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. Another 8603 subjects formed a validation cohort. A risk scoring system was developed and its performance was assessed using the area under the receiver operating characteristic curve (AUROC). RESULTS: The risk scores were assigned based on the following criteria: (a) patients who were admitted from inpatient colonoscopy (2.2) or not (1); (b) with three or more chronic diseases (hypertension, diabetes mellitus, hyperlipidemia, heart disease, or cancer) (1.7) or not (1); (c) anemia (1.3) or without anemia (1); (d) receiving aspirin (0.5) or not (1); (e) receiving other nonsteroidal anti-inflammatory drugs (0.3) or not (1); (f) male (1.2) or female gender (1); (g) age <55 (1), 55-64 (1.4), 65-69 (2), 70 years or above (2.2). ACN was more common in those with scores of 2.192 or higher, and they were classified as high risk (HR). The prevalence of ACN in the validation cohort was 13.28% and 3.56% in the HR and low-risk groups, respectively. In both the derivation and validation cohorts, AUROC of the risk-scoring model was 0.7138. CONCLUSION: Physicians are recommended to utilize this validated score for risk-stratification of patients having subcentimetric polyps.

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. The COVID-19 patients requiring hospitalisation in Hong Kong from January 1, 2022, to November 13, 2022, an Omicron-predominated period, were identified. Severe clinical outcomes were defined as ICU admission and inpatient death after the first positive PCR results as well as a composite outcome of both. RESULTS: Of the 74,396 hospitalised COVID-19 patients admitted, 1,290 asthma patients and 18,641 non-asthma patients were included in the matched cohort. The rates of death and the composite outcome were 15·3% and 17·2%, respectively, among the non-asthma patients,12·2% and 13·6%, respectively, among the asthma patients, with adjusted hazard ratios equal to 0·775 (95% CI: 0·660-0·909) and 0·770 (95% CI: 0·662-0·895), respectively. The negative association was more apparent in the elderly and female groups. Asthma remained a factor that lowered the risk of disease severity even though the patients were not fully vaccinated with at least two doses. CONCLUSIONS: We used real-world data to demonstrate that asthma was not a risk factor for COVID-19 severity of the infections of Omicron variant, even though the patients were not fully vaccinated.

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. This study aims to develop a risk scoring system for liver cancer prediction among diabetes patients and a sub-model among diabetes patients without cirrhosis/chronic viral hepatitis. METHODS: A retrospective cohort study was performed using electronic health records of Hong Kong. Patients who received diabetes care in general outpatient clinics between 2010 and 2019 without cancer history were included and followed up until December 2019. The outcome was diagnosis of liver cancer during follow-up. A risk scoring system was developed by applying random survival forest in variable selection, and Cox regression in weight assignment. RESULTS: The liver cancer incidence was 0.92 per 1000 person-years. Patients who developed liver cancer (n = 1995) and those who remained free of cancer (n = 1969) during follow-up (median: 6.2 years) were selected for model building. In the final time-to-event scoring system, presence of chronic hepatitis B/C, alanine aminotransferase, age, presence of cirrhosis, and sex were included as predictors. The concordance index was 0.706 (95%CI: 0.676-0.741). In the sub-model for patients without cirrhosis/chronic viral hepatitis, alanine aminotransferase, age, triglycerides, and sex were selected as predictors. CONCLUSIONS: The proposed scoring system may provide a parsimonious score for liver cancer risk prediction among diabetes patients.

Risk prediction of advanced colorectal neoplasia among diabetic patients: A derivation and validation study.

Background and Aim: Colorectal cancer (CRC) is the third most common cancer in the world. This study devises and validates a clinical scoring system for risk prediction of advanced colorectal neoplasia (ACN) to guide colonoscopy evaluation among diabetic patients. Methods: We identified 55 964 diabetic patients who received colonoscopies from a large database in a Chinese population (2008-2018). We recruited a derivation cohort based on random sampling. The risk factors of CRC evaluated by univariate analysis were examined for ACN, defined as advanced adenoma, CRC, or any combination thereof using binary logistic regression analysis. We used the adjusted odds ratios (aORs) for independent risk factors to devise a risk score, ranging from 0 to 6: 0-4 "average risk" (AR) and 5-6 "high risk" (HR). The other subjects acted as an independent validation cohort. Results: The prevalence of ACN in both the derivation and validation cohorts was 2.0%. Using the scoring system constructed, 78.5% and 21.5% of patients in the validation cohort were classified as AR and HR, respectively. The prevalence of ACN in the AR and HR groups was 1.5% and 4.1%, respectively. Individuals in the HR group had a 2.78-fold increased prevalence of ACN than the AR group. The concordance (c-) statistics was 0.70, implying a good discriminatory capability of the risk score to stratify high-risk individuals who should consider colonoscopy. Conclusion: The clinical risk scoring system based on age, gender, smoking, presence of hypertension, and use of aspirin is useful for ACN risk prediction among diabetic patients.

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. PARTICIPANTS: Patients with diabetes without a history of malignancy (n=391 921). PRIMARY OUTCOME MEASURES: The outcomes of interest were diagnosis of site-specific cancers (colon and rectum, liver, pancreas, bladder, kidney and stomach) during follow-up. Cox proportional hazards regression was applied to assess the associations between metabolic dysfunction and other clinical factors with each site-specific cancer. RESULTS: Each 0.1 increase in waist-to-hip ratio was associated with an 11%-35% elevated risk of colorectal, bladder and liver cancers. Each 1% increase in glycated haemoglobin was linked to a 4%-9% higher risk of liver and pancreatic cancers. While low-density lipoprotein cholesterol and triglycerides were inversely associated with the risk of liver and pancreatic cancers, high-density lipoprotein cholesterol was negatively associated with pancreatic, gastric and kidney cancers, but positively associated with liver cancer. Furthermore, liver cirrhosis was linked to a 56% increased risk of pancreatic cancer. No significant association between hypertension and cancer risk was found. CONCLUSIONS: Metabolic dysfunction-associated profiles contribute to different obesity-related cancer outcomes differentially among patients with diabetes. This study may provide evidence to help identify cancer prevention targets during routine diabetes care.

A machine learning-based risk score for prediction of infective endocarditis among patients with Staphylococcus aureus bacteraemia - The SABIER score.

BACKGROUND: Early risk assessment is needed to stratify Staphylococcus aureus infective endocarditis (SA-IE) risk among Staphylococcus aureus bacteraemia (SAB) patients to guide clinical management. The objective of this study is to develop a novel risk score independent of subjective clinical judgment and can be used early at the time of blood culture positivity. METHODS: We conducted a retrospective big data analysis from territory-wide electronic data and included hospitalized patients with SAB between 2009 and 2019. We applied a random forest risk scoring model to select variables from an array of parameters, according to the statistical importance of each feature in predicting SA-IE outcome. The data was divided into derivation and validation cohorts. The areas under the curve of the receiver operating characteristic (AUCROC) were determined. RESULTS: We identified 15,741 SAB patients, among them 4.18% had SA-IE. The AUCROC was 0.74 (95%CI 0.70-0.76), with a negative predictive value of 0.980 (95%CI 0.977-0.983). The four most discriminatory features were age, history of infective endocarditis, valvular heart disease, and being community-onset. CONCLUSION: We developed a novel risk score with good performance as compared to existing scores and can be used at the time of SAB and prior to subjective clinical judgment.

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. Our analyses revealed that features representing the internal built environment of the working poor, such as the size of the living space, air quality, access to light, architectural design conducive to social connection, and age of the building, were assigned greater statistical importance than other more commonly examined predisposing factors for pain interference, such as age, occupation, the severity and locations of pain, BMI, serum blood sugar, and blood pressure. The findings were discussed in the context of their benefit in informing community pain screening to target residential areas whose built environment contributed most to pain interference and informing the design of intervention programs to minimize pain interference among those who suffered from chronic pain and showed specific characteristics. The findings support the call for good architecture to provide the spirit and value of buildings in city development.

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. We used the adjusted odds ratios (aORs) for independent risk factors to devise a risk score, ranging from 0 to 5: 0-2 'average risk' (AR) and 3-5 'high risk' (HR). RESULTS: A total of 5905 people with diabetes, among whom 123 people with BCa were included. The prevalence rate in the derivation (n = 4174) and validation cohorts (n = 1731) was 2.2% and 1.8% respectively. Using the scoring system constructed, 79.6% and 20.4% in the derivation cohort were classified as AR and HR respectively. The prevalence rate in the AR and HR groups was 1.57% and 4.58% respectively. The risk score consisted of age (18-70: 0; >70: 2), male sex (1), ever/ex-smoker (1) and duration of diabetes (≥10 years: 1). Individuals in the HR group had 3.26-fold (95% CI = 1.65-6.44, p = 0.025) increased prevalence of bladder than the AR group. The concordance (c-) statistics was 0.72, implying a good discriminatory capability of the risk score to stratify high-risk individuals who should consider earlier cystoscopy. CONCLUSIONS: The risk prediction algorithm may inform urgency of cystoscopy appointments, thus allowing a more efficient use of resources and contributing to early detection of BCa among people planned to be referred.

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. A binary logistic regression model was constructed to examine the predictors of gastric cancer in a derivation cohort (n = 4,347,224), followed by model evaluation in a validation cohort (n = 1,862,473). The algorithm's discriminatory ability was evaluated as the area under the curve (AUC) of the mathematically constructed receiver operating characteristic (ROC) curve. RESULTS: Age, male gender, history of Helicobacter pylori infection, use of proton pump inhibitors, non-use of aspirin, non-steroidal anti-inflammatory drugs (NSAIDs), and statins were significantly associated with gastric cancer. A scoring of ≤8 was designated as "average risk (AR)". Scores at 9 or above were assigned as "high risk (HR)". The prevalence of gastric cancer was 1.81% and 0.096%, respectively, for the HR and LR groups. The AUC for the risk score in the validation cohort was 0.834, implying an excellent fit of the model. CONCLUSIONS: This study has validated a simple, accurate, and easy-to-use scoring algorithm which has a high discriminatory capability to predict gastric cancer. The score could be adopted to risk stratify subjects suspected as having gastric cancer, thus allowing prioritized upper digestive tract investigation.

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). The outcome variable included recurrence of ACN at surveillance colonoscopy. We examined the association between length of surveillance and the outcome variable, whilst controlling for risk factors of colorectal cancer. RESULTS: We include 109 768 subjects who have received a baseline colonoscopy from our dataset. They were aged 67.35 (SD 9.84) years, and 60.9% of them were male subjects. The crude 5-year and 10-year recurrence rate of ACN was 1.50% and 2.42%, respectively (crude odds ratio = 1.629, 95% CI 1.362 to 1.949, P < 0.001). From the binary logistic regression model, individuals with surveillance colonoscopy performed at 10 years had a statistically higher recurrence rate of ACN than those followed-up at 5 year (adjusted odds ratio [aOR] = 1.544, 95% CI 1.266 to 1.877, P < 0.001), but the effect size of aOR is small. CONCLUSIONS: There is a small difference in recurrence of ACN between individuals who received colonoscopy workup at 5 years vs 7-10 years. These findings support a 7-10 years surveillance period after baseline NAA was polypectomized.

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. Little remains known about the protection of commonly-administered vaccines against transmission of Omicron BA.2 variants. METHODS: In this retrospective cohort study, we identified 17 535 laboratory-confirmed COVID-19 cases using contact tracing information during the Omicron-predominant period between January and June 2022 in Hong Kong. Demographic characteristics, time from positive test result to case reporting, isolation, or hospital admission, as well as contact tracing history and contact setting were extracted. Transmission pairs were reconstructed through suspected epidemiological links according to contact tracing history, and the number of secondary cases was determined for each index case as a measurement for risk of transmission. The effectiveness of mRNA vaccine (BNT162b2) and inactivated vaccine (Sinovac) against transmission of BA.2 variants was estimated using zero-inflated negative binomial regression models. RESULTS: Vaccine effectiveness against transmission for patients who received the 2-dose BNT162b2 vaccine was estimated at 56.2% (95% CI: 14.5, 77.6), 30.6% (95% CI: 13.0, 44.6), and 21.3% (95% CI: 2.9, 36.2) on 15 - 90, 91 - 180, and 181 - 270 days after vaccination, respectively, showing a significant decrease over time. For 3-dose vaccines, vaccine effectiveness estimates were 41.0% (95% CI: 11.3, 60.7) and 41.9% (95% CI: 6.1, 64.0) on 15 - 180 days after booster doses of Sinovac and BNT162b2, respectively. Although significant vaccine effectiveness was detected in household settings, no evidence of such protective association was detected in non-household settings for either Sinovac or BNT162b2. CONCLUSION: Moderate and significant protection against Omicron BA.2 variants' transmission was found for 2 and 3 doses of Sinovac or BNT162b2 vaccines. Although protection by 2-dose BNT162b2 may evidently wane with time, protection could be restored by the booster dose. Here, we highlight the importance of continuously evaluating vaccine effectiveness against transmission for emerging SARS-CoV-2 variants.

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. The algorithm's discriminatory ability was evaluated as the area under the curve (AUC) of the mathematically constructed receiver operating characteristic curve. RESULTS: Age, male gender, inpatient setting, abnormal aspartate transaminase/alanine transaminase, white blood cell, plasma gamma-glutamyl transferase, high-density lipoprotein cholesterol, triglycerides, and hemoglobin A1c were significantly associated with ACN. A scoring of < 2.65 was designated as "low risk (LR)." Scores at 2.65 or above had prevalence higher than the overall prevalence and hence were assigned as "high risk (HR)." The prevalence of ACN was 32% and 11%, respectively, for HR and LR groups. The AUC for the risk score in the derivation and validation cohort was 70.12%. CONCLUSIONS: This study has validated a simple, accurate, and easy-to-use scoring algorithm, which has a high discriminatory capability to predict ACN in symptomatic patients. Future studies should examine its predictive performance in other population groups.

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. METHODS: We extracted data from eligible patients in a Chinese population database from 2008 to 2018. The outcome variables included recurrence of advanced adenoma and advanced neoplasia, respectively, at follow-up colonoscopy. Binary logistic regression modeling was constructed to examine the association between length of surveillance and the outcome variables, controlling for risk factors of colorectal cancer, including age, gender, smoking, alcohol drinking, body mass index and chronic diseases. RESULTS: We included 147,270 subjects who have received a baseline colonoscopy from our dataset. They were aged 69.3 years and 59.7% of them were male subjects. The crude 1-year and 3-year recurrence rate of ACN was 7.57% and 7.74%. From a binary logistic regression model, individuals with surveillance colonoscopy performed at 3 years did not have significantly higher recurrence rate of ACN than those followed-up at 1 year. CONCLUSIONS: No statistically significantly difference in recurrence of ACN between individuals who received workup at 1vs. 3 years. These findings support a 3-year surveillance period after baseline ACN was polypectomised.

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. Severe clinical outcomes including intensive care unit admission and inpatient death after the first positive results of reverse transcription polymerase chain reaction as well as a composite outcome of both were studied. Cox proportional hazard models were applied to estimate the crude and adjusted hazard ratios (HR). Findings: Of 60,903 hospitalised COVID-19 patients admitted, 40,459 were included for matching, among which 3821 (9.4%) were prescribed antidepressants. The rates of intensive care unit admission, inpatient death, and the composite event were 3.9%, 25.5%, and 28.3% respectively in the unexposed group, 1.3%, 20.0%, and 21.1% respectively in the exposed group, with adjusted HR equal to 0.332 (95% CI, 0.245-0.449), 0.868 (95% CI, 0.800-0.942), and 0.786 (95% CI, 0.727-0.850) respectively. The result was generally consistent when stratified by selective serotonin reuptake inhibitors (SSRIs) and non-SSRIs. Antidepressants with functional inhibition of acid sphingomyelinase activity, specifically fluoxetine, were also negatively associated with the outcomes. The effect of antidepressants was more apparent in female and fully vaccinated COVID-19 patients. Interpretation: Antidepressant use was associated with a lower risk of severe COVID-19. The findings support the continuation of antidepressants in patients with COVID-19, and provide evidence for the treatment potential of antidepressants for severe COVID-19. Funding: This research was supported by Health and Medical Research Fund [grant numbers COVID190105, COVID19F03, INF-CUHK-1], Collaborative Research Fund of University Grants Committee [grant numbers C4139-20G], National Natural Science Foundation of China (NSFC) [71974165], and Group Research Scheme from The Chinese University of Hong Kong.

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. We estimated the time-varying reproduction number (Rt) and dispersion parameter (k), a measure of superspreading potential, and related them to the mobility proxy of dining out in eateries. We compared the relative contribution to the superspreading potential with other common proxies derived by Google LLC and Apple Inc. RESULTS: A total of 6391 clusters involving 8375 cases were used in the estimation. A high correlation between dining-out mobility and superspreading potential was observed. Compared to other mobility proxies derived by Google and Apple, the mobility of dining-out behavior explained the highest variability of k (ΔR-sq=9.7%, 95% credible interval: 5.7% to 13.2%) and Rt (ΔR-sq=15.7%, 95% credible interval: 13.6% to 17.7%). CONCLUSIONS: We demonstrated that there was a strong link between dining-out behaviors and the superspreading potential of COVID-19. The methodological innovation suggests a further development using digital mobility proxies of dining-out patterns to generate early warnings of superspreading events.

Estimation of Vaccine Effectiveness of CoronaVac and BNT162b2 Against Severe Outcomes Over Time Among Patients With SARS-CoV-2 Omicron.

Importance: Few studies have evaluated the waning of vaccine effectiveness against severe outcomes caused by SARS-CoV-2 Omicron infection. Hong Kong is providing inactivated and mRNA vaccines, but the population had limited protection from natural infections before the Omicron variant emerged. Objective: To examine the change in vaccine effectiveness against hospitalization and mortality due to the Omicron variant over time. Design, Setting, and Participants: This case-control study included adults with SARS-CoV-2 Omicron variant infection who died or were hospitalized in Hong Kong from January 1 to June 5, 2022 (ie, case participants), and adults with SARS-CoV-2 Omicron, sampled from the public health registry during the study period (ie, control participants), who were matched to case participants by propensity score. Exposures: Vaccination status of the individuals. Main Outcomes and Measures: Estimated vaccine effectiveness against death, death or hospitalization, and death among hospitalized patients. Vaccine effectiveness was calculated as 1 - adjusted odds ratio obtained by conditional logistic regression adjusted with covariates for each period following vaccination. Results: There were 32 823 case participants (25 546 [77.8%] ≥65 years; 16 930 [47.4%] female) and 131 328 control participants (100 041 [76.2%] ≥65 years; 66 625 [46.6%] female) in the sample analyzed for the death or hospitalization outcome. Vaccine effectiveness against death or hospitalization was maintained for at least 6 months after the second dose of both CoronaVac (74.0%; 95% CI, 71.8%-75.8%) and BNT162b2 (77.4%; 95% CI, 75.5%-79.0%) vaccines. Vaccine effectiveness against death in those aged 18 to 49 years was 86.4% (95% CI, 85.8%-87.0%) and 92.9% (95% CI, 92.6%-93.2%) for those receiving 2 doses of CoronaVac and BNT162b2, respectively, while for patients aged 80 years or older, it dropped to 61.4% (95% CI, 59.8%-63.2%) and 52.7% (95% CI, 50.2%-55.6%) for CoronaVac and BNT162b2, respectively. Nevertheless, overall vaccine effectiveness against death at 4 to 6 months after the third dose was greater than 90% for CoronaVac, BNT162b2, and the mixed vaccine schedule (eg, mixed vaccines: vaccine effectiveness, 92.2%; 95% CI, 89.2%-95.1%). Conclusions and Relevance: While vaccines were generally estimated to be effective against severe outcomes caused by SARS-CoV-2 Omicron infection, this analysis found that protection in older patients was more likely to wane 6 months after the second dose. Hence, a booster dose is recommended for older patients to restore immunity. This is especially critical in a setting like Hong Kong, where third-dose coverage is still insufficient among older residents.

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. The data of the electronic health records (EHRs) contain cohorts (aged 60+) in a major public hospital in Hong Kong. Two temporal offset functions accounted for the associations between exposure time and parameters corresponding to the zero-inflated logistic component and the Poisson distribution's expected count. tZIP was externally validated with a retrospective cohort's rehospitalisation events up to 12 months after the discharge date. Subsequently, tZIP was validated prospectively after piloting its implementation at the study hospital. Patients discharged within the pilot period were tagged, and the proposed model's prediction of their rehospitalisation was verified monthly. Using a hybrid machine learning (ML) approach, the tZIP-based risk estimator's marginal effect on 28-day rehospitalisation was further validated, competing with other factors representing different post-acute and clinical statuses. RESULTS: The tZIP prediction of rehospitalisation from 28 days to 365 days was achieved at above 80% discrimination accuracy retrospectively and prospectively in two out-of-sample cohorts. With a large margin, it outperformed the Cox proportional and linear models built with the same predictors. The hybrid ML revealed that the risk estimator's contribution to 28-day rehospitalisation outweighed other features relevant to service utilisation and clinical status. CONCLUSIONS: A novel rehospitalisation risk model was introduced, and its risk estimators, whose importance outweighed all other factors of diverse post-acute care and clinical conditions, were derived. The proposed approach relies on four easily accessible variables easily extracted from EHR. Thus, clinicians could visualise patients' rehospitalisation risk from 28 days to 365 days after discharge and screen high-risk older patients for follow-up care at the proper time.

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. METHODS: Live births in public hospitals in Hong Kong during 01 Jan 2006-31 Dec 2017 were included. Relevant data were extracted from the electronic medical records. Gestational age-specific mortality was calculated, and the trends were analyzed using the Cochran-Armitage trend test. Causes of death were summarized, and risk factors were identified in multivariate logistic regression analysis. RESULTS: In 490,034 live births, 755 cases (1.54‰) died during the neonatal period, and 293 (0.6‰) died during the post-neonatal period. The neonatal mortality remained similar overall (P = 0.17) and among infants born at 24-29 weeks' gestation (P = 0.4), while it decreased in those born at 23 (P = 0.04), 30-36 (P < 0.001) and ≥ 37 (P < 0.001) weeks' gestation. Neonates born at < 27 weeks' gestation accounted for a significantly increased proportion among cases who died (27.6% to 51.9%), with hemorrhagic conditions (24%) being the leading cause of death. Congenital anomalies were the leading cause of death in neonates born ≥ 27 weeks' gestation (52%), but its cause-specific mortality decreased (P = 0.002, 0.6‰ to 0.41‰), with most of the decrease attributed to trisomy 13/18 and multiple anomalies. CONCLUSION: Reduction of neonatal mortality in developed regions may heavily rely on improved quality of perinatal and neonatal care among extremely preterm infants.

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. The robust model can be transformed into a mixed integer program, for which we provide two decomposition-based approaches to accelerate computation. Through a real-data case study, we verify the effectiveness of the proposed models. We show that robust solutions can protect against service time variations and achieve better performance while incurring a small additional cost over deterministic ones. We provide insights into choosing the level of conservatism and human resource planning for practitioners.