Dataset Information

Smoking behaviours of Hong Kong Chinese hospitalised patients and predictors of smoking abstinence after discharge: a cross-sectional study.

ABSTRACT: OBJECTIVES:Patients admitted to hospitals represent an excellent teachable moment for smoking cessation, as they are required to abstain from tobacco use during hospitalisation. Nevertheless, smoking behaviours of hospitalised patients, and factors that lead to smoking abstinence thereafter, remain relatively underexplored, particularly in a Hong Kong Chinese context. This study aimed to examine the smoking behaviours of hospitalised patients and explore factors leading to their abstaining from cigarette use after being hospitalised. DESIGN:A cross-sectional design was employed. SETTING:This study was conducted in three outpatient clinics in different regions in Hong Kong. PARTICIPANTS:A total of 382 recruited Chinese patients. PRIMARY AND SECONDARY OUTCOME MEASURES:The patients were asked to complete a structured questionnaire that assessed their smoking behaviours before, during and after hospitalisation. RESULTS:The results indicated 23.6% of smokers smoked secretly during their hospital stay, and about 76.1% of smokers resumed smoking after discharge. Multivariate logistic regression analysis found that number of days of hospitalisation admission in the preceding year (OR 1.02; 95%?CI 1.01 to 1.27; p=0.036), patients' perceived correlation between smoking and their illness (OR 1.08; 95%?CI 1.01 to 1.17; p=0.032), withdrawal symptoms experienced during hospitalisation (OR 0.75; 95%?CI 0.58 to 0.97; p=0.027) and smoking cessation support from healthcare professionals (OR 1.18; 95%?CI 1.07 to 1.36; p=0.014) were significant predictors of smoking abstinence after discharge. CONCLUSIONS:The results of this study will aid development of appropriate and innovative smoking cessation interventions that can help patients achieve more successful smoking abstinence and less relapse. TRIAL REGISTRATION NUMBER:NCT02866760.

SUBMITTER: Ho KY

PROVIDER: S-EPMC6303614 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Smoking behaviours of Hong Kong Chinese hospitalised patients and predictors of smoking abstinence after discharge: a cross-sectional study.

Ho Ka Yan KY Li William Ho Cheung WHC Lam Katherine Ka Wai KKW Wang Man Ping MP Xia Wei W Ho Lok Yin LY Tan Kathryn Choon Beng KCB Sin Hubert Kit Man HKM Cheung Elaine E Mok Maisy Pik Hung MPH Lam Tai Hing TH

BMJ open 20181219 12

<h4>Objectives</h4>Patients admitted to hospitals represent an excellent teachable moment for smoking cessation, as they are required to abstain from tobacco use during hospitalisation. Nevertheless, smoking behaviours of hospitalised patients, and factors that lead to smoking abstinence thereafter, remain relatively underexplored, particularly in a Hong Kong Chinese context. This study aimed to examine the smoking behaviours of hospitalised patients and explore factors leading to their abstaini ...[more]

PMID: 30573486

Similar Datasets

Project description:BACKGROUND:Type 2 diabetes (T2D) is a progressive disease whereby there is often deterioration in glucose control despite escalation in treatment. There is significant heterogeneity to this progression of glycemia after onset of diabetes, yet the factors that influence glycemic progression are not well understood. Given the tremendous burden of diabetes in the Chinese population, and limited knowledge on factors that influence glycemia, we aim to identify the clinical and genetic predictors for glycemic progression in Chinese patients with T2D. METHODS AND FINDINGS:In 1995-2007, 7,091 insulin-naïve Chinese patients (mean age 56.8 ± 13.3 [SD] years; mean age of T2D onset 51.1 ± 12.7 years; 47% men; 28.4% current or ex-smokers; median duration of diabetes 4 [IQR: 1-9] years; mean HbA1c 7.4% ± 1.7%; mean body mass index [BMI] 25.3 ± 4.0 kg/m2) were followed prospectively in the Hong Kong Diabetes Register. We examined associations of BMI and other clinical and genetic factors with glycemic progression defined as requirement of continuous insulin treatment, or 2 consecutive HbA1c ?8.5% while on ?2 oral glucose-lowering drugs (OGLDs), with validation in another multicenter cohort of Hong Kong Diabetes Biobank. During a median follow-up period of 8.8 (IQR: 4.8-13.3) years, incidence of glycemic progression was 48.0 (95% confidence interval [CI] 46.3-49.8) per 1,000 person-years with 2,519 patients started on insulin. Among the latter, 33.2% had a lag period of 1.3 years before insulin was initiated. Risk of progression was associated with extremes of BMI and high HbA1c. On multivariate Cox analysis, early age at diagnosis, microvascular complications, high triglyceride levels, and tobacco use were additional independent predictors for glycemic progression. A polygenic risk score (PRS) including 123 known risk variants for T2D also predicted rapid progression to insulin therapy (hazard ratio [HR]: 1.07 [95% CI 1.03-1.12] per SD; P = 0.001), with validation in the replication cohort (HR: 1.24 [95% CI 1.06-1.46] per SD; P = 0.008). A PRS using 63 BMI-related variants predicted BMI (beta [SE] = 0.312 [0.057] per SD; P = 5.84 × 10-8) but not glycemic progression (HR: 1.01 [95% CI 0.96-1.05] per SD; P = 0.747). Limitations of this study include potential misdiagnosis of T2D and lack of detailed data of drug use during follow-up in the replication cohort. CONCLUSIONS:Our results show that approximately 5% of patients with T2D failed OGLDs annually in this clinic-based cohort. The independent associations of modifiable and genetic risk factors allow more precise identification of high-risk patients for early intensive control of multiple risk factors to prevent glycemic progression.

Dataset Information

Smoking behaviours of Hong Kong Chinese hospitalised patients and predictors of smoking abstinence after discharge: a cross-sectional study.

Publications

Smoking behaviours of Hong Kong Chinese hospitalised patients and predictors of smoking abstinence after discharge: a cross-sectional study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure