Prevalence of tobacco related chronic diseases and its role in smoking cessation among smokers in a rural area of Shanghai, China: a cross sectional study.

BACKGROUND: Tobacco smoking is a recognized risk factor for many chronic diseases and previous study evidences have indicated that smokers receive smoking cessation service after the diagnosis of chronic diseases increases successful rate in quitting. But the prevalence of tobacco related chronic diseases (TCD) among smokers, as well as the role of TCD diagnosis in smoking cessation is still unclear in China. METHODS: From June 2016 to December 2017, we sampled 36, 698 residents aged over 18 years by a three stage sampling in Songjiang district, Shanghai. We conducted a cross-sectional study to understand the prevalence of TCD among smokers, and the role of TCD diagnosis in smoking cessation among ex-smokers as well as the smoking cessation attempt among current smokers. RESULTS: Over all, the prevalence of current smoking is 19.78% (48.36% for male and 0.22% for female). 15.93% of smokers have stopped smoking successfully (1, 376/8, 636). The prevalence of ten selected TCDs among smokers range from 0.63% (Chronic Obstructive Pulmonary Disease, COPD) to 36.31% (hypertension). All of 1, 376 ex-smokers had at least one kind of TCD, and 52.33% of them stop smoking after the diagnosis of TCD, the time interval between TCD diagnosis and smoking cessation ranges from 0 to 65 years, with a median of 9 years. Smokers with TCD had higher prevalence of quit smoking, and current smokers with TCD had higher smoking cessation attempt proportion. CONCLUSIONS: The prevalence of current smoking is still very high among male residents in rural area of Shanghai, and the occurrence of TCD even non-lethal one could provide an opportunity for doctors to assist the smoking cessation among smokers.

'Outbreak Gold Standard' selection to provide optimized threshold for infectious diseases early-alert based on China Infectious Disease Automated-alert and Response System.

The China Infectious Disease Automated-alert and Response System (CIDARS) was successfully implemented and became operational nationwide in 2008. The CIDARS plays an important role in and has been integrated into the routine outbreak monitoring efforts of the Center for Disease Control (CDC) at all levels in China. In the CIDARS, thresholds are determined using the "Mean+2SD? in the early stage which have limitations. This study compared the performance of optimized thresholds defined using the "Mean +2SD? method to the performance of 5 novel algorithms to select optimal "Outbreak Gold Standard (OGS)? and corresponding thresholds for outbreak detection. Data for infectious disease were organized by calendar week and year. The "Mean+2SD?, C1, C2, moving average (MA), seasonal model (SM), and cumulative sum (CUSUM) algorithms were applied. Outbreak signals for the predicted value (Px) were calculated using a percentile-based moving window. When the outbreak signals generated by an algorithm were in line with a Px generated outbreak signal for each week, this Px was then defined as the optimized threshold for that algorithm. In this study, six infectious diseases were selected and classified into TYPE A (chickenpox and mumps), TYPE B (influenza and rubella) and TYPE C [hand foot and mouth disease (HFMD) and scarlet fever]. Optimized thresholds for chickenpox (P55), mumps (P50), influenza (P40, P55, and P75), rubella (P45 and P75), HFMD (P65 and P70), and scarlet fever (P75 and P80) were identified. The C1, C2, CUSUM, SM, and MA algorithms were appropriate for TYPE A. All 6 algorithms were appropriate for TYPE B. C1 and CUSUM algorithms were appropriate for TYPE C. It is critical to incorporate more flexible algorithms as OGS into the CIDRAS and to identify the proper OGS and corresponding recommended optimized threshold by different infectious disease types.

How to select a proper early warning threshold to detect infectious disease outbreaks based on the China infectious disease automated alert and response system (CIDARS).

BACKGROUND: China Centre for Diseases Control and Prevention (CDC) developed the China Infectious Disease Automated Alert and Response System (CIDARS) in 2005. The CIDARS was used to strengthen infectious disease surveillance and aid in the early warning of outbreak. The CIDARS has been integrated into the routine outbreak monitoring efforts of the CDC at all levels in China. Early warning threshold is crucial for outbreak detection in the CIDARS, but CDCs at all level are currently using thresholds recommended by the China CDC, and these recommended thresholds have recognized limitations. Our study therefore seeks to explore an operational method to select the proper early warning threshold according to the epidemic features of local infectious diseases. METHODS: The data used in this study were extracted from the web-based Nationwide Notifiable Infectious Diseases Reporting Information System (NIDRIS), and data for infectious disease cases were organized by calendar week (1-52) and year (2009-2015) in Excel format; Px was calculated using a percentile-based moving window (moving window [5 week*5 year], x), where x represents one of 12 centiles (0.40, 0.45, 0.50….0.95). Outbreak signals for the 12 Px were calculated using the moving percentile method (MPM) based on data from the CIDARS. When the outbreak signals generated by the 'mean + 2SD' gold standard were in line with a Px generated outbreak signal for each week during the year of 2014, this Px was then defined as the proper threshold for the infectious disease. Finally, the performance of new selected thresholds for each infectious disease was evaluated by simulated outbreak signals based on 2015 data. RESULTS: Six infectious diseases were selected in this study (chickenpox, mumps, hand foot and mouth diseases (HFMD), scarlet fever, influenza and rubella). Proper thresholds for chickenpox (P75), mumps (P80), influenza (P75), rubella (P45), HFMD (P75), and scarlet fever (P80) were identified. The selected proper thresholds for these 6 infectious diseases could detect almost all simulated outbreaks within a shorter time period compared to thresholds recommended by the China CDC. CONCLUSIONS: It is beneficial to select the proper early warning threshold to detect infectious disease aberrations based on characteristics and epidemic features of local diseases in the CIDARS.

'Outbreak Gold Standard'Selection to Provide Optimized Threshold for Infectious Diseases Early-alert Based on China Infectious Disease Automated-alert and Response System / 华中科技大学学报（医学）（英德文版）

The China Infectious Disease Automated-alert and Response System (CIDARS) was successfully implemented and became operational nationwide in 2008.The CIDARS plays an important role in and has been integrated into the routine outbreak monitoring efforts of the Center for Disease Control (CDC) at all levels in China.In the CIDARS,thresholds are determined using the'Mean+2SD'in the early stage which have limitations.This study compared the performance of optimized thresholds defined using the'Mean +2SD'method to the performance of 5 novel algorithms to select optimal 'Outbreak Gold Standard (OGS)'and corresponding thresholds for outbreak detection.Data for infectious disease were organized by calendar week and year.The'Mean+2SD',C1,C2,moving average (MA),seasonal model (SM),and cumulative sum (CUSUM) algorithms were applied.Outbreak signals for the predicted value (Px) were calculated using a percentile-based moving window.When the outbreak signals generated by an algorithm were in line with a Px generated outbreak signal for each week,this Px was then defined as the optimized threshold for that algorithm.In this study,six infectious diseases were selected and classified into TYPE A (chickenpox and mumps),TYPE B (influenza and rubella) and TYPE C [hand foot and mouth disease (HFMD) and scarlet fever].Optimized thresholds for chickenpox (P55),mumps (P50),influenza (P40,P55,and P75),rubella (P45 and P75),HFMD (P65 and P70),and scarlet fever (P75 and Ps0) were identified.The C1,C2,CUSUM,SM,and MA algorithms were appropriate for TYPE A.All 6 algorithms were appropriate for TYPE B.C1 and CUSUM algorithms were appropriate for TYPE C.It is critical to incorporate more flexible algorithms as OGS into the CIDRAS and to identify the proper OGS and corresponding recommended optimized threshold by different infectious disease types.

The influence of estimated retail tobacco sale price increase on smokers' smoking habit in Jiangxi province, China: a cross-sectional study.

INTRODUCTION: China is the biggest tobacco producer and consumer in the world. Raising cigarette taxes and increasing tobacco retail prices have been prove as effective strategies to reduce tobacco consumption and the prevalence of smoking in western countries. But in China, it is uncertain how an increase of cigarette retail price will influence the tobacco consumption. METHODS: From April to July, 2012, we selected 4025 residents over 15 years by a three stage random sampling in four cities, Jiangxi Province, China. We conducted interviews of their current smoking habits and how they would change their smoking behavior if tobacco retail prices increase. RESULTS: Overall, the prevalence of smoking is 27 % (47 % for male, 3.1 % for female). 15 % of smokers have tried to quit smoking in the past but all relapsed (168/1088), and over 50 % of current smokers do not want to quit, The average cigarette price per pack is 1.1 USD (range = 0.25-5.0). If retail cigarette prices increases by 50 %, 45 % of smokers say they will smoke fewer cigarettes, 20 % will change to cheaper brands and 5 % will attempt to quit smoking. Smokers who have intention to quit smoking are more sensitive to retail cigarette price increase. With retail cigarette price increases, more smokers will attempt to quit smoking. CONCLUSION: Chinese smokers will change their smoking habits if tobacco retail prices increase. Consequently the Chinese government should enact tobacco laws which increase the retail cigarette price. The implementation of new tobacco laws could result in lowering the prevalence of smoking. Meanwhile, price increase measures need to apply to all cigarette brands to avoid smokers switching cigarettes to cheaper brands.