Point-Based Prediction Model for Bladder Cancer Risk in Diabetes: A Random Survival Forest-Guided Approach.

Previous epidemiological studies have shown that diabetes is associated with an increased risk of several cancers, including bladder cancer. However, prediction models for bladder cancer among diabetes patients remain scarce. This study aims to develop a scoring system for bladder cancer risk prediction among diabetes patients who receive routine care in general outpatient clinics using a machine learning-guided approach.

The valuation of older adult homecare services under a joint medical-social budgetary perspective.

Background: Homecare, a cornerstone of public health, is essential for health systems to achieve the Sustainable Development Goal (SDG) of universal health coverage while maintaining its own sustainability. Notwithstanding homecare's system-level significance, there is a lack of economic evaluations of homecare services in terms of their system-wide cost-savings. Specifically, decisions informed by a joint medical-social budgetary perspective can maximize the allocative efficiency of assigning a diverse service mix to address the complex needs of the older adult population.

Screening for frequent hospitalization risk among community-dwelling older adult between 2016 and 2023: machine learning-driven item selection, scoring system development, and prospective validation.

Background: Screening for frequent hospitalizations in the community can help prevent super-utilizers from growing in the inpatient population. However, the determinants of frequent hospitalizations have not been systematically examined, their operational definitions have been inconsistent, and screening among community members lacks tools. Nor do we know if what determined frequent hospitalizations before COVID-19 continued to be the determinant of frequent hospitalizations at the height of the pandemic.

Predicting the risk of colorectal cancer among diabetes patients using a random survival forest-guided approach.

Background: Colorectal cancer (CRC) is the third most frequently diagnosed cancer worldwide. Diabetes and CRC share many overlapping lifestyle risk factors such as obesity, heavy alcohol use, and diet. This study aims to develop a risk scoring system for CRC prediction among diabetes patients using routine medical records.

Development of a risk scoring system for predicting advanced colorectal neoplasia within subcentimetric polyps: A population-based study.

Objective: To determine a risk scoring system for predicting advanced colorectal neoplasia (ACN) within subcentimetric polyps in a large Asian population.

Methods: A retrospective study was conducted in Hong Kong SAR, China involving participants who underwent colonoscopy between 2008 and 2015. A random sample of 20 072 subjects were included as the derivation cohort to assess ACN-associated independent factors using logistic regression modeling.

Scoring System for Predicting the Risk of Liver Cancer among Diabetes Patients: A Random Survival Forest-Guided Approach.

Background: Most liver cancer scoring systems focus on patients with preexisting liver diseases such as chronic viral hepatitis or liver cirrhosis. Patients with diabetes are at higher risk of developing liver cancer than the general population. However, liver cancer scoring systems for patients in the absence of liver diseases or those with diabetes remain rare.

Association between asthma and COVID-19 severity during Omicron epidemic: a retrospective cohort study using real-world data.

Background: The available evidence presented inconsistencies and inconclusive findings regarding the associations between co-existing asthma and mortality among COVID-19 patients. The objective of the current study is to investigate the relationship between asthma and severe outcomes after SARS-CoV-2 Omicron infection in an infection-naïve population.

Methods: A retrospective cohort study using propensity score matching was conducted.

Metabolic dysfunction-associated profiles and subsequent site-specific risk of obesity-related cancers among Chinese patients with diabetes: a retrospective cohort study.

Objectives: To compare metabolic dysfunction-associated profiles between patients with diabetes who developed different obesity-related site-specific cancers and those who remained free of cancer during follow-up.

Design: Retrospective cohort study.

Setting: Public general outpatient clinics in Hong Kong.

A Machine Learning-Based Risk Score for Prediction of Infective Endocarditis Among Patients With Staphylococcus aureus Bacteremia-The SABIER Score.

Background: Early risk assessment is needed to stratify Staphylococcus aureus infective endocarditis (SA-IE) risk among patients with S. aureus bacteremia (SAB) to guide clinical management. The objective of the current study was to develop a novel risk score that is independent of subjective clinical judgment and can be used early, at the time of blood culture positivity.

Impact of Environment on Pain among the Working Poor: Making Use of Random Forest-Based Stratification Tool to Study the Socioecology of Pain Interference.

Pain interferes with one's work and social life and, at a personal level, daily activities, mood, and sleep quality. However, little research has been conducted on pain interference and its socioecological determinants among the working poor. Noting the clinical/policy decision needs and the technical challenges of isolating the intricately interrelated socioecological factors' unique contributions to pain interference and quantifying the relative contributions of each factor in an interpretable manner to inform clinical and policy decision-making, we deployed a novel random forest algorithm to model and quantify the unique contribution of a diverse ensemble of environmental, sociodemographic, and clinical factors to pain interference.

Prediction algorithm for gastric cancer in a general population: A validation study.

Background: Worldwide, gastric cancer is a leading cause of cancer incidence and mortality. This study aims to devise and validate a scoring system based on readily available clinical data to predict the risk of gastric cancer in a large Chinese population.

Methods: We included a total of 6,209,697 subjects aged between 18 and 70 years who have received upper digestive endoscopy in Hong Kong from 1997 to 2018.

Risk of recurrent advanced colorectal neoplasia in individuals with baseline non-advanced neoplasia followed up at 5 vs 7-10 years.

Background And Aim: Colorectal cancer (CRC) is one of the commonest cancers, especially among the Asian populations. We compared the recurrence rate of advanced colorectal neoplasia (ACN) at 5 year vs 7-10 years among individuals with non-advanced adenoma (NAA) detected and polypectomized at baseline colonoscopy in a large Chinese population.

Methods: We extracted data of a large Chinese population with NAA polypectomized who received surveillance colonoscopy after 5 or 7-10 years from a large database (2008-2018).

Risk prediction of bladder cancer among person with diabetes: A derivation and validation study.

Aims: This study aimed to devise and validate a clinical scoring system for risk prediction of bladder cancer to guide urgent cystoscopy evaluation among people with diabetes.

Methods: People with diabetes who received cystoscopy from a large database in the Chinese population (2009-2018). We recruited a derivation cohort based on random sampling from 70% of all individuals.

Effectiveness of BNT162b2 and Sinovac vaccines against the transmission of SARS-CoV-2 during Omicron-predominance in Hong Kong: A retrospective cohort study of COVID-19 cases.

Background: In 2022, SARS-CoV-2 Omicron variants circulated globally, generating concerns about increased transmissibility and immune escape. Hong Kong, having an infection-naive population with a moderate 2-dose vaccine coverage (63% by the end of 2021), experienced a COVID-19 epidemic largely seeded by Omicron BA.2 variants that led to the greatest outbreak in the region to date.

Prediction of advanced colorectal neoplasia based on metabolic parameters among symptomatic patients.

Background And Aim: Worldwide, colorectal cancer (CRC) is the third most common cancer and ranks second among the leading causes of cancer death. This study aims to devise and validate a scoring system based on metabolic parameters to predict the risk of advanced colorectal neoplasia (ACN) in a large Chinese population.

Methods: This was a cohort study of 495 584 symptomatic subjects aged 40 years or older who have received colonoscopy in Hong Kong from 1997 to 2017.

Relationship between antidepressants and severity of SARS-CoV-2 Omicron infection: a retrospective cohort study using real-world data.

Background: Few studies have used real-world data to evaluate the impact of antidepressant use on the risk of developing severe outcomes after SARS-CoV-2 Omicron infection.

Methods: This is a retrospective cohort study using propensity-score matching to examine the relationship between antidepressant use and COVID-19 severity. Inpatient and medication records of all adult COVID-19 patients in Hong Kong during the Omicron-predominated period were obtained.

Recurrence rates of advanced colorectal neoplasia (ACN) in subjects with baseline ACN followed up at different surveillance intervals.

Background: Current clinical guidelines recommend that a baseline finding of advanced colorectal neoplasia (ACN) should be followed-up within 1-3 years.

Aim: We compared the recurrence rate of ACN at 1 year vs. 3 years among individuals with ACN detected and polypectomised at baseline colonoscopy.

Dining-Out Behavior as a Proxy for the Superspreading Potential of SARS-CoV-2 Infections: Modeling Analysis.

Background: While many studies evaluated the reliability of digital mobility metrics as a proxy of SARS-CoV-2 transmission potential, none examined the relationship between dining-out behavior and the superspreading potential of COVID-19.

Objective: We employed the mobility proxy of dining out in eateries to examine this association in Hong Kong with COVID-19 outbreaks highly characterized by superspreading events.

Methods: We retrieved the illness onset date and contact-tracing history of all laboratory-confirmed cases of COVID-19 from February 16, 2020, to April 30, 2021.

A hybrid machine learning framework to improve prediction of all-cause rehospitalization among elderly patients in Hong Kong.

Background: Accurately estimating elderly patients' rehospitalisation risk benefits clinical decisions and service planning. However, research in rehospitalisation and repeated hospitalisation yielded only models with modest performance, and the model performance deteriorates rapidly as the prediction timeframe expands beyond 28 days and for older participants.

Methods: A temporal zero-inflated Poisson (tZIP) regression model was developed and validated retrospectively and prospectively.

Robust meal delivery service for the elderly: a case study in Hong Kong.

Service providers in a community center in Hong Kong deliver meals to community-dwelling elderly, first from a central kitchen to intermediate depots by a van and then to the homes of the elderly via walking. We propose a modified two-echelon vehicle routing model with concerns of both delivery efficiency and workload fairness among workers, incorporating important practical aspects, such as continuity of care and unique features of buildings and served elderly. Notably, we employ robust optimization to address service time uncertainties that differentiate between frail and ordinary elderly.

Gestational age-specific neonatal mortality in Hong Kong: a population-based retrospective study.

Background: The neonatal period is the most vulnerable period during childhood, with the risk of death being the highest even in developed countries/regions. Hong Kong's neonatal mortality (1‰) is among the world's lowest and has remained similar for 15 years. This study aimed to explore neonatal deaths in Hong Kong in detail and determine whether neonatal mortality is reducible at such a low level.

Influencing factors of Barthel index scores among the community-dwelling elderly in Hong Kong: a random intercept model.

Background: Barthel Index (BI) is one of the most widely utilized tools for assessing functional independence in activities of daily living. Most existing BI studies used populations with specific diseases (e.g.

Health Informatics and Technology for Integrated Elderly Care in the Context of Hong Kong: A Case Study.

The aging population creates tremendous pressure to healthcare. To resolve, scholars recognized the solution to this challenge is integrated care. To facilitate integrated care, health information technologies (HIT) is a critical enabler.