Impact of Smoke-Free Legislation on Acute Myocardial Infarction and Subtypes of Stroke Incidence in Shenzhen, China, 2012-2016: An Interrupted Time Series Analysis.

Objective: This study assesses the impact of smoke-free legislation on the incidence rate for acute myocardial infarction (AMI) and stroke in Shenzhen. Methods: Data on ischemic ( n = 72,945) and hemorrhagic ( n = 18,659) stroke and AMI ( n = 17,431) incidence covering about 12 million people in Shenzhen from 2012 to 2016 were used. Immediate and gradual changes in incidence rates were analyzed using segmented Poisson regression. Results: Following the smoke-free legislation, a 9% (95% CI: 3%-15%) immediate reduction was observed in AMI incidence, especially in men (8%, 95% CI: 1%-14%) and in those aged 65 years and older (17%, 95% CI: 9%-25%). The gradual annual benefits were observed only in hemorrhagic and ischemic stroke incidence, with a 7% (95% CI: 2%-11%) and 6% (95% CI: 4%-8%) decrease per year, respectively. This health effect extended gradually to the 50-64 age group. In addition, neither the immediate nor gradual decrease in stroke and AMI incidence rates did not show statistical significance among the 35-49 age group ( P > 0.05). Conclusion: Smoke-free legislation was enforced well in Shenzhen, which would generate good experiences for other cities to enact and enforce smoke-free laws. This study also provided more evidence of the health benefits of smoke-free laws on stroke and AMI.

[The control study on risk factors of high-risk population in diabetes].

OBJECTIVE: To examine the significance of individual risk on diabetes to subjects who underwent diabetes screening. METHODS: 2003 asymptomatic diabetes subjects with high-risk factors of diabetes as family history, obesity, hypertension, and/or dyslipidemia, fetal giant history were screened. 5362 subjects having no risk factors but from the same community were allocated as controls. RESULTS: There were 131 (6.54%) diabetes identified in the screening group and 1547 (77.23%) subjects having 1 risk factor, 387 (19.27%) having 2 risk factors, 70 (3.49%) having 3 or more risk factors. There were 96 (1.79%) diabetes identified in the control group. Compared with control group, the OR (95% CI) value was 2.68 (2.20-3.25) after adjusted on age among the high risk group. The OR value of those having 1 risk factor was 2.89, but these having 3 or more risk factors increased to 4.68. CONCLUSION: The relation between the risk of high-risk group with diabetes and the number of risk factors of diabetes presented positive correlation. Early and regular screening for diabetes was essential in these individuals with high-risk factors.