the condition.
11 May 2026
Read appraisal →Calcified tissue international
Diagnostic Indices for Epidemiological Assessment of Molar Incisor Hypomineralization: A Systematic Review
Molar incisor hypomineralization (MIH) is a qualitative developmental defect of enamel and one of the most prevalent oral health conditions in childhood. Over the last two decades, increasing attention has been given to its prevalence and etiology; however, marked heterogeneity in diagnostic criteria and indices has limited the comparability of epidemiological findings. This systematic review aimed to identify and describe the diagnostic indices used for the epidemiological assessment of MIH in the general pediatric population. A systematic literature search was conducted in PubMed, Scopus, and Google Scholar to identify epidemiological studies published between January 2001 and December 2024 reporting MIH prevalence in children. Study selection, data extraction, and methodological quality assessment were performed independently by two reviewers in accordance with PRISMA guidelines. The review protocol was registered in PROSPERO (CRD42022345224). A total of 198 articles were included, corresponding to 201 independent epidemiological studies, as three articles reported results from two distinct study populations. The European Academy of Paediatric Dentistry (EAPD) criteria were used in 171 studies (75.7%), representing the most widely adopted diagnostic index. Other indices, including the modified Developmental Defects of Enamel index (mDDE), Mathu-Muju and Wright, Wetzel and Reckel, MIH Severity Scoring System (MIH-SSS), the MIH Treatment Need Index (MIH TNI) criteria, and FDI criteria, were used infrequently. Studies using EAPD criteria accounted for the largest cumulative sample size and showed a descriptively aggregated MIH prevalence of 12.9%, whereas prevalence estimates varied across alternative diagnostic indices. In conclusion, the lack of uniform diagnostic standards and nomenclature hampers global surveillance and limits the comparability of prevalence data. The adoption of MIH-specific diagnostic criteria, supported by adequate examiner calibration and transparent reporting, is essential to improve the quality, consistency, and interpretability of future epidemiological research.
11 May 2026
Read appraisal →Environmental health : a global access science source
Building the model: a review of input structures in extreme temperature-health
BACKGROUND: The health impacts of extreme temperatures have been extensively studied through epidemiological models. However, limited attention has been paid to the specification of these models, particularly regarding input structure and model selection. Although exposure metrics and statistical techniques have evolved over time, a comprehensive synthesis of the variables included in these models, and the rationale behind their inclusion, is still lacking. This gap limits the comparability of studies and may compromise the robustness of temperature-health evidence. METHODS: We conducted a systematic review of peer-reviewed studies published between 2014 and 2024 that employed quantitative epidemiological models to estimate the association between extreme temperatures and health outcomes. Following PRISMA guidelines, we selected 119 studies through searches conducted in Scopus, PubMed, and Web of Science. Each study was analysed in terms of spatial coverage, modelling framework, and model inputs. Inputs were classified into six functional groups: thermal exposures; environmental covariates (including both non-thermal meteorological variables and air pollutants); temporal controls; socio-demographic factors; health system indicators; and built environment characteristics. RESULTS: Substantial heterogeneity was observed in both input selection and model specification. Daily mean temperature was the dominant exposure metric, though rarely justified over alternatives. Environmental covariates were inconsistently included: while relative humidity was frequent, other meteorological modifiers and air pollutants were often omitted without clear rationale. Temporal adjustments were common but heterogeneous. Distributed lag non-linear models were the prevailing framework, varying greatly in lag structure, spline specification, and covariate integration. Socio-economic, health, and infrastructural indicators appeared in less than one third of studies, typically as effect modifiers in meta-regression analyses, highlighting uneven integration of contextual determinants. No consensus currently exists on what constitutes a minimum model specification necessary to ensure reliable and interpretable effect estimates. CONCLUSIONS: Current temperature and health modelling remains fragmented, with notable variability in input specification and transparency. Strengthening methodological coherence through clearer guidance on input selection is essential. Greater integration of socio-economic and infrastructural variables would further enhance models' capacity to capture contextual vulnerability. To ensure reliability and policy relevance, future research should develop shared guidelines for input specification, define minimum modelling standards, and promote transparent reporting of analytical decisions.
11 May 2026
Read appraisal →Annals of epidemiology
Population-based clustering of co-occurring social determinants: An application of unsupervised machine learning
PURPOSE: This study aimed to develop a cluster-based measure of multiple co-occurring social determinants of health by applying unsupervised machine learning to a population-based cohort, offering a data-driven approach to organize complex social exposures. METHODS: Unsupervised clustering was applied to a population-based cohort of Ontario respondents to six-cycles of the Canadian Community Health Survey (2001-2012) linked to the Canadian census and vital statistics data. Clusters were evaluated using internal metrics, visualization techniques, descriptive analysis and theoretical considerations to determine the optimal number of clusters. Sensitivity analyses were integrated across the iterative clustering process. Premature mortality rates were generated assess validity. RESULTS: Optimal clustering solutions included 4-clusters and 6-clusters. Both cluster solutions revealed distinct social typologies. The 6-cluster solution offered greater granularity and theoretical interpretability. The 4-cluster solution showed greater heterogeneity within certain marginalized groups. Premature mortality rates differed meaningfully across clusters, supporting the clustering approach in capturing risk associated with social exposure. CONCLUSIONS: Unsupervised machine learning methods identified meaningful population subgroups reflecting complex patterns of social exposures. This approach offers a flexible, data-driven method for characterizing social exposures that can be considered alongside theoretical frameworks and used for equity monitoring, intervention planning and policy development.
6 May 2026
Read appraisal →Molecular psychiatry
A five-year risk prediction model of cardiovascular disease in individuals with bipolar disorder: a nationwide register study from Sweden
Cardiovascular disease (CVD) risk prediction models for the general population may not provide accurate predictions in individuals with bipolar disorder (BD) who have elevated risks of cardiometabolic conditions and premature mortality. Therefore, we aimed to: 1) develop a five-year CVD risk prediction model in this population by using nationwide register data from Sweden, 2) investigate whether the performance improved when we considered additional risk factors, including psychiatric comorbidity, psychotropic medication, and socio-demographic variables, compared to using established CVD risk factors only, and 3) whether machine learning approach provided improvements compared to standard logistic regression models. We followed 33,933 persons with BD aged 30-82 years old, without previous CVD, from the date of BD diagnosis registered between 2007-2014, for up to five years. The logistic regression model containing only established risk factors yielded an area under the receiver operating characteristic curve (AUC) of 0.76 (95% confidence interval 0.74-0.78) in the test dataset, while the logistic regression model and the best performing machine learning model including additional predictors yielded similar results (AUC was 0.77 (0.75, 0.79) in both models). The performance of logistic regression models slightly improved with additional predictors when continuous risk scores were used. In conclusion, standard logistic regression and established CVD risk factors may be sufficient to predict CVD in individuals with BD when using population register-based data from Sweden. External validation across diverse healthcare settings and rigorous assessment of clinical impact will be crucial next steps before implementing these models in clinical practice.
4 May 2026
Read appraisal →PloS one
A GNN-based approach for accurate trade balance forecasting and interpretable analysis
In this study, we developed a machine learning pipeline to predict trade balances across 229 countries, utilizing a Graph Neural Network (GNN), and compared it with several deep learning and regression-based models. The data preprocessing involved handling missing values, normalizing features, and conducting exploratory data analysis to uncover key patterns. Feature selection was performed using a Random Forest Regressor to identify the most influential predictors of trade balances. We then evaluated multiple models, including a complex Deep Neural Network (DNN), Transformer with multi-head attention, Random Forest, and a hybrid ensemble model, using various regression metrics. Among these, the GNN proved to be the most effective model, achieving an MSE of 0.06, RMSE of 0.26, MAE of 0.18, and an R² of 0.91. These results demonstrate that GNN outperforms other models in terms of accuracy, robustness, and consistency in predicting trade balances. We compared models across several key evaluation metrics and conducted a detailed comparison of residual plots to assess prediction quality and error distribution. Residual plots and ROC curves were used to validate the reliability and performance of the GNN and other models, ensuring robust and accurate predictions across the board. This study highlights the potential of machine learning techniques to improve trade balance forecasting, providing policymakers and economists with a more adaptable and precise tool for navigating complex global trade dynamics. The findings contribute to more informed economic strategies and enhanced forecasting methodologies.
4 May 2026
Read appraisal →The Proceedings of the Nutrition Society
Good practices and common pitfalls of machine learning in nutrition research
Machine learning is increasingly being utilised across various domains of nutrition research due to its ability to analyse complex data, especially as large datasets become more readily available. However, at times, this enthusiasm has led to the adoption of machine learning techniques prior to a proper understanding of how they should be applied, leading to non-robust study designs and results of questionable validity. To ensure that research standards do not suffer, key machine learning concepts must be understood by the research community. The aim of this review is to facilitate a better understanding of machine learning in research by outlining good practices and common pitfalls in each of the steps in the machine learning process. Key themes include the importance of generating high-quality data, employing robust validation techniques, quantifying the stability of results, accurately interpreting machine learning outputs, adequately describing methodologies, and ensuring transparency when reporting findings. Achieving this aim will facilitate the implementation of robust machine learning methodologies, which will reduce false findings and make research more reliable, as well as enable researchers to critically evaluate and better interpret the findings of others using machine learning in their work.
4 May 2026
Read appraisal →Scandinavian journal of primary health care
Overdiagnosis in atrial fibrillation screening with wearables
PURPOSE: Wearable technologies are increasingly used to screen for atrial fibrillation (AF), often outside formal clinical pathways. While this holds promise for early detection, the impact on patient outcomes remains unclear. We aimed to examine the hypothesis whether intermittent and continuous AF screening using wearable-like technologies leads to overdiagnosis in asymptomatic, high-risk individuals. METHODS: This hypothesis-generating analysis draws on available RCTs screening asymptomatic, high-risk individuals for AF using wearable-like ECG technologies. It is not a systematic review, as the field remains too early-stage for formal evidence synthesis. Eligible studies included either intermittent (≥10 recordings) or continuous monitoring for ≥1 week, reflecting typical smartwatch use. Primary outcomes included AF diagnoses, anticoagulant use, and adverse events. Overdiagnosis was inferred when excess AF detection did not translate into improved clinical outcomes. Analyses used Mantel-Haenszel random-effects models. RESULTS: Continuous screening nearly tripled AF diagnoses (OR 2.84; 95% CI, 1.61-5.02) and doubled anticoagulant use compared to controls. Intermittent screening showed a non-significant 1.8-fold increase. Neither method significantly reduced adverse outcomes. A pooled analysis revealed a marginal reduction in negative events (OR 0.94, 95% CI 0.90-0.99). Overdiagnosis estimates varied by method, ranging from 8-29% based on persistent differences in cumulative incidence after the screening period has ended and 47-65% based on increased diagnoses and treatment during the screening phase that are not followed by improved patient-relevant outcomes. CONCLUSION: Wearable-enabled screening for AF in high-risk patients increases diagnosis and treatment without clear benefit in clinical outcomes. These findings support the hypothesis of overdiagnosis and findings highlight the need for cautious integration of consumer technologies into primary care. Further trials with extended follow-up in low-risk populations are needed to clarify when AF detection improves outcomes versus when it leads to unnecessary medicalisation and harm.
30 Apr 2026
Read appraisal →PloS one
Resilient road safety modeling through spatially disaggregated explainable AI
Understanding spatial disparities in traffic accident severity is essential not only for improving transport safety but also for advancing sustainable and inclusive transportation systems. Road crashes represent a persistent public health and equity challenge, directly linked to the United Nations Sustainable Development Goals (SDG 3: Good Health and Well-Being and SDG 11: Sustainable Cities and Communities). This study proposes an explainable, spatially segmented machine learning framework to examine urban-rural heterogeneity in crash outcomes, using disaggregated accident data from Kent, UK (2022-2024). By treating urban and rural systems as separate analytical units, the study captures risk heterogeneity that conventional pooled approaches often obscure. Among five tested models, Random Forest achieved the best performance and was further interpreted using SHapley Additive exPlanations (SHAP) to uncover how key factors differ in influence across spatial contexts. The results reveal a behavioral risk profile dominating in urban areas and infrastructure-driven risks in rural environments. These findings highlight the need for context-sensitive, evidence-based interventions that ensure transport equity across regions. Additionally, the study contributes to sustainable governance frameworks, enabling spatially adaptive risk mitigation, inclusive policy design and the long-term resilience of transport infrastructures.
27 Apr 2026
Read appraisal →Behavior research methods
A review and evaluation of doubly robust approaches for estimating average treatment effects
In nonexperimental studies, obtaining an unbiased estimate of the average treatment effect (ATE) typically requires two key assumptions: that all relevant covariates are measured (i.e., no unmeasured confounding) and that the statistical model used for covariate adjustment is correctly specified. Two common approaches for adjustment are specifying an outcome model and propensity score weighting. To mitigate bias from model misspecification, doubly robust methods combine both approaches, ensuring unbiased ATE estimates if either the outcome model or the propensity score model is correctly specified. In this study, we review four doubly robust methods that have received considerable attention in the methodological literature but remain underutilized in psychological research: augmented inverse probability weighting, regression weighted by the inverse propensity score, regression incorporating the inverse propensity score as a covariate, and calibrated propensity score weighting. Using two simulation studies, we compare these methods with regression estimation and inverse probability weighting estimators. Our results suggest that doubly robust methods-particularly regression weighted by the inverse propensity score-offer greater protection against bias from model misspecification across various data-generating scenarios. We also discuss practical considerations for implementing doubly robust methods, including weight normalization, propensity score truncation, and potential efficiency losses due to overfitting. The different methods for estimating the ATE are illustrated in a data example.
26 Apr 2026
Read appraisal →BMJ open
Course of psychological symptoms and the initial management strategies in general practice using electronic health records in the Netherlands
BACKGROUND: Patients who consult their GP about psychological complaints, such as feeling anxious or depressed, are often initially given a psychological symptom diagnosis. However, it remains unclear whether these symptoms will develop into psychiatric conditions, which is crucial for informing patients about their prognosis and guiding GPs in their management. AIMS: To explore the course of psychological symptom diagnoses and compare GPs' management strategies for (a) psychological symptom diagnoses that persisted for more than a year and (b) those that changed into psychiatric conditions during the first year of care. METHODS: We performed a retrospective cohort study using the Family-Medicine Network database. We included all episodes of care (EoC) that started with a psychological symptom diagnosis between 2008 and 2021. We performed negative binomial analyses and logistic regression analyses to compare management strategies and number of contacts between EoC that changed into psychiatric conditions and persistent psychological symptoms (>1 year) during the first year of care. RESULTS: Out of the 14 633 EoC that started with a psychological symptom diagnosis, 79.4% resolved within 1 year, 12.8% persisted as psychological symptoms and 7.8% changed into psychiatric conditions. In EoC that changed into psychiatric conditions, as compared with EoC that persisted as psychological symptoms, we observed a significantly higher number of contacts with the GP (RR=1.76, 95% CI 1.63 to 1.91) as well as an increased total number of interventions (RR=1.71, 95% CI 1.58 to 1.84). CONCLUSION: Most psychological symptoms remain for only a short period of time and only a few persist or change into psychiatric conditions. Future research should investigate the factors that influence patients' decisions to seek further help from their GPs as well as those that contribute to the transition from psychological symptom diagnoses to a psychiatric condition.
25 Apr 2026
Read appraisal →Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco
Computer Vision Models for Detecting Large Cigars on Social Media
INTRODUCTION: Large cigars, which include premium cigars and large manufactured cigars, are widely promoted on social media and digital media, highlighting the critical need for scalable methods to monitor such content. To fill this need, we aimed to train and test an automated computer vision model to identify large cigars. METHODS: In 2022, we obtained 876 large cigar images and 1526 non-cigar images (ie, cigarettes, e-cigarettes, and pens) from Reddit, a global social media platform organized into subcommunities (subreddits) focused on specific topic areas, discussions, and communities. We used the You Only Look Once Version 7 (YOLOv7) computer vision model to train large cigar detection and evaluated its performance using recall, precision, and F1 scores (ie, harmonic mean of recall and precision scores). The data were split into 75% (594 cigar images and 250 non-cigar images) for training and 25% (282 cigar images) for validating and testing. Non-cigar images (n = 1276) were used to evaluate for false positives. RESULTS: Our model achieved a recall of 0.98, a precision of 0.99, and an F1 score of 0.98, demonstrating high accuracy in correctly identifying large cigars while minimizing false positives. It successfully distinguished large cigars from similarly shaped objects with an accuracy of 97%. CONCLUSIONS: Our computer vision model accurately distinguished large cigars from non-cigar images, demonstrating its potential as an automated, scalable tool for monitoring this understudied tobacco product on social media and other digital media. IMPLICATIONS: This computer vision model offers a novel and scalable approach to tobacco monitoring by automatically detecting large cigars in social media content. Its application may enhance the efficiency of product identification and facilitate research on use patterns, user perceptions, and emerging marketing strategies, thereby generating evidence to inform regulatory decision-making and policies aimed at preventing initiation.
24 Apr 2026
Read appraisal →Journal of the American College of Cardiology
African Representation in Randomized Controlled Trials Published in Leading Medical and Cardiovascular Journals, 2019-2024
BACKGROUND: Despite accounting for a substantial proportion of the global population and disease burden, African countries are underrepresented in randomized controlled trials (RCTs), including those informing cardiovascular (CV) care. OBJECTIVES: In this study, we sought to quantify African representation in RCTs published from 2019 to 2024 in: 1) 5 leading general medical journals; and 2) 3 leading CV journals. METHODS: We conducted a systematic review of RCTs published from 2019 to 2024 in the British Medical Journal, the Journal of the American Medical Association, The Lancet, Nature Medicine, and the New England Journal of Medicine, and in Circulation, the European Heart Journal, and the Journal of the American College of Cardiology. Eligible studies included traditional, pragmatic, cluster, and stepped-wedge RCTs. African representation was assessed by trial scope (Africa-only vs multicontinental), country and regional participation, disease category, and African authorship. RESULTS: Among 2,138 RCTs published in leading general medical journals, only 83 (3.9%) were conducted exclusively in Africa, and 195 (9.1%) were multicontinental studies including at least 1 African site. In the CV journals, 2 out of 334 RCTs (0.6%) were conducted exclusively in Africa, and African sites were included in only 9 multicontinental trials (2.7%). South Africa accounted for the majority of Africa-based RCTs across both journal categories. Regionally, southern Africa predominated and central Africa was minimally represented. Trials published in general medical journals and conducted exclusively in Africa largely focused on infectious diseases (n = 63; 75.9%), with only 3 addressing cardiovascular disease (CVD). In contrast, Africa-including multicontinental trials more frequently investigated noncommunicable diseases, including CVD. African leadership was common in Africa-only trials but rare in multicontinental studies. CONCLUSIONS: African countries are profoundly underrepresented in RCTs published in the world's most influential medical and CV journals. Addressing this imbalance requires expanding African participation in global trials, investing in local research capacity, and promoting equitable leadership to strengthen the relevance and validity of clinical evidence. (Underrepresentation of African Countries in Randomized Controlled Trials: A Systematic Review of Leading General Medical and Cardiovascular Journals; CRD42024603157).
24 Apr 2026
Read appraisal →PloS one
Forecasting under-five stunting in Ethiopia using classical and machine learning time series models
BACKGROUND: The prevalence of under-five stunting in Ethiopia remains above 30%, which, according to World Health Organization (WHO), constitutes a major public health concern. Stunting has long-term consequences for child growth, development, and overall health. Accurate forecasting of its prevalence is therefore essential to guide policymakers and inform targeted interventions. This study aimed to forecast the prevalence of under-five stunting in Ethiopia for the period 2025-2030 using historical data and time series modeling. METHODS: Annual under-five stunting prevalence data for Ethiopia from 2000 to 2024 were retrieved from the WHO Global Health Observatory. Time series forecasting models, including Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS), Multilayer Perceptron (MLP), and Long Short-Term Memory (LSTM), were developed and evaluated. Model performance was assessed using time series cross-validation with mean absolute error (MAE), mean absolute percentage error (MAPE), and R² as evaluation metrics. The best-performing model was applied to forecast stunting prevalence for 2025-2030. RESULTS: The ETS model demonstrated the best predictive performance (MAE = 1.09, MAPE = 2.71%, R² = 0.903) and was selected for forecasting. Forecasting indicated a gradual decline in under-five stunting prevalence in Ethiopia from 33.95% in 2025 to 31.95% in 2030. The projected prevalence for 2029 is 32.35% (95% CI: 26.90-37.79%), above the national target of 19%, and the 2030 forecast remains well above the SDG target of ending all forms of malnutrition. CONCLUSION AND RECOMMENDATION: Under-five stunting in Ethiopia is projected to remain above national and SDG targets by 2030, indicating current nutrition efforts are insufficient; national program evaluation and evidence-based policy adjustments are recommended.
24 Apr 2026
Read appraisal →PloS one
Hepatitis C distribution across diverse population groups in the Eastern Mediterranean Region: An umbrella review
BACKGROUND: Hepatitis C virus (HCV) remains a major public health challenge in the Eastern Mediterranean Region (EMR), with prevalence varying across population groups. This umbrella review summarizes the distribution of HCV infection across diverse populations in the region. METHODS: We conducted an umbrella review of systematic reviews with meta-analyses reporting HCV prevalence in the EMR. PubMed, Web of Science, Scopus, Iranian databases, including Magiran and the Scientific Information Database (SID), were searched. Google Scholar was additionally screened to ensure comprehensive coverage. Pooled estimates were extracted across four population groups: apparently healthy individuals; those with clinical or healthcare-associated exposure risk; patients co-infected with hepatitis viruses or other liver-related diseases; and key populations at increased risk (groups disproportionately affected due to their behaviors and higher vulnerability). For time-period analyses, meta-analyses were grouped into two intervals (before 2015 vs. 2015 and after) based on the median year of data collection of primary studies. Pooled prevalence was calculated using a random-effects model in STATA version 17. RESULTS: A total of 55 meta-analyses were included. The pooled HCV prevalence was 31.0% (95% CI: 27.0-38.0) among key populations, and also 31.0% (95% CI: 12.0-49.0) among patients co-infected with other hepatitis viruses or liver-related diseases. Those with clinical or healthcare-associated exposure risk showed a prevalence of 28.0% (95% CI: 23.0-32.0), whereas the apparently healthy population had the lowest prevalence at 2.0% (95% CI: 1.0-2.0). Subgroup analysis indicated a decline in prevalence among clinically exposed populations after 2015, from 29% to 10%, coinciding with the direct-acting antivirals (DAAs) and strengthened infection-control practices. The highest prevalence was observed among key populations in Libya, healthcare-exposed populations in Morocco, and apparently healthy individuals in Egypt. CONCLUSION: Variation in HCV prevalence across populations in the EMR highlights the need for population-specific strategies to support progress toward World Health Organization elimination targets.
24 Apr 2026
Read appraisal →Infectious diseases of poverty
Progress and challenges in development of minimum essential dataset for disease surveillance through a One Health lens: a scoping review
BACKGROUND: Minimum essential dataset (MED) enables One Health surveillance by facilitating cross-sectoral data sharing. Yet, existing MED research represents insufficient integration of the One Health concept, and its progress in disease surveillance remains unclear. This study aims to evaluate the current progress and gaps in MED research in disease surveillance through a One Health lens. METHODS: This scoping review systematically searched Embase, PubMed, Scopus, and Web of Science from the inception of databases to November 30th, 2024 to identify studies published in English. Studies were independently screened by two reviewers for inclusion based on their relevance to the development of MEDs for disease surveillance. The progress and challenges were synthesized based on data extracted from eligible studies. RESULTS: This review includes 28 eligible studies for analysis, all of which focused on infectious diseases. MED development is predominantly concentrated on the human health interface (89.3%), whereas animal and environmental interfaces are comparatively limited (≤ 10.7%). The One Health concept has been adopted in 16 of the eligible studies; however, there is still insufficient interdisciplinary and cross-sectoral collaboration (57.1%). Meanwhile, numerous methods have been employed in the development of MEDs, with qualitative approaches being the most prevalent (42.9%), while data-driven approaches remain scarce. The absence of a standardized approach is recognized as a primary barrier. Thus, we propose a pathway and operational tool to detail the specific steps, which could facilitate future research on MED. CONCLUSION: This review highlights critical gaps in MED development at the human-animal-environment interface, and proposes a pathway and operational tool to inform future development of MED, including scope identification, establishment, modification and improvement. This study promotes the improvement of disease surveillance system and provides insights to enhance future preparedness for reducing the global public health burden.
23 Apr 2026
Read appraisal →Veterinaria italiana
A One Health systematic review and meta-analysis of Coxiella burnetii prevalence in humans, animals, and vectors in Algeria
Coxiella burnetii, the causative agent of Q fever, is a globally distributed zoonotic pathogen affecting humans, domestic animals, and arthropod vectors. In Algeria, fragmented data suggest widespread circulation, yet no comprehensive quantitative assessment exists. This study aimed to systematically review and meta-analyse the prevalence of C. burnetii in humans, animals, and vectors in Algeria, using a One Health perspective. A systematic search of PubMed, Scopus, Web of Science, and Google Scholar was conducted up to January 2026. Studies reporting serological or molecular detection of C. burnetii in Algeria were included. Random-effects meta-analysis was performed to estimate pooled prevalence, with subgroup analyses by host species, region, and diagnostic method. A total of 35 studies were included, encompassing 8,372 samples and 80 prevalence observations. Pooled prevalence was highest in camels (73.7%; 95% CI: 66.5-79.8%), followed by small ruminants (15.8%; 95% CI: 10.6-22.9%) and cattle (11.8%; 95% CI: 7.2-18.8%). Ticks showed a pooled prevalence of 10.0% (95% CI: 3.2-27.1%), while human infection had a pooled prevalence of 3.7% (95% CI: 0.9-13.9%). The south-eastern region exhibited the highest prevalence, and serological methods generally reported higher rates than molecular methods. High heterogeneity (I² > 75%) was observed across studies. Coxiella burnetii is widely circulating among humans, domestic animals, camels, and ticks in Algeria, with notable variation between hosts and regions. Camels and small ruminants act as major reservoirs, while humans remain at risk, particularly those in close contact with livestock. Integrated One Health surveillance and targeted control strategies are urgently needed to reduce the burden of Q fever.
22 Apr 2026
Read appraisal →International journal of biometeorology
Random forest-based medical geography: geographical distribution of reference values for LP-PLA2 in healthy adults in China
Lipoprotein-associated phospholipase A2 (LP-PLA2), a vascular-specific inflammatory marker, is associated with coronary heart disease (CHD), a highly prevalent condition among middle-aged and older adults. It explores the relationship between environmental factors in environmentscience, geography, statistics and medicine and the reference value of LP-PLA2 in China. A geographic distribution prediction model for Lp-PLA2 medical reference values was developed using optimized machine learning approaches. The support vector machine (SVM) model employed a polynomial kernel function, with hyperparameters tuned via grid search and cross-validation to determine the optimal classifier configuration. Concurrently, the random forest algorithm was applied to mitigate overfitting and perform regression analysis on the dataset. Further considering the influence of geographical factors on the 100 LP-PLA2 medical reference values in city and county units from 2000 to 2024, using 24 geographical factors as independent variables, to fit 2317 LP-PLA2 medical reference values, and then explore the spatial variation characteristics of LP-PLA2 medical reference values. Further spatial interpolation was visualized on the national map. The medical reference value of LP-PLA2 was significantly correlated with longitude, altitude, annual average wind speed, topsoil clay percentage, topsoil reference capacity, topsoil organic matter content, and total convertible amount of topsoil, respectively. The geospatial distribution map shows that the whole value was high in the east and low in the west, with significant differences in longitude. The geographical characteristics of any given region, when analyzed through either the SVM prediction model or the derived geographical distribution map, enable precise determination of region-specific Lp-PLA2 reference values. This establishes an evidence-based foundation for clinical decision-making and research design in healthcare institutions and medical research organizations.
20 Apr 2026
Read appraisal →Medicine
Trends and determinants of tuberculosis incidence in Turkey: A secondary data analysis
Tuberculosis remains a major public health concern, with incidence rates influenced by social determinants of health. Although tuberculosis incidence in Turkey has declined markedly in recent decades, the factors associated with this decline have not been comprehensively evaluated at the country-level. This study aimed to examine trends in tuberculosis incidence in Turkey from 1982 to 2021 and to identify determinants of tuberculosis incidence rates between 2000 and 2021, considering variables categorized under composite development, economic indicators, population, and health services. A longitudinal ecological study was conducted using secondary data to examine trends and determinants of tuberculosis incidence in Turkey. Univariate and multivariable linear regression were employed to evaluate the associations between tuberculosis incidence rates and 10 selected variables. Tuberculosis incidence in Turkey declined substantially over the 40-year period analyzed, with an overall decrease of 86.6% and sharper reductions observed in certain intervals. In multivariable linear regression, only the Human Development Index remained independently associated with tuberculosis incidence (β = -1.041, P < .001), indicating that higher Human Development Index values were associated with lower tuberculosis incidence rates in Turkey. These findings indicate that tuberculosis incidence in Turkey has declined alongside changes in human development, as reflected by the composite Human Development Index.
20 Apr 2026
Read appraisal →Scientific reports
Food preferences and mortality risk in the prospective cohort of UK Biobank participants.
Food preferences may serve as less biased proxies for self-reported food intake in predicting health outcomes but have not yet been linked to mortality risk. We included 177,148 UK Biobank participants who completed a food preference questionnaire (FPQ), with a mean follow-up of 3.4 years (607,779 person-years; 3355 deaths). High versus low preference for 140 food items was tested using Cox regression with Holm adjustment. Sensitivity analyses excluded participants with key health conditions, additional supplement use, or short follow-up, added BMI and physical activity as covariates, and redefined preference groups. Higher preference for asparagus (hazard ratio (95% confidence interval)): 0.69 (0.62, 0.77)), aubergine (0.79 (0.72, 0.87)), black pepper (0.72 (0.64, 0.81)), broccoli (0.69 (0.61, 0.79)), butternut squash (0.75 (0.68, 0.83)), and extra virgin olive oil (0.67 (0.58, 0.78)) was linked to lower all-cause mortality, whereas higher preference for regular fizzy drinks (1.38 (1.24, 1.54)) was linked to higher mortality across all analyses. Specific food preferences are associated with all-cause mortality risk. FPQs may, therefore, complement conventional dietary assessments. Further studies should examine their associations with metabolic and cardiovascular outcomes.
19 Apr 2026
Read appraisal →PloS one
Operationalization of Severe Maternal Morbidity surveillance in administrative databases of the Brazilian public and private health systems: Brazil 2015-2022
The study of severe maternal morbidity (SMM) is an important strategy for improving the quality of obstetric care. The objective of this study is to assess SMM cases recorded in the Hospital Information System of the Unified Health System (SIH/SUS), which contains public funded hospitalizations, and in the database of the National Supplementary Health Agency (ANS), which contains hospitalizations paid for by health plans. This is a cross-sectional study using obstetric hospitalizations of women aged 10-49 years from both databases in the period 2015-2022. We assessed SMM cases using the World Health Organization's criteria for potentially life-threatening conditions (PLTC). Of the 26 PLTC criteria, 22 were operationalized through diagnoses during hospitalization, using International Classification of Diseases-10 codes, or medical procedures, using specific codes of each database. We estimated the frequency and odds ratio of death for each isolated criterion, for grouped criteria and for the total number of SMM cases, as well as the frequency of deaths according to the number of SMM criteria. We identified 18,807,757 obstetric hospitalizations in the SIH/SUS and 3,776,986 in the ANS database. The estimated proportion of SMM was 8.51% and 7.30% in SIH/SUS and ANS, respectively. Most cases presented only one criterion. Differences were observed among sectors, with a higher frequency of procedure-based criteria in the ANS database, compatible with the greater availability of resources in private services. The proportion of deaths increased with the increase in the number of criteria, and more severe criteria presented a higher odds ratio of death. The results indicate that it is possible to estimate the number of SMM cases in the country, both in public and private hospitalizations. Continued investment in the quality of recording in both databases are necessary so that the estimates can truly reflect the maternal morbidity of Brazilian women.
18 Apr 2026
Read appraisal →Journal of global health
National, regional and provincial prevalence of peripheral artery disease in Chinese adults in 2023: an updated systematic review and modelling study
BACKGROUND: Peripheral artery disease (PAD), a common atherosclerotic condition characterised by the narrowing or obstruction of peripheral arteries, has become a substantial public health concern in China. However, its prevalence across the country at the provincial level has never been quantified. Through an updated systematic review and modelling study, we aimed to estimate the national, regional, and provincial prevalence of PAD among Chinese individuals aged 30-89 years in 2023. METHODS: We conducted an updated literature search in PubMed, Embase, MEDLINE, the China National Knowledge Infrastructure, Wanfang, and the Chinese Science and Technology Journal Database to identify studies published between 4 March 2017 and 12 August 2024 reporting on the prevalence of PAD in the general Chinese population. We also identified and included studies from previous systematic reviews. We then used a multilevel mixed-effects meta-regression model to generate the age- and sex-specific prevalence estimates of PAD among individuals aged 30-89 years at the national level. We calculated pooled odds ratios (ORs) for factors associated with PAD using a random-effects meta-analysis and incorporated them into an 'associated factor-based model' to estimate regional and provincial prevalence. RESULTS: We included 54 articles from all searches. Model-based estimates indicated that the overall prevalence of PAD among Chinese adults aged 30-89 years in 2023 was 5.47% (95% confidence interval (CI) = 4.64-6.44), equivalent to 49.90 million (95% CI = 42.32-58.73) affected individuals. Prevalence increased progressively with age, ranging from 4.23% (95% CI = 3.58-5.00) among individuals aged 30-34 years to 18.37% (95% CI = 15.90-21.13) among those aged 85-89 years. The overall prevalence was higher in females (6.19%; 95% CI = 5.25-7.27) than males (4.76%; 95% CI = 4.03-5.61). Regionally, Northeast China had the highest prevalence of PAD at 5.87% (95% CI = 4.98-6.91), while Central China had the lowest at 5.26% (95% CI = 4.46-6.19). At the provincial level, the prevalence of PAD was the highest in Beijing (6.40%; 95% CI = 5.43-7.53) and lowest in Tibet (4.35%; 95% CI = 3.68-5.12). Female sex, current smoking, hypertension, diabetes, coronary heart disease, and stroke were identified as factors significantly associated with PAD. CONCLUSIONS: We found that PAD is prevalent in China, with pronounced age-related trends, sex differences, and regional disparities. These findings could inform targeted public health initiatives and appropriate resource allocation. REGISTRATION: PROSPERO: CRD420251118923.
18 Apr 2026
Read appraisal →African journal of reproductive health
Mapping research fronts of reproductive medicine using the NHANES database: A bibliometric and thematic analysis
Reproductive health, infertility, and perinatal diseases significantly challenge human health development and continuity, requiring multidisciplinary approaches for enhanced academic understanding and practical solutions. This study used bibliometric analysis to examine related articles in the web of science (WoS) database, employing tools including bibliometrix and VOSviewer for systematic literature analysis. To capture the broader context of high-impact research, we first analyzed 1,026 highly-cited and hot papers in reproductive medicine, which revealed evolving thematic trends and global collaboration patterns. Then we examined 232 literatures using NHANES datasets for reproductive health research to identify research gaps and opportunities for further research in this field. The analysis revealed key trends, areas of concentration, and potential knowledge gaps in the literature, while the thematic analysis of reproductive health studies utilizing the NHANES database further underscored the wealth of data amenable to multidisciplinary investigation. Future research directions to explore new dimensions of reproductive health, improving health policies and clinical practices in this field using NHANES database are also suggested. The study concludes that multidisciplinary research using comprehensive datasets like NHANES is crucial for advancing understanding of reproductive health, infertility, and perinatal outcomes. La santé reproductive, l'infertilité et les maladies périnatales constituent un défi majeur pour le développement et la continuité de la santé humaine, nécessitant des approches multidisciplinaires afin d'améliorer la compréhension académique et de proposer des solutions pratiques. Cette étude a utilisé une analyse bibliométrique pour examiner les articles pertinents dans la base de données WoS, en employant des outils tels que Bibliometrix et VOSviewer pour une analyse systématique de la littérature. Afin de saisir le contexte plus large de la recherche à fort impact, nous avons d'abord analysé 1 026 articles hautement cités et à tendance chaude en médecine reproductive, ce qui a révélé les tendances thématiques évolutives et les modèles de collaboration internationale. Nous avons ensuite examiné 232 publications utilisant les jeux de données NHANES pour la recherche sur la santé reproductive, afin d'identifier les lacunes de recherche et les opportunités pour de futures études dans ce domaine. L'analyse a mis en évidence les tendances clés, les domaines de concentration et les lacunes de connaissances potentielles dans la littérature, tandis que l'analyse thématique des études sur la santé reproductive utilisant les jeux de données NHANES a en outre souligné la richesse des données susceptibles d'être étudiées de manière multidisciplinaire. Les orientations futures de la recherche visant à explorer de nouvelles dimensions de la santé reproductive, à améliorer les politiques sanitaires et les pratiques cliniques dans ce domaine à l'aide des jeux de données NHANES sont également proposées. L'étude conclut que la recherche multidisciplinaire utilisant des jeux de données complets tels que NHANES est cruciale pour faire progresser la compréhension de la santé reproductive, de l'infertilité et des issues périnatales.
17 Apr 2026
Read appraisal →JMIR medical informatics
Research on Risk Transfer Pathways for Lung Cancer Among Middle-Aged and Older Individuals Using Deep Reinforcement Learning: Retrospective Cohort Study
BACKGROUND: Nowadays, lung cancer has a significantly high incidence rate worldwide. The mortality rate of lung cancer continues to rise; it is more common in middle-aged and older individuals and poses a great threat to human health. OBJECTIVE: This study aimed to assess the lung cancer risk among middle-aged and older individuals in a timely manner and to establish an efficient pathway for the risk transfer. METHODS: We proposed a deep reinforcement learning model based on deep Q-network (DQN) to explore the risk transfer pathway for lung cancer among middle-aged and older individuals. Risk stratification of lung cancer occurrence was deduced through deep neural network. The DQN model was developed using the Health and Retirement Study cohort for model training and internal validation. We also used the China Health and Retirement Longitudinal Study cohort for model external validation. Transfer simulation of multiple pathways in different cycles was calculated in a stratified risk groups-leveraged DQN model. RESULTS: We developed and evaluated the DQN method for optimizing the risk transfer pathway among middle-aged and older individuals, with accuracy ranging from 0.917 (95% CI 0.896-0.928) to 0.949 (95% CI 0.909-0.961) and area under curve ranging from 0.906 (95% CI 0.887-0.933) to 0.927 (95% CI 0.893-0.938). External validation was conducted to assess the model's effectiveness and availability. A total of 8780 and 3763 samples from the Health and Retirement Study were used for model training and internal testing, respectively, and 16,442 samples from the China Health and Retirement Longitudinal Study were used for external validation. The results showed that DQN models illuminated the optimal risk transfer pathways for stratified risk groups. Lung cancer incidence in the high risk group had declined by 68.2% through risk transfer of model-based simulation, which had declined by 56.9% in the medium risk group. Through simulative intervention and risk transition deduced from the DQN model, lung cancer incidences of the high risk, medium risk, and low risk groups were obviously decreased. CONCLUSIONS: A DQN-based deep reinforcement learning model was proposed and validated to develop and simulate a risk transfer pathway of lung cancer among middle-aged and older individuals. Risk stratification supplied an effective foundation for lung cancer risk transition.
17 Apr 2026
Read appraisal →BMJ open
Trends and characteristics of syphilis incidence in Japan: a nationwide claims-based analysis of working-age populations and their dependents, 2016-2023
OBJECTIVES: To examine trends and demographic characteristics of syphilis incidence in Japan using a large nationwide claims database with family linkage, with particular focus on differences by sex, age, HIV status and family relationships. DESIGN: Retrospective cohort study. SETTING: JMDC claims database (JMDC Inc, Tokyo, Japan), a nationwide administrative claims database in Japan, using data from 2016 to 2023. PARTICIPANTS: Individuals aged 16-59 years enrolled in the JMDC database, including employees of medium-to-large companies and their dependents (n=12.5 million). OUTCOME MEASURES: Syphilis cases were defined by International Classification of Diseases, 10th Revision (ICD-10) codes (A50-A53) with concurrent treatment with relevant antibiotics. We determined syphilis incidence rates per 100 000 person-years, stratified by sex, age, HIV status and family relationships. We also investigated within-couple concordance patterns and reinfection rates. RESULTS: Among 16.4 million individuals, 9357 syphilis infections were identified among 8881 individuals. Incidence increased markedly during the pandemic, reaching 48.2 (men) and 12.9 (women) per 100 000 person-years in 2023. Men showed consistently high incidence in their 20s-50s, whereas female incidence peaked in the 10s-20s. Among 2 294 184 married couples, dependent women (ie, housewives) showed comparably high incidence to age-matched men (10-20 per 100 000 person-years). In 1286 couples with at least one syphilis case, 12.4% of wives in their 20s were also diagnosed, compared with 2%-3% in older groups. In 20s couples, the proportion of syphilis among wives only and husbands only was similar. Subgroup analysis revealed notably high incidence among unmarried female dependent youths (2022: 66.7 per 100 000 person-years). Individuals living with HIV had substantially elevated incidence (3000-15 000 per 100 000 person-years) and reinfection rates. CONCLUSIONS: Using a large claims database with family linkage, we found that while male syphilis incidence remained dominant, high rates were also observed among dependent women and youths. These findings suggest that syphilis risk may extend beyond traditionally recognised high-risk populations and emphasise the need for targeted screening and preventive strategies in broader demographic groups.
17 Apr 2026
Read appraisal →Journal of affective disorders
Association of social isolation and loneliness with cancer prognosis: an analysis of the UK Biobank
BACKGROUND: Evidence is scarce for the association between social isolation and loneliness and prognosis of any cancer or other common cancer types. METHODS: To address this gap, we performed a cohort study from 2006 to 2022 using data from the UK Biobank (UKB). Social isolation and loneliness were identified based on the questionnaire of the UKB. Cases of death were obtained from the death registry, where both primary and contributory causes were recorded using ICD-10 codes. The Cox proportional hazard models were used to estimate hazard ratios (HRs) and associated 95% confidence intervals (CIs) of death in relation to social isolation and loneliness among patients with cancer. RESULTS: Among cancer patients studied, 6206 (9.6%) were socially isolated and 3130 (4.8%) reported loneliness. Over a median follow-up of 6.7 years, there were 2378 deaths (38.3%) among exposed patients and 16,566 deaths (29.1%) among unexposed patients. Social isolation was associated with poorer cancer prognosis overall (HR 1.21, 95% CI 1.16-1.26, P < 0.001), while loneliness correlated with a similar risk (HR 1.18, 95% CI 1.11-1.25, P < 0.001). Social isolation specifically impacted survival in colon, melanoma, breast, prostate, kidney, and bladder cancers, as well as myeloproliferative neoplasms. Loneliness was linked to poorer outcomes in breast, prostate, and bladder cancers. CONCLUSIONS: Our findings suggested that social support for these vulnerable patients might have the potential to improve prognosis.
15 Apr 2026
Read appraisal →Journal of affective disorders
Adverse psychosocial work exposures and 2-year incident prescription of psychotropic medications: a prospective French study using the national healthcare reimbursement database
OBJECTIVE: Despite the abundant literature on the associations between psychosocial work exposures and mental health outcomes, prospective studies using medico-administrative data are lacking. The objective was to study the associations between various psychosocial work exposures and incident prescription of psychotropic medications. METHODS: The study relied on three waves (2010, 2012, 2014) of the French ESPS survey (Health, Health Care and Insurance Survey) (baseline data), linked to the national database of healthcare reimbursements (follow-up data). Each survey wave was conducted among a randomly selected national sample of people aged 18+. The study included a total of 8689 observations among 7695 workers without psychotropic medication prescription within the 6 months preceding survey wave. Ten psychosocial work exposures (quantitative demands, tensions with the public, freedom, possibilities for learning new things, colleague support, recognition, salary satisfaction, job insecurity, temporary contract, and redundancy plan) were assessed at each survey wave. Covariates included gender, age, marital status, employee/self-employed worker, private/public sector, occupation, and full/part-time work. The outcome was psychotropic medication prescription within the 2 years following survey wave. Mixed effects Cox regression modelling was used. RESULTS: Seven of the ten psychosocial work exposures were associated with incident psychotropic medication prescription. Associations were observed more with incident prescription of antidepressants and anxiolytics than with hypnotics/sedatives. The number of psychosocial work exposures increased the risk linearly, except for hypnotics/sedatives. No gender interaction was found for any of these associations. CONCLUSION: The findings may support that poor psychosocial working conditions increase the risk of subsequent psychotropic medication prescription.
15 Apr 2026
Read appraisal →PloS one
Use of administrative data for evaluating trends in medically-attended Lyme disease, Manitoba, Canada, 2010-2021
While Lyme disease (LD) is the most common tick-borne disease reported to public health surveillance in North America and is increasingly recognized as a public health threat in Canada, it's incidence may be underreported. Our study aims to estimate medically-attended LD incidence in Manitoba, Canada, using administrative health data. We identified medically-attended LD cases in Manitoba from 2010 - 2021 in a claims database (Manitoba Population Research Data Repository), which contains the health records of >95% of the residents of Manitoba, using diagnostic codes, antibiotic dispensations, and laboratory results. The incidence of claims-based LD cases ranged from 8.4 to 28.5 per 100,000 population per year, 5.1 to 11.0 times higher than the incidence of surveillance-reported LD cases. The incidence of claims-based LD cases was particularly higher than the incidence of surveillance-reported LD cases in females and health regions with a low surveillance-reported incidence. Our study suggests that medically attended LD is more common than reported in surveillance. Further study is required to identify barriers to reporting. Interventions are needed to reduce the substantial burden of LD in Manitoba.
15 Apr 2026
Read appraisal →Neurology
Type 1 Diabetes and Incident Dementia: An Analysis in the All of Us Cohort
BACKGROUND AND OBJECTIVES: Although diabetes mellitus (DM) is a well-established determinant of dementia risk, most studies have evaluated type 2 DM (T2DM) or any DM. The influence of type 1 DM (T1DM) on dementia risk remains unclear. We evaluated associations of T1DM and T2DM, separately, with incident dementia using linked electronic health records (EHRs). METHODS: This prospective cohort study used previously collected survey and EHR data from the All of Us (AoU) cohort, a convenience sample of US adults. Eligible participants were 50 years or older and completed baseline surveys. Enrollment began in 2017, with data available through October 2023, along with records before enrollment. Mean follow-up from baseline was 2.4 years. We developed an algorithm to distinguish DM type based on count of T1DM encounters. This algorithm was validated against 2 reference measures: self-reported diabetes type and C-peptide values. Using AoU data, we classified participants as having no DM, T1DM, or T2DM. We ascertained incident dementia using ICD-9, ICD-10, and Systematized Nomenclature of Medicine codes in participants' EHRs. We estimated hazard ratios (HRs) and 95% CIs for the association of diabetes type with incident dementia using Cox proportional hazards models. RESULTS: Among 283,772 participants (mean [SD] age 64.62 [8.96] years; 56.7% women), 60.3% identified as non-Hispanic White and 13.3% as Hispanic/Latino. Optimal DM classification algorithm cutoffs varied by reference standard: self-reported diabetes: ≥1 T1DM EHR encounter (sensitivity: 0.59; specificity: 0.90) and C-peptide: ≥3 T1DM EHR encounters (sensitivity: 0.76; specificity: 0.79). Defining T1DM as having ≥1 T1DM encounter, 5,442 participants had T1DM. Compared with those without DM, participants with T1DM had higher dementia incidence (sociodemographic-adjusted HR 2.82; 95% CI 2.28-3.48) and those with T2DM also had elevated risk (sociodemographic-adjusted HR 2.08; 95% CI 2.87-2.31). Results were similar across sex, race, and ethnicity strata. DISCUSSION: In AoU, DM was associated with elevated dementia risk, with the highest risk among those with T1DM. These findings highlight the need to better understand mechanisms tying T1DM to dementia.
14 Apr 2026
Read appraisal →Epidemiology and psychiatric sciences
Cross-cultural validity of the generalized anxiety disorder 7-item scale (GAD-7): a systematic review and standardized assessment
AIMS: The Generalized Anxiety Disorder 7-Item Scale (GAD-7) is a brief self-reported measure for screening for anxiety symptoms. However, the evidence about its cross-cultural validity is fragmentary and usually focused on specific settings. Therefore, we aimed to critically review and synthesize the existing evidence about the cross-cultural validity of the GAD-7. METHODS: We conducted a systematic review of studies assessing the cross-cultural validity of the GAD-7 in following the PRISMA guidelines. Additionally, the quality of the studies was assessed following the COSMIN guidelines, and the quality of the evidence was assessed with the GRADE. Data were synthesized narratively. RESULTS: Out of 1,965 unique records, 9 unique studies were deemed eligible for the COSMIN appraisal and the narrative synthesis (total sample: 11,894, 53.7% females and 20 different cultural groups). Most studies (7) had adequate quality and showed evidenced of the unitary structure of the GAD-7 across cultural groups. In 4 studies also assessing possible cultural bias, the effect on the general score was deemed negligible. CONCLUSIONS: The evidence about the cross-cultural validity of the GAD-7 is very limited. Although more research is needed, the evidence available shows that the GAD-7 could be a cross-culturally valid tool for the assessment of anxiety symptoms in clinical contexts and epidemiological studies. Until new high-quality evidence will be available, these results would constitute a key first step for supporting the use of the GAD-7 in multi-cultural clinical settings and to inform clinical, public health and global health decision making in relation to anxiety.
14 Apr 2026
Read appraisal →Vaccine
Adenovirus type 5 vector-based COVID-19 vaccine does not increase the likelihood of HIV infection
BACKGROUND: There is a concern about the potential increased risk of acquiring HIV infection after vaccination with adenovirus type 5 (Ad5) vector-based vaccines. We aimed to investigate whether Ad5-nCoV increases the risk of HIV infection. METHODS: Between September 2020 and March 2021, a Phase 3 clinical trial was executed to evaluate the efficacy of Ad5-nCoV. This trial, employing a double-blind, randomized, and placebo-controlled design, enrolled 44,247 participants from 66 clinical sites spanning Argentina, Chile, Mexico, Russia, and Pakistan. Participants were allocated randomly in a 1:1 ratio to receive either Ad5-nCoV or a placebo, and their HIV-negative status was assessed by self-report and confirmed by HIV testing on a baseline blood sample at the end of the study. Consequently, individuals initially administered a placebo received one dose of Ad5-nCoV, while those initially given Ad5-nCoV finally received two doses, with an interval of approximately 6-10 months between administrations. The trial design and primary outcomes have been previously reported. This analysis involves a comprehensive assessment of the associations between pre-existing Ad5 neutralizing antibodies, HIV prevalence and incidence before and after vaccination while accounting for demographic variables. RESULTS: A total of 43,780 participants who had valid HIV baseline testing results were included in this study. Prior to vaccination, there was no discernible difference in HIV prevalence between the placebo group (0·39% of 21,877 participants) and the Ad5-nCoV group (0·38% of 21,893 participants). This similarity persisted when the data were stratified by country, gender, age, and race. Paired analyses assessing HIV incidence were conducted on participants who tested negative for HIV before vaccination, with data collected over a 6-month period post-vaccination. The results revealed notable similarity in HIV incidence across all groups: 0·29% for Group A (Ad5-nCoV group), 0·21% for Group B (Placebo group), 0·27% for group A + D (Ad5-nCoV group and 1 × Placebo+1 × Ad5-nCoV group), 0·1% for Group C (2 × Ad5-nCoV group), and 0·25% for Group A + C + D (participants who received at least one dose of Ad5-nCoV). Testing approximately 100 randomly chosen participants in each country revealed a similar distribution of pre-existing Ad5 neutralizing antibodies (Nab), with the ratio above a titer of 200 ranged from 65·42% to 67·71%. Notably, the heightened presence of pre-existing Ad5 Nab did not result in an increased risk of HIV infection. CONCLUSION: The Ad5-nCoV did not increase the short-term risk of HIV infection in this large and diverse sample of the general population, regardless of the presence of pre-existing Ad5 Nab providing valuable insights into the safety of Ad5 vector-based vaccines. CLINICALTRIALS: gov, NCT04526990.
12 Apr 2026
Read appraisal →Global public health
Seroprevalence of hepatitis C virus among refugees, asylum seekers, and internally displaced populations within Africa: A systematic review and meta-analysis
Hepatitis C virus (HCV), an often overlooked public health concern, is highly prevalent among marginalized groups. Refugees, asylum seekers, and internally displaced people (RASIDP) constitute an underserved population in healthcare. Given that displacement rates are increasing and that HCV is endemic to many African countries, we aimed to determine the prevalence of HCV among RASIDP communities in Africa. Studies investigating HCV prevalence within RASIDP communities across Africa were identified via the following database searches: Ovid MEDLINE, EMBASE, Web of Science, APA PsycINFO, and the WHO Africa Index Medicus. Seroprevalence estimates were meta-analysed using a random-effects model, with data stratified by participant region, age, and gender. Across 9 studies, we estimated a pooled HCV seroprevalence of 3.63% (95% CI: 0.54, 9.16) among RASIDP in Africa. Seroprevalence varied by region of origin and age, with those from Northern Africa and those aged > 40 years having the highest seroprevalence, at 10.03% (95% CI:9.03, 11.13) and 5.70% (95% CI: 0.81, 14.21), respectively. RASIDP communities represent a marginalised and vulnerable population that often faces significant barriers to accessing healthcare. Our findings show that HCV seroprevalence within these communities varies by region and age, thus suggesting a need for targeted screening approaches.
12 Apr 2026
Read appraisal →JMIR aging
Global, Regional, and National Burden of Falls Among Older Adults Aged 65 Years and Above: Secondary Data Analysis of the Global Burden of Disease Study 2021
BACKGROUND: Falls are a leading cause of injury, disability, and death among older adults, posing significant public health challenges. However, comprehensive global analyses of fall-related burdens in older populations remain scarce. OBJECTIVE: This study aimed to explore the patterns and distribution of the global, regional, and national burden of falls among adults aged 65 years and older. METHODS: Data from the Global Burden of Disease study 2021 were used to assess the overall, disability, and mortality burden of falls among adults aged 65 years and older. Age-standardized rates of disability-adjusted life years (DALYs), years lived with disability (YLDs), and years of life lost were calculated to compare burdens across countries. Health inequalities were evaluated via the slope index of inequality and the concentration index. Frontier analysis identified optimal burden levels by sociodemographic index (SDI). Bayesian age-period-cohort models projected trends up to 2050. RESULTS: DALY age-standardized rates showed a U-shaped distribution across SDI regions: lower-SDI countries faced higher mortality burdens, while higher-SDI countries had elevated disability burdens. Despite an increase in absolute overall burden inequality from 1990 to 2021, absolute inequalities in YLDs and years of life lost declined, with DALYs and YLDs exhibiting relatively more balanced distributions. Frontier analysis pinpointed countries with the greatest burden reduction potential. Projections suggest decreasing overall and mortality burdens by 2050 but rising disability burdens. CONCLUSIONS: Higher- and lower-SDI countries face distinct fall-related challenges. Reducing cross-national health inequalities and closing gaps between the observed burden and the optimal burden level achievable at a similar SDI level are critical. Despite projected declines in the overall burden (DALYs), the rising disability burden (YLDs) could present evolving challenges, potentially underscoring the importance of proactive preparedness.
11 Apr 2026
Read appraisal →Acta dermato-venereologica
Predicting Melanoma Impact on the Swedish Healthcare System from the Adult Population Using Machine Learning on Registry Data
Melanoma incidence has increased in Western countries over the past 50 years, leading to significant healthcare costs. In Sweden, comprehensive healthcare registries enable large-scale prediction studies using machine learning. Several machine learning models were evaluated to predict melanoma diagnoses using Swedish registry data, assessing the added value of diagnostic and medication data beyond demographics. The study included all adults in Sweden with continuous residency for 9.5 years (n = 6,036,186). The outcome was a melanoma diagnosis, including melanoma in situ, recorded within 5 years after the index date (31 December 2014). Predictors included age, sex, income, education, marital status, region of birth, diagnoses, and dispensed drugs. Models tested were logistic regression, gradient boosting, random forests, and a neural network. A total of 38,582 individuals (0.64%) developed melanoma. The gradient boosting model using all predictors performed best, with an area under the receiving operating characteristics curve (AUC) of 0.735 (95% confidence interval [CI], 0.725-0.746). When diagnosis and medication data were excluded, AUC dropped to 0.681 (95% CI: 0.670-0.691). The findings highlight that including healthcare codes improves predictive performance, and demonstrate the utility of Swedish registries for computational phenotyping. This approach may support early detection of melanoma and targeted follow-up.
10 Apr 2026
Read appraisal →The Tohoku journal of experimental medicine
Age-Specific Trends and Health Status of Children Aged 12-19 Years with Specified Chronic Pediatric Diseases in Japan - Insights from a National Database Study
Japan has a national financial assistance program called the "Specified Chronic Pediatric Diseases of Japan" (SCPDJ; also called "Shouman" in Japanese) for children with chronic diseases designated by the Japanese Ministry of Health, Labour and Welfare. The information of registrants in the system is digitized for research. We utilized the database to clarify the characteristics of children with diseases designated by the SCPDJ program. Analysis was performed with a particular focus on patients aged 12-19 years, which are considered the important ages for transition support from pediatric to adult medical care. The data included 69,061 cases aged 0 to 19 years from April 2016 to March 2017. The number of registrants showed a cumulative increase from 2 years toward adulthood. There were four distribution patterns for registered age and age at onset by disease category: 1) both age and age of onset peaked at age 0; 2) age distribution was mostly flat, while the most common age of onset was age 0; 3) age distribution peaked in older age groups, while age of onset of 0 was most common; and 4) both age and age of onset peaked at older ages. Approximately half of the patients aged 12-19 years were diagnosed after reaching school age. Although the disease condition of most patients remained good, some aged 12-19 years experienced exacerbation. The database was analyzed to provide an overview of children with diseases designated by the SCPDJ program and issues related to transition to adult care systems.
10 Apr 2026
Read appraisal →Journal of the American Heart Association
Exposure to Volatile Organic Compounds and Cardiovascular Diseases: A Systematic Review and Meta-Analysis
BACKGROUND: Environmental pollution is an often-neglected contributor to cardiovascular disease (CVD). Volatile organic compounds (VOCs) are a subset of ubiquitous pollutants. Recent evidence has suggested exposure to VOCs as a possible contributor to CVD. We aimed to review the evidence for such a link between VOC exposure and cardiovascular outcomes. METHODS: PubMed, Web of Science, and Scopus were systematically searched for ecological and observational studies evaluating associations between exposure to VOCs and cardiovascular outcomes. The available evidence was synthesized qualitatively, and meta-analyses were conducted wherever multiple estimates were identified for a shared association. Sensitivity analyses were performed to evaluate the influence of individual studies on the pooled estimates. RESULTS: Thirty-six studies were included in the review. Pooled random-effects estimates for associations between VOC mixtures (odds ratio, 1.57 [95% CI, 0.79-3.11])/toluene, ethylbenzene, and xylene and CVD trended toward a positive effect while being imprecise. The pooled estimate for the association between exposure to benzene and CVD death also trended toward a positive effect (meta relative risk, 1.09 [95% CI, 0.99-1.21]; I2=91.4 [95% prediction interval, 0.82-1.46]). However, serious inconsistencies were noted on the significance and the magnitude of association. Fixed-effect analyses indicated significant positive effects in all evaluated associations. CONCLUSIONS: Evidence from studies evaluating ambient and individual VOC exposures suggests a possible association with increased CVD and CVD death. However, the heterogeneity noted between specific VOCs' effects, possible confounding, limited long-term exposure assessment, and sparse evaluation of mixture/copollutant effects warrant cautious interpretation of these findings and highlight the need for future high-quality prospective studies.
8 Apr 2026
Read appraisal →Psychological medicine
Joint effects of childhood adversity and genetic risk for psychosis on psychopathology in the UK Biobank
BACKGROUND: The individual effects of genetic factors and adverse childhood experiences (ACEs) on risk of psychosis, including schizophrenia (SCZ) and bipolar disorder (BIP), have been widely acknowledged, but their interaction effects on individual psychopathological symptoms remain unclear. METHODS: Based on data from 163,704 individuals in the UK Biobank, we investigated the joint effects of polygenic risk scores (PRSs) of SCZ and BIP and ACEs on psychopathology. ACEs status and 55 psychopathological symptoms from seven domains were measured retrospectively using an online mental health questionnaire in 2016. Recent genome-wide association studies for SCZ and BIP were combined with genotype data to generate PRSs. Logistic regression analyses were then conducted to explore univariate and joint main effects of PRSs and ACEs on psychopathological symptoms, as well as their additive and multiplicative interaction effects. RESULTS: The interaction mechanisms for PRSs and ACEs varied across symptom domains: additive interactions were observed on the depression (RERIBIP-ACEs = 0.20-0.25), anxiety (RERISCZ-ACEs = 0.20; RERIBIP-ACEs = 0.22-0.26), help-seeking (RERISCZ-ACEs = 0.24; RERIBIP-ACEs = 0.23), and cognition domains (RERISCZ-ACEs = -0.23 to -0.17), whereas multiplicative interactions were only detected on the psychotic (betaSCZ-ACEs = -0.543; betaBIP-ACEs = -0.181), mania (betaBIP-ACEs = -0.195), self-harm or suicide (betaSCZ-ACEs = -0.118), and cognitive domains (betaSCZ-ACEs = -0.204 to -0.157). CONCLUSIONS: The interplay mechanisms for genetic liability to SCZ and BIP and ACEs vary across symptom domains. This study reveals heterogeneity in gene-ACEs interaction mechanisms underlying psychosis and may provide personalized guidance for psychological care after ACEs.
8 Apr 2026
Read appraisal →BMJ open
Adverse childhood experiences and cognitive outcomes among older adults in low-income and middle-income countries: a systematic review
OBJECTIVES: While adverse childhood experiences (ACEs) have been consistently linked to poorer cognitive outcomes in later life, far less is known about ACEs' contribution to dementia and cognitive ageing risk in low-income and middle-income countries (LMICs), despite their growing and disproportionate share of global Alzheimer's disease and related dementias (ADRD) burden. This study aimed to systematically review existing evidence on the association between individual ACEs and cognitive outcomes among adults aged 40 years and older in LMIC settings. DESIGN: Systematic review and narrative synthesis. DATA SOURCE: We searched Medline, Embase, PsycINFO and CINAHL from the inception of each database to January 2026. ELIGIBILITY CRITERIA: Observational studies examining the association between individual ACEs and cognitive outcomes among adults aged 40 and older in LMICs. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently screened each record, assessed risk of bias using the Joanna Briggs Institute critical appraisal tool and extracted data. Results were illustrated using descriptive forest plots and a narrative synthesis. RESULTS: Our systematic review included 14 studies, primarily from upper-middle-income countries, that assessed the relationship between individual ACEs and cognitive impairment (n=4) and function (n=10). 10 studies reported significant associations between ACEs and poorer cognitive function and increased risk of cognitive impairment. No study assessed the association between ACEs and ADRD. Parental death, neglect and mental health issues in the family showed consistent associations with cognitive outcomes, whereas experiencing hunger and poor health in childhood showed inconsistent associations. CONCLUSIONS: Our review corroborated evidence from high-income countries that exposure to ACEs is associated with a long-term risk of poorer cognitive outcomes among older populations in LMICs. However, the available literature remains limited, with most studies originating from upper-middle-income countries, few examining cognitive impairment and none assessing ADRD. These findings underscore substantial gaps in knowledge and highlight the need for research across diverse LMIC settings to clarify the role of broader psychosocial and contextual childhood experiences in shaping cognitive ageing and dementia risk. PROSPERO REGISTRATION NUMBER: CRD42024501816.
8 Apr 2026
Read appraisal →PloS one
Perception and acceptance of micronutrient-Fortified Bouillon among Non-Index Household Members: A longitudinal sub-study nested within a randomized trial in Northern Ghana
BACKGROUND: Fortified bouillon cubes offer a scalable strategy to address micronutrient deficiencies in West Africa. However, success depends on household members' perceptions and acceptance. This study assessed changes in perception and acceptance of fortified bouillon cubes over time and explored factors influencing these outcomes among non-index household members (NIHMs) from trial households in Northern Ghana. METHODS: This longitudinal mixed-methods study was nested within a double-masked randomised controlled trial. Households received micronutrient-fortified bouillon cubes (vitamin A, vitamin B12, folic acid, iron, zinc, iodine) or control cubes (iodine only) for nine months. One NIHM (≥15 years) per household (n = 1,047) was enrolled. Structured interviews were conducted during early (months 1-2; n = 731) and late (months 7-9; n = 796) phases. Composite perception (8 items) and acceptance (10 items) scores (range: 1-5; higher scores = more positive perceptions/greater acceptance) were derived from an 18-item validated instrument. Bayesian linear mixed-effects models assessed temporal changes and associated individual and household factors. Twenty-four focus group discussions (months 4-5) explored drivers and barriers. RESULTS: Mean age of NIHMs was 40 ± 14.7 years; 59% were female. Perception scores increased over seven months, from 3.4 to 4.1 (adjusted posterior mean difference [aPMD] = 0.69; 95% credible interval [CrI]: 0.57, 0.81). Acceptance remained high (4.59 to 4.58; aPMD = -0.02; 95% CrI: -0.07, 0.03). No differences were observed between trial arms. At early timepoint, perception scores were lower among household heads compared with other household members, and lower among formal employees compared with farmers. Acceptance was higher among individuals from severely food-insecure households and lower among those from mildly food-insecure households. Focus groups revealed that perceived health benefits, sensory appeal, and cultural fit drove acceptance, while infertility-related myths and food colour changes were barriers. CONCLUSION: Fortified bouillon cubes were accepted, with perceptions improving over time. Public education will be essential to address misconceptions. TRIAL REGISTRATION: ClinicalTrials.gov (NCT05178407); Pan-African Clinical Trial Registry (PACTR202206868437931). The authors confirm that all ongoing and related trials for this intervention are registered.
5 Apr 2026
Read appraisal →BMJ open respiratory research
Associations between respiratory diseases and lung cancer risk: a secondary analysis of the large-scale prospective UK biobank cohort
INTRODUCTION: Respiratory diseases are significant risk factors for lung cancer; however, the association between acute respiratory infections and lung cancer incidence requires further exploration. METHODS: We performed a secondary analysis of the prospective UK Biobank cohort, including participants aged 37-73 years recruited from 22 assessment centres across the UK between 2006 and 2010. Cox proportional hazards models estimated HRs for incident lung cancer according to respiratory disease status, which was defined based on linked hospital inpatient records. Mediation analysis explored potential biomarkers, and Mendelian randomisation assessed causal relationships. RESULTS: During a mean follow-up of 10.44 years (4 790 738 person-years), 2189 participants developed lung cancer. Among 107 007 individuals with respiratory diseases, 1322 cases occurred (incidence rate 27.6 per 10 000 person-years), compared with 867 cases among 351 876 participants without respiratory diseases (7.9 per 10 000 person-years). Overall, respiratory diseases were associated with increased lung cancer risk (HR 2.97, 95% CI 2.75 to 3.21). Acute respiratory infections, including acute nasopharyngitis (HR 3.41; 95% CI 1.83 to 6.34), influenza (HR 3.90; 95% CI 2.53 to 6.01), viral pneumonia (HR 9.86; 95% CI 6.55 to 14.85) and bacterial pneumonia (HR 6.28; 95% CI 4.40 to 8.96), showed strong associations with lung cancer incidence. In subtype analyses, squamous cell carcinoma exhibited the highest risk elevation (HR 3.65; 95% CI 3.06 to 4.36). Mediation analysis indicated that neutrophil counts partially mediated these associations (proportion mediated up to 8%). CONCLUSION: Acute respiratory infections were associated with higher lung cancer incidence, providing hypothesis-generating evidence that may inform future risk stratification research.
5 Apr 2026
Read appraisal →Neurology
Long-Term Exposure to Ambient Air Pollution and Incident Amyotrophic Lateral Sclerosis: A Prospective Cohort Analysis of the UK Biobank
BACKGROUND AND OBJECTIVES: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease with a complex etiology. Although a range of genetic and lifestyle factors have been implicated, the potential role of environmental airborne pollution exposure is uncertain. This study examined the association between long-term ambient exposure to air pollutants and the incidence of ALS in UK Biobank participants. METHODS: This prospective cohort study was based on the UK Biobank participants aged 40-69 years. The analytical sample comprised participants free of ALS at baseline and had complete data on air pollution exposure. Long-term exposure (2006-2021) to nitrogen dioxide (NO2), nitrogen oxides (NOX), fine particulate matter (PM2.5; <2.5 µm), and coarse particulate matter (PM10; <10 µm) was assessed using data from the UK Department for Environment, Food and Rural Affairs at a spatial resolution of 1 × 1 km. To evaluate the association between these pollutants and ALS risk, we used multivariable time-varying Cox proportional hazards models. Several sensitivity analyses were conducted to assess the robustness of the results. We also examined for gene-environment interaction stratified by C9orf72 status and UNC13A genotype. RESULTS: Among the 501,308 participants with a mean age of 56.5 (SD 8.1) years at baseline, 272,764 (54.4%) were female. Over a median follow-up of 8.4 years, 687 individuals developed ALS. We did not observe any associations for any of the examined pollutants and ALS risk. Specifically, the hazard ratios per SD increment for PM10, PM2.5, NOX, and NO2 were 1.03 (95% CI 0.92-1.15), 1.00 (95% CI 0.88-1.14), 1.01 (95% CI 0.90-1.13), and 1.00 (95% CI 0.89-1.12), respectively. Individuals living in areas with the highest tertile of air pollutant exposure, compared with those in the lowest tertile, did not show a higher risk of ALS across any of the pollutants examined (p for trend >0.05). Restricted cubic spline analyses revealed no nonlinear associations between air pollution and ALS risk (all p for nonlinearity >0.05). These results remained robust in various subgroup and sensitivity analyses. No evidence of gene-environment interaction was found. DISCUSSION: In this large population-based study with high statistical power, ambient air pollution was not a risk factor for the development of ALS.
5 Apr 2026
Read appraisal →BMJ global health
Impacts of rising food prices on nutritional outcomes and mortality of children in low and middle-income countries: a systematic review
BACKGROUND: Estimates indicate that 45% of mortality among children under 5-year old is linked to nutrition. Increased food prices can reduce access to nutritious foods, increasing nutritional and health risks for children, particularly in low and middle-income countries (LMICs). We synthesised evidence on the relationship of rising food costs with nutritional outcomes and mortality in children in LMICs. METHODS: EMBASE, MEDLINE, Global Health, EconLit, Web of Science and six grey literature sources were searched in September 2023. Searches included key phrases such as 'food price shocks' and 'child malnutrition'. We included any longitudinal study design, with any measure of food price increase and any measure of child malnutrition or mortality as outcomes. Results were analysed narratively with risk of bias assessed using the Newcastle Ottawa Scale. RESULTS: A total of 18 studies were included, covering 104 LMICs. There were 16 repeated cross-sectional or panel studies, one longitudinal cohort study and one quasi-experimental study. Five of these studies were multicountry. 16/18 studies reported a range of adverse nutritional outcomes associated with higher food costs, including mortality (five studies), stunting and low height for age (six studies), and wasting and low weight for height (five studies). Low birth weight, admissions to feeding centres, reduced consumption of rice, fruit, vegetables and animal protein, and reduced haemoglobin concentrations were also associated with higher food prices in one study each. Subgroup analyses were limited although two studies identified larger impacts on children in urban areas, and two studies identified larger impacts where children were exposed to higher food prices at younger ages. DISCUSSION: Rising food prices present a risk to child health and nutrition in LMICs. Policies should prioritise increasing household income alongside structural reforms to mitigate the nutritional impacts of elevated food prices on young children and pregnant women. PROSPERO REGISTRATION NUMBER: CRD42023469198.
4 Apr 2026
Read appraisal →Ann Allergy Asthma Immunol
Current updates in the epidemiology and comorbidities of atopic dermatitis
Atopic dermatitis (AD) is a chronic, inflammatory skin disorder that affects individuals across the lifespan, with significant implications for both public health and individual well-being. This paper provides an overview of the epidemiology of AD, focusing on its prevalence, incidence, and risk factors across different populations and regions. We evaluate the growing global burden of AD, highlighting trends in both high-income and low- to middle-income countries, and the impact of socioeconomic, environmental, and genetic factors on disease manifestation. The review also explores the variations in disease severity, age of onset, and comorbidities such as asthma and allergic rhinitis. This paper emphasizes the need for more robust epidemiologic studies to further elucidate the complex interactions between genetic predisposition and environmental exposures in the development and progression of AD and explores the potential role of technology (eg, artificial intelligence) in helping to collect robust epidemiologic data. Understanding the epidemiology of AD is crucial for identifying high-risk groups, informing preventive strategies, and optimizing management approaches.
1 Nov 2025
Read appraisal →