A Machine Learning Model for Identifying Sexual Health Influencers to Promote the Secondary Distribution of HIV Self-Testing Among Gay, Bisexual, and Other Men Who Have Sex With Men in China: Quasi-Experimental Study.

BACKGROUND: Sexual health influencers (SHIs) are individuals actively sharing sexual health information with their peers, and they play an important role in promoting HIV care services, including the secondary distribution of HIV self-testing (SD-HIVST). Previous studies used a 6-item empirical leadership scale to identify SHIs. However, this approach may be biased as it does not consider individuals' social networks. OBJECTIVE: This study used a quasi-experimental study design to evaluate how well a newly developed machine learning (ML) model identifies SHIs in promoting SD-HIVST compared to SHIs identified by a scale whose validity had been tested before. METHODS: We recruited participants from BlueD, the largest social networking app for gay men in China. Based on their responses to the baseline survey, the ML model and scale were used to identify SHIs, respectively. This study consisted of 2 rounds, differing in the upper limit of the number of HIVST kits and peer-referral links that SHIs could order and distribute (first round ≤5 and second round ≤10). Consented SHIs could order multiple HIV self-testing (HIVST) kits and generate personalized peer-referral links through a web-based platform managed by a partnered gay-friendly community-based organization. SHIs were encouraged to share additional kits and peer-referral links with their social contacts (defined as "alters"). SHIs would receive US $3 incentives when their corresponding alters uploaded valid photographic testing results to the same platform. Our primary outcomes included (1) the number of alters who conducted HIVST in each group and (2) the number of newly tested alters who conducted HIVST in each. We used negative binomial regression to examine group differences during the first round (February-June 2021), the second round (June-November 2021), and the combined first and second rounds, respectively. RESULTS: In January 2021, a total of 1828 men who have sex with men (MSM) completed the survey. Overall, 393 SHIs (scale=195 and ML model=198) agreed to participate in SD-HIVST. Among them, 229 SHIs (scale=116 and ML model=113) ordered HIVST on the web. Compared with the scale group, SHIs in the ML model group motivated more alters to conduct HIVST (mean difference [MD] 0.88, 95% CI 0.02-2.22; adjusted incidence risk ratio [aIRR] 1.77, 95% CI 1.07-2.95) when we combined the first and second rounds. Although the mean number of newly tested alters was slightly higher in the ML model group than in the scale group, the group difference was insignificant (MD 0.35, 95% CI -0.17 to -0.99; aIRR 1.49, 95% CI 0.74-3.02). CONCLUSIONS: Among Chinese MSM, SHIs identified by the ML model can motivate more individuals to conduct HIVST than those identified by the scale. Future research can focus on how to adapt the ML model to encourage newly tested individuals to conduct HIVST. TRIAL REGISTRATION: Chinese Clinical Trials Registry ChiCTR2000039632; https://www.chictr.org.cn/showprojEN.html?proj=63068. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s12889-021-11817-2.

Incremental value of automatically segmented perirenal adipose tissue for pathological grading of clear cell renal cell carcinoma: a multicenter cohort study.

OBJECTIVES: Accurate preoperative prediction of the pathological grade of clear cell renal cell carcinoma (ccRCC) is crucial for optimal treatment planning and patient outcomes. This study aims to develop and validate a deep-learning (DL) algorithm to automatically segment renal tumours, kidneys, and perirenal adipose tissue (PRAT) from computed tomography (CT) images and extract radiomics features to predict the pathological grade of ccRCC. METHODS: In this cross-ethnic retrospective study, a total of 614 patients were divided into a training set (383 patients from the local hospital), an internal validation set (88 patients from the local hospital), and an external validation set (143 patients from the public dataset). A two-dimensional TransUNet-based DL model combined with the train-while-annotation method was trained for automatic volumetric segmentation of renal tumours, kidneys, and visceral adipose tissue (VAT) on images from two groups of datasets. PRAT was extracted using a dilation algorithm by calculating voxels of VAT surrounding the kidneys. Radiomics features were subsequently extracted from three regions of interest of CT images, adopting multiple filtering strategies. The least absolute shrinkage and selection operator (LASSO) regression was used for feature selection, and the support vector machine (SVM) for developing the pathological grading model. Ensemble learning was used for imbalanced data classification. Performance evaluation included the Dice coefficient for segmentation and metrics such as accuracy and area under curve (AUC) for classification. The WHO/International Society of Urological Pathology (ISUP) grading models were finally interpreted and visualized using the SHapley Additive exPlanations (SHAP) method. RESULTS: For automatic segmentation, the mean Dice coefficient achieved 0.836 for renal tumours and 0.967 for VAT on the internal validation dataset. For WHO/ISUP grading, a model built with features of PRAT achieved a moderate AUC of 0.711 (95% CI, 0.604-0.802) in the internal validation set, coupled with a sensitivity of 0.400 and a specificity of 0.781. While model built with combination features of the renal tumour, kidney, and PRAT showed an AUC of 0.814 (95% CI, 0.717-0.889) in the internal validation set, with a sensitivity of 0.800 and a specificity of 0.753, significantly higher than the model built with features solely from tumour lesion (0.760; 95% CI, 0.657-0.845), with a sensitivity of 0.533 and a specificity of 0.767. CONCLUSION: Automated segmentation of kidneys and visceral adipose tissue (VAT) through TransUNet combined with a conventional image morphology processing algorithm offers a standardized approach to extract PRAT with high reproducibility. The radiomics features of PRAT and tumour lesions, along with machine learning, accurately predict the pathological grade of ccRCC and reveal the incremental significance of PRAT in this prediction.

Identifying dementia from cognitive footprints in hospital records among Chinese older adults: a machine-learning study.

Background: By combining theory-driven and data-driven methods, this study aimed to develop dementia predictive algorithms among Chinese older adults guided by the cognitive footprint theory. Methods: Electronic medical records from the Clinical Data Analysis and Reporting System in Hong Kong were employed. We included patients with dementia diagnosed at 65+ between 2010 and 2018, and 1:1 matched dementia-free controls. We identified 51 features, comprising exposures to established modifiable factors and other factors before and after 65 years old. The performances of four machine learning models, including LASSO, Multilayer perceptron (MLP), XGBoost, and LightGBM, were compared with logistic regression models, for all patients and subgroups by age. Findings: A total of 159,920 individuals (40.5% male; mean age [SD]: 83.97 [7.38]) were included. Compared with the model included established modifiable factors only (area under the curve [AUC] 0.689, 95% CI [0.684, 0.694]), the predictive accuracy substantially improved for models with all factors (0.774, [0.770, 0.778]). Machine learning and logistic regression models performed similarly, with AUC ranged between 0.773 (0.768, 0.777) for LASSO and 0.780 (0.776, 0.784) for MLP. Antipsychotics, education, antidepressants, head injury, and stroke were identified as the most important predictors in the total sample. Age-specific models identified different important features, with cardiovascular and infectious diseases becoming prominent in older ages. Interpretation: The models showed satisfactory performances in identifying dementia. These algorithms can be used in clinical practice to assist decision making and allow timely interventions cost-effectively. Funding: The Research Grants Council of Hong Kong under the Early Career Scheme 27110519.

The Impact of Expert Opinions in COVID-19 News Coverage on Vaccination During the Fifth Outbreak in Hong Kong: A Time Series Analysis.

The role of experts in news coverage has become increasingly prominent, but the evidence regarding the effectiveness of expert opinions in affecting public behavior remains mixed. This study seeks to examine the influence of expert opinions covered in the news on the public's response to public health crises. By adopting a macro-level framing perspective, we investigated how framing consistency, a macro-level concept indicating the agreement between expert opinions in news coverage and government policies or among peer experts, evolves over time and its temporal causal relationship with public behavior. Specifically, this study collected all press news coverage in Hong Kong over four months during the fifth outbreak, including 1,416 articles with 650 expert opinions, as well as the vaccination data that paralleled with this period. We constructed time series of expert opinions and vaccination behavior, and then conducted Vector Autoregressive (VAR) models with Granger causality analysis to examine how framing consistency of expert opinions in news coverage influenced vaccination. The results indicate that the consistent framing between expert opinions and government policies increased COVID-19 vaccination during the fifth outbreak in Hong Kong, while conflicting opinions responding to government policies had no significant effect on vaccination. Opinions among medical experts on COVID-19 issues also did not significantly impact vaccination. The implications for designing communication strategies and enhancing public behavioral support during public health crises are discussed.

Community engagement in vaccination promotion: A systematic review and meta-analysis.

BACKGROUND: Community engagement takes predominance in global immunization strategies with capacity to eliminate vaccination hesitancy and boost vaccination confidence. Despite strong support for community engagement in promoting health, evidence for community engagement in vaccination promotion emerges in fragments with uncertain qualities. OBJECTIVE: The current review aims to systematic examine the effectiveness of different contents and extents of community engagement for promoting vaccination rates. METHODS: This study was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A comprehensive and exhaustive literature search was performed in four English databases (PubMed, Embase, Web of Science, and Cochrane Library) and Two Chinese databases (CNKI and Wan Fang) to locate all possible articles. Original research article with an experimental study design examined the effectiveness of community engagement in vaccination promotion was eligible for inclusion. Two reviewers independently performed the literature search, study selection, quality assessment and data extraction. Discrepancies were resolved through discussion, with the arbitration of a third reviewer where necessary. RESULTS: A total of 20 articles out of 11,404 records over the period of 2006 to 2021 were retrieved as the sample set to quantified the effectiveness of community engagement in vaccination promotion. These included studies were performed in various designs, 12 used single group pre-post study designs, 5 used cluster RCTs and 3 used non-randomized controlled trials. These included studies targeted multiple vaccine, 8 studies focused on children immunization, 8 studies focused on HPV vaccine, 3 studies focused on HBV vaccine, and 1 study focused on COVID-19 vaccine. Meta-analysis reported significant increases in vaccination rates in both pre-post comparation ((rate difference) RD:0.34; 95% CI: 0.21-0.47) and between group comparation (RD: 0.18; 95% CI: 0.07-0.29). Meta-analysis about the contents of community engagement found that participant recruitment yielded the largest effect size (RD: 0.51; 95% CI: 0.37-0.67; I2=99.5%), followed by intervention development (RD: 0.36; 95% CI: 0.23-0.50; I2=99.7%), intervention implementation (RD: 0.35; 95% CI: 0.22-0.47; I2=99.8%), and data collection (RD: 0.34; 95% CI: 0.19-0.50; I2=99.8%). Meta-analysis about the extents of community engagement found that high community engagement extent yielded the largest effect size (RD: 0.49; 95% CI: 0.17-0.82; I2 = 99.5%), followed by moderate community engagement extent (RD: 0.45; 95% CI: 0.33-0.58; I2 = 99.4%), and low community engagement extent (RD: 0.15; 95% CI: 0.05-0.25; I2 = 98.6%). Meta-analysis about the types of intervention strategies found that "health service support" endorsed the largest effect sizes (RD: 0.45; 95% CI: 0.25-0.65; I2 = 99.9%), followed by "health education and discussion" (RD: 0.39; 95% CI: 0.20-0.58; I2 = 99.7%), "follow-up and reminder" (RD: 0.33; 95% CI: 0.23-0.42; I2 = 99.3%), and "social marketing campaign (SMC) and community mobilization (CM)" (RD: 0.24; 95% CI: 0.06-0.41; I2 = 99.9%). CONCLUSIONS: The results of this meta-analysis supported the effectiveness of community engagement in vaccination promotion with variations in terms of engagement contents and extents. Community engagement required a "fit for purpose" approach rather than "one size fits all" approach to maximize the effectiveness of vaccine promotion. CLINICALTRIAL: This review protocol was registered in the PROSPERO database (CRD42022339081).

Influences of mpox disease perceptions, sources and contents of information exposure on mpox vaccine uptake among gay, bisexual, and other men who have sex with men in Hong Kong, China.

BACKGROUND: Following the global mpox outbreak in 2022, multiple regions in Asia have been reporting ongoing mpox cases within high-risk groups, including gay, bisexual, and other men who have sex with men (GBMSM). An optimal level of vaccination rate is essential to prevent further mpox outbreaks. However, no existing studies have examined mpox vaccine uptake among GBMSM in East Asia. METHODS: A cross-sectional survey was conducted among a sample of 531 GBMSM in Hong Kong, China, between March and October 2023. The study used multivariable logistic regression models to investigate the associations between mpox-related disease perceptions, exposures to sources and contents of mpox-related information, and mpox vaccine uptake. RESULTS: The prevalence of mpox vaccine uptake among GBMSM in Hong Kong was 21.7%, with 7.7% completing one dose and 13.9% completing two doses. GBMSM who were younger or earning less monthly income were less likely to have been vaccinated. After adjusting for confounding variables, participants who perceived more negative impacts on their lives if they were to contract mpox, more severe symptoms, and a more coherent understanding of mpox were positively associated with mpox vaccine uptake. In addition, more frequent exposure to information through the following sources: TV, newspaper, radio and posters, government websites, news websites or apps, other people's social media, and communication over the phone or face-to-face was positively associated with mpox vaccine uptake. Finally, more frequent exposure to the following information contents: mpox statistics from other countries, the Hong Kong government's responses to mpox cases, negative information about patients with mpox, and information on prevention and treatment of mpox were positively associated with mpox vaccine uptake. CONCLUSIONS: This study provides timely and evidence-based implications to address health communication and messaging needs in promoting mpox vaccination among GBMSM in Hong Kong, relevant to regions with similar sociocultural contexts.

Deep reinforcement learning identifies personalized intermittent androgen deprivation therapy for prostate cancer.

The evolution of drug resistance leads to treatment failure and tumor progression. Intermittent androgen deprivation therapy (IADT) helps responsive cancer cells compete with resistant cancer cells in intratumoral competition. However, conventional IADT is population-based, ignoring the heterogeneity of patients and cancer. Additionally, existing IADT relies on pre-determined thresholds of prostate-specific antigen to pause and resume treatment, which is not optimized for individual patients. To address these challenges, we framed a data-driven method in two steps. First, we developed a time-varied, mixed-effect and generative Lotka-Volterra (tM-GLV) model to account for the heterogeneity of the evolution mechanism and the pharmacokinetics of two ADT drugs Cyproterone acetate and Leuprolide acetate for individual patients. Then, we proposed a reinforcement-learning-enabled individualized IADT framework, namely, I$^{2}$ADT, to learn the patient-specific tumor dynamics and derive the optimal drug administration policy. Experiments with clinical trial data demonstrated that the proposed I$^{2}$ADT can significantly prolong the time to progression of prostate cancer patients with reduced cumulative drug dosage. We further validated the efficacy of the proposed methods with a recent pilot clinical trial data. Moreover, the adaptability of I$^{2}$ADT makes it a promising tool for other cancers with the availability of clinical data, where treatment regimens might need to be individualized based on patient characteristics and disease dynamics. Our research elucidates the application of deep reinforcement learning to identify personalized adaptive cancer therapy.

New-onset syncope in diabetic patients treated with sodium-glucose cotransporter-2 inhibitors versus dipeptidyl peptidase-4 inhibitors: a Chinese population-based cohort study.

BACKGROUND AND AIMS: Syncope is a symptom that poses an important diagnostic and therapeutic challenge, and generates significant cost for the healthcare system. Sodium-glucose cotransporter-2 inhibitors (SGLT2i) have demonstrated beneficial cardiovascular effects, but their possible effects on incident syncope have not been fully investigated. This study compared the effects of SGLT2i and dipeptidyl peptidase-4 inhibitors (DPP4i) on new-onset syncope. METHODS AND RESULTS: This was a retrospective, territory-wide cohort study enrolling type 2 diabetes mellitus (T2DM) patients treated with SGLT2i or DPP4i between 1 January 2015 and 31 December 2020, in Hong Kong, China. The outcomes were hospitalization of new-onset syncope, cardiovascular mortality, and all-cause mortality. Multivariable Cox regression and different approaches using the propensity score were applied to evaluate the association between SGLT2i and DPP4i with incident syncope and mortality. After matching, a total of 37 502 patients with T2DM were included (18 751 SGLT2i users vs. 18 751 DPP4i users). During a median follow-up of 5.56 years, 907 patients were hospitalized for new-onset syncope (2.41%), and 2346 patients died from any cause (6.26%), among which 471 deaths (1.26%) were associated with cardiovascular causes. Compared with DPP4i users, SGLT2i therapy was associated with a 51% lower risk of new-onset syncope [HR 0.49; 95% confidence interval (CI) 0.41-0.57; P < 0.001], 65% lower risk of cardiovascular mortality (HR 0.35; 95% CI 0.26-0.46; P < 0.001), and a 70% lower risk of all-cause mortality (HR 0.30; 95% CI 0.26-0.34; P < 0.001) in the fully adjusted model. Similar associations with syncope were observed for dapagliflozin (HR 0.70; 95% CI 0.58-0.85; P < 0.001), canagliflozin (HR 0.48; 95% CI 0.36-0.63; P < 0.001), and ertugliflozin (HR 0.45; 95% CI 0.30-0.68; P < 0.001), but were attenuated for empagliflozin (HR 0.79; 95% CI 0.59-1.05; P = 0.100) after adjusting for potential confounders. The subgroup analyses suggested that, compared with DPP4i, SGLT2i was associated with a significantly decreased risk of incident syncope among T2DM patients, regardless of gender, age, glucose control status, Charlson comorbidity index, and the association remained constant amongst those with common cardiovascular drugs and most antidiabetic drugs at baseline. CONCLUSION: Compared with DPP4i, SGLT2i was associated with a significantly lower risk of new-onset syncope in patients with T2DM, regardless of gender, age, degree of glycaemic control, and comorbidity burden.

Ultrasmall PtMn nanoparticles as sensitive manganese release modulator for specificity cancer theranostics.

Manganese-based nanomaterials (Mn-nanomaterials) hold immense potential in cancer diagnosis and therapies. However, most Mn-nanomaterials are limited by the low sensitivity and low efficiency toward mild weak acidity (pH 6.4-6.8) of the tumor microenvironment, resulting in unsatisfactory therapeutic effect and poor magnetic resonance imaging (MRI) performance. This study introduces pH-ultrasensitive PtMn nanoparticles as a novel platform for enhanced ferroptosis-based cancer theranostics. The PtMn nanoparticles were synthesized with different diameters from 5.3 to 2.7 nm with size-dominant catalytic activity and magnetic relaxation, and modified with an acidity-responsive polymer to create pH-sensitive agents. Importantly, R-PtMn-1 (3 nm core) presents "turn-on" oxidase-like activity, affording a significant enhancement ratio (pH 6.0/pH 7.4) in catalytic activity (6.7 folds), compared with R-PtMn-2 (4.2 nm core, 3.7 folds) or R-PtMn-3 (5.3 nm core, 2.1 folds), respectively. Moreover, R-PtMn-1 exhibits dual-mode contrast in high-field MRI. R-PtMn-1 possesses a good enhancement ratio (pH 6.4/pH 7.4) that is 3 or 3.2 folds for T1- or T2-MRI, respectively, which is higher than that of R-PtMn-2 (1.4 or 1.5 folds) or R-PtMn-3 (1.1 or 1.2 folds). Moreover, their pH-ultrasensitivity enabled activation specifically within the tumor microenvironment, avoiding off-target toxicity in normal tissues during delivery. In vitro studies demonstrated elevated intracellular reactive oxygen species production, lipid peroxidation, mitochondrial membrane potential changes, malondialdehyde content, and glutathione depletion, leading to enhanced ferroptosis in cancer cells. Meanwhile, normal cells remained unaffected by the nanoparticles. Overall, the pH-ultrasensitive PtMn nanoparticles offer a promising strategy for accurate cancer diagnosis and ferroptosis-based therapy.

Identification of Key Influencers for Secondary Distribution of HIV Self-Testing Kits Among Chinese Men Who Have Sex With Men: Development of an Ensemble Machine Learning Approach.

BACKGROUND: HIV self-testing (HIVST) has been rapidly scaled up and additional strategies further expand testing uptake. Secondary distribution involves people (defined as "indexes") applying for multiple kits and subsequently sharing them with people (defined as "alters") in their social networks. However, identifying key influencers is difficult. OBJECTIVE: This study aimed to develop an innovative ensemble machine learning approach to identify key influencers among Chinese men who have sex with men (MSM) for secondary distribution of HIVST kits. METHODS: We defined three types of key influencers: (1) key distributors who can distribute more kits, (2) key promoters who can contribute to finding first-time testing alters, and (3) key detectors who can help to find positive alters. Four machine learning models (logistic regression, support vector machine, decision tree, and random forest) were trained to identify key influencers. An ensemble learning algorithm was adopted to combine these 4 models. For comparison with our machine learning models, self-evaluated leadership scales were used as the human identification approach. Four metrics for performance evaluation, including accuracy, precision, recall, and F1-score, were used to evaluate the machine learning models and the human identification approach. Simulation experiments were carried out to validate our approach. RESULTS: We included 309 indexes (our sample size) who were eligible and applied for multiple test kits; they distributed these kits to 269 alters. We compared the performance of the machine learning classification and ensemble learning models with that of the human identification approach based on leadership self-evaluated scales in terms of the 2 nearest cutoffs. Our approach outperformed human identification (based on the cutoff of the self-reported scales), exceeding by an average accuracy of 11.0%, could distribute 18.2% (95% CI 9.9%-26.5%) more kits, and find 13.6% (95% CI 1.9%-25.3%) more first-time testing alters and 12.0% (95% CI -14.7% to 38.7%) more positive-testing alters. Our approach could also increase the simulated intervention's efficiency by 17.7% (95% CI -3.5% to 38.8%) compared to that of human identification. CONCLUSIONS: We built machine learning models to identify key influencers among Chinese MSM who were more likely to engage in secondary distribution of HIVST kits. TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR) ChiCTR1900025433; https://www.chictr.org.cn/showproj.html?proj=42001.

PS-MPs or their co-exposure with cadmium impair male reproductive function through the miR-199a-5p/HIF-1α-mediated ferroptosis pathway.

Microplastics (MPs) and cadmium (Cd) exist extensively in ambient environments and probably influence negatively on human health. However, the potential reproductive toxicity of MPs or MPs + Cd remains unknown. This study was aimed to observe the reproductive changes of male mice treated orally for 35 days with PS-MPs (100 mg/kg), CdCl2 (5 mg/kg) and PS-MPs plus CdCl2 mixture. We found that subchronic exposure to PS-MPs damaged mouse testicular tissue structure, reduced sperm quality and testosterone levels. Moreover, the reproductive toxicity in 0.1 µm group was stronger than 1 µm group, and mixture group was more severe than single particle size ones. Meanwhile, co-exposure of PS-MPs and Cd exacerbated reproductive injury in male mice, with an ascending toxicity of Cd, 1 µm + Cd, 0.1 µm + Cd, and 0.1+1 µm + Cd. In addition, we discovered that the testicular damage induced by PS-MPs or PS-MPs + Cd was associated with interfering the miR-199a-5p/HIF-1α/ferroptosis pathway. Promisingly, these findings will shed new light on how PS-MPs and PS-MPs + Cd damage male reproductive function.

Chatbot-Delivered Online Intervention to Promote Seasonal Influenza Vaccination During the COVID-19 Pandemic: A Randomized Clinical Trial.

Importance: Receiving seasonal influenza vaccination (SIV) is important for adults during the COVID-19 pandemic. There are few robust evaluations of tailored interventions for improving SIV uptake among adults 65 years or older. Objective: To evaluate the relative efficacy of a stages of change (SOC)-tailored online intervention compared with a standard, non-SOC-tailored online intervention in increasing SIV uptake among Hong Kong residents 65 years or older. Design, Setting, and Participants: This nonblinded parallel-group randomized clinical trial was conducted between December 1, 2021, and July 31, 2022, in Hong Kong, China. Eligible participants were 65 years or older, had Cantonese- and/or Mandarin-speaking skills, were community-dwelling, had Hong Kong residency, were smartphone users, and had not received SIV for the 2021 to 2022 influenza season. Participants were recruited through random telephone calls, and those who completed the baseline telephone survey were randomized to either the intervention or control group. Both complete case and intention-to-treat (ITT) analyses were performed. Intervention: In the intervention group, a simplified rule-based chatbot first assessed participants' SOC related to SIV uptake and then automatically selected and sent participants SOC-tailored online health promotion messages (videos) through a messaging application (WhatsApp; Meta) once every 2 weeks for 4 sessions. In the control group, the chatbot sent a link to access through the messaging application a standard online health promotion message (video) covering general SIV information every 2 weeks for 4 sessions. Main Outcomes and Measures: The primary outcome was self-reported SIV uptake at month 6, which was validated by the research team. The secondary outcome was SOC measured at both baseline and month 6 by validated questions. Results: A total of 396 participants (mean [SD] age of 70.2 [4.3] years; 249 females [62.9%]) were randomized to the intervention (n = 198) or control (n = 198) group. The ITT analysis showed that the validated SIV uptake rate was higher in the intervention group than the control group at month 6 (50.5% vs 35.3%; P = .002). The mean (SD) SOC score was higher in the intervention group than the control group (2.8 [1.4] vs 2.4 [1.4]; P = .02). More participants in the intervention group completed at least 1 episode of intervention than in the control group (77.3% vs 62.6%; P < .001). Conclusions: Results of this trial indicate that the SOC-tailored online intervention was more effective than the non-SOC-tailored intervention and may be a sustainable new method in increasing SIV uptake among adults 65 years or older. Trial Registration: ClinicalTrials.gov Identifier: NCT05155241.

A network approach to understand co-occurrence and relative importance of different reasons for suicide: a territory-wide study using 2002-2019 Hong Kong Coroner's Court reports.

Background: Suicide is a complex and multifaceted issue, and suicidal behaviors are often driven by multiple, interacting factors. It has been challenging to identify reasons for suicide using existing scientific methodologies. This study aims to identify critical reasons for suicide and suicidal behaviors through the application of novel network science methods. Methods: Based on cases investigated by the Hong Kong Coroner's Court from 2002 to 2019, we modelled identified reasons for 13,001 suicide cases as a co-occurrence network, and calculated each reason's eigencentrality to determine their respective relative importance. We then analyzed the temporal and demographic changes in the structure and eigencentrality of the network. We further conducted simulation studies based on the United Nations population projection to assess potential burden of different reasons for suicide on the population in the coming years. Findings: School-related issues had the highest eigencentrality (eigencentrality = 0.49) for individuals younger than 20 years of age. Financial issues were crucial for adults aged 20-59 years, but their importance differed between males (eigencentrality = 0.51) and females (eigencentrality = 0.14). Physical illness (eigencentrality = 0.80) was the core concern for adults over 60 years. Across the Hong Kong population, the reasons for suicide appear to have shifted from financial issues in the early 2000s (eigencentrality = 0.46) to issues related to physical illnesses since 2011 (eigencentrality = 0.58). Simulation findings indicate that, by 2050, most suicides in Hong Kong will be due to physical illness-related issues (eigencentrality = 0.69) due to the rapidly aging population. Interpretation: There have been important sex and age differences over time, in reasons for suicide. Given the projected increasing age of the Hong Kong population over the next decades, older adults with physical illnesses appear to be the highest contributors to suicide cases in the overall population. This novel network analysis approach provides important data-driven information upon which to base effective proactive public health suicide prevention strategies and interventions. Funding: Hong Kong Jockey Club Charities Trust, Collaborative Research Fund (C7151-20G), and General Research Fund (17606521).

Associations between COVID-19 Vaccination and Behavioural Intention to Receive Seasonal Influenza Vaccination among Chinese Older Adults: A Population-Based Random Telephone Survey.

During the Coronavirus Disease 2019 (COVID-19) pandemic, seasonal influenza remained a significant health threat for older adults. Seasonal influenza vaccination (SIV) is highly effective and safe for older adults. This study investigated the associations of COVID-19 vaccination, perceptions related to COVID-19 and SIV, with the behavioural intention to receive SIV among older adults in Hong Kong, China. A random telephone survey was conducted among 440 community-dwelling Hong Kong residents aged 65 years or above, between November 2021 and January 2022. Among the participants, 55.7% intended to receive SIV in the next year. After adjustment for significant background characteristics, concern about whether SIV and COVID-19 vaccination would negatively affect each other was associated with a lower intention to receive SIV, while a perceived higher risk of co-infection with COVID-19 and seasonal influenza was positively associated with the dependent variable. In addition, the perceived severe consequences of seasonal influenza, perceived benefits of SIV, received cues to action from doctors and participants' family members or friends, and the perception that more older people would receive SIV was associated with a higher behavioural intention. Future programmes promoting SIV among older adults should modify perceptions related to COVID-19 vaccination and SIV at the same time.