Recalibration of a Non-Laboratory-Based Risk Model to Estimate Pre-Diabetes/Diabetes Mellitus Risk in Primary Care in Hong Kong.

INTRODUCTION/OBJECTIVES: A non-laboratory-based pre-diabetes/diabetes mellitus (pre-DM/DM) risk prediction model developed from the Hong Kong Chinese population showed good external discrimination in a primary care (PC) population, but the estimated risk level was significantly lower than the observed incidence, indicating poor calibration. This study explored whether recalibrating/updating methods could improve the model's accuracy in estimating individuals' risks in PC. METHODS: We performed a secondary analysis on the model's predictors and blood test results of 919 Chinese adults with no prior DM diagnosis recruited from PC clinics from April 2021 to January 2022 in HK. The dataset was randomly split in half into a training set and a test set. The model was recalibrated/updated based on a seven-step methodology, including model recalibrating, revising and extending methods. The primary outcome was the calibration of the recalibrated/updated models, indicated by calibration plots. The models' discrimination, indicated by the area under the receiver operating characteristic curves (AUC-ROC), was also evaluated. RESULTS: Recalibrating the model's regression constant, with no change to the predictors' coefficients, improved the model's accuracy (calibration plot intercept: -0.01, slope: 0.69). More extensive methods could not improve any further. All recalibrated/updated models had similar AUC-ROCs to the original model. CONCLUSION: The simple recalibration method can adapt the HK Chinese pre-DM/DM model to PC populations with different pre-test probabilities. The recalibrated model can be used as a first-step screening tool and as a measure to monitor changes in pre-DM/DM risks over time or after interventions.

Factors associated with health-seeking for urinary incontinence in Hong Kong Chinese women: a cross-sectional study.

INTRODUCTION AND HYPOTHESIS: To explore levels of urinary incontinence (UI) knowledge among Hong Kong Chinese women and the factors affecting patients' help-seeking behavior. METHODS: Chinese women with age ≥ 40 years who attended General Out-patient Clinics between May 1 and June 30, 2022, were invited to complete the questionnaire. The questionnaire consisted of four sections: (1) demographic data, (2) knowledge of UI (UI quiz), (3) severity of UI (UDI-6) and impairment of quality of life (QOL) (IIQ-7), and (4) barriers to seeking medical help. Severity of UI, impairment of QOL, knowledge of UI, and sociodemographic characteristics were analyzed regarding their relationship with help-seeking behavior. RESULTS: Of 351 women, 53.2% had symptoms of UI but only 25.0% symptomatic patients had consulted a doctor. The UI quiz score was 6.82 ± 2.76 (total 14) and the highest incorrect item was "Urinary incontinence is the normal aging" (incorrect rate 86.3%). The most frequent barrier for help-seeking was "The symptoms are not severe and do not affect me." Logistic regression analysis showed that UDI-6 score (adjusted OR = 1.06, P < 0.0001) and UI quiz score (adjusted OR = 1.30, P = 0.001) were the independent impact factors of patient's help-seeking behavior. CONCLUSIONS: Hong Kong Chinese women's knowledge toward UI is not sufficient, and the consultation rate is low. Besides symptom severity, the knowledge level of UI is the other independent factor affecting patients' help-seeking behavior. Community education on UI may encourage women to seek early help.

Non-laboratory-based risk assessment model for case detection of diabetes mellitus and pre-diabetes in primary care.

INTRODUCTION: More than half of diabetes mellitus (DM) and pre-diabetes (pre-DM) cases remain undiagnosed, while existing risk assessment models are limited by focusing on diabetes mellitus only (omitting pre-DM) and often lack lifestyle factors such as sleep. This study aimed to develop a non-laboratory risk assessment model to detect undiagnosed diabetes mellitus and pre-diabetes mellitus in Chinese adults. METHODS: Based on a population-representative dataset, 1,857 participants aged 18-84 years without self-reported diabetes mellitus, pre-diabetes mellitus, and other major chronic diseases were included. The outcome was defined as a newly detected diabetes mellitus or pre-diabetes by a blood test. The risk models were developed using logistic regression (LR) and interpretable machine learning (ML) methods. Models were validated using area under the receiver-operating characteristic curve (AUC-ROC), precision-recall curve (AUC-PR), and calibration plots. Two existing diabetes mellitus risk models were included for comparison. RESULTS: The prevalence of newly diagnosed diabetes mellitus and pre-diabetes mellitus was 15.08%. In addition to known risk factors (age, BMI, WHR, SBP, waist circumference, and smoking status), we found that sleep duration, and vigorous recreational activity time were also significant risk factors of diabetes mellitus and pre-diabetes mellitus. Both LR (AUC-ROC = 0.812, AUC-PR = 0.448) and ML models (AUC-ROC = 0.822, AUC-PR = 0.496) performed well in the validation sample with the ML model showing better discrimination and calibration. The performance of the models was better than the two existing models. CONCLUSIONS: Sleep duration and vigorous recreational activity time are modifiable risk factors of diabetes mellitus and pre-diabetes in Chinese adults. Non-laboratory-based risk assessment models that incorporate these lifestyle factors can enhance case detection of diabetes mellitus and pre-diabetes.

Clinical Audit on Chronic Obstructive Pulmonary Disease (COPD) Management in Primary Care: A Quality Improvement Project from Hong Kong.

Objective: To enhance the quality of COPD management in primary care via a two-phase clinical audit in Hong Kong. Methods: COPD patients aged 40 or above and had attended any of the 73 public primary care clinics under the Hospital Authority of Hong Kong (HAHK) for follow up (FU) during the audit period were included. Performance of six evidence-based audit criteria on COPD care was reviewed in phase 1 from 1st April 2017 to 31st March 2018. Service gaps were identified and a series of quality improvement strategies were executed in the one-year implementation phase. The outcome of the service enhancement was assessed in phase 2 from 1st April 2019 to 31st March 2020. Student's t-test and the chi-square test were used to examine the statistically significant differences between the two phases. Results: Totally 10,385 COPD cases were identified in phase 1, the majority were male (87.7%) and the mean age was 75.3±9.9 years. Among the 3102 active smokers, 1788 (57.6%) were referred to receive the smoking cessation counselling and 1578 (50.9%) actually attended it. A total of 4866 cases (46.9%) received seasonal influenza vaccine (SIV) and 4227 cases (40.7%) received pneumococcal vaccine (PCV). A total of 1983 patients (19.1%) had spirometry test done before and 1327 patients (12.8%) had history of hospital admission due to acute exacerbation of COPD (AECOPD). After the proactive implementation phase, performance on all criteria was significantly improved in phase 2, with a marked increase in the SIV and PCV uptake rate and spirometry performance rate. Most importantly, a significant reduction in AECOPD rate leading to hospital admission had been achieved (9.6%, P<0.00001). Conclusion: COPD care at all public primary care clinics of HAHK had been significantly improved for all audit criteria via the systematic team approach, which, in turn, reduced the hospital admission rate and helped relieve the burden of the health care system.