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SSRN 2025年

作者： Latt, Phyu M. Soe, Nyi N. Fairley, Christopher K. Chow, Eric P.F. Johnson, Cheryl C. Shah, Purvi Maatouk, Ismail Zhang, Lei Ong, Jason J. Artificial Intelligence and Modelling in Epidemiology Program Melbourne Sexual Health Centre Alfred Health Melbourne Australia School of Translational Medicine Faculty of Medicine Nursing and Health Sciences Monash University Melbourne Australia Melbourne Sexual Health Centre Alfred Health Melbourne Australia Centre for Epidemiology and Biostatistics Melbourne School of Population and Global Health The University of Melbourne Melbourne Australia Global HIV Hepatitis and STIs Programmes World Health Organization Geneva Switzerland Regional Support Team Asia Pacific UNAIDS Thailand Clinical Medical Research Centre Children’s Hospital of Nanjing Medical University Jiangsu Province Nanjing China Department of Clinical Research London School of Hygiene and Tropical Medicine London United Kingdom

Background: Despite advances in sexually transmitted infection (STI) prevention, optimising risk estimation remains challenging. While machine learning (ML) approaches hold promise for enhancing STI risk estimation, the empirical evidence supporting their clinical utility remains incompletely understood. This study aimed to systematically evaluate the performance of ML models in estimating individual risk for HIV, syphilis, gonorrhoea, and ***: Following PRISMA guidelines, we systematically searched six electronic databases (Embase, MEDLINE, Scopus, CINAHL, Web of Science, and IEEE), three preprint archives, and conference proceedings from January 2010 to April 2024. Studies were eligible if they reported quantitative performance metrics for supervised ML-based STI risk prediction models. Two independent reviewers assessed the risk of bias using PROBAST (Prediction Model Risk Of Bias Assessment Tool). We employed bivariate random-effects meta-analysis to estimate pooled sensitivity, specificity, and area under the curve (AUC) with 95% confidence intervals. We conducted sequential analyses of studies with complete and reconstructed confusion matrices. Subgroup analyses and meta-regression explored potential sources of heterogeneity, including study population (general versus specific populations such as men who have sex with men (MSM) and other gender-specific groups), sample size (Results: Among 3,877 records screened, 25 studies comprising 45 unique models met inclusion criteria, with sample sizes ranging from 547 to 4,384,178 participants across 14 countries. For HIV risk estimation, the SROC curve analysis of studies reporting complete confusion matrices (7 studies with 9 contingency tables) demonstrated a summary AUC of 0.91 (95% CI: 0.88-0.93), with a pooled sensitivity of 0.84 (0.76-0.90) and specificity of 0.84 (0.70-0.93). The analyses demonstrated substantial heterogeneity (I2 > 98%). While meta-regression identified significant differences in

关键词： Adversarial machine learning

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artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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Clustering analysis of sexual behavioral patterns and risk of sexually transmitted infections in Chinese men who have sex with men

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Journal of Infection and Public Health 2025年第8期18卷 102786-102786页

作者： She, Bingyang Liu, Yiming Lin, Siqi Lu, Fang Liu, Yi Sun, Jiajun Li, Gaixia Hu, Yawu Su, Shu Zhang, Lei Phase I clinical trial research ward The Second Affiliated Hospital of Xi'an Jiaotong University No.157 Xi Wu Road Shaanxi Province Xi'an 710004 China School of Translational Medicine Faculty of Medicine Nursing and Health Sciences Monash University Melbourne Australia Guizhou Provincial Department of Agriculture and Rural Affairs China Artificial Intelligence and Modelling in Epidemiology Program Melbourne Sexual Health Centre Alfred Health Melbourne Australia Department of Epidemiology and Biostatistics The Second Affiliated Hospital of Chongqing Medical University Chongqing China China-Australia Joint Research Center for Infectious Diseases School of Public Health Xi'an Jiaotong University Health Science Center Shaanxi Xi'an 710061 China

Introduction: This study identifies clusters in Chinese men who have sex with men (MSM) by sexual behavioral patterns and defines high-risk subgroups for gonorrhea, chlamydia, and syphilis infections. Methods: We conducted a longitudinal study of Chinese MSM from November 2020 to January 2022, collecting weekly data over 12 consecutive weeks on sequential sexual act patterns and testing for gonorrhea, chlamydia, and syphilis infections. Hierarchical clustering identified sexual behavior patterns, and survival analysis estimated infection incidence within each cluster. Results: We identified three main clusters (n = 91, 100, 62), one small cluster (n = 8) and 41 unclustered participants. Participants in cluster 1 engaged in both insertive and receptive roles in oral, anal sex and rimming during sexual encounters. Cluster 2 participants predominately performed insertive sexual acts, while cluster 3 performed receptive sexual acts. During follow-up, cluster 1 exhibited a significantly higher incidence of overall gonorrhea infection (581.51/100,000 person-days), pharyngeal (355.45/100,000 person-days), anorectum gonorrhea (487.63/100,000 person-days) and syphilis (17.19 %) while comparing with cluster 2 and 3. Consistently, cluster 1 was significantly more likely to engage in kissing (40.08 %) and insertive rimming (6.77 %) and receptive rimming (7.18 %), in sexual act pairs such as ‘receptive oral sex-insertive oral sex’ (8.71 %), ‘receptive rimming-insertive anal sex’ (4.59 %), ‘kissing-receptive oral sex’ (13.62 %), ‘insertive anal sex-receptive anal sex’ (5.38 %) and ‘insertive rimming-receptive rimming’ (6.83 %). Conclusion: Sexual role preference is a significant factor in clustering Chinese MSM. Those who assume both insertive and receptive roles demonstrate a substantial higher risk of gonorrhea and syphilis infections. © 2025 The Author(s)

关键词： Hierarchical clustering MSM Sequential sexual acts Sexual behavioral patterns STI risk

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Machine Learning for Personalised Risk Assessment of HIV, Syphilis, Gonorrhoea, and Chlamydia: A Systematic Review and Meta-analysis

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International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases 2025年 157卷 107922页

作者： Phyu M Latt Nyi N Soe Christopher K Fairley Eric P F Chow Cheryl C Johnson Purvi Shah Ismail Maatouk Lei Zhang Jason J Ong Artificial Intelligence and Modelling in Epidemiology Program Melbourne Sexual Health Centre Alfred Health Melbourne Australia School of Translational Medicine Faculty of Medicine Nursing and Health Sciences Monash University Melbourne Australia. Electronic address: phyu.latt@monash.edu. School of Translational Medicine Faculty of Medicine Nursing and Health Sciences Monash University Melbourne Australia. School of Translational Medicine Faculty of Medicine Nursing and Health Sciences Monash University Melbourne Australia Melbourne Sexual Health Centre Alfred Health Melbourne Australia. Melbourne Sexual Health Centre Alfred Health Melbourne Australia Centre for Epidemiology and Biostatistics Melbourne School of Population and Global Health The University of Melbourne Melbourne Australia. Global HIV Hepatitis and STIs Programmes World Health Organization Geneva Switzerland. Global HIV Hepatitis and STIs Programmes World Health Organization Geneva Switzerland Regional Support Team Asia Pacific UNAIDS Thailand. Clinical Medical Research Centre Children's Hospital of Nanjing Medical University Nanjing Jiangsu Province China. Department of Clinical Research London School of Hygiene and Tropical Medicine London United Kingdom.

BACKGROUND:Machine learning (ML) shows promise for sexually transmitted infection (STI) risk prediction, but systematic evidence of its effectiveness remains fragmented. METHODS:We systematically searched six electronic databases, three preprint archives, and conference proceedings (January 2010-April 2024). Studies reporting quantitative performance metrics for supervised ML-based STI risk prediction models were included. We used a bivariate random-effects model to estimate pooled sensitivity, specificity, and area under the curve (AUC). The risk of bias was assessed using the PROBAST tool. We conducted sequential analyses of studies with complete and reconstructed confusion matrices. Subgroup analyses and meta-regression explored potential sources of heterogeneity. RESULTS:Among 3,877 records screened, 25 studies comprising 45 unique models met inclusion criteria. For HIV, analysis of studies with complete confusion matrices (7 studies, 9 contingency tables) demonstrated summary AUC of 0.91 (95%CI:0.88-0.93), pooled sensitivity 0.84 (0.76-0.90), and specificity 0.84 (0.70-0.93). Substantial heterogeneity persisted across subgroups (I²>98%). For other STIs, individual studies reported AUCs ranging from 0.75-0.87 for syphilis (n=5), 0.73-1.00 for gonorrhoea (n=6), and 0.67-1.00 for chlamydia (n=6). DISCUSSION:While ML models show promising performance, particularly for HIV, significant heterogeneity complicates interpretation. Future research should prioritise external validation, standardised guidelines, and multi-centred robust implementation studies to evaluate clinical impact.

关键词： Chlamydia Gonorrhoea HIV Machine learning Risk assessment Syphilis Systematic review meta-analysis

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Using Deep Learning Systems for Diagnosing Common Skin Lesions in Sexual Health

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SSRN 2024年

作者： Soe, Nyi Nyi Latt, Phyu Mon Lee, David Yu, Zhen Schmidt, Martina Bissessor, Melanie Aung, Ei T. Ge, Zongyuan Ong, Jason J. Fairley, Christopher K. Zhang, Lei Artificial Intelligence and Modelling in Epidemiology Program Melbourne Sexual Health Centre Alfred Health Melbourne Australia School of Translational Medicine Faculty of Medicine Nursing and Health Sciences Monash University Melbourne Australia Melbourne Sexual Health Centre Alfred Health Melbourne Australia for Health lab Faculty of Information Technology Monash University Melbourne Australia eResearch Centre Clinical Medical Research Centre Children’s Hospital of Nanjing Medical University Jiangsu Province Nanjing210008 China

Background: Sexually transmitted infections (STIs) are a major public health concern, affecting millions of individuals worldwide. Early identification and treatment of STIs prevents complications and improves STI control. However, there are obstacles to delivering accessible care, particularly for genital conditions. A deep learning system (DLS) that automates the diagnosis of skin lesions may facilitate access to health care but has not been evaluated across a comprehensive range of sexual health-related skin conditions. Methods: We developed a DLS using 15,891 clinical images from public repositories and the Melbourne Sexual Health Centre (MSHC) to classify 33 anogenital dermatological conditions, including STIs and non-STIs. The DLS was trained on 80% of images and validated on a 20% hold-out test set using 5-fold cross-validation. We prospectively collected 336 images to evaluate the DLS’s accuracy and compared it to the clinician diagnosis. We also evaluated whether DLS recommendations aligned with clinical urgency for seeking care based on the diagnosis. Results: On the hold-out test dataset, the DLS achieved an accuracy of 59.2% (top-1) (standard deviation (SD) 0.7%) and the correct diagnosis was included in the top five diagnoses (top-5) with an accuracy of 82.1% (SD 13.3%). On the 8-month prospective dataset at MSHC, the DLS achieved a top-1 accuracy of 52.1%, top-3 of 73.8%, and top-5 of 89.9%. The performance varies across 33 different diagnoses, with the majority (77%) of the diagnoses achieving over 80.0% for top-5 accuracy. Top-1 accuracy ranged from 15.0-100.0% and was highest for dermatoses, discharge-associated gonorrhoea or chlamydia, molluscum, mpox, but lowest for herpes zoster, scabies, and penile papules. The DLS recommendation based on top-5 diagnoses for seeking care maintained 100% sensitivity for urgent cases (e.g. syphilis) but a lower positive predictive value (59.5%). The recommendation based on top-1 diagnosis provided more balanced se

关键词： Learning systems

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What Do People Want from an AI-Assisted App for Diagnosing Images of Sexually Transmitted Infections: A Discrete Choice Experiment

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SSRN 2024年

作者： Soe, Nyi Nyi Latt, Phyu Mon King, Alicia J. Lee, David Phillips, Tiffany R. Fairley, Christopher K. Zhang, Lei Ong, Jason J. Artificial Intelligence and Modelling in Epidemiology Program Melbourne Sexual Health Centre Alfred Health Melbourne Australia School of Translational Medicine Faculty of Medicine Nursing and Health Sciences Monash University Melbourne Australia Melbourne Sexual Health Centre Alfred Health Melbourne Australia Clinical Medical Research Centre Children’s Hospital of Nanjing Medical University Jiangsu Province Nanjing210008 China Faculty of Infectious Diseases London School of Hygiene and Tropical Medicine London United Kingdom

Background: One of the World Health Organization (WHO) recommendations to achieve its global targets for sexually transmitted infections (STIs) is the increased use of digital technologies. Melbourne Sexual Health Centre (MSHC) has developed an AI-assisted diagnosis application (app) called AiSTi for the detection of common STI-related anogenital skin conditions. This study aims to understand the community’s preference for using the AiSTi app. Methods: We used a discrete choice experiment (DCE) to understand community preferences regarding the attributes of the AiSTi app for checking anogenital skin lesions. The DCE design included the attributes: data type;AI accuracy;verification of result by clinician;details of result;speed;professional support;and cost. The anonymous DCE survey was distributed to clients attending MSHC and through social media channels in Australia between January and March 2024. Participant preferences on various app attributes were examined using random parameters logit (RPL) and latent class analysis (LCA) models. Results: The median age of 411 participants was 32 years (interquartile range, 26-40), with 64% assigned male at birth. Of the participants, 177 (43.1%) identified as same-sex attracted and 137 (33.3%) as heterosexual. In the RPL model, the most influential attribute was the cost of using the app (24.1%), followed by the clinician’s verification of results (20.4%), the AI accuracy (19.5%) and the speed of receiving the result (19.1%). The LCA identified two distinct groups: "all-rounders" (88%), who considered every attribute as important;and a "cost-focused" group (12%), who mainly focused on the price. Based on the currently available app attributes, the predicted uptake was 72%. In the short-term, a more feasible scenario of improving AI accuracy to 80-89% with clinician verification at a $5 cost could increase uptake to 90%. A long-term optimistic scenario with AI accuracy over 95%, no clinician verification and no cost could i

关键词： Diagnosis

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Determinants and prediction of hypertension among Chinese middle-aged and elderly adults with diabetes: A machine learning approach

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Heliyon 2024年第18期10卷 e38124页

作者： Mao, Lijun Lin, Luotao Shi, Zumin Song, Hualing Zhao, Hailei Xu, Xianglong School of Public Health Shanghai University of Traditional Chinese Medicine Shanghai China Nutrition and Dietetics Program Department of Individual Family and Community Education University of New Mexico United States Human Nutrition Department College of Health Sciences QU Health Qatar University Qatar School of Translational Medicine Faculty of Medicine Nursing and Health Sciences Monash University Melbourne VIC Australia Artificial Intelligence and Modelling in Epidemiology Program Melbourne Sexual Health Centre Alfred Health Carlton VIC Australia

Objective: Multimorbidity, particularly diabetes combined with hypertension (DCH), is a significant public health concern. Currently, there is a gap in research utilizing machine learning (ML) algorithms to predict hypertension risk in Chinese middle-aged and elderly diabetic patients, and gender differences in DCH comorbidity patterns remain unclear. We aimed to use ML algorithms to predict DCH and identify its determinants among middle-aged and elderly diabetic patients in China. Study design: Cross-sectional study. Methods: Data were collected on 2775 adults with diabetes aged ≥45 years from the 2015 China Health and Retirement Longitudinal Study. We employed nine ML algorithms to develop prediction models for DCH. The performance of these models was evaluated using the area under the curve (AUC). Additionally, we conducted variable importance analysis to identify key determinants. Results: Our results showed that the best prediction models for the overall population, men, and women were extreme gradient boosting (AUC = 0.728), light gradient boosting machine (AUC = 0.734), and random forest (AUC = 0.737), respectively. Age, waist circumference, body mass index, creatinine level, triglycerides, taking Western medicine, high-density lipoprotein cholesterol, blood urea nitrogen, total cholesterol, low-density lipoprotein cholesterol, and sleep disorders were identified as common important predictors by all three populations. Conclusions: ML algorithms showed accurate predictive capabilities for DCH. Overall, non-linear ML models outperformed traditional logistic regression for predicting DCH. DCH predictions exhibited variations in predictors and model accuracy by gender. These findings could help identify DCH early and inform the development of personalized intervention strategies. © 2024 The Authors

关键词： China Diabetes Hypertension Machine learning Middle-aged and elderly adults Multimorbidity Prediction model

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Using AI to Differentiate Mpox From Common Skin Lesions in a Sexual Health Clinic: Algorithm Development and Validation Study

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Journal of Medical Internet Research 2024年 26卷 e52490页

作者： Soe, Nyi Nyi Yu, Zhen Latt, Phyu Mon Lee, David Samra, Ranjit Singh Ge, Zongyuan Rahman, Rashidur Sun, Jiajun Ong, Jason J. Fairley, Christopher K. Zhang, Lei Artificial Intelligence and Modelling in Epidemiology Program Melbourne Sexual Health Centre Alfred Health Melbourne Australia School of Translational Medicine Faculty of Medicine Nursing and Health Sciences Monash University Melbourne Australia Augmented Intelligence and Multimodal analytics (AIM) for Health Lab Faculty of Information Technology Monash University Melbourne Australia Melbourne Sexual Health Centre Alfred Health Melbourne Australia Department of Infectious Diseases Alfred Hospital Alfred Health Melbourne Australia Clinical Medical Research Center Children’s Hospital of Nanjing Medical University Nanjing China

Background: The 2022 global outbreak of mpox has significantly impacted health facilities, and necessitated additional infection prevention and control measures and alterations to clinic processes. Early identification of suspected mpox cases will assist in mitigating these impacts. Objective: We aimed to develop and evaluate an artificial intelligence (AI)–based tool to differentiate mpox lesion images from other skin lesions seen in a sexual health clinic. Methods: We used a data set with 2200 images, that included mpox and non-mpox lesions images, collected from Melbourne Sexual Health Centre and web resources. We adopted deep learning approaches which involved 6 different deep learning architectures to train our AI models. We subsequently evaluated the performance of each model using a hold-out data set and an external validation data set to determine the optimal model for differentiating between mpox and non-mpox lesions. Results: The DenseNet-121 model outperformed other models with an overall area under the receiver operating characteristic curve (AUC) of 0.928, an accuracy of 0.848, a precision of 0.942, a recall of 0.742, and an F₁-score of 0.834. Implementation of a region of interest approach significantly improved the performance of all models, with the AUC for the DenseNet-121 model increasing to 0.982. This approach resulted in an increase in the correct classification of mpox images from 79% (55/70) to 94% (66/70). The effectiveness of this approach was further validated by a visual analysis with gradient-weighted class activation mapping, demonstrating a reduction in false detection within the background of lesion images. On the external validation data set, ResNet-18 and DenseNet-121 achieved the highest performance. ResNet-18 achieved an AUC of 0.990 and an accuracy of 0.947, and DenseNet-121 achieved an AUC of 0.982 and an accuracy of 0.926. Conclusions: Our study demonstrated it was possible to use an AI-based image recognition algorit

关键词： artificial intelligence deep learning mpox sexually transmitted infections skin lesion

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Preferences for Attributes of an artificial intelligence-Based Risk Assessment Tool for HIV and Sexually Transmitted Infections: A Discrete Choice Experiment

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SSRN 2024年

作者： Latt, Phyu Mon Soe, Nyi Nyi King, Alicia J. Lee, David Phillips, Tiffany R. Xu, Xianglong Chow, Eric P.F. Fairley, Christopher K. Zhang, Lei Ong, Jason J. Artificial Intelligence and Modelling in Epidemiology Program Melbourne Sexual Health Centre Alfred Health Melbourne Australia School of Translational Medicine Faculty of Medicine Nursing and Health Sciences Monash University Melbourne Australia Melbourne Sexual Health Centre Alfred Health Melbourne Australia School of Public Health Shanghai University of Traditional Chinese Medicine Shanghai China Centre for Epidemiology and Biostatistics Melbourne School of Population and Global Health The University of Melbourne Melbourne Australia Clinical Medical Research Center Children’s Hospital of Nanjing Medical University Jiangsu Province Nanjing China Department of Clinical Research London School of Hygiene and Tropical Medicine London United Kingdom

Introduction: Early detection and treatment of HIV and sexually transmitted infections (STIs) are crucial to their effective control. We developed MySTIRisk, an artificial intelligence (AI)-based risk self-assessment tool that predicts an individual’s risk of HIV and STIs. We examined the attributes of MySTIRisk that encourage potential users to use the ***: Between January and March 2024, we sent text message invitations to the Melbourne Sexual Health Centre (MSHC) attendees to participate in an online survey. We also advertised the survey on social media platforms, the clinic's website, and posters in affiliated general practice clinics. This anonymous survey used a discrete choice experiment (DCE) to examine which attributes of MySTIRisk would encourage potential users to use the tool. We analysed the data using random parameters logit (RPL) and latent class analysis (LCA) ***: The median age of 415 participants was 31 years (interquartile range, 26-38 years), with a proportion of participants identifying as straight or heterosexual (31.8%, n=132). The choice to use MySTIRisk was most influenced by two attributes: cost and accuracy, followed by the availability of a pathology request form, level of anonymity, speed of receiving results, and whether the tool was a web or mobile application. LCA revealed two classes: "The Precisionists" (66.0% of respondents), who demanded high accuracy and "The Economists" (34.0% of respondents), who prioritised low cost. Simulations predicted a high uptake (97.7%) for a tool designed with the most preferred attribute levels, contrasting with lower uptake (22.3%) for the least preferred ***: Participants were more likely to use MySTIRisk if it was free, highly accurate, and could send pathology request forms. Tailoring the tool to meet the needs of distinct user segments could further enhance its uptake and effectiveness in promoting early detection and prevention of HIV and ***: EPFC is

关键词： Pathology

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Disparities in stage at diagnosis for liver cancer in China

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Journal of the National Cancer Center 2023年第1期3卷 7-13页

作者： Tianhao Shan Xianhui Ran Huizhang Li Guoshuang Feng Siwei Zhang Xuehong Zhang Lei Zhang Lingeng Lu Lan An Ruiying Fu Kexin Sun Shaoming Wang Ru Chen Li Li Wanqing Chen Wenqiang Wei Hongmei Zeng Jie He National Central Cancer Registry National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina Cancer Hospital of University of Chinese Academy of Sciences/Zhejiang Cancer Hospital Department of Cancer PreventionInstitute of Cancer Research and Basic Medical SciencesChinese Academy of SciencesHangzhouChina Big Data Center Beijing Children’s HospitalCapital Medical UniversityNational Center for Children’s HealthBeijingChina Channing Division of Network Medicine Department of MedicineBrigham and Women’s Hospital and Harvard Medical SchoolBostonUSA Department of Nutrition T.H.Chan School of Public HealthHarvard UniversityBostonUSA China-Australia Joint Research Center for Infectious Diseases School of Public HealthXi’an Jiaotong University Health Science CenterXi’anChina Artificial Intelligence and Modelling in Epidemiology Program Melbourne Sexual Health CentreAlfred HealthMelbourneAustralia Central Clinical School Faculty of MedicineMonash UniversityMelbourneAustralia Department of Epidemiology and Biostatistics College of Public HealthZhengzhou UniversityZhengzhouChina Department of Chronic Disease Epidemiology Yale School of Public HealthYale Cancer CenterNew HavenUSA Department of Thoracic Surgery National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina

Background:The stage at diagnosis is a major factor in making treatment strategies and cancer control ***,the stage distribution for liver cancer in China was not well *** this multi-center hospital-based study,we aimed to identify the distribution and factors associated with stage at diagnosis for liver cancer in ***:We included patients diagnosed with primary liver cancer in 13 hospitals of 10 provinces covering various geographic and socioeconomic populations during 2016-2017 in *** stage distribution overall,and by sex and age at diagnosis were *** used logistic regression to identify the factors associated with stage Ⅲ-Ⅳ *** further compared these estimates with data from the ***:We included 2,991 patients with known stage at diagnosis in *** proportion of patients diagnosed with stageⅠ,Ⅱ,Ⅲ,and Ⅳ was 17.5%,25.6%,29.3%,and 27.6%,*** proportion of stage Ⅲ-Ⅳ cases was higher in women[65.1%vs 54.9%,adjusted odds ratio(OR)=1.5,95%CI:1.2,1.8]and those≥60 years(61.6%vs 52.8%,OR=1.4,95%CI:1.2,1.6).We found an increased risk of stage Ⅲ-Ⅳ among drinkers and those without a family history of *** to the USA,our study population had a substantially higher proportion of stage Ⅲ-Ⅳ cases(56.9%vs 45.6%).Conclusion:The disparities in liver cancer stage at diagnosis among different populations within China,and between China and the USA,imply the necessity for improving cancer awareness and early detection for liver cancer in China.

关键词： Liver cancer Stage at diagnosis Disparity China USA

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