BackgroundMachine learning offers new avenues for complementing traditional epidemiological approaches by analyzing routinely collected, population-based administrative health data.ObjectiveThis study aimed to identify potential exposomic predictors (hypothesis generation) for Parkinson's disease (PD) across the entire French agricultural workforce.MethodsWe applied XGBoost adapted for Cox proportional hazards modeling to assess approximately 180 exposomic factors derived from nationwide administrative health data within the TRACTOR project. Shapley Additive Explanation (SHAP) values were used to assess the importance of each predictor. To provide both model-based and statistical perspectives, SHAP analysis was complemented with classical Cox regression, allowing for transparent assessment of each predictor's contribution to the model and its statistical association with survival. Sensitivity analyses incorporating different exposure lags were conducted. The study included 424,725 farm managers (6,265 PD cases) and 544,788 farmworkers (2,848 PD cases) aged 50+, analyzed separately due to differences in available variables and coding structures.ResultsSeveral occupational factors, including duration of involvement in crop farming and viticulture, emerged as key promoting predictors, surpassing age in predictive importance. Beyond conventional predictors such as type 2 diabetes, less conventional predictors were identified, including work diversification, seasonal employment, hypercholesterolemia, epilepsy, antidepressant use, anxiolytic use, and antibiotic use.ConclusionsThese results contribute to a growing body of evidence supporting the integration of occupational health considerations into PD research and highlight the importance of exploring and identifying potential farming-related risk factors in PD development.Plain language summary titleUsing nationwide French farming data and machine learning to explore potential factors linked to Parkinson's disease. This study investigates how work-related and health-related factors may contribute to Parkinson's disease risk among farm managers and farmworkers.

Les hydrocarbures aromatiques polycycliques (HAP) forment une famille de polluants cancérigènes ubiquitaires, classés prioritaires par l’Union européenne. En raison de leur toxicité et de leur présence dans de très nombreux procédés industriels (métallurgie, sapeurs-pompiers, cokeries, bitumes, gaz d’échappement, produits carbonés), Il est essentiel de quantifier l’exposition interne des populations exposées aux HAP. Le 1-hydroxypyrène (1-OHP), métabolite urinaire du pyrène, est le biomarqueur le plus dosé. L’ACGIH précise que la valeur biologique d’interprétation (VBI) actuelle de ce marqueur (1μmol/mol créatinine), basée sur la génotoxicité, doit être adaptée en fonction de la proportion relative de pyrène (non cancérogène) et de Benzo(a)pyrène (cancérogène) dans l’air sur le lieu de travail. Cependant, la complexité et le coût des mesures atmosphériques rendent leur réalisation systématique impossible. Pour répondre à cette problématique et dans une optique de formation et de sensibilisation à la surveillance biologique des expositions professionnelles (SBEP), un outil interactif d’aide à l’interprétation de la SBEP aux HAP a été développé : SurvBioHAP. Cet outil s’appuie sur une revue de la littérature regroupant plus de 5000 métrologies atmosphériques provenant de 19 pays, 15 secteurs industriels, 79 processus d’émission et 213 activités professionnelles. Disponible gratuitement, SurvBioHAP permet d’estimer la VBI du 1-OHP selon le contexte professionnel (secteur, poste, activité) en tant compte du ratio Pyrène/BaP atmosphérique. L’outil offre diverses fonctionnalités : prise en compte du port d’un équipement de protection respiratoire et de son facteur de protection assigné (FPA), génération ou import de fichiers de concentrations urinaires de 1-OHP, estimation des probabilités de dépassement de la VBI. Des boutons d’aide guident l’utilisateur dans la configuration et l’interprétation des résultats. Tous les résultats sont téléchargeables. Une version avancée en anglais, appelée PAH HBM tool, a également été développée. SurvBioHAP est un outil en évolution continue, régulièrement enrichi de nouvelles données et fonctionnalités. Il s’adresse principalement aux médecins et infirmiers du travail (SPSTI, SPSTA, CRPPE), en leur fournissant un cadre de recommandation avec des VBI spécifiques au contexte professionnel, pour faciliter l’interprétation de la SBEP aux HAP. Une démonstration de l’outil peut être réalisée en direct.

Background:Mobile health (mHealth) technologies can improve hypertension self-management, yet real-world adoption remains limited and unequally distributed. Objective:This study aimed to characterize the profiles, usage patterns, and engagement of active users of a hypertension self-management app (Hypertension.APP) in Germany, with a focus on user engagement and potential digital divides. Methods:We conducted a cross-sectional online survey among adult users of Hypertension.APP in Germany between January and September 2023. An 88-item questionnaire assessed app usage patterns, perceived utility, integration into clinical care, sociodemographic and clinical data, and digital health literacy (eHealth Literacy Scale; scores 16-40). Digital health literacy was categorized as low (16-23.99), moderate (24-31.99), or high (32-40). Descriptive statistics and univariable ordinal logistic regression were used to explore associations between sociodemographic and clinical variables and app usage frequency. Results:Of 254 respondents, the mean age was 53.6 years, and 54.3% (138/254) were male. A total of 44.5% (113/254) had a university or technical college degree, and 44.5% (113/254) reported a monthly net income higher than €2500 (US $2950). Most participants (224/254, 88.2%) reported access to at least two digital devices. Overall, 88.2% (224/254) had moderate or high digital health literacy (eHealth Literacy Scale ≥24). App engagement was high: 80.7% (205/254) reported using the app at least weekly, and 52.4% (133/254) reported using the app to prepare for medical visits. However, only 20.1% (51/254) reported that the app was formally integrated into their medical care, and 11.8% (30/254) indicated that medication had been adjusted based on app data. In univariable ordinal logistic regression analyses, higher education, longer duration of hypertension, and living in a small town (5000-20,000 inhabitants) were associated with more frequent app use, whereas systolic blood pressure of 140 mm Hg or higher was associated with less frequent use. Digital health literacy was not clearly associated with app usage frequency among current users. Conclusions:Users of this hypertension self-management app were predominantly well-educated, digitally literate individuals with established hypertension, reinforcing concerns about a persistent digital divide. While app usability and engagement were high, formal clinical integration remained limited. Simply making an app available is insufficient; strategies to promote equitable access, strengthen clinical integration, and support patients with lower digital health literacy are needed for mHealth to contribute effectively to hypertension management.

The triple planetary crisis (climate change, pollution, and biodiversity loss) is reshaping health conditions worldwide, yet public health itself contributes to the problem through carbon-intensive and polluting activities. What distinguishes public health is that its environmental footprint arises both from activities shared with other scientific disciplines (e.g., laboratory work, computing, and academic travel) and from activities specific to the field, including surveillance systems, mass screening programs, vaccination campaigns, and community-based interventions. In this perspective, we argue that environmental sustainability should be considered a core dimension of research quality and ethical integrity in public health, consistent with the fundamental principle of “do no harm.” Embedding sustainability requires both systemic transformation and individual engagement. This includes integrating environmental criteria into funding, governance, and evaluation frameworks, as well as adopting frugal, low-carbon, low-pollution practices in everyday work. Importantly, public health holds a distinctive lever compared with other disciplines. Its preventive mission has the potential to reduce the emissions-intensive burden associated with downstream curative care. In this sense, investing in prevention is also an investment in decarbonization and lower pollution. While acknowledging the complexity of measuring environmental impacts and the primacy of health benefits, we contend that a more sustainable, climate-resilient, and environmentally responsible public health is not a compromise; it is a more coherent, effective, and credible approach.

Background:Cardiovascular diseases remain a major health burden in Germany, and telemedicine (TM) offers promising solutions for outpatient care, yet barriers limit uptake. While prior studies relied on qualitative or conventional statistical methods, they often struggled with model uncertainty and complex relationships. Building on a national survey, this study applies Bayesian Model Averaging (BMA) and extreme gradient boosting (XGBoost). Objectives:This study aimed to explore candidate associations and patterns related to TM use among physicians in the German outpatient sector. Methods:We conducted a secondary analysis of a web-based survey carried out between 2023 and 2024. BMA was applied to identify explanatory associations with TM use, explicitly accounting for model uncertainty. XGBoost with SHAP values was used to explore classification patterns in a hypothesis-generating framework using repeated nested cross-validation. Results:Of the 165 respondents, 95 (58%) reported using TM. BMA revealed a limited number of variables with moderate to high posterior inclusion probabilities (PIP), alongside substantial overall model uncertainty, with TM use associated with receiving information from professional associations or insurers and perceived TM benefits (e.g., improving patients' everyday quality of life, improving doctor-patient relationship). Practicing in Lower Saxony was associated with lower TM use. XGBoost demonstrated limited discriminative ability, with performance statistically indistinguishable from chance. SHAP-based analyses therefore identified exploratory patterns, including features such as information status, workplace, perceptions of TM's benefits for patients (e.g., health literacy, compliance and adherence) and TM's barriers (e.g., data protection, implementation incompatibility), as well as employment status. Conclusion:TM adoption in Germany's outpatient sector appears associated with structural-economic factors and physicians' perceptions of patient-related benefits. However, given the substantial model uncertainty in BMA and the limited predictive performance of the machine learning model, all findings should be interpreted cautiously. The machine learning component should be considered exploratory and hypothesis-generating.

Planetary health is an interdisciplinary field that examines the complex connections between human health and the earth's ecosystems. Established by the Rockefeller Foundation-Lancet Commission in 2015, it seeks to understand and address health risks arising from anthropogenic environmental change. As an emerging but impactful discipline, we aimed to explore the evolution of planetary health research landscape for the first decade through a bibliometric analysis of publications during the last decade from January 2015 to December 2024, sourced from databases including PubMed, Scopus, and Web of Science. Utilizing the bibliometrix, and ArcGIS software, we visualized the keywords, co-occurrence networks and publication trends. A total of 587 publications, with a peak in 2022, were included in our analysis. These publications were published in 244 journals, written by authors from 102 countries. Key themes identified were climate change, sustainability, food security, and public health, with prominent links to One Health and Eco Health though major gaps exist in indigenous health, health equity, policy and education. While the contributions were dominated by high-income countries and specialized journals, regions most affected by planetary health challenges, were underrepresented. Amid global health and environmental inequalities, our paper demonstrates the growing interdisciplinary engagement of planetary health and underscores the unmet need for the equitable and growing collaborations with most vulnerable and resource poor settings, and the planetary health integration into global policy frameworks.

AbstractDigital 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.

Motor (MS) and non-motor symptoms (NMS) could contribute to mobility impairment in Parkinson’s disease (PD), but the relative role of each remain unclear. While supervised mobility assessments capture capacity, wearable devices enable evaluation of real-world performance. In this cross-sectional study, 105 individuals with mild-to-moderate PD performed standardized tests of forward, backward walking and turning, and wore a consumer smartwatch for five consecutive days to record average daily steps. MS and NMS were evaluated using validated clinical scales. Robust multivariable regressions showed that MS and NMS explained a modest proportion of mobility variance, with stronger effects on capacity- than performance-related metrics. Tremor-dominant and postural instability/gait disorder subscore predicted supervised mobility, while akinetic-rigid subscore was associated with average daily steps. Executive dysfunction and fatigue emerged as the main NMS determinants of mobility. Overall, these findings suggest a complex interplay between MS and NMS and warrant the need for multidimensional mobility assessment in PD.

Concept L’épidémiologie classique se base sur une donnée de haute qualité. Cependant, pour des raisons pratiques, économiques et techniques, ces travaux ciblent généralement une seule pathologie et sont limités dans l’espace et/ou le temps ainsi que dans la représentativité de la population incluse. Aujourd’hui, les sciences des données et l’IA, appliquées aux bases de données médico-administratives, ouvrent des perspectives complémentaires inédites. Il est par exemple possible de passer au crible l’ensemble des événements de santé sans a priori pour mettre en évidence des associations statistiques et générer des hypothèses sur les causes professionnelles possibles de maladies. Introduction La Mutualité Sociale Agricole (MSA) dispose de données longitudinales, administratives et de remboursement de soins sur l’intégralité de la population agricole, dont les travailleurs sont, en fonction de leurs activités, confrontés à des combinaisons uniques d’expositions et facteurs de risques. Objectif Il s’agit ici de présenter la synthèse des résultats de ce projet d’exploitation systématisée des données de la MSA collectées en routine sur l’ensemble de la population agricole métropolitaine pour permettre l’analyse de manière quasi-automatique des liens entre activités professionnelles agricoles et pathologies. Méthodes Plusieurs bases de données (> 1To et>400 fichiers) couvrant l’intégralité de la population agricole française métropolitaine–exploitants agricoles et salariés agricoles–, ont été mises en lien au niveau de l’individu (grâce à un identifiant crypté), nettoyées et préparées pour l’analyse épidémiologique dans le cadre du projet TRACTOR. Des modélisations statistiques ont ensuite été programmées puis automatisées pour étudier les associations entre activités professionnelles agricoles et incidence de plusieurs centaines de maladies. Résultats Ces travaux ont notamment donné lieu à 9 publications internationales (Lancet Reg Health Europe, Eur J Epidemiol, Int J Cancer, etc). Des associations statistiques dénotant d’un risque accru de plusieurs maladies associé à certaines activités agricoles ont pu être mises en évidence, notamment pour la maladie d’Alzheimer, la dépression, les tumeurs du système nerveux central, les dysthyroïdies, et les maladies inflammatoires chroniques de l’intestin, ou les maladies auto-immunes. Conclusion Une approche systématique est aujourd’hui possible à des fins de vigilance. Elle est en mesure d’extraire des connaissances utiles qui, sinon, demeurent cachées au sein de la masse de données. Une implantation en routine au sein d’un outil évolutif parait aujourd’hui incontournable pour contribuer à la veille sanitaire en continu des risques professionnels.

BACKGROUND:Frailty affects 42% of older patients with cancer and is a predictor of cancer recurrence and mortality. Its impact on older cancer survivors has not yet been extensively studied. OBJECTIVE:Assess the impact of a multicomponent home-based intervention on frailty prevalence in older adults in remission from cancer. DESIGN:Bicentric before-and-after non-randomised intervention study (NCT04746768). SETTING:Community-dwelling. SUBJECTS:Patients ≥70 years old in remission from cancer who met at least one criterion for the Fried phenotype. METHODS:A 6-month multicomponent home-based intervention was performed combining supervised exercise training and personalised nutritional support. The primary endpoint was the proportion of patients who achieved frailty or prefrailty improvement from the initial assessment (M0) to M6. RESULTS:The mean age of the 110 participants was 77 (SD = 5) years. At baseline, 45% were frail and none were robust. Eighty-one completed the intervention (Completion Group: CG). In CG, functional, physical performance measures and the T-stage of cancer were associated with baseline frailty, while none of the other cancer-related characteristics were. Among the CG, 62% improved their frailty status at M6: 78% of frails improved, 51% of pre-frail became robust. Frailty severity decreased at M6 in 74% of participants. Vitamin D showed a group × time interaction, suggesting that frailty trajectories differed according to vitamin D status. CONCLUSIONS:These preliminary findings suggest a potential beneficial effect of a home-based multicomponent intervention to improve frailty in community-dwelling older cancer survivors. Further controlled and adequately powered trials are required to confirm these findings.

In this secondary analysis of a German cross-sectional survey data, we investigated key determinants and predictors of telemedicine (TM) use among healthcare professionals (HCPs) treating cardiology patients. We applied Bayesian Model Averaging (BMA) for explanatory analysis and Machine Learning (ML) for predictive modeling. BMA identified TM determinants after excluding collinear variables and selecting variables based on LASSO regression. The extreme gradient boosting (XGBoost) ML algorithm predicted TM use and identified key predictors, using nested cross-validation to prevent overfitting. ML model performance was assessed via area under the receiver operating characteristic curve (AUROC), while predictor importance was evaluated using Shapley additive explanations. Among 112 HCPs, 64 (57%) used TM. BMA identified 12 determinants, including positive associations with TM knowledge, being a cardiologist, female gender, and perceiving TM as suitable for heart failure and for monitoring events. Negative associations included concerns about insufficient patient benefits, perceptions that TM is less suitable for acute events, and skepticism regarding its relevance for extending aftercare intervals. The XGBoost model showed strong predictive performance (AUROC: 0.88 [95% CI: 0.75; 1.00], accuracy: 0.79) for TM use. Key promoting factors included TM knowledge, being a cardiologist, female gender, number of average patients per quarter, and perceiving TM as suitable for arrhythmias, device follow-up, and heart failure. Limiting factors included older age, personal use of TM for one's own health, and skepticism about TM's relevance in acute situations. These findings emphasize the importance of knowledge and attitudes in shaping TM adoption and show that ML can accurately identify healthcare professionals most likely to use TM, supporting targeted interventions and safer implementation in cardiology.

Abstract Spondyloarthropathies (SpA) are characterized by low back pain and limited mobility. Therefore, physical activity (PA) is an essential part of the treatment, yielding positive effects on clinical symptoms. Digital health applications (DHAs) present new opportunities to promote clinical outcomes, however, their long-term effectiveness is often limited by low adherence and high dropout rates.This study investigates whether integrating personalized or AI-driven coaching enhances the therapeutic benefits of DHA in patients with SpA. SpAs patients were randomized into one of 3 groups. They were instructed to exercise at least 2–3 times per week for 6 months with the DHA according to their group (intervention groups: ViViRA (with personal coaching) or Kaia Health (with AI-based coaching); control group: ViViRA (without coaching)). Personal coaching consisted of a one-time, 30-min online coaching session prior to using DHA, while the AI coaching consisted of video-based AI integrated into DHA to provide movement guidance during each session. At baseline, after 3 and 6 months sociodemographic, questionnaires and mobility were assessed. Data from 78 participants were analyzed (mean age 51 years; 68% female). All three digital interventions showed a significant improvement in mobility (Bath Ankylosing Spondylitis Metrology Index (BASM), range: 0–10, lower scores = better mobility; BL-3 month: mean BASMI change − 0.6 to − 0.7; all p < 0.001). Pain intensity decreased substantially in all arms (PainDETECT, neuropathic pain, range: 0–38, higher scores = more severe pain; BL-6 month: mean reduction − 4.6 to − 6.6 points; all p ≤ 0.006). PAHCO (Physical Activity-related Health Competence) control competence increased over time and reached statistical significance only in the ViViRA + coaching group (PAHCO: higher scores = better physical activity-related health competence; BL-6 month: + 1.02, p = 0.013) but did not exceed the other interventions in a direct comparison. Overall, none of the coaching strategies showed significant superiority over the stand-alone digital therapy. Adherence was the same in all groups after 3 months (2–3 weekly use of DHA). Digital movement therapy with the use of DHA improves mobility and pain independently of coaching in SpAs patients. In contrast, personal coaching has been shown to improve health-related skills which could indicate potential benefits for self-management and long-term treatment adherence. Trial registration The study is registered in the German clinical trial registry (DRKS) under the following ID: DRKS00035191, https://www.drks.de/search/de/trial/DRKS00035191/details, Registration date: 01.10.2024.

Data have become central to scientific discovery. While primary data collection remains vital, there is growing recognition of the benefits of reusing existing datasets. However, identifying suitable datasets for specific research questions is increasingly difficult due to the fragmentation and heterogeneity of the big data ecosystem. Despite the expansion of data sharing, efficient dataset discovery remains elusive, with limited empirical research on how datasets are identified, interpreted, and reused. Current dataset search practices often lack standardization, leading researchers to rely on convenience rather than systematic criteria. Unlike bibliographic research, dataset selection lacks a formal methodology, increasing the risks of bias, inefficiencies, and reduced generalizability. To address this gap, we introduce datagraphy, a structured approach to dataset identification and evaluation. Analogous to bibliographic methods but designed for datasets, datagraphy encompasses not only discovery but also critical assessment of dataset quality, relevance, interoperability, completeness, sustainability, and ethical use. By formalizing dataset search as a research practice, datagraphy seeks to improve transparency, reproducibility, and interdisciplinary collaboration, while also reducing research redundancy and environmental impact. We present a 9-step framework to operationalize datagraphy and explore challenges such as inconsistent metadata and variability among dataset discovery tools. This framework provides a foundation for systematically and reproducibly identifying and synthesizing reusable datasets. To demonstrate the application of the proposed framework, we conducted a datagraphic search focused on the exposome. We discuss major challenges faced by datagraphy with respect to metadata availability, repository heterogeneity, dataset accessibility, and dataset quality, as well as highlight how datagraphy could enhance transparency, reproducibility, and efficiency at the researcher level. Datagraphy is intended to complement repository-level improvements. Aligning researcher practices with standardized, machine-readable metadata, persistent identifiers, artificial intelligence integration, and lightweight packaging frameworks such as RO-Crates and FAIR (Findable, Accessible, Interoperable, and Reusable) Digital Objects could enable automated discovery and sustainable dataset reuse. By integrating structured researcher-level methodology with systemic improvements and community efforts, datagraphy could offer a scalable approach for systematic, FAIR-aligned data-driven research across disciplines.

BackgroundAlthough agricultural health has gained importance, to date, much of the existing research relies on traditional epidemiological approaches that often face limitations related to sample size, geographic scope, temporal coverage, and the range of health events examined. To address these challenges, a complementary approach involves leveraging and reusing data beyond its original purpose. Administrative health databases (AHDs) are increasingly reused in population-based research and digital public health, especially for populations such as farmers, who face distinct environmental risks. ObjectiveWe aimed to explore the reuse of AHDs in addressing health issues within farming populations by summarizing the current landscape of AHD-based research and identifying key areas of interest, research gaps, and unmet needs. MethodsWe conducted a scoping review and bibliometric analysis using PubMed and Web of Science. Building upon previous reviews of AHD-based public health research, we conducted a comprehensive literature search using 72 terms related to the farming population and AHDs. To identify research hot spots, directions, and gaps, we used keyword frequency, co-occurrence, and thematic mapping. We also explored the bibliometric profile of the farming exposome by mapping keyword co-occurrences between environmental factors and health outcomes. ResultsBetween 1975 and April 2024, 296 publications across 118 journals, predominantly from high-income countries, were identified. Nearly one-third of these publications were associated with well-established cohorts, such as Agriculture and Cancer and Agricultural Health Study. The most frequently used AHDs included disease registers (158/296, 53.4%), electronic health records (124/296, 41.9%), insurance claims (106/296, 35.8%), population registers (95/296, 32.1%), and hospital discharge databases (41/296, 13.9%). Fifty (16.9%) of 296 studies involved >1 million participants. Although a broad range of exposure proxies were used, most studies (254/296, 85.8%) relied on broad proxies, which failed to capture the specifics of farming tasks. Research on the farming exposome remains underexplored, with a predominant focus on the specific external exposome, particularly pesticide exposure. A limited range of health events have been examined, primarily cancer, mortality, and injuries. ConclusionsThe increasing use of AHDs holds major potential to advance public health research within farming populations. However, substantial research gaps persist, particularly in low-income regions and among underrepresented farming subgroups, such as women, children, and contingent workers. Emerging issues, including exposure to per- and polyfluoroalkyl substances, biological agents, microbiome, microplastics, and climate change, warrant further research. Major gaps also persist in understanding various health conditions, including cardiovascular, reproductive, ocular, sleep-related, age-related, and autoimmune diseases. Addressing these overlooked areas is essential for comprehending the health risks faced by farming communities and guiding public health policies. Within this context, promoting AHD-based research, in conjunction with other digital data sources (eg, mobile health, social health data, and wearables) and artificial intelligence approaches, represents a promising avenue for future exploration.

Objective: To assess the potential association between congenital hypothyroidism (CH) and third-trimester maternal exposure to nitrate and perchlorate in tap water and to particulate matter (PM) in outdoor air. Methods: Using the French National Health Data System (SNDS), a retrospective ecological cohort was created, including all children born in France between 2014 and 2019. Ecological data for each child's municipality were used to examine associations, measured as odds ratios (OR), between mean exposure levels and CH in 1,417,402 newborns . Both single and multipollutant analyses were undertaken. To limit residual bias from the administrative nature of SNDS, analyses were conducted at both national and regional levels. The Benjamini-Hochberg approach was used to account for multiple testing. Results: Higher exposure to nitrates was associated with a greater risk of permanent CH (OR [95%CI] = 1.017 [1.001;1.035] in the Nord-Pas-de-Calais region and 1.024 [1.001;1.048] for the Pays-de-la-Loire region). Higher exposure to PM2.5 or PM10 was associated with a lower risk of permanent CH (OR [95%CI] = 0.928 [0.873;0.982] for PM10 in the Nord-Pas-de-Calais). These findings were confirmed in multi-pollutant analyses. Conclusions: In some French regions, prenatal exposure to nitrate ions in tap water was significantly associated with an elevated risk of permanent HC, while exposure to PM was significantly associated with a decreased risk. However, using municipal-level exposure proxies may introduce ecological bias, and results were not robust enough to draw firm conclusions about pollutant influence on CH risk.

Parkinson’s disease (PD) is characterized by motor symptoms altering gait domains such as slow walking speed, reduced step and stride length, and increased double support time. Gait disturbances occur in the early, mild to moderate, and advanced stages of the disease in both backward walking (BW) and forward walking (FW), but are more pronounced in BW. At this point, however, no information is available about BW performance and disease stages specified using the Hoehn and Yahr (H Y) scale. The objectives of this study were to examine the link between clinical scores and gait parameters in PD, and to assess gait parameters in both FW and BW among PD patients in early disease stages (H Y: 1–2) and advanced disease stages (H Y: 3–4), as well as among PD patients with mild and moderate disease severity as per the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale Part III (MDS-UPDRS III). Spatiotemporal gait parameters were analyzed during FW and BW over a 5-meter walkway at a comfortable speed using 3D motion capture. Correlations and regressions between clinical scores and gait parameters were examined. Wilcoxon Mann-Whitney rank sum tests were used to compare PD patients in early and advanced disease stages and assess differences in gait parameters for both FW and BW conditions. The study included a total of 25 PD patients (aged 65 ± 9 years), among whom 10 were in the H Y stages 1–2 and 15 in stages 3–4. All participants were evaluated with the MDS-UPDRS III, with 17 having a total score ≤ 32 (mild impairment and disability) and 8 having a total score > 32 (moderate impairment and disability). During BW, PD patients with H Y stages 1–2 had significantly (p < 0.05) longer step lengths, stride lengths, and a higher walk ratio compared to those with H Y stage 3–4. Regardless of the walking condition, no difference was found between PD patients with a MDS-UPDRS III total score ≤ 32 and patients with a MDS-UPDRS III total score > 32. The study demonstrates that individuals with PD in H Y stages 3–4 exhibit compromised FW and BW abilities in comparison to those in stages 1–2. Notably, the disparities are more prominent in the realm of backward walking. These findings substantiate the existence of distinct gait patterns between the early and advanced stages of the disease, with the variations being particularly accentuated in the context of backward walking. Taken together, our results suggest that backward walking may hold greater clinical utility in assessing and managing PD patients. The research procedure was approved by the ethical committee of the Medical Faculty of Kiel University (D438/18). The study is registered in the German Clinical Trials Register on 20,200,904 (DRKS00022998).

Farmers are exposed to numerous stressors that can negatively impact their mental health, leading to conditions such as depression. However, most studies examining depression risk in farmers are limited by small sample sizes, narrow geographic coverage, and a focus predominantly on male farmers and general agricultural contexts. To complement these traditional studies, big data and machine learning (ML) can advantageously be harnessed. While ML algorithms have shown high accuracy in identifying depression predictors in mental health research, no study has yet applied ML in farmers. We aimed to identify key predictors of depression among the entire French farmer workforce across professional categories, activities, and sexes using ML (XGBoost). A secondary analysis of large-scale administrative health databases (TRACTOR project) was conducted. Potential predictors (n=128 for farm managers and 123 for farmworkers) included a broad range of sociodemographic, health, lifestyle, and occupational variables. The predictor’s importance was determined using Shapley’s additive explanation. There were 83,592 depression cases among 1,088,561 farm managers and 149,285 depression cases among 5,831,302 farmworkers. Models performed well, with F1 scores ranging from 0.65 to 0.94. We noted differences, even though several predictors were common across populations, activities, and/or sexes. The top predictors of depression included working year, age, sex, experience, job security, income, and preexisting health conditions. The working year, which reflects the cumulative impact of external factors (e.g., harsh weather) on farmers’ mental health, emerged as the most important predictor. These findings highlight the potential of ML applied to real-world data for identifying modifiable predictors, thus enhancing early detection and prevention strategies. By differentiating predictors across farming groups, our results suggest that tailored mental health interventions could be developed to better address the unique needs of various farming populations. These insights could inform the development of clinical tools (e.g., risk calculators) to assist in clinical decision-making.

Exposome represents one of the most pressing issues in the environmental science research field. However, a comprehensive summary of worldwide human exposome research is lacking. We aimed to explore the bibliometric characteristics of scientific publications on the human exposome. A bibliometric analysis of human exposome publications from 2005 to December 2024 was conducted using the Web of Science in accordance with PRISMA guidelines. Trends/hotspots were investigated with keyword frequency, co-occurrence, and thematic map. Sex disparities in terms of publications and citations were examined. From 2005 to 2024, 931 publications were published in 363 journals and written by 4529 authors from 72 countries. The number of publications tripled during the last 5 years. Publications written by females (51

The risk of Parkinson’s disease (PD) associated with farming has received considerable attention, in particular for pesticide exposure. However, data on PD risk associated with specific farming activities is lacking. We aimed to explore whether specific farming activities exhibited a higher risk of PD than others among the entire French farm manager (FM) population. A secondary analysis of real-world administrative insurance claim data and electronic health/medical records (TRACTOR project) was conducted to estimate PD risk for 26 farming activities using data mining. PD cases were identified through chronic disease declarations and antiparkinsonian drug claims. There were 8845 PD cases among 1,088,561 FMs. The highest-risk group included FMs engaged in pig farming, cattle farming, truck farming, fruit arboriculture, and crop farming, with mean hazard ratios (HRs) ranging from 1.22 to 1.67. The lowest-risk group included all activities involving horses and small animals, as well as gardening, landscaping and reforestation companies (mean HRs: 0.48–0.81). Our findings represent a preliminary work that suggests the potential involvement of occupational risk factors related to farming in PD onset and development. Future research focusing on farmers engaged in high-risk farming activities will allow to uncover potential occupational factors by better characterizing the farming exposome, which could improve PD surveillance among farmers.