medRxiv : the preprint server for health sciences最新文献

{"title":"Timeliness of Yellow Fever Specimen Collection and Transport in Ghana, 2018-2022.","authors":"Seth D Judson, Lee Schroeder, Franklin Asiedu-Bekoe, Dennis Odai Laryea, Gifty Boateng, Horlali Gudjinu, Robert Ossom, Jerry Fosu Danquah, David W Dowdy, Ernest Kenu","doi":"10.1101/2025.06.19.25329877","DOIUrl":"10.1101/2025.06.19.25329877","url":null,"abstract":"<p><p>Yellow fever is a mosquito-borne viral hemorrhagic fever that has caused recent outbreaks in African countries, including Ghana (2021-2022). Delayed diagnosis of yellow fever may cause increased morbidity and mortality. To improve timely detection of yellow fever, we need to better understand the factors contributing to diagnostic delays. We analyzed the diagnostic testing timeline of all suspected yellow fever cases in Ghana from 2018-2022. For these patients we calculated the days from symptom onset to specimen collection and arrival at the National Public Health and Reference Laboratory (NPHRL) for testing. We compared these times to World Health Organization (WHO) metrics. For suspected yellow fever cases, the time from symptom onset to specimen arrival had a median of 10 days (interquartile range 6-17). 5892/6345 (93%) of specimens were collected within 14 days of symptom onset, and 2653/6471 (41%) of specimens arrived within 3 days of collection (WHO metrics). Overall, we find that the timing of yellow fever testing varies among districts in Ghana. While specimens are generally collected in a timely manner, delays in specimen arrival are common. Improving specimen transport for yellow fever and/or expanded testing could lead to more timely detection of outbreaks.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204298/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144532283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decoding the mechanophysiology for inhaled onset of smallpox with model-based implications for mpox spread.","authors":"Mohammad Yeasin, Mohammad Mehedi Hasan Akash, Abir Malakar, Azadeh A T Borojeni, Aditya Tummala, Jihong Wu, William D Bennett, Wanda M Bodnar, Julia S Kimbell, Arijit Chakravarty, Julia R Port, Saikat Basu","doi":"10.1101/2025.06.17.25329814","DOIUrl":"https://doi.org/10.1101/2025.06.17.25329814","url":null,"abstract":"<p><p>Orthopoxviruses can transmit via inhalation of virus-laden airborne particulates, with the initial infection triggered along the respiratory pathway. Understanding the flow physics of inhaled aerosols and droplets within the respiratory tract is crucial for improving transmission mitigation strategies and elucidating disease pathology. Here, we introduce an experimentally-validated physiological fluid dynamics model simulating inhaled onset of smallpox caused by the variola virus of Orthopoxvirus genus. Using high-fidelity Large Eddy Simulations, we modeled airflow and particulate motion within anatomical airway domains reconstructed from medical imaging. By integrating these simulations with viral concentration and individual immune factors, we estimated critical exposure durations for infection onset to be between 1 - 19 hours, aligning with existing smallpox literature. To formalize the broader applicability of this framework, we extended our analysis to mpox virus, a circulating pathogen from same genus. For mpox, the mechanophysiological computations indicate a critical exposure window of 24 - 40 hours; however, this can vary significantly-from as short as 8 hours to as long as 127 hours-depending on virion concentration fluctuations within inhaled particulates, assuming happenstance of viral evolution. Predictably longer than the critical exposure durations for smallpox, the mpox findings still strongly suggest the possibility for airborne inhaled transmission during prolonged proximity.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204288/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144532250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying Key Predictors of Smoking Cessation Success: Text-Based Feature Selection Using a Large Language Model.","authors":"Thuy T T Le, Jiongxuan Yang, Zimo Zhao, Kaidi Zhang, Wenjun Li, Yan Hu","doi":"10.1101/2025.06.18.25329854","DOIUrl":"https://doi.org/10.1101/2025.06.18.25329854","url":null,"abstract":"<p><strong>Background: </strong>The most effective way to reduce mortality and morbidity among current smokers is to quit smoking. Although about half of smokers attempted to quit, only one-tenth succeeded in 2022.</p><p><strong>Objective: </strong>To identify key predictors of smoking cessation success to inform cessation interventions and increase quitting rates.</p><p><strong>Methods: </strong>We analyzed data from waves 5 and 6 of the Population Assessment of Tobacco and Health (PATH) study (December 2018 to November 2021). Using OpenAI's GPT-4.1, we identified the top 45 variables from wave 5 that are highly predictive of 12-month smoking abstinence in wave 6, based on descriptions of survey variables. We then validated the predictive power of the GPT-4.1-selected variables by comparing the performance of eXtreme Gradient Boosting (XGBoost) trained on different sets of variables. Finally, we derived insights into the top 10 variables, ranked according to their SHapley Additive exPlanations values.</p><p><strong>Results: </strong>The performance of XGBoost trained with all possible wave 5 variables and the 45 selected variables was almost identical (AUC:0.749 vs AUC:0.752). The top 10 variables included past 30-day smoking frequency, minutes from waking up to smoking first cigarette, important people's views on tobacco use, prevalence of tobacco use among close associates, daily electronic nicotine product use, emotional dependence, and health harm concerns.</p><p><strong>Conclusion: </strong>This study demonstrates the ability of OpenAI's GPT-4.1 to identify the top 45 PATH wave 5 variables associated with 12-month smoking abstinence using only their descriptions. This approach could help researchers design more effective survey questionnaires and improve efficiency of data collection.</p><p><strong>What is already known on this topic: </strong>Generative artificial intelligence models have recently been applied to assess their potential in addressing various tobacco-related issues, such as detecting tobacco products in social media videos and promoting vaping cessation. However, their application in identifying the most significant predictors of tobacco use behavior, based on survey data, remains unexplored.</p><p><strong>What this study adds: </strong>GPT-4.1 successfully assigned high-quality importance scores to survey variables for predicting 12-month smoking abstinence over two years among current established smokers. It accomplished this using only the textual descriptions of the survey variables, without accessing the actual survey data. Based on these importance scores, GPT-4.1 can aid in identifying the most crucial variables for predicting smoking cessation success.</p><p><strong>How this study might affect research practice or policy: </strong>This study demonstrates the capacity of GPT-4.1 to perform feature selection, paving the way for future exploration of this innovative approach to address other tobacco-related issues.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204296/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144532279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comprehensive Study of Circulating Blood Linear RNA nominates CD55 and DLD as novel causal genes and early-stage biomarkers for Parkinson's Disease.","authors":"Aleksandra Beric, Sarp Sahin, Santiago Sanchez, Zining Yang, Ravindra Kumar, Isabel Alfradique-Dunham, Jessie Sanford, Daniel Western, Bridget Phillips, John P Budde, Richard J Perrin, Paul T Kotzbauer, Joel S Perlmutter, Scott A Norris, Carlos Cruchaga, Laura Ibanez","doi":"10.1101/2025.06.20.25329948","DOIUrl":"https://doi.org/10.1101/2025.06.20.25329948","url":null,"abstract":"<p><p>We leveraged transcriptomic data from 4,343 participants from four independent datasets to robustly identify and annotate circulating PD-associated transcripts. We identified 296 differentially expressed transcripts, 28 of which were transcribed from known PD-associated loci. Further, we found a significant overlap between our findings and transcripts dysregulated in brain, as well as proteins differentially accumulated in CSF. Expression of the identified transcripts was affected by genetic background including ancestry and PD-related mutations, and nearly half of the identified transcripts were dysregulated before symptom onset. The differentially expressed transcripts were utilized to develop three predictive models that distinguished between PD and healthy controls with a ROC AUC of 0.727-0.733. The predictive models were capable of detecting PD transcriptomic signatures even before symptom onset. One transcript, DLD, showed particular promise as an early stage, minimally invasive PD biomarker that was differentially expressed in whole blood, brain and CSF. This transcript significantly related to PD in the eQTL analyses and in two of the three predictive models.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204277/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144532227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the Socio-geographic and lifestyle Factors Impacting Epithelial Ovarian Cancer Outcomes: A Retrospective Study Based on County Health Ranking in Missouri.","authors":"Carlye Goldenberg, Kavita Krell, Edgar Diaz Miranda, Sooah Ko, Maya Demirchian, Grace Anne Dyer, Mark Hunter, Erin Tuller, Amanda Hull, Lei Lei","doi":"10.1101/2025.06.18.25329863","DOIUrl":"https://doi.org/10.1101/2025.06.18.25329863","url":null,"abstract":"<p><strong>Objective: </strong>This study examined how obesity, smoking, and pregnancy history, characterized as lifestyle factors, are associated with survival of epithelial ovarian cancer, and investigated whether epithelial ovarian cancer presentation, survival, and cancer recurrence are affected by patient home geographic location.</p><p><strong>Methods: </strong>A retrospective analysis was conducted on all patients with epithelial ovarian cancer treated at the University of Missouri and Ellis Fischel Cancer Center between 2008 and 2023. Patient charts were reviewed for cancer history, lifestyle factors, patient status, laboratory values, and residential zip codes which were categorized using Missouri ZIP Health Rankings. Survival, cox univariate and multivariate logistic regression, and association analyses were performed.</p><p><strong>Results: </strong>In this cohort, stage at diagnosis, histologic type, age at diagnosis and initial CA125 proved to be significant predictors of survival, while lifestyle factors including BMI, smoking, and pregnancy were not. Notably, patients residing in communities with the lowest zip code health rankings experienced higher rates of cancer recurrence, despite a lower overall number of cases compared to higher-ranked communities.</p><p><strong>Conclusion: </strong>Although the lifestyle factors investigated in this study were not significantly associated with survival, a geographic disparity in recurrence rates and total cases was clear, suggesting possible underdiagnosis and barriers to accessing care in lower ranked zip codes. These findings emphasize an evident need to further investigate community-specific healthcare access and delivery, as well as other lifestyle factors that may be contributing to these differences.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204272/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144532243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning NTCP model for late dysphagia after radiotherapy for head and neck cancer patients based on 3D dose, CT and segmentations.","authors":"S P M de Vette, H Neh, L van der Hoek, D C MacRae, H Chu, A Gawryszuk, R J H M Steenbakkers, P M A van Ooijen, C D Fuller, K A Hutcheson, J A Langendijk, N M Sijtsema, L V van Dijk","doi":"10.1101/2025.06.20.25329926","DOIUrl":"https://doi.org/10.1101/2025.06.20.25329926","url":null,"abstract":"<p><strong>Background & purpose: </strong>Late radiation-associated dysphagia after head and neck cancer (HNC) significantly impacts patient's health and quality of life. Conventional normal tissue complication probability (NTCP) models use discrete dose parameters to predict toxicity risk but fail to fully capture the complexity of this side effect. Deep learning (DL) offers potential improvements by incorporating 3D dose data for all anatomical structures involved in swallowing. This study aims to enhance dysphagia prediction with 3D DL NTCP models compared to conventional NTCP models.</p><p><strong>Materials & methods: </strong>A multi-institutional cohort of 1484 HNC patients was used to train and validate a 3D DL model (Residual Network) incorporating 3D dose distributions, organ-at-risk segmentations, and CT scans, with or without patient- or treatment-related data. Predictions of grade ≥2 dysphagia (CTCAEv4) at six months post-treatment were evaluated using area under the curve (AUC) and calibration curves. Results were compared to a conventional NTCP model based on pre-treatment dysphagia, tumour location, and mean dose to swallowing organs. Attention maps highlighting regions of interest for individual patients were assessed.</p><p><strong>Results: </strong>DL models outperformed the conventional NTCP model in both the independent test set (AUC=0.80-0.84 versus 0.76) and external test set (AUC=0.73-0.74 versus 0.63) in AUC and calibration. Attention maps showed a focus on the oral cavity and superior pharyngeal constrictor muscle.</p><p><strong>Conclusion: </strong>DL NTCP models performed better than the conventional NTCP model, suggesting the benefit of using 3D-input over the conventional discrete dose parameters. Attention maps highlighted relevant regions linked to dysphagia, supporting the utility of DL for improved predictions.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204244/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144532252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genome-Wide Insights into the Genes and Pathways Shaping Human Foveal Development.","authors":"Callum Hunt, Ha-Jun Yoon, Alvin Lirio, Kayesha Coley, Jun Wang, Nick Shrine, Jianming Shao, Gail DE Maconachie, Zhanhan Tu, Jonathan H Zippin, Pirro G Hysi, Christopher J Hammond, Ala Moshiri, Rui Chen, Martin D Tobin, Chiara Batini, Mervyn G Thomas","doi":"10.1101/2025.06.18.25329836","DOIUrl":"https://doi.org/10.1101/2025.06.18.25329836","url":null,"abstract":"<p><p>Here we report the first genome-wide association study of foveal pit depth. In a cohort of 61,269 individuals, we identified 123 genome-wide significant loci associated with pit depth, including 47 novel associations not previously linked to macular traits. Using 12 complementary variant-to-gene mapping strategies, we prioritised 128 putative causal genes, 64 of which have not previously been implicated in foveal development. Our findings reveal previously unrecognised biological influences on foveal morphogenesis, including retinoic acid metabolism (implicating <i>CYP26A1</i> for the first time in human foveal development), extracellular matrix and cytoskeletal dynamics, and retinal cell fate determination. In addition, rare-variant analysis uncovered two further gene associations, including <i>ESYT3</i> , a gene not previously linked to foveal structure. Together, these results provide new insights into the genetic architecture and molecular pathways underlying human foveal development, and offer a foundation for future functional studies aimed at characterising foveal development and disease.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204292/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144532273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cohort-level analysis of human <i>de novo</i> mutations points to drivers of clonal expansion in spermatogonia.","authors":"Vladimir Seplyarskiy, Mikhail A Moldovan, Evan Koch, Prathitha Kar, Matthew Dc Neville, Raheleh Rahbari, Shamil Sunyaev","doi":"10.1101/2025.01.03.25319979","DOIUrl":"https://doi.org/10.1101/2025.01.03.25319979","url":null,"abstract":"<p><p>In renewing tissues, mutations conferring selective advantage may result in clonal expansions ^1-4 . In contrast to somatic tissues, mutations driving clonal expansions in spermatogonia (CES) are also transmitted to the next generation. This results in an effective increase of <i>de novo</i> mutation rate for CES drivers ^5-8 . CES was originally discovered through extreme recurrence of <i>de novo</i> mutations causing Apert syndrome ⁵ . Here, we develop a systematic approach to discover CES drivers as hotspots of human <i>de novo</i> mutation. Our analysis of 54,715 trios ascertained for rare conditions ^9-13 , 6,065 control trios ^12,14-19 , and population variation from 807,162 mostly healthy individuals ²⁰ identifies genes manifesting rates of <i>de novo</i> mutations inconsistent with plausible models of disease ascertainment. We propose 23 genes hypermutable at loss-of-function (LoF) sites as candidate CES drivers. An additional 17 genes feature hypermutable missense mutations at individual positions, suggesting CES acting through gain-of-function (GoF). Among candidates are 5 of 13 known CES drivers ^7,8 , 13 drivers of somatic expansions, and 21 members of major signaling pathways; notably, 17 genes show CES evidence in direct sperm sequencing ²¹ . CES increases the average mutation rate ∼17-fold for LoF genes in both control trios and sperm and ∼500-fold for pooled GoF sites in sperm. Positive selection in the male germline elevates the prevalence of genetic disorders and increases polymorphism levels, masking the effect of negative selection in human populations. Despite the excess of mutations in disease cohorts for 19 LoF CES driver candidates, only 9 show clear evidence of disease causality ²² , suggesting that CES may lead to false-positive disease associations.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204251/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144532291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Population-based assessment of neurodevelopmental and mental health diagnoses among pediatric patients with Turner Syndrome: A PEDSnet study.","authors":"Christa Hutaff-Lee, Morgan Jolliffe, Karli S Swenson, Holly Wakeman, Deanna Swain, Anna Furniss, Natalie Nokoff, Jen Hansen-Moore, Chijioke Ikomi, Vaneeta Bamba, Rachel E Lean, Skyler Leonard, Shanlee M Davis","doi":"10.1101/2025.06.17.25329800","DOIUrl":"https://doi.org/10.1101/2025.06.17.25329800","url":null,"abstract":"<p><p>Individuals with Turner syndrome (TS) are known to be at increased risk for neurodevelopmental disorders (NDD) and mental health (MH) conditions, but data from large, population-based pediatric samples remain limited. We examined the prevalence of NDD and MH diagnoses among youth with TS (N = 2,145) compared to matched female controls (N = 8,580) across six U.S. pediatric health systems. Odds ratios (OR) and 95% confidence intervals (CI) were calculated using generalized estimating equations. Youth with TS had significantly higher odds of an NDD diagnosis (24.2% vs. 11.9%; OR 2.37, 95% CI 2.11-2.67), particularly for speech-language, motor, learning, and attentional disorders. Increased odds were also observed for autism spectrum disorder (ASD) and intellectual developmental disorder (IDD), though these remained relatively uncommon. In contrast, MH diagnoses, such as anxiety and mood disorders, were not more prevalent in TS compared to controls (17.3% vs. 18.5%; OR 0.92, 95% CI 0.81-1.05). These findings support the need for proactive neurodevelopmental screening in TS and raise important questions about the recognition and documentation of MH conditions in this population. Additional research is warranted to understand whether MH symptoms are underdiagnosed in youth with TS or emerge later in development.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204300/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144532317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Azithromycin mass drug administration to reduce child mortality in Niger (AVENIR II): a master protocol for a cluster-randomized adaptive platform trial to evaluate community-based health interventions.","authors":"Ahmed M Arzika, Abdou Amza, Sani Ousmane, Ramatou Maliki, Ibrahim Almou, Nasser Galo, Nasser Harouna, Alio Mankara, Bawa Aichatou, Ousseini Boubacar, Elodie Lebas, Brittany Peterson, Carolyn Brandt, Andrea Picariello, Angela Cheng, Travis C Porco, Thuy Doan, Benjamin F Arnold, Thomas M Lietman, Kieran S O'Brien","doi":"10.1101/2025.06.17.25329431","DOIUrl":"https://doi.org/10.1101/2025.06.17.25329431","url":null,"abstract":"<p><strong>Background: </strong>Trials have demonstrated that azithromycin mass drug administration (MDA) to children 1-59 months old reduces mortality, but increases antimicrobial resistance (AMR). The World Health Organization recommends that programs include mortality and AMR monitoring. Niger is expanding the azithromycin MDA for child survival program nationwide.</p><p><strong>Methods: </strong>To establish program monitoring and leverage the infrastructure to evaluate other community health interventions, AVENIR II is designed as a cluster-randomized adaptive platform trial with monitoring and re-randomization every 2 years. The initial focus is to monitor under-5 mortality, AMR, implementation, and safety as the program expands in Niger. All eligible primary health center catchment areas (Centre de Santé Intégrés, CSIs) will be included in biannual oral azithromycin MDA to children 1-59 months old. A subset will be randomized to delay MDA for the first 2 years, after which they will receive MDA and another subset will be randomized to stop MDA for the next 2 years. The proportion randomized to delay or stop will be determined using an adaptive algorithm including: 1) results of prior azithromycin MDA mortality trials, 2) expert opinion on the appropriate ethical balance between delivering the program and monitoring AMR, and 3) statistical power to detect a programmatically relevant difference between arms. We anticipate 5-10% of CSIs will be randomized to delay or stop at each randomization. Mortality and AMR will be monitored at baseline and every 2 years. Implementation and safety outcomes will be monitored continuously. To enable ongoing monitoring while ensuring program access, CSIs receiving MDA will be re-randomized using the adaptive algorithm updated with new mortality results and no CSI will go without MDA for more than 2 years. In this platform design, additional arms may be added or dropped based on information from other studies, updates to guidelines, or preferences of Niger policymakers, and other interventions may be evaluated.</p><p><strong>Discussion: </strong>The risk of AMR has led to caution in the implementation of azithromycin MDA. We present a design that enables continued rigorous evaluation of program impact on key outcomes, with flexibility to evaluate other interventions as well.</p><p><strong>Trial registration: </strong>clinicaltrials.gov ( NCT06358872 ), registered April 2024.</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204265/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144532247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}