Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 38 appraisals
Bulletin of the World Health Organization
Digital epidemiology investments, Saudi Arabia
PROBLEM: Traditional epidemiological surveillance methods are often limited by delays in reporting and fragmented data systems. Saudi Arabia faces additional public health challenges from mass gatherings during Hajj and Umrah, an increasing burden of noncommunicable diseases and rapid urbanization, highlighting the need for investing in digital epidemiology. APPROACH: Saudi Arabia has accelerated digital transformation in health care through Vision 2030 initiatives. The strategies include health information exchange platforms, analytics driven by artificial intelligence, telemedicine services and digital monitoring systems used during Hajj. We review current initiatives to invest in digital epidemiology in Saudi Arabia, implementation challenges and policy priorities. LOCAL SETTING: Saudi Arabia's health system operates under a predominantly public model. The health ministry is the main provider, regulator and finance provider of most health-care services. Health-care coverage is nearly universal, with citizens receiving services free of charge through the public system. Ongoing reforms aim to gradually decentralize certain functions. RELEVANT CHANGES: The initiatives under Vision 2030 have supported disease surveillance, data integration and public health response capacities. Existing digital health reforms have created a foundation for integrating digital epidemiology into routine public health practice. However, challenges remain, including fragmented interoperability between institutions, workforce shortages, unequal digital access, and concerns about data governance, privacy and algorithmic bias. LESSONS LEARNT: Saudi Arabia's experience suggests that digital epidemiology is more effective when integrated within broader digital health reforms. Successful implementation requires not only digital infrastructure, but also workforce development, ethical governance, transparency and mechanisms for integrating digital data into public health decision-making. PROBLÈME: Les méthodes traditionnelles de surveillance épidémiologique sont souvent limitées par des retards dans la transmission des données et par la fragmentation des systèmes de données. L’Arabie saoudite est confrontée à des défis supplémentaires en matière de santé publique liés aux rassemblements de masse lors du Hajj et de la Omra, au fardeau croissant des maladies non transmissibles et à l’urbanisation rapide, ce qui souligne la nécessité d’investir dans l’épidémiologie numérique. APPROCHE: L’Arabie saoudite a accéléré la transformation numérique dans le domaine des soins de santé grâce aux initiatives de la Vision 2030. Ces stratégies comprennent des plateformes d’échange d’informations de santé, des analyses alimentées par l’intelligence artificielle, des services de télémédecine et des systèmes de surveillance numérique employés pendant le Hajj. La présente étude passe en revue les initiatives actuelles visant à investir dans l’épidémiologie numérique en Arabie saoudite, les défis liés à leur mise en œuvre et les priorités politiques. ENVIRONNEMENT LOCAL: Le système de santé saoudien fonctionne selon un modèle majoritairement public. Le ministère de la Santé est le principal prestataire, régulateur et bailleur de fonds de la plupart des services de santé. La couverture des soins de santé est pratiquement universelle, les citoyens bénéficiant de services gratuits au travers du système public. Les réformes en cours visent à décentraliser progressivement certaines fonctions. CHANGEMENTS SIGNIFICATIFS: Les initiatives menées dans le cadre de la Vision 2030 ont favorisé la surveillance des maladies, l’intégration des données et le renforcement des capacités d’intervention de santé publique. Les réformes existantes en matière de santé numérique ont jeté les bases nécessaires à l’intégration de l’épidémiologie numérique dans les pratiques courantes de santé publique. Toutefois, des défis subsistent, notamment le manque d’interopérabilité entre les établissements, la pénurie de main-d’œuvre, les inégalités d’accès au numérique, ainsi que des préoccupations en matière de gouvernance des données, de confidentialité et de biais dû aux algorithmes. LEÇONS TIRÉES: L’expérience de l’Arabie saoudite suggère une plus grande efficacité de l’épidémiologie numérique lorsque celle-ci s’inscrit dans le cadre de réformes plus larges en matière de santé numérique. La réussite de la mise en œuvre nécessite non seulement une infrastructure numérique, mais aussi le développement des ressources humaines, une gouvernance éthique, la transparence ainsi que des mécanismes d’intégration des données numériques dans la prise de décision en matière de santé publique. SITUACIÓN: Los métodos tradicionales de vigilancia epidemiológica suelen verse limitados por retrasos en la notificación y por sistemas de datos fragmentados. Arabia Saudí se enfrenta además a desafíos de salud pública derivados de las concentraciones masivas de personas durante el Hach y la Umrah, del aumento de la carga de las enfermedades no transmisibles y de la rápida urbanización, lo que destaca la necesidad de invertir en epidemiología digital. ENFOQUE: Arabia Saudí ha acelerado la transformación digital de la atención sanitaria mediante las iniciativas de la Visión 2030. Las estrategias incluyen plataformas de intercambio de información sanitaria, análisis basados en inteligencia artificial, servicios de telemedicina y sistemas digitales de vigilancia utilizados durante el Hach. Se examinan las iniciativas actuales de inversión en epidemiología digital en Arabia Saudí, los desafíos para su implementación y las prioridades en materia de políticas. MARCO REGIONAL: El sistema sanitario de Arabia Saudí funciona bajo un modelo predominantemente público. El ministerio de salud es el principal proveedor, regulador y financiador de la mayoría de los servicios sanitarios. La cobertura sanitaria es prácticamente universal y la ciudadanía recibe servicios gratuitos a través del sistema público. Las reformas en curso tienen por objeto descentralizar gradualmente determinadas funciones. CAMBIOS IMPORTANTES: Las iniciativas de la Visión 2030 han fortalecido la vigilancia de enfermedades, la integración de datos y la capacidad de respuesta en salud pública. Las reformas existentes en materia de salud digital han creado una base para integrar la epidemiología digital en la práctica habitual de la salud pública. No obstante, persisten desafíos, entre ellos la interoperabilidad fragmentada entre instituciones, la escasez de personal, las desigualdades en el acceso digital y las preocupaciones relacionadas con la gobernanza de los datos, la privacidad y los sesgos algorítmicos. LECCIONES APRENDIDAS: La experiencia de Arabia Saudí sugiere que la epidemiología digital es más eficaz cuando se integra en reformas más amplias de salud digital. Su implementación satisfactoria requiere no solo infraestructura digital, sino también desarrollo de capacidades del personal, gobernanza ética, transparencia y mecanismos para incorporar los datos digitales a la toma de decisiones en salud pública. المشكلة: غالبًا ما تكون طرق الرصد الوبائي التقليدية محدودة بسبب التأخيرات في الإبلاغ وأنظمة البيانات المشتتة. وتواجه المملكة العربية السعودية تحديات إضافية في مجال الصحة العامة نتيجة للتجمعات الكبيرة خلال موسمي الحج والعمرة، وتزايد عبء الأمراض غير المعدية، والتوسع الحضري السريع، مما يُبرز الحاجة إلى الاستثمار في علم الأوبئة الرقمي. الأسلوب: سارعت المملكة العربية السعودية في التحول الرقمي في مجال الرعاية الصحية من خلال مبادرات رؤية 2030. وتشمل هذه الاستراتيجيات منصات تبادل المعلومات الصحية، والتحليلات المدعومة بالذكاء الاصطناعي، وخدمات العلاج الطبي عن بُعد، وأنظمة المراقبة الرقمية المستخدمة خلال موسم الحج. نستعرض في هذا البحث المبادرات الحالية للاستثمار في علم الأوبئة الرقمي في المملكة العربية السعودية، وتحديات التنفيذ، وأولويات السياسة الحكومية. المواقع المحلية: يعمل النظام الصحي في المملكة العربية السعودية بموجب نموذج حكومي بشكل رئيسي. وتُعد وزارة الصحة هي المزود الرئيسي، والجهة المنظمة، والممول لمعظم خدمات الرعاية الصحية. وتعد تغطية الرعاية الصحية شاملة تقريبًا، حيث يحصل المواطنون على الخدمات مجانًا من خلال النظام الحكومي. وتهدف الإصلاحات الجارية إلى تطبيق اللامركزية تدريجيًا على وظائف محددة. التغيّرات ذات الصلة: إن المبادرات بموجب رؤية 2030 تدعم رصد الأمراض، وتكامل البيانات، وقدرات الاستجابة في مجال الصحة العامة. لقد أدت إصلاحات الصحة الرقمية إلى إنشاء أساس لدمج علم الأوبئة الرقمي في ممارسات الصحة العامة الروتينية. ومع ذلك، لا تزال هناك تحديات، تشمل التشغيل المشترك المشتت بين المؤسسات، ونقص الكوادر، والوصول غير العادل إلى البيانات الرقمية، والمخاوف المتعلقة بحوكمة البيانات، والخصوصية، والتحيز الخوارزمي. الدروس المستفادة: تشير تجربة المملكة العربية السعودية إلى أن علم الأوبئة الرقمي يكون أكثر فعالية عند دمجه ضمن إصلاحات أوسع نطاقًا في مجال الصحة الرقمية. ويتطلب التنفيذ الناجح ليس فقط بنية تحتية رقمية، بل أيضًا تطوير القوى العاملة، والحوكمة الأخلاقية، والشفافية، وآليات دمج البيانات الرقمية في عملية صنع القرار في مجال الصحة العامة. 问题: 传统流行病学监测手段常受制于报告滞后与数据系统碎片化的问题。沙特阿拉伯还面临朝觐与副朝期间大型聚众活动、非传染性疾病负担攀升以及快速城市化带来的额外公共卫生难题，凸显出投资发展数字流行病学的必要性。. 方法: 沙特阿拉伯依托《2030 愿景》(Vision 2030) 相关举措，加快推进医疗健康领域数字化转型。相关举措包括医疗信息交互平台、人工智能驱动的数据分析、远程医疗服务以及朝觐期间启用的数字化监测系统。本文梳理了沙特阿拉伯的当前数字流行病学投资项目、项目实施方面的挑战及政策优先发展方向。. 当地状况: 沙特阿拉伯的医疗卫生体系以公立运营模式为主。卫生部是大多数医疗护理服务的主要提供方、监管机构和出资主体。医疗卫生保障基本实现全民覆盖，本国公民可通过公立医疗体系免费就医。现行改革旨在逐步下放部分职能。. 相关变化: 《2030 愿景》框架下的各项举措，助力完善疾病监测、数据整合与公共卫生应急处置能力建设。现有的数字医疗改革为将数字流行病学融入常规公共卫生工作奠定了基础。然而，仍存在一些挑战：医疗机构间数据互通割裂、人才短缺、数字资源获取不均衡，以及数据治理、隐私安全与算法偏见等隐患问题。. 经验教训: 沙特阿拉伯的实践经验表明，将数字流行病学融入大范围数字医疗改革体系中，其实施成效更佳。实施成功不仅需要数字化基础设施，还需人才队伍建设、合规伦理治理、信息公开制度，以及把数字化数据纳入公共卫生决策的配套机制。. ПРОБЛЕМА: Традиционные методы эпидемиологического надзора часто ограничены задержками в предоставлении отчетности и фрагментированностью систем данных. Саудовская Аравия сталкивается с дополнительными проблемами общественного здравоохранения, связанными с массовыми скоплениями людей во время хаджа и умры, растущим бременем неинфекционных заболеваний и стремительной урбанизацией. Все это подчеркивает необходимость инвестиций в цифровую эпидемиологию. ПОДХОД: Саудовская Аравия ускорила цифровую трансформацию здравоохранения в рамках инициатив программы Vision 2030. Среди ключевых направлений – платформы для обмена медицинской информацией, аналитические системы на основе искусственного интеллекта, телемедицинские сервисы и цифровые системы мониторинга, используемые во время хаджа. Авторы рассматривают текущие инвестиционные инициативы в области цифровой эпидемиологии в Саудовской Аравии, проблемы внедрения и приоритетные направления политики. МЕСТНЫЕ УСЛОВИЯ: Система здравоохранения Саудовской Аравии опирается преимущественно на государственную модель. Министерство здравоохранения является основным поставщиком медицинских услуг, регулятором и главным источником финансирования большинства медико-санитарных услуг. Охват медицинской помощью практически всеобщий: граждане получают услуги бесплатно через государственную систему. Текущие реформы направлены на постепенную децентрализацию отдельных функций. ОСУЩЕСТВЛЕННЫЕ ПЕРЕМЕНЫ: Инициативы в рамках программы Vision 2030 способствовали развитию эпидемиологического надзора, интеграции данных и укреплению потенциала реагирования системы общественного здравоохранения. Проводимые реформы в области цифрового здравоохранения заложили фундамент для внедрения цифровой эпидемиологии в повседневную практику общественного здравоохранения. Однако сохраняются проблемы, включая недостаточную совместимость информационных систем разных учреждений, нехватку кадров, неравномерный доступ к цифровым технологиям, а также вопросы управления данными, конфиденциальности и алгоритмической предвзятости. ВЫВОДЫ: Опыт Саудовской Аравии показывает, что цифровая эпидемиология наиболее эффективна, когда интегрирована в более широкие реформы цифрового здравоохранения. Для успешного внедрения необходима не только цифровая инфраструктура, но и развитие кадрового потенциала, этическое регулирование, прозрачность и механизмы интеграции цифровых данных в процессы принятия решений в сфере общественного здравоохранения.
2 Aug 2026
Read appraisal →BMJ global health
A systematic review of MHPSS interventions targeting non-clinical Arabic-speaking refugees and/or displaced populations in the MENA region
BACKGROUND: Arabic-speaking refugees and displaced populations in the Middle East and North Africa (MENA) face ongoing adversity that negatively impacts their mental health. Despite increasing mental health needs, there is no published systematic review evaluating non-clinical mental health and psychosocial support (MHPSS) interventions in the MENA region for this population, particularly through a culturally grounded framework. METHODS: We conducted a systematic review of MHPSS interventions published from 2011 to 2022 targeting non-clinical Arabic-speaking adult refugees and/or displaced persons in MENA countries. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines and PROSPERO registration (CRD42023421057), we searched ten databases and grey literature sources. Objectives were to identify, appraise and synthesise eligible MHPSS interventions and assess their alignment with the Integrative Complexity (IC)-Adaptation and Development After Persecution and Trauma (ADAPT) framework. Studies were appraised using four validated quality assessment tools and a novel tool assessing links to the IC-ADAPT framework, which integrates an eco-psycho-social model (ADAPT) and a cognitive-interactionist model (IC). A subset of ten top-rated interventions was synthesised for IC-ADAPT alignment. RESULTS: Thirty-eight studies met the inclusion criteria. Most interventions were conducted in Lebanon or Jordan, group-based and delivered by non-specialist facilitators in community settings. Parenting, resilience-building and trauma-focused therapies were the most common modalities. Most studies prioritised feasibility and effectiveness over efficacy. While none explicitly applied IC-ADAPT, several implicitly aligned with its eco-psychosocial systems (eg, bonds/networks, safety/security and roles/identities) and IC (eg, increased 'differentiation'), reporting reduced harsh parenting and improved reflective capacity. Culturally adapted interventions that promoted complex thinking, relational healing and community participation were linked with more favourable outcomes. CONCLUSION: This review highlights the value of culturally sensitive, non-clinical MHPSS interventions in enhancing mental health outcomes for Arabic-speaking refugee populations in MENA. Programmes grounded in eco-psychosocial and cognitive complexity frameworks-such as IC-ADAPT-offer effective, scalable strategies for addressing refugee mental health. Future work should embed these models explicitly to optimise intervention design, relevance and impact. PROSPERO REGISTRATION NUMBER: CRD42023421057.
31 July 2026
Read appraisal →Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Suicide in neurodegenerative diseases: a systematic review.
BACKGROUND AND OBJECTIVE: Suicide is a public health issue, which differs from suicidality, the continuum from suicidal ideation to the suicidal act, including suicide attempts and completed suicide. The main goal of the present study is to determine the relationship between Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and multiple sclerosis (MS) with suicidality. METHODS: This is a systematic review aiming to determine the relationship between AD, PD, ALS, and MS with suicidality following PRISMA 2020 guidelines by collecting cross-sectional, case-control, and cohort studies; case series; case reports; and retrospective and prospective studies from Google Scholar, PubMed, and Cochrane Library. The protocol of this systematic review was registered on PROSPERO; the registration number is CRD420261422354. RESULTS: From 2247 records identified from electronic databases, only 24 articles were included: three studies focusing on AD, nine on PD, and six studies focusing on ALS and MS, respectively. These studies exhibited moderate to low risk of bias. Despite the broad differences regarding the neurochemistry, pathophysiology, diagnosis, symptoms, and treatments of the selected diseases, patients are at a higher risk of suicidality. Depression and low social connectivity are the most prevalent risk factors. Suicidality is mainly detected during the first years post-diagnosis in PD, ALS, and MS patients, while the results in AD are confusing. CONCLUSIONS: Data about this topic is scarce and largely varying. Further research is required to elucidate this paradigmatic realm, fostering awareness, enhancing therapies, and providing explanations and interpretations of the mechanisms involved.
30 July 2026
Read appraisal →PloS one
The economic burden of Type 2 Diabetes by social determinants of health: A systematic review.
BACKGROUND: The unequal distribution of resources in society generates social gradients that translate into health inequalities and differential use of health care resources and their costs. Non-medical factors such as employment, income, ethnicity and education impact the prevalence and treatment outcomes of patients with type 2 diabetes mellitus (T2DM); however, there is a scarcity of articles assessing the relationship between health inequalities and the economic costs of treatment. Therefore, we conducted a systematic review of published studies examining the cost differences of treating T2DM across social determinants of health (SDH). METHODS: We systematically searched MEDLINE, Embase, PsycINFO, EconLit, and NHS EED for original peer-reviewed articles that provided cost differences of treating T2DM by SDH: education, income, employment, residency and ethnicity. We grouped the studies by each SDH and calculated the percentage differences where possible between the lowest and highest ends of the gradient (education, income and employment). Residency was categorised as rural vs. urban and ethnicity as white or general population vs other ethnic minorities. RESULTS: We included 19 articles retrieved internationally from varying healthcare systems. Results were contextualised given the healthcare financing model. In countries with high out-of-pocket expenses, Black and Hispanic ethnic backgrounds and rural residence were associated with lower direct health care and costs likely to be determined by ability to pay rather than clinical need. Indirect costs such as lost productivity due to absenteeism were also lower in unemployed, and lower income groups. CONCLUSIONS: There are evident health disparities in the direct and indirect economic consequences of T2DM. The effect of decreased healthcare use and costs on treatment outcomes needs to be further explored to inform policies to ensure healthcare delivery is based on clinical need rather than socio-economic factors.
28 July 2026
Read appraisal →Current psychiatry reports
Postpartum Suicidality beyond Depression: a Systematic Review of Risk Profiles and Prevention Gaps
PURPOSE OF REVIEW: Suicidal ideation and behavior during the postpartum period are increasingly recognized as important contributors to maternal morbidity and mortality, particularly in high-income countries. Although postpartum depression is a well-established risk factor, emerging evidence suggests that suicidality may also occur independently and therefore remain underrecognized in clinical practice. This systematic review aimed to synthesize current evidence regarding the prevalence of suicidal ideation and behavior in the postpartum period and to identify associated risk factors. RECENT FINDINGS: A systematic search of PubMed, Web of Science, EMBASE, and PsycINFO was conducted up to March 2025 following PRISMA guidelines. Twenty studies met inclusion criteria, encompassing 352,726 postpartum women across diverse international settings. Reported prevalence of suicidal ideation ranged from 2.2% to over 50%, depending on the population studied and assessment methods used. The most frequently identified risk factors included postpartum depression, anxiety, trauma history, intimate partner violence, unplanned pregnancy, low socioeconomic status, low social support, and adverse childhood experiences. Notably, several studies reported suicidal ideation even in the absence of clinical depression. Only a limited number of studies addressed preventive interventions. Postpartum suicidality appears to be a multifactorial phenomenon that cannot be explained solely by depressive symptomatology. The findings highlight the need for broader screening strategies that incorporate psychosocial and trauma-related factors in addition to depression. Integrated and trauma-informed approaches to screening and prevention may improve early identification of at-risk individuals. Future research should prioritize longitudinal designs and culturally sensitive investigations to better inform prevention and intervention strategies.
28 July 2026
Read appraisal →Journal of medical Internet research
Digital Health Technologies Are Bridging the Maternal Mortality Gap
Disparities in maternal outcomes along socioeconomic, racial, ethnic, and geographic lines are well established. In this News and Perspectives article, JMIR Correspondent Anika Nayak reports on the growth and potential of digital health interventions in bridging the rural-urban maternal mortality gap.
26 July 2026
Read appraisal →Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
Shorter Antitubercular Regimens Versus 9 Months of Isoniazid for Latent Tuberculosis in Children: A Systematic Review and Meta-Analysis
BACKGROUND: We conducted a systematic review and meta-analysis to compare effectiveness and safety of 9 months of isoniazid (9H) versus shorter rifamycin-containing regimens for treating latent tuberculosis infection (TBI) in children. METHODS: We systematically searched MEDLINE, Embase, and Cochrane Central Register of Controlled Trials to June 2025 for randomized, controlled trials (RCTs) and cohort studies that compared regimens that were shorter than 9 months of isoniazid in children aged 1-18 years. Outcomes were development of TB disease, treatment completion, and adverse events. Risk of bias was assessed using RoB 2.0 and the Risk Of Bias In Non-Randomized Studies - of Interventions (ROBINS-I) tool; certainty of evidence was graded using Grading of Recommendations Assessment, Development, and Evaluation (GRADE). RESULTS: Five RCTs and 7 nonrandomized studies that enrolled approximately 2950 children in trials and >25 000 in observational cohorts were included. In pooled analysis of 3 RCTs, shorter rifamycin-containing regimens resulted in little to no difference in development of TB disease compared with 9H (odds ratio [OR], 0.19; 95% confidence interval [CI], .03-1.12; moderate-certainty evidence). Treatment completion was probably higher with shorter regimens (OR, 0.51; 95% CI, .42-0.62; moderate-certainty evidence). Adverse events were similar between groups, but evidence is uncertain (low-certainty evidence). Observational data were consistent with these findings, showing higher completion rates and lower hepatotoxicity with shorter treatments. CONCLUSIONS: Shorter rifamycin-containing regimens for pediatric TBI probably increase treatment completion and have similar safety outcomes, with no important difference in development of TB disease compared with the standard regimen. These findings support current guideline recommendations that favor shorter regimens in children.
25 July 2026
Read appraisal →JMIR human factors
Japanese Health Information Technology Usability Evaluation Scale for Sexually Transmitted Infection-Related Chatbots: Development and Psychometric Validation Study
BACKGROUND: The rapid expansion of mobile technology has accelerated the integration of health applications and conversational AI into clinical and public health practices. To ensure these tools are effective and sustainable, usability evaluations and early user engagement during development are essential. The Health Information Technology Usability Evaluation Scale (Health-ITUES) is a validated and flexible usability assessment instrument that is available in multiple languages and applicable across diverse contexts. However, a Japanese version of this scale has not yet been developed. OBJECTIVE: This study aimed to translate and validate a Japanese version of the Health-ITUES, customized for a sexually transmitted infection (STI)-related chatbot, and to support the usability assessment of emerging mobile health tools in Japan. METHODS: We developed a Japanese version of the Health-ITUES using a chatbot under development as a consultation tool for young women regarding STIs. First, the original scale was customized to reflect the chatbot's specific purpose and intended usage context. Following established translation guidelines, we conducted forward translation from English to Japanese, back translation, expert review, and reconciliation. We then evaluated the reliability and validity of the Japanese version in a sample of 301 young women. RESULTS: The Japanese version of the Health-ITUES demonstrated high internal consistency (Cronbach α=0.85-0.98). Confirmatory factor analysis supported acceptable construct validity (root mean square error of approximation is 0.10, comparative fit index>0.90). Additionally, the Health-ITUES scores showed strong correlations with satisfaction and usage intention for the tool (r=0.779 and 0.797, respectively). CONCLUSIONS: The Japanese version of the Health-ITUES provides initial evidence of reliability and validity in an STI-related scenario among young women and may facilitate more rigorous usability evaluations of mHealth and conversational AI tools in Japan.
24 July 2026
Read appraisal →Behavior research methods
Measuring complex constructs in large-scale text with computational social mixed methods
A growing convergence between social science and machine learning enables, in principle, large-scale analyses of complex social phenomena through text. Yet, approaches leveraging supervised text classification based on human-annotated data for statistical analysis often treat conceptual validity and technical performance as separate challenges, impairing measurement quality. We provide guidelines to bridge this gap in what we call computational social mixed methods pipelines across three stages: data annotation, model training, and statistical analysis. Building on best practices and our own methodological innovations, such as "Iterative Annotation" and "Training on Confident Examples", we address recurring pitfalls like unbalanced training data or stagnant model performance. We also discuss when large language models constitute a viable alternative to transformer-based classifiers. Using a case study on countering online hate, we illustrate how consequently integrating social science and machine learning expertise improves the validity and comparability of computational social science.
24 July 2026
Read appraisal →Scientific reports
The role of AI in combating misinformation: leveraging text mining and social networking analysis
Misinformation on social media can be a severe threat to social trust, safety, and health of the population, especially in times of an epidemic like the Monkeypox outbreak. This study specifically focuses on a hybrid RoBERTa-GRU architecture designed to capture both contextual semantics and temporal dependencies in social media discourse. This research presents how the combination of text mining and social network analysis enables Artificial Intelligence (AI) to support misinformation detection. The proposal of a hybrid architecture that integrates RoBERTa and GRU-based embeddings in a contextual fashion and GRU-based modelling of sequential patterns helps identify and substantiate misinformation in social media posts. Based on a curated X (formerly Twitter) dataset consisting of Monkeypox posts (5787 posts), the model provided state-of-the-art results, with ROC-AUC 0.9979 and Cohen's kappa 0.9887; standalone baselines were also surpassed. Results reveal that the proposed transformer-RNN hybrid effectively captures both semantic depth and temporal relationships in misinformation detection tasks. In addition to performance, the paper addresses the limitations of dataset bias, multilingual constraint issues, and scalability as related to cross-linguistic applicability, multimodal study, and performance in real-time and resource-limited systems. The study adds value in this emerging body of knowledge on AI-driven social media analytics by offering practical guidance for mitigating health-related misinformation online.
22 July 2026
Read appraisal →Environmental science & technology
Bridging the Gap between External and Internal Exposure: Challenges and a Machine Learning Approach Validated Using Benzene Ring-Containing Pollutants
Establishing quantitative linkages between external and internal exposures is essential for accurate exposure and health risk assessment, yet this goal remains hindered by multiple challenges. This Perspective systematically reviews major challenges limiting the quantitative linkage across three dimensions: (i) External exposure: complex multimedia and multipathway exposure, environmental factor variability, and substantial data gaps; (ii) Internal exposure: difficulties in biological sample collection, large-scale biomonitoring, and appropriate biomarker selection; and (iii) External-internal exposure linkage: interindividual variability, scarce toxicokinetic parameters, and methodological limitations. In this context, the Perspective proposes ML-EIExpLink (machine learning-based external-internal exposure linkage), a framework integrating multisource, multidimensional variables, including pollutant physicochemical properties, ADME parameters, exposure factors, environmental conditions, and socioeconomic indicators, to model complex external-internal exposure relationships. An exploratory case study on benzene ring-containing pollutants demonstrates the framework's feasibility and reliability. Using gradient boosting decision trees as the core predictive engine, the model achieved robust predictive performance, with an external validation Qext2 of 0.784. Most key variables, including external air concentration, molecular planarity, and temperature, exhibited nonlinear and threshold effects on predicted blood concentrations. This Perspective introduces a novel ML-based framework for establishing robust, interpretable external-internal exposure linkages, thereby providing a useful basis for improved exposure assessment and health risk evaluation.
22 July 2026
Read appraisal →Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention
Natural language processing tool for extracting information about opioid overdoses in the USA from case narratives in the violent death reporting system.
BACKGROUND: Improving the infrastructure for drug overdose surveillance is critical for identifying new threats and responding to emerging trends. We aimed to develop a prototype tool using the principles of natural language processing that can extract information from the death records of drug overdose victims. METHODS: Data were obtained from the Violent Death Reporting System on drug overdose deaths. Narratives were manually labelled for 12 attributes of interest, totalling 82 labels about the circumstances of the overdose. Narratives were passed through the 'Excel Extractor' to identify and extract a target phrase and subsequently map the extracted phrase to predetermined code values. The output from the Excel Extractor was compared with manually labelled data to determine accuracy. Performance was compared against multiple machine learning models. RESULTS: The Excel Extractor performed well across the attributes of interest, achieving an F1 Score over 0.8 on nine of the 12 attributes. The Excel Extractor was the highest performing model on seven of the 12 attributes. The Excel Extractor achieved an F1 Score of 0.8 or higher on 46 of 82 (56%) of the labels, and a score of 0.9 or higher on nearly one-third (25 out of 82) of the labels. CONCLUSION: This work demonstrates it is feasible to develop a spreadsheet-formula-based natural language processing tool to accurately extract information about drug overdose deaths from narratives; for most attributes, a rule-based search performs well or better than deep learning. The Excel Extractor has the potential to streamline data abstraction for epidemiologists gathering data about drug overdose deaths.
21 July 2026
Read appraisal →International journal of medical informatics
Examining user-AI interaction patterns in health-information queries
In this study, we examine how individuals utilize generative artificial intelligence (GAI) when seeking health-related information. Using a dataset of user-GAI chat logs available on Hugging Face, we analyzed real-world interactions in which users posed health-related questions to a generative model. We applied a combination of data and text-analytic methods to categorize these interactions, including supervised machine learning techniques such as Support Vector Machines (SVMs). SVMs were selected for their efficiency and strong performance in high-dimensional text classification tasks, and used to identify recurrent themes in user queries and interactions. We found that users frequently consult AI chatbots for symptom exploration, medical education, mental health support, and general health advice. The findings suggest that GAI tools may not only function as informational resources, but also as preliminary support tools that can shape users' health knowledge and encourage them to seek consultation with medical professionals.
16 July 2026
Read appraisal →BMC health services research
Computational forecasting using Swedish data in the vaccination phase of the COVID-19 pandemic: a systematic literature review deliberating modelling relevance for public health and healthcare.
BACKGROUND: The benefits of computational forecasting in the later phase of the COVID-19 pandemic when vaccines and clinical pharmaceutical interventions were available have seldom been assessed. We aimed to evaluate computational forecasting research applied to Swedish populations in the vaccination phase of the pandemic. METHODS: A systematic search was performed on March 8, 2024 in the electronic databases PubMed, Scopus, Cochrane library, Embase, Love platform and Epistemikos. An updated version of the Risk of bias Opinion Tool (ROBOT) was used to assess the quality of evidence reported in the papers identified in the search. The articles fulfilling the quality criteria were assessed for suitability for meta-analysis. Data were extracted from the selected articles for synthesis of characteristics, and a thematic analysis was used for a qualitative synthesis of the contents. RESULTS: Of 2034 unique publications identified in the database search, 6 articles satisfied the selection and quality criteria. Variability in the reporting of forecasting performance results was found to make a quantitative meta-analysis of forecast performance infeasible. The data synthesis showed that statistical modeling using Bayesian calibration was the most common methodological approach. No external model validation was reported, but 5/6 articles included internal model corroboration data. The primary theme resulting from the qualitative synthesis of article content was design or refinement of computational models with demonstration of model use in health service practice as a secondary theme. None of the articles referred to health service policymaking as the primary research context. CONCLUSION: Computational forecasting research using Swedish population data from the vaccination phase of the COVID-19 pandemic was deployed in a model design context. While methodological knowledge was developed, most of the research was not initiated to solve the public health and healthcare problems at hand. Our results indicate that the alignment between computational forecasting research and policymaking needs in the vaccination phase of pandemics can be enhanced.
13 July 2026
Read appraisal →Vaccine
Instruments for measuring parents' vaccine hesitancy towards their children based on the COSMIN guidelines: A systematic review
BACKGROUND: Parental vaccine hesitancy undermines immunization gains, leading to resurgent outbreaks and increased child mortality. Reliable assessment instruments are urgently needed to guide effective interventions and improve coverage. Despite multiple vaccine hesitancy assessment instruments, no systematic reviews have evaluated the measurement properties of these instruments against established methodological standards, limiting evidence-based selection. OBJECTIVE: To systematically evaluate the methodological quality and measurement properties of vaccine hesitancy assessment instruments for parents of 0-18 children, providing an evidence-based basis for selecting appropriate instruments. DESIGN: Measurement properties are systematically reviewed according to the Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) guidelines. METHODS: Six electronic databases were systematically searched from inception until July 15, 2025. Methodological quality was assessed using the COSMIN Risk of Bias Checklist and measurement properties were synthesized according to the COSMIN criteria. A modified Grading, Recommendations, Assessment, Development, and Evaluation system was used to assess the certainty of evidence. RESULTS: Forty studies evaluating 31 vaccine hesitancy assessment instruments were included. Based on COSMIN evidence grading, the Malay version of the modified vaccine hesitancy scale (MVHS-M), the Vietnamese version of the parent attitudes about childhood vaccines survey (PACV-Viet), and the parental attitude scale towards vaccination (PASV) received Category A recommendation. The general vaccine hesitancy scale (GVHS), vaccination attitudes examination (VAX), vaccine acceptance instrument (VAI), and vaccine barriers assessment tool (VBAT) received Category C recommendation, while others were Category B recommendation. CONCLUSIONS: This systematic review identified MVHS-M, PACV-Viet, and PASV as effective tools for assessing vaccine hesitancy in parents, suitable for both research and clinical use. Future research should comprehensively assess their measurement properties, especially exploring measurement error and responsiveness. REGISTRATION: A protocol was registered on the PROSPERO (CRD420251070194).
12 July 2026
Read appraisal →PloS one
Machine learning-based analysis of drug resistance mutations in Mycobacterium tuberculosis
Tuberculosis is a deadly airborne disease caused by Mycobacterium tuberculosis. Drug-resistant tuberculosis presents significant challenges for treatment and control of the disease. Resistant strains of Mycobacterium tuberculosis arise from specific mutations in the bacterium. Identification and characterization of resistance-associated mutations are crucial for effective treatment strategies because the first- and second-line drugs for the disease target distinct genes in the bacterium and serve different purposes. Our study developed a machine learning prediction model to analyze mutations across multiple drug-resistance types. The proposed framework predicts drug-resistance mutations across four drug-resistance types, including Rifampicin Resistance, Isoniazid Resistance, Multidrug Resistance, and Pre-extensively Drug-Resistant tuberculosis. The NIAID-NIH TB portal is a publicly available dataset of tuberculosis patients, including drug-resistance information. Our study analyzed 3,065 cases of drug-resistant TB. Eight supervised ML algorithms were implemented for the study. A Random Forest classifier with 10-fold cross-validation shows higher predictive performance than the other seven algorithms considered for further analysis. Significant drug resistance mutations were identified using SHapley Additive exPlanations feature importance. The World Health Organisation mutation catalogues, considered the gold standard for drug-resistant mutations, were used to evaluate prediction results. Mutations not reported in the WHO catalogues were identified during the post-prediction comparative analysis stage, as they may represent potential resistance-conferring markers warranting further investigation, including structural and functional validation or experimental validation. The mutations are rpoB-I480T, rpoC-G332R, L527V, gyrA-D94V, KatG-G99E, A106V, W191R, W328C, T380I, and M420T. The study further checks the stability and pathogenicity of the mutations using computational tools, including I-Mutant 2.0 and PredictSNP. The findings added more clarity and further evidence for the significance of the mutation, based on its contribution to drug resistance.
11 July 2026
Read appraisal →International journal of biometeorology
Scorpion sting stratification in Iran: A systematic review of epidemiological patterns, determinants, and climate change impacts
This systematic review provides a comprehensive synthesis of the epidemiology of scorpion sting stratification across Iran's geography, a critical public health concern in its arid and semi-arid regions. Conducted in accordance with PRISMA 2020 guidelines, the review analyzes the spatial and temporal patterns of envenomations, identifies key ecological, climatic, and anthropogenic determinants, and evaluates the growing evidence for climate change as a driver of increased risk. The study confirms hyperendemic levels of scorpion stings in the southwestern and western provinces, with species from the genera Hemiscorpius and Androctonus presenting the most severe medical threat. A meta-analysis of national data yields a pooled incidence rate of 92.7 per 100,000 population per year (95% CI: 68.4-117.0), with extreme heterogeneity (I² = 98.9%) reflecting stark regional disparities. The primary determinants of scorpion distribution and human-scorpion conflicts include climatic variables (temperature, precipitation, and humidity), land-use practices, unplanned urbanization, and occupational exposure. Recent evidence from species distribution modeling strongly suggests that climate change is expanding suitable habitats for scorpions, prolonging their active seasons, and increasing human-scorpion conflicts. This review highlights the critical need for improved surveillance, predictive spatial risk models, and climate-adaptive public health strategies to address this growing threat.
11 July 2026
Read appraisal →ACS infectious diseases
Machine Learning Approaches Using High-Throughput Profiling Data for Antibiotic Discovery
Antibiotic-resistant bacterial infections continue to increase globally, creating an urgent need for new antibacterials with novel mechanisms of action. Early stages of antibiotic discovery are often limited by the difficulty of identifying compounds that act through previously unrecognized pathways and by challenges in determining their mechanisms. Machine learning (ML) integrated with high-throughput profiling now provides systematic approaches to overcome these barriers. Beyond initial hit discovery, multilayer profiling using morphological phenotyping, transposon sequencing, transcriptomics, proteomics, and metabolomics captures cellular responses that reflect the mechanisms of action. Because profiling data sets are typically high-dimensional and contain defined features and variables, ML can extract complex patterns associated with pathway-level responses and predict mechanisms for unknown compounds. In this review, we summarize current progress in high-throughput profiling and describe how ML applied to each data set can accelerate the identification of antibacterials with new mechanisms. These approaches accelerate the transition from large-scale compound screening to mechanistic validation and enable effective prioritization of lead compounds in early-stage antibiotic discovery.
11 July 2026
Read appraisal →PloS one
Intergenerational patterns of digital use: Evidence from a large cross-sectional study
This study aims to determine the level of digital use across six generations (Greatest, Silent, Baby Boomers, and Generations X, Y and Z) within a district in North East England (population ~200,000). A cross-sectional descriptive study was conducted via an online and paper-based survey from February to May 2022, mailed to households in the district (N = 98,260). Respondents estimated their weekly use of digital tools and the internet at home. One-way ANOVA and Tukey-Kramer HSD tests were used to analyse differences in digital resource uses across six generations. To account for underlying sociodemographic characteristics, a follow-up ANCOVA analysis was also performed. Content Analysis was used for qualitative responses from an open-ended survey question. A total of 9,181 completed surveys were analysed. The sample was skewed towards older, homeowner adults (mean age 63, 60% female). Findings revealed that respondents spend less time online than other British cohorts. Baby Boomers and Generation X self-reported statistically significant differences in the level of digital use compared to all other generations. Younger generations (Y and Z) self-reported, on average, a larger amount of time spent on both digital tools and the internet. Members of Greatest and Silent Generations had the lowest hours spent on digital tools and the internet. The results suggest that public health initiatives should prioritise strategies bridging the digital divide between generations. Targeted training programs pairing younger, tech-savvy individuals with older adults could enhance digital literacy. Additionally, integrating user-friendly digital health platforms that cater to varying levels of technological proficiency will encourage wider adoption. These strategies not only foster intergenerational collaboration but also drive successful digital health and care transformations, ensuring equitable access to technological advancements for all age groups.
11 July 2026
Read appraisal →PloS one
Air quality index prediction using machine learning regression models: A comparative analysis
Air plays a vital role in human life, and poor air quality can lead to respiratory infections. Given the significant impact of air quality on people's health, monitoring and assessing air quality is crucial. With advancements in machine learning (ML) and artificial intelligence (AI), we now have extensive tools to measure the Air Quality Index (AQI). Air quality is influenced by various pollutants, including carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), and sulfur dioxide (SO2), which are prevalent in highly polluted areas and contribute to a wide range of illnesses. Particulate matter, such as PM2.5 (particles with an aerodynamic diameter of less than 2.5 µm) and PM10, poses additional health risks. To address these concerns, this study focuses on predicting AQI values for major cities in Pakistan, specifically Karachi and Peshawar, using four prominent ML algorithms: Random Forest (RF), Gradient Boosting (GB), Linear Regression (LR), and Ridge Regression (RR). The results indicate that the models effectively predicted AQI using evaluation metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R2). This research's novelty lies in using the latest AQI datasets for Karachi and Peshawar and applying standard scaling for AQI normalization. Additionally, the study compares evaluation metric results across different cities, highlighting the importance of using standard scalers to achieve optimal model performance. This research underscores the value of advanced ML techniques for accurate AQI prediction and analysis.
10 July 2026
Read appraisal →Cell host & microbe
Mining the code of life for new antibiotics.
Antimicrobial resistance (AMR) is outpacing antibiotic development, creating an urgent need for discovery strategies that are faster, broader, and more systematic. Here, we review the transition from classical "dirt mining" and phenotypic screening toward digital discovery approaches that treat chemical structures and biological sequences as searchable, engineerable substrates for antibiotic innovation. Modern extensions of conventional screening, including in situ cultivation, co-culture, and microfluidics, have broadened access to previously uncultured microbes. Computer-aided approaches spanning virtual screening, molecular networking, and deep learning have enabled identification of unconventional antibacterial scaffolds from ultra-large chemical libraries. Mining genomes, proteomes, and metagenomes has uncovered antimicrobial peptides, encrypted peptides, and biosynthetic gene clusters encoding novel small-molecule antibiotics. Generative AI now enables design of peptides and small molecules under multiobjective constraints, including potency, toxicity, stability, and resistance risk. Together, these advances point toward discovery platforms that improve novelty, hit rates, and long-term durability in the face of AMR.
10 July 2026
Read appraisal →Journal of the American Heart Association
Social Determinants of Health and Cardiovascular Disease-Related Outcome Disparities Among Breast Cancer Survivors: A Systematic Review
BACKGROUND: Breast cancer is the most commonly diagnosed malignancy worldwide. Breast cancer survivors face an increased risk of cardiovascular disease (CVD), a leading cause of death in this group. Social determinants of health (SDoHs), operationalized as economic, environmental, and psychosocial factors, play an important role in CVD disparities. However, few studies have examined how SDoHs are associated with CVD disparities in this population, and no systematic review has addressed their multilevel influences. METHODS: This systematic review summarizes the current evidence on the relationships between SDoHs at different levels and CVD disparities among breast cancer survivors. Using the 2024 American College of Cardiology/American Heart Association framework, SDoHs were categorized at the individual, interpersonal, or community levels. Studies published in peer-reviewed journals between January 1, 2010, and November 30, 2025, were identified through searching electronic databases (PubMed, Embase, Cumulative Index to Nursing and Allied Health Literature, Web of Science, PsycINFO) and citations. RESULTS: Of 6550 unique records, 37 articles that addressed the impact of SDoHs on CVD outcomes were selected. Most (n=31) were conducted in the United States and used retrospective designs (n=29). Most (n=30) focused on individual-level SDoHs such as race, income, or education; 2 on interpersonal-level SDoHs (ie, psychosocial stress); and 13 on community-level SDoHs, including neighborhood socioeconomic status and residential area. Black race, lower neighborhood socioeconomic status, and rural residence were associated with a higher incidence of CVD and increased cardiovascular death. CONCLUSIONS: This review highlights the urgent need to address SDoHs and emphasizes the importance of multilevel interventions to reduce CVD disparities among breast cancer survivors.
9 July 2026
Read appraisal →Chemical communications (Cambridge, England)
An update on polydiacetylene-based solid-supported sensing platforms for environmental and health monitoring.
Polydiacetylenes (PDAs) are a unique class of conjugated polymers renowned for their pronounced chromo- and fluorogenic responses to external stimuli, enabling direct optical transduction of chemical and biological interactions. While early PDA-based sensing systems predominantly relied on solution-phase assemblies, their limited structural stability, reproducibility, and deployability have constrained practical applications. In recent years, the immobilization of PDAs onto solid supports has emerged as a powerful strategy to overcome these limitations, transforming responsive supramolecular polymers into robust, functional sensing platforms. Solid-supported PDA architectures offer enhanced mechanical stability, improved environmental tolerance, controlled molecular organization, and compatibility with portable and field-deployable devices. This review provides a comprehensive and application-oriented overview of solid-supported polydiacetylene systems for environmental and health monitoring. Emphasis is placed on the role of support materials, fabrication strategies, and structure-process-property relationships that govern sensing performance. Key applications in environmental pollutant detection and biomedical diagnostics are critically examined, alongside comparative performance metrics and design considerations. Current challenges, including signal irreversibility, environmental interference, and scalability, are discussed, and future directions toward sustainable fabrication, wearable devices, and integrated smart sensing platforms are outlined. By positioning solid-supported PDAs as adaptive interfaces between molecular recognition and macroscopic readout, this review highlights their potential as next-generation materials for decentralized and real-world sensing applications.
8 July 2026
Read appraisal →Scientific reports
Differentially private federated learning for localized control of infectious disease dynamics
In times of epidemics, swift reaction is necessary to mitigate epidemic spreading. For this reaction, localized approaches have several advantages, limiting necessary resources and reducing the impact of interventions on a larger scale. However, training a separate machine learning (ML) model on a local scale is often not feasible due to limited available data. Centralizing the data is also challenging because of its high sensitivity and privacy constraints. In this study, we consider a localized strategy based on the German counties and communities managed by the related local health authorities (LHA). For the preservation of privacy to not oppose the availability of detailed situational data, we propose a privacy-preserving forecasting method that can assist public health experts and decision makers. ML methods with federated learning (FL) train a shared model without centralizing raw data. Considering the counties, communities or LHAs as clients and finding a balance between utility and privacy, we study a FL framework with client-level differential privacy (DP). We train a shared multilayer perceptron on sliding windows of recent case counts to forecast the number of cases in the future, while clients exchange only norm-clipped updates and the server aggregates updates with DP noise. We evaluate the approach on COVID-19 data on county-level during two phases: November 2020 and March 2022 (Omicron). As expected, very strict privacy ([Formula: see text]) yields unstable, unusable forecasts. At a moderately strong but still privacy-preserving level ([Formula: see text]), the DP model closely approaches the non-DP model: [Formula: see text] (vs. 0.96) and mean absolute percentage error (MAPE) [Formula: see text] in November 2020; [Formula: see text] (vs. 0.90) and MAPE [Formula: see text] in March 2022. Overall, our results support the feasibility of privacy-preserving collaboration among health authorities for local forecasting. In the evaluated COVID-19 phases, client-level DP-FL delivered useful county-level predictions with formal privacy guarantees under the stated threat model. The appropriate privacy budget should nevertheless be re-evaluated for other epidemic phases and applications.
8 July 2026
Read appraisal →BMJ global health
Enhancing malaria-in-pregnancy monitoring: stakeholder experiences and data integration into the BornFyne-PNMS digital platform in Cameroon
Malaria in pregnancy (MiP) is a significant public health concern in high-burden malaria countries and a key predictor of high-risk pregnancies in Cameroon. One of the challenges of the National Malaria Control Programme (NMCP) in Cameroon is to generate reliable, timely, accurate and actionable data to inform decision-making, effective monitoring and planning. Discussions with the NMCP at the Ministry of Public Health (MoPH) identified poor data quality, lack of clear guidelines, manual data processing at the district level and delays in feedback on data as key bottlenecks in effective implementation of malaria control in Cameroon. This paper documents the data gaps for MiP identified by the digital health platform, BornFyne, project team in collaboration with the MoPH. We outlined lessons learnt in identifying and defining MiP-related indicators through stakeholder engagement across various levels, and identifying priority MiP elements for health facility registers for integration into the BornFyne digital platform. These lessons and insights will provide valuable guidance for other countries looking to incorporate malaria-related content into their antenatal care packages and/or to integrate similar content into their digital health platforms, and suggestions for strengthening the WHO digital adaptation kit MiP content.
8 July 2026
Read appraisal →Transactions of the Royal Society of Tropical Medicine and Hygiene
Extreme weather effects on health services and communities in low and lower-middle income countries: a thematic systematic review
Most previous research about the dangers of extreme weather events was applicable to populations in high-income countries. Data summarising harms related to extreme weather events in low-income settings are lacking. A systematic review thematically summarising evidence about weather event-linked harms and responses in low- and lower-middle-income countries was conducted. Peer-reviewed and grey literature was systematically searched and selected. Data were extracted about harms, responses and outcomes relevant to six WHO building blocks of healthcare systems. Framework analysis was used to identify predominant themes related to harms, responses and the WHO building blocks. In total, 183 reports were included. Flooding and high winds were the most common types of extreme weather events documented. The main community experience themes identified were the displacement of populations and disruption. The main themes identified for health service delivery were vulnerability, disruption and resilience. Documented examples of resilience or recovery were far fewer for all six WHO healthcare system building blocks than descriptions of vulnerability and disruption. Extreme weather events can be highly disruptive and harmful to healthcare systems and communities in LMIC settings that are often already highly vulnerable.
8 July 2026
Read appraisal →Journal of epidemiology
Comparison of Influenza Epidemic Trends Based on a Large-scale Claims Database and National Infectious Disease Surveillance in Japan
BACKGROUND: Seasonal influenza is a recurrent respiratory infection, and timely detection is essential for public health. In Japan, surveillance is conducted through sentinel medical institutions under the National Epidemiological Surveillance of Infectious Diseases (NESID). Recently, access to large claims databases, such as the JMDC claims database (JMDCdb), has increased. While both are sample-based systems, JMDCdb covers a much larger population. We aimed to assess consistency between these sources in estimating influenza cases and the effective reproduction number (Rt) and to explore their utility in epidemic analysis. METHODS: We analyzed data from week 36 of 2016 to week 35 of 2019. Influenza cases were estimated from NESID (reported cases and cases per sentinel) and JMDCdb (cases with influenza-related diagnoses and antiviral prescriptions). Daily infection counts were derived to estimate Rt. RESULTS: Although minor differences appeared at epidemic peaks, estimates from NESID reports aligned well with JMDCdb. Estimates based on cases per sentinel were lower. Rt values were consistent across data sources. Rt exceeded 1.0 when cases per sentinel surpassed 0.2-0.3. Using a threshold of 0.25 cases per sentinel enabled detection of epidemic onset 4-5 weeks earlier than current standards. CONCLUSION: Claims data, such as those from JMDCdb, may be useful for retrospective examination of influenza trends. Moreover, a detailed analysis of the number of cases reported per sentinel suggested the potential to propose threshold values that enable earlier prediction of epidemics than conventional criteria.
7 July 2026
Read appraisal →The Tohoku journal of experimental medicine
At-Home Deaths and Regional Factors in Japan
In Japan, at-home deaths are not always popular, yet many people hope to spend their end-of-life at home, which makes this a socio-medical issue. Regional factors are associated with at-home deaths, but their relative importance has not been known. The present study investigated the relationship between at-home deaths and regional factors and ranked their importance at the municipal level in Japan. We used national open data on at-home deaths, population, medical resources, causes of death, economic factors and geographic factors as well as built the prediction model for at-home deaths using Extreme Gradient Boosting (XGBoost) regression. The Shapley Additive Explanations (SHAP) value was applied to rank the importance of regional factors and explain the XGBoost model. As a result, the median prevalence of at-home deaths across municipalities was 12.7%. A higher prevalence of at-home deaths was observed in large metropolitan areas such as Tokyo, Osaka, and Nagoya. SHAP values revealed that latitude was the most important factor in rank, followed by the number of clinics providing end-of-life care, longitude, population density, and population change rate. In conclusion, geographic and population-related factors were ranked highly among regional factors associated with at-home deaths. These findings may be informative for community planning to realize at-home deaths nationwide.
7 July 2026
Read appraisal →International health
Antimicrobial stewardship interventions currently implemented at primary healthcare settings across low- and lower-middle-income countries (LLMICs)
BACKGROUND: Antimicrobial resistance (AMR) is a top global public health and development threat. Antimicrobial stewardship programs (AMSPs) are one of the most cost-effective interventions to optimize the use of antimicrobials. This study reviews AMSPs that have been implemented in low- and lower middle-income countries. METHODS: A systematic search was conducted on electronic databases including MEDLINE, PubMed, Embase, OVID, Web of Science and Cochrane Library on 18 July 2024 for published papers from 2014 to 2024 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline. Relevant published literature was then selected based on the established inclusion/exclusion criteria. Each article was screened by two independent reviewers. Data were extracted and synthesized in the review. RESULTS: Of the 425 articles screened, only 13 were eligible for review and included in this study. Two studies were multinationals. Five studies were randomized controlled trials. Among the three key focuses of AMSPs, most of the interventions focused on optimizing antibiotic use (n=8), followed by improving diagnostics and monitoring (n=3) and education and training (n=2). The most commonly reported barriers to implementing AMSPs was a lack of resources (n=9). Facilitators reported included knowledge of AMS (n=8), availability of educational and training resources (n=8), adequate funding (n=6), accountable and transparent procedures (n=5) and positive communication within healthcare facilities (n=4). CONCLUSIONS: All included studies show improvement in AMS through innovative programs. However, only a few have been adopted nationwide and influence policy formulation in the country. We recommend adoption of effective AMSPs into the national strategic planning and implementation across primary health settings.
5 July 2026
Read appraisal →BMJ open
Impact of climatic variables on postoperative outcomes in various surgical disciplines: a systematic review.
OBJECTIVES: The impact of climate change on health is well recognised, yet its influence on postoperative complications remains underexplored. This systematic review examines how climatic factors-particularly temperature, humidity and seasonal variations-affect surgical outcomes. DESIGN: Systematic review with narrative synthesis following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines. DATA SOURCES: A comprehensive search of PubMed, Web of Science and the Cochrane Library from June 2015 to June 2025 was conducted. An additional supplementary PubMed search was performed to identify further relevant studies. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Included were studies published in English or German that examined associations between climatic factors, including temperature, humidity, precipitation or seasonality and postoperative outcomes in surgical patients. Studies without a surgical or climatic focus, animal studies, reviews and commentaries were excluded. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently screened studies and extracted data using predefined criteria. Risk of bias was assessed using the Newcastle-Ottawa Scale. Due to methodological heterogeneity, findings were synthesised narratively. RESULTS: Fifteen studies were included. High ambient temperatures were frequently associated with increased risks of surgical site infections (SSIs). Some large studies reported that even a one-degree rise significantly elevated infection rates. Humidity was also linked to higher infection rates, with evidence suggesting an independent effect in some cases. Seasonal changes influenced complication rates across regions and surgical disciplines, with infection and pneumonia peaks in summer or winter months. A few studies reported increased risks during heavy rainfall or institutional transitions such as the 'July effect'. CONCLUSION: Overall, elevated temperature and humidity appear to be associated with increased postoperative risks, especially SSIs. Seasonal patterns may further influence outcomes depending on geography and procedure type. While the available evidence supports a link between climate and surgical complications, methodological differences and limited adjustment for confounders highlight the need for more rigorous studies.
5 July 2026
Read appraisal →PloS one
Delayed health-seeking behavior and its associated factors among cancer patients in Ethiopia: A systematic review and meta-analysis, 2025
BACKGROUND: Delayed Health-seeking behavior among cancer patients is a major contributor to late diagnosis, poor prognosis, and high mortality, particularly in low-resource settings like Ethiopia. However, evidence on the magnitude and determinants of delayed care-seeking remains fragmented. OBJECTIVE: This systematic review and meta-analysis aimed to estimate the pooled prevalence of delayed Health-seeking behavior among cancer patients in Ethiopia and to identify associated factors influencing delays. METHODS: This study employed a systematic review and meta-analysis design to assess delayed Health-seeking behavior and its influencing factors among cancer patients in Ethiopia. A systematic search was conducted in PubMed, Scopus, Web of Science, CINAHL, AJOL, Google Scholar, and Ethiopian University repositories until April 27, 2025. The data were extracted from March 10-20 and analyzed from March 21-30, with report generation till April 27, 2025, using R software. Meta-analysis was performed using a random-effects model, with forest plots illustrating pooled prevalence and associated factors. Heterogeneity was assessed using the I² statistic, and study quality was evaluated using a validated tool. RESULTS: Seven studies conducted across multiple regions of Ethiopia were included in the final analysis with a total of 2,641 participants. The pooled prevalence of delayed Health-seeking behavior among cancer patients was 54% (95% CI: 39%-68%). Meta-analysis of associated factors showed that rural residence was significantly associated with delayed Health-seeking behavior, with patients residing in rural areas having more than threefold higher odds of delay (AOR = 3; 95% CI: 1.81-4.19), poor knowledge about cancer was strongly associated with delay, with nearly seven times higher odds among patients with poor knowledge compared to those with adequate knowledge (AOR = 6.63; 95% CI: 2.21-11.05), lack of cancer awareness was also a significant predictor of delayed Health-seeking behavior (AOR = 2.63; 95% CI: 1.75-3.51), and patients without pain were over three times more likely to delay Healthcare(AOR = 3.38; 95% CI: 2.44-4.67) were factors associated with delayed Health-seeking behavior. CONCLUSIONS: Our review showed that half of the cancer patients in Ethiopia experienced delayed health-seeking behavior. Delayed care-seeking was associated with rural residence, poor knowledge, limited awareness of cancer, and absence of pain symptoms. Targeted interventions, including public awareness campaigns, expansion of healthcare services in rural areas, and financial support initiatives, are urgently needed to reduce delays and improve early cancer diagnosis and outcomes. PROSPERO REGISTRATION NUMBER: CRD420251037845.
4 July 2026
Read appraisal →BMJ health & care informatics
Predicting health and disease: a conceptual framework for AI in preventive and precision medicine
The conventional medical approach of treating symptoms as they appear with restricted screening often limits intervention to slowing disease progression rather than fully reversing it. A new approach leveraging artificial intelligence (AI) and computational technologies across expanding multimodal biomedical datasets holds the promise to enable predicting actionable future changes in health before symptom onset. This article presents a conceptual framework for a preventive paradigm in precision medicine and healthcare, integrating recent advancements in AI and biomedical datasets. Key remaining challenges facing computational systems, real-world clinical validation and implementation, and preventive interventions together with recommendations and prioritised future directions are highlighted. Such an approach could pave the way for more proactive and preventive medical interventions to effectively address the growing burden of chronic disease.
4 July 2026
Read appraisal →Nature
Deep learning four decades of human migration
Human migration is a fundamental driver of global demographic change, shaping population structure, labour markets and social policy across countries1-3. Although long-term migration patterns are often linked to economic development4, they can shift rapidly in response to shocks such as conflict, environmental crises and political change5. Despite its importance, migration remains difficult to measure consistently: existing data are sparse, concentrated in high-income settings and are fragmented across incompatible definitions, temporal resolutions and data types6-8. Past efforts have relied on partial datasets, including flow records, stock estimates and model-based reconstructions with limited coverage9-14. A central challenge is therefore to construct a globally consistent, high-resolution account of migration flows over time. Here we present a new dataset of annual origin-destination migration across 230 countries and regions from 1990 to the present, integrating diverse data sources into a unified modelling framework. By combining official statistics, census-based stocks, net migration estimates and past flow reconstructions, our approach produces temporally detailed and spatially comprehensive estimates that substantially extend existing resources. Using an ensemble of deep recurrent neural networks informed by geographic, economic, cultural and political covariates, we capture both persistent trends and short-term responses to changing conditions-all while propagating uncertainty to generate confidence bounds. Our results outperform existing five-year flow estimates on held-out data and provide finer temporal resolution, revealing previously obscured dynamics in global migration patterns. This framework highlights regions in which uncertainty remains high and data collection is most urgently needed. By releasing all data, code and trained models, we provide a transparent and reproducible foundation for future work. These advances enable a more timely and detailed understanding of human mobility, with implications for research and policy in an increasingly dynamic global system.
3 July 2026
Read appraisal →Telemedicine journal and e-health : the official journal of the American Telemedicine Association
Utilization of Telemedicine Among Offshore Health Care Workers and Its Influencing Factors
INTRODUCTION: Telemedicine has become an essential tool for providing health care to offshore workers. Despite its advancements, there is still a lack of understanding about its optimal use and the factors affecting it among health care providers in offshore settings. The objective of this work was to study and determine the level of telemedicine utilization among offshore health care workers and its associated factors in Malaysia. METHODS: In this cross-sectional study, online questionnaires were distributed to health care workers involved in offshore medical services. The questionnaire includes sociodemographic information and the validated Malay version of the Telemedicine Acceptance Model (TAM) Questionnaire. A total of 73 offshore health care workers were recruited from a local company using universal sampling. Data analysis involved descriptive statistics as well as simple and multiple logistic regression. RESULTS: A total of 65.8% of offshore health care workers reported using telemedicine in their practice. Multiple logistic regression analysis indicated that Chinese and Indian workers were significantly more likely to use telemedicine compared with Malay workers, with adjusted odds ratios of 32.11 (p = 0.045) and 33.51 (p = 0.037), respectively. Workers with a good attitude toward telemedicine had 11.84 times higher odds of utilization (p = 0.004), while those with high behavioral intention were 25.80 times more likely to use telemedicine compared with their counterparts with low behavioral intention (p < 0.001). CONCLUSIONS: A majority of offshore health care workers utilize telemedicine, with Chinese and Indian ethnicity, good attitude, and high behavioral intention significantly predicting its utilization.
3 July 2026
Read appraisal →PloS one
Forecasting COVID-19 new cases using NBEATS deep learning and mobility data
COVID-19 is a highly contagious disease transmitted primarily through human contact. Therefore, understanding population mobility is essential for predicting COVID-19 case trends. In this paper, we propose a novel deep learning approach for forecasting new COVID-19 cases using a neural architecture called Neural Basis Expansion Analysis for Interpretable Time Series (N-BEATS). The N-BEATS model effectively handles long input sequences and large output horizons without information loss or increased computational complexity. We compare the performance of N-BEATS with a state-of-the-art benchmark model, LSTM-Markov, across four major countries: the United States, the United Kingdom, Russia, and Brazil. Three distinct COVID-19 datasets from Google, Apple, and Our World in Data (OWID) were used in this study. Incorporating Google and Apple mobility data as covariates enhances both the accuracy and interpretability of the N-BEATS model. Our results show that N-BEATS consistently outperforms LSTM-Markov across all datasets and countries, consistently yielding lower Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). Furthermore, the N-BEATS model with covariates outperforms its counterpart without covariates, indicating that mobility data provide substantial value for forecasting new COVID-19 cases. Overall, this study demonstrates the effectiveness of the N-BEATS architecture in capturing pandemic dynamics and offers valuable insights for policymakers and public health officials in managing future outbreaks.
30 June 2026
Read appraisal →Cancer causes & control : CCC
HPV in breast cancer: prevalence and comparison with healthy tissue-a systematic review and meta-analysis
INTRODUCTION: Breast cancer (BC) is the most common cancer among women worldwide, with over 2.3 million new cases annually. Recent studies suggest that Human Papillomavirus (HPV), a known oncogenic virus, may be involved in BC development. This study investigates HPV prevalence in BC samples and its potential role in tumorigenesis. METHODS: Random-effects meta-analyses were conducted to estimate raw proportions and odds ratio (OR), with 95% confidence intervals (CIs). Heterogeneity was assessed using I2. Statistical significance was set at p < 0.05. Analyses were performed in R 4.5.0 RESULTS: Our meta-analysis encompassed 82 studies and evaluated 7,683 breast cancer (BC) tissue samples to assess the presence of HPV. The overall prevalence of HPV in BC specimens was estimated at 23% (95%CI: 19%-28%). When stratified by continent, Oceania exhibited the highest regional prevalence at 38%. Comparative analysis between BC tissues and healthy controls revealed a significantly increased likelihood of HPV detection in the cancer group (OR 5.06; P < 0.001). This association remained statistically robust in both case-control (OR 6.34; P < 0.001) and cross-sectional designs (OR 2.83; P < 0.001). Among continents, South America demonstrated the most pronounced association (OR 11.66; P = 0.005). Subgroup analysis based on economic classification indicated that countries with low-income settings had the highest HPV prevalence (34%; 95%CI: 9%-73%). Evaluation by BC subtype revealed that luminal B had the highest HPV-positive rate (44%; 95%CI: 27%-61%). CONCLUSION: This meta-analysis reveals a global presence of HPV in BC and suggests a possible link. Further well-designed studies are needed to confirm its role in tumorigenesis.
26 June 2026
Read appraisal →European journal of hospital pharmacy : science and practice
A bibliometric analysis of the application trends of information technology in antimicrobial stewardship within hospitals
OBJECTIVES: This study uses bibliometric analysis to systematically map research trends, knowledge structure and evolution of information technology (IT) in hospital antimicrobial stewardship (AMS) over the last 20 years. METHODS: A Web of Science Core Collection search (2000-2025) yielded 258 English-language publications on IT applications in hospital AMS. A bibliometric analysis, utilising CiteSpace and Bibliometrix, quantitatively evaluated domain evolution through temporal analysis, network mapping, keyword clustering, burst detection and examination of highly cited publications. RESULTS: The bibliometric analysis reveals a fluctuating yet overall increasing trend in annual publications, peaking at 49 articles in 2024. The US (131 publications) and European nations demonstrate significant research output (centrality >0.2), with major collaborative networks coalescing around institutions including Harvard University and Imperial College London. Keyword analysis identifies 'AMS' as a core theme, closely associated with technological keywords such as 'machine learning (ML)', 'clinical decision support systems (CDSS)', 'electronic health records (EHR)' and 'artificial intelligence (AI)'. Emerging trends suggest a shift in research focus from foundational strategies to data-driven prediction of antimicrobial resistance (AMR) and precision interventions. Highly cited literature emphasises the integration of EHR and ML technologies for optimising prescriptions and predicting resistance patterns. CONCLUSIONS: IT-driven AMS research has shifted from empirical management to data science. Despite EHR integration, and ML and CDSS support, challenges remain in data standardisation, technical deployment and ethics. Future work must emphasise global collaboration, standardisation, design refinement and ethical guidelines, and provide clear algorithm explanations to enhance AMR mitigation.
26 June 2026
Read appraisal →Journal of medical Internet research
The Open Syndrome Definition as a Machine-Readable Standard for Public Health: Design and Implementation Study
BACKGROUND: Case definitions are essential for effectively communicating public health threats. However, the absence of a standardized, machine-readable format poses significant challenges to interoperability, epidemiological research, data sharing, and the application of computational methods, including artificial intelligence. These barriers complicate collaboration across regions and organizations and hinder technological progress in public health. OBJECTIVE: This study aims to propose and release the first open, machine-readable format for representing case and syndrome definitions, together with tools and resources that enable their standardized and scalable use. METHODS: We developed the Open Syndrome Definition, a structured, machine-readable schema for representing case and syndrome definitions. We compiled official public health case definitions from multiple institutions and converted them into standardized, machine-readable representations using open-source tools. These tools, available through GitHub under the Massachusetts Institute of Technology license, automate the translation of narrative definitions into structured data. We also created a platform for browsing, analyzing, and contributing new definitions on our initiative website. RESULTS: The Open Syndrome Definition format enabled consistent, automated representation of case definitions across different diseases and jurisdictions. The conversion tools achieved high semantic fidelity, as assessed by qualitative expert review, between narrative and structured representations, supporting human verification and automated analysis. The dataset and accompanying tools demonstrated structural and semantic interoperability by standardizing definitions from various health systems into a unified format and integrating existing medical ontologies through JSON for Linked Data. To further illustrate practical applicability and downstream usage, we introduced a data filtering prototype that allows users to upload their own datasets and verify the results against the standardized definitions. CONCLUSIONS: The Open Syndrome Definition establishes a foundation for consistent and machine-readable public health definitions, facilitating reproducible research and interoperability at scale. By enabling systematic data exchange and artificial intelligence-driven analysis, it strengthens public health preparedness and supports more rapid, coordinated responses to emerging health threats.
19 June 2026
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