The negative spillover impact of the COVID-19 pandemic on Hong Kong diabetes patients' mortality and cardiovascular disease.

BACKGROUND: The spillover impact from disrupted healthcare services for non-COVID-infected diabetes mellitus (DM) patients caused by the reshuffling of the manpower during the pandemic remains understudied, especially in Hong Kong where healthcare resources were already strained before the pandemic. AIM: To evaluate the spill-over effect of the Pandemic on Hong Kong diabetes patients, we examined the change in all-cause mortality and the incidence of cardiovascular disease (CVD) from 2012 to 2021. METHOD: This retrospective cohort study analyzed data from Hong Kong Hospital Authority healthcare records covering all publicly provided care. Adults diagnosed with DM on/before December 31, 2010, without CVD before January 2012 were included. The 2016-2019 average all-cause mortality and the incidence of CVD after age-standardization represented the pre-pandemic levels. Subjects would leave the cohort after being infected with COVID-19. RESULTS: A cohort of 159,693 patients with diabetes was identified and followed up for 10 years from January 2012 to December 2021. Compared to the pre-pandemic levels, 2020 saw a 12% increase in age-standardized mortality per 10,000 diabetic patients (incidence rate ratio [95% CI]: 1.12 [1.10 - 1.14]), but no significant change in age-standardized CVD incidence. However, in 2021, there were 11% (1.11[1.10 - 1.13]) and 13% (1.13 [1.11 - 1.15]) more new CVD cases and deaths, respectively, versus the pre-pandemic period. CONCLUSION: The COVID-19 outbreak in 2020 had negative spillover impacts on DM patients without COVID-19 in Hong Kong, with a higher mortality in 2020 and 2021 compared with the pre-pandemic level.

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.