Smoking cessation and related factors in middle-aged and older Chinese adults: Evidence from a longitudinal study.

OBJECTIVES: There are more than 300 million smokers in China. This study aimed to evaluate the rate of smoking cessation, smoking relapse and related factors in middle-aged and older smokers in China.
METHODS: We performed a secondary analysis of data from China Health and Retirement Longitudinal Study (CHARLS) that recruited a nationally representative sample of adults aged 45 and older. Participants were 3708 smokers in 2011 who completed two waves of follow-up interviews in 2013 and 2015. Self-reported quit and relapse rates at follow-ups were estimated. Multiple logistic regressions were conducted to identify factors associated with smoking cessation and relapse.
RESULTS: The overall quit rate was 8.5% (95% CI 7.7% - 9.5%) at the 2-year follow-up in 2013, and 16.6% (95% CI 15.5% - 17.9%) at the 4-year follow up. Smoking cessation in 2013 was associated with not living in the northeast region (p = 0.003), fewer cigarettes smoked daily (p <0.001), and longer time to the first cigarette in the morning (p<0.001). Smoking cessation in 2015 was associated with older age (p = 0.049), smoking initiation at age ≥20 years (p<0.001), longer time to the first cigarette in the morning (p<0.001), and self-perceived poor health (p<0.001). Of the 317 participants who stopped smoking in 2013, 13.3% (95% CI 9.9% - 17.5%) relapsed by 2015. Smoking relapse was associated with younger age (p = 0.025), shorter time to the first cigarette in the morning (p = 0.003), and self-perception of not poor health (p = 0.018).
CONCLUSION: The overall quit rate was 8.5% at the 2-year follow up, and 16.6% at the 4-year follow up in the middle-aged and older smokers, but 13% of quitters returned to smoking in two years. Successful smoking cessation was associated with older age, lower nicotine dependence, and self-perceived poor health.

Introduction

There are more than 300 million smokers, and the death toll from smoking-related diseases is estimated to exceed one million annually in China [1-3]. Tobacco control and smoking cessation are important actions to achieve the "Healthy China" official target [4].

Smoking cessation reduces tobacco-related harms regarding a variety of related diseases, including cancer, coronary heart disease, and chronic obstructive pulmonary disease [5, 6]. To promote smoking cessation, it is important to have an appropriate understanding of smoking cessation and its related factors. Previous studies reported that possible predictors of, or factors associated with, smoking cessation included age, socioeconomic status, health conditions, and the severity of nicotine dependence [7]. Evidence on smoking cessation and related factors is rare in China, particularly from longitudinal studies. A one-year follow-up study of participants of ‘Quit and Win’ in 2002 in China found that smoking cessation was associated with motivation to quit, age, and marital status [8]. China Seven Cities Study revealed that smoking abstinence was associated with lower nicotine dependence, perception of less stress and reduced hostility [9]. Findings from International Tobacco Control (ITC) China Survey identified older age, quitting intentions, nicotine dependence, and geographical location as important predictors of quit attempts or successful quitting [10].

However, previous studies of smoking cessation and related factors in China included only smokers from large cities. The present study used data from a large-scale longitudinal study of nationally representative samples to estimate the quit rates of current smokers, and to provide additional evidence on factors associated with successful smoking cessation in the middle-aged and older smokers.

Methods

Study design and sample

Data for this study came from China Health and Retirement Longitudinal Study (CHARLS) [11]. CHARLS is a longitudinal study that assessed the social, economic, and health status of a nationally representative sample of Chinese adults aged ≥45 years. The study covers 28 provinces, 150 county-level units and 450 village-level units and uses probability proportional to size (PPS) sampling. Regular follow-up with strict quality control maintained the representativeness of the middle-aged and elderly population, and formed a large-scale cohort data on aging problems in China [11].

The Biomedical Ethics Review Committee of Peking University approved the CHARLS study in January 2011. All participants were informed and provided informed consents before interviews. For this study, we applied to the CHARLS team and obtained anonymous data of participants.

The 2011 CHARLS survey provided baseline data. The follow-up rate of participants recruited in 2011 was 88% in 2013 and 87% in 2015. The present study included 3708 participants who were current smokers in 2011, and were followed up in both 2013 and 2015. We checked the data integrity, and excluded 109 participants with missing or abnormal data. Our study was a secondary analysis of data from the CHARLS.

Measurements

Ever smokers in the CHARLS study were defined as participants who smoked more than 100 cigarettes in their lifetime, according to answers to the following question: “have you ever chewed tobacco, smoked a pipe, smoked self-rolled cigarettes, or smoked cigarettes/cigars?”. Ever smokers were classified as current or former smokers (quitters) through the question “do you still have the habit or have you totally quit?”. Current smokers in 2011 were re-interviewed in 2013 and classified into two categories: continued smokers or quitters in 2013. Current smokers in 2013 were classified as remained smokers and quitters in the 2015 survey, and quitters in 2013 were classified as quitters or relapsers in 2015 (Fig 1).

Fig 1

Flow diagram for the process of participant selection and follow-up.

The CHARLS study measured the level of nicotine dependence based on cigarettes per day, age at starting smoking, and time to the first cigarette after waking up. The CHARLS data also included the following information on participants: sociodemographic variables including age, sex, rural/urban location, educational level, marital status, region and self-perceived health condition. The perception of health by participants was measured by asking the following question: "Would you say your health is very good, good, fair, poor or very poor?"

Statistical analysis

The quit rate was estimated by dividing the number of quitters at the end of the follow-ups in 2013 and 2015 by the number of current smokers before the follow-up in 2011 and 2013 (Fig 1). Therefore, quit rate in this study was self-reported point quit prevalence. To facilitate comparisons with different studies, we estimated the annualized quit rate (q) based on an assumption of constant quit rate during the follow-up period: q = 1−exp {ln (1−nq/ns)/yr}, in which ns refers to smokers before the follow-ups, nq refers to the number of quitters at the end of follow-ups, and yr refers to the number of follow-up years. The annualized quit rate had been used in previous studies with different years of follow-ups [12].

Between smokers and quitters, the differences in characteristics in the frequency of categorical variables were analyzed using the chi-squared test. Logistic regressions modeled the association between smoking cessation (dependent variable) and participant characteristics (independent variables). The selection of independent variables was restricted by data availability, and according to our understanding of factors that may be possibly associated with the dependent variable. We used the following independent variables: age, sex, rural/urban location, marital status, geographical region, educational level, self-perceived health status, cigarettes per day, age at starting smoking, and time to the first cigarette after waking up. Data were analyzed using Stata/MP 16 (Statacorp, TX). Two sided p ≤0.05 was considered as statistically significant.

Results

The 3,708 participants included 3323 males with an average age of 58.7 years, and 385 females with an average age of 61.5 years (Table 1). A total of 317 smokers reported that they stopped smoking in 2013, and 617 smokers stopped smoking in 2015 (Fig 1). The overall quit rate was 8.5% (95% CI: 7.7% - 9.5%) at the 2-year follow-up in 2013, and 16.6% (95% CI: 15.5% - 17.9%) at the 4-year follow-up, which corresponded to an annualized quit rate of 4.4%. There were 275 participants who quit smoking at both follow-ups in 2013 and 2015, with a quit rate of 7.4% (95% CI 6.6% - 8.3%). Of the 317 quitters in 2013, 42 returned to smoking again in 2015, with a relapse rate of 13.3% (95% CI: 9.9% - 17.5%) within two years.

Table 1

Participant characteristics and smoking cessation rates.

Variables	N	2013			2015
		Quit rate (%) (95% CI)		p	Quit rate (%) (95% CI)	p
Total	3078		8.5 (7.7–9.5)	-	10.1 (9.1–11.2)	-
Age(years)
45–54	1229	7.65(6.22–9.28)		0.068	13.51(11.64–15.55)	<0.001
55–64	1543	8.68(7.32–10.20)			16.98(15.14–18.95)
65–74	745	8.86(6.92–11.13)			20.40(17.56–23.48)
≥75	191	12.04(7.79–17.52)			19.37(14.02–25.70)
Sex
Male	3323	8.70(7.76–9.71)		0.344	16.79(15.54–18.11)	0.464
Female	385	7.27(4.89–10.34)			15.32(11.88–19.32)
Rural/urban
Rural	2491	8.35(7.29–9.51)		0.535	16.5(15.06–18.02)	0.743
Urban	1217	8.96(7.41–10.70)			16.93(14.86–19.15)
Educational status
Illiteracy	579	8.29(6.18–10.84)		0.268	17.62(14.60–20.97)	0.606
Primary & junior high school	2663	8.26(7.24–9.37)			16.45(15.06–17.91)
High school & above	466	10.52(7.88–13.66)			17.26(13.87–21.10)
Marital status
Married	3342	8.65(7.72–9.65)		0.517	16.91(15.65–18.22)	0.188
Other	366	7.65(5.14–10.87)			14.21(10.80–18.21)
Region
East	1167	9.00(7.42–10.79)		0.376	17.91(15.75–20.23)	0.046
Central	1035	9.03(7.35–10.95)			17.29(15.04–19.74)
West	1191	8.62(7.06–10.40)			16.20(14.16–18.42)
Northeast	315	6.03(3.67–9.26)			11.43(8.13–15.47)
Health status in 2011
Poor	1828	9.13(7.85–10.55)		0.211	-	-
Other	1880	7.98(6.79–9.30)			-
Health status in 2013
Poor	1802	9.10(7.81–10.52)		0.243	18.31(16.55–20.18)	0.008
Other	1906	8.03(6.85–9.34)			15.06(13.48–16.74)
Health status in 2015
Poor	1864	8.91(7.65–10.29)		0.435	19.1(17.34–20.96)	<0.001
Other	1844	8.19(6.98–9.54)			14.15(12.59–15.83)
Changes in self-perceived health status in two years
Worse	1184	8.36(6.85–10.09)		0.78	18.51(16.36–20.82)	0.033
Other	2524	8.64(7.57–9.80)			15.73(14.33–17.22)
Time to the first cigarette after waking up
≤30 minutes	2004	5.19(4.26–6.25)		<0.001	11.08(9.74–12.53)	<0.001
>30 minutes	1704	12.5(10.97–14.16)			23.18(21.20–25.26)
Cigarettes per day
<10	749	15.09(12.60–17.85)		<0.001	24.30(21.27–27.54)	<0.001
10–19	881	9.42(7.57–11.55)			18.62(16.10–21.35)
≥20	2078	5.82(4.85–6.91)			13.04(11.62–14.57)
Age started smoking(years)
<20	1299	7.01(5.68–8.54)		0.014	13.09(11.30–15.04)	<0.001
≥20	2409	9.38(8.25–10.62)			18.56(17.03–20.18)

Table 1 shows the quit rates by participant characteristics. Quitting in 2013 was significantly associated with longer time to the first cigarette after waking up, fewer cigarettes per day, and starting smoking at age ≥20 years. Smoking cessation in 2015 was significantly associated with older age, not living in the northeast region, longer time to the first cigarette after waking up, fewer cigarettes per day, and smoking initiation at age ≥20 years. Smoking cessation was significantly associated with deterioration of self-perceived health during 2011–2015. There were no significant associations between smoking cessation and other factors, including sex, rural or urban location, educational background, or marital status (Table 1).

The results of multivariable logistic regressions are shown in Table 2. Smoking cessation in 2013 was significantly associated with not living in the northeast region, fewer cigarettes smoked daily, and longer time to the first cigarette after waking up. For current smokers in 2013, smoking cessation in 2015 was significantly associated with older age, later age (≥20 years) at starting smoking, longer time to the first cigarette after waking up, and self-perceived poor health (Table 2).

Table 2

Results of logistic regressions analyses of factors associated with smoking cessation in 2013 and 2015.

Variables	Quit in 2013		Quit in 2015
	OR (95% CI)	p	OR (95% CI)	p
Age in 2011	1.01 (0.99–1.03)	0.139	1.01 (1.00–1.03)	0.049
Sex (male vs. female)	1.30 (0.84–2.00)	0.237	1.43 (0.95–2.17)	0.088
Education (above vs. junior primary or below)	1.19 (0.92–1.55)	0.187	0.88 (0.68–1.14)	0.331
Married vs. other	1.20 (0.79–1.84)	0.390	1.37 (0.91–2.08)	0.134
Unban vs. rural	1.08 (0.84–1.39)	0.528	1.04 (0.82–1.33)	0.730
Northeast vs. others region	0.40 (0.22–0.73)	0.003	0.88 (0.57–1.35)	0.544
Age started smoking	1.00 (0.99–1.02)	0.565	1.02 (1.01–1.04)	<0.001
Cigarettes per day	0.97 (0.96–0.98)	<0.001	1.00 (0.99–1.01)	0.747
Smoking ≤30 minutes after waking up vs. >30	0.47 (0.37–0.61)	<0.001	0.54 (0.42–0.69)	<0.001
Self-perceived poor health vs. other	1.17 (0.92–1.48)	0.203	1.61 (1.27–2.02)	<0.001

Notes to Table 2: Current smokers (n = 3708) in 2011 were included in the analysis for quitting in 2013, and continued smokers in 2013 (n = 3391) were included in the analysis for quitting in 2015.

Table 3 shows the results of the logistic regressions of factors associated with smoking relapse in the 317 quitters in 2013. Smoking relapse during 2013–2015 was associated with younger age (p = 0.024), shorter time to the first cigarette after waking up (p = 0.003), and self-perceived not poor health (p = 0.018). Smoking relapse was not significantly associated with sex, education, marital status, urban/rural location or region.

Table 3

Results of multiple variable logistic regressions of factors associated with smoking relapse in 2015.

Variables	OR (95% CI)	p
Age in 2011	0.95 (0.91–0.99)	0.024
Sex (male vs. female)	4.14 (0.51–33.39)	0.183
Education (above junior primary)	0.76 (0.35–1.64)	0.480
Married vs. other	0.35 (0.11–1.13)	0.080
Urban vs. rural	0.95 (0.45–2.02)	0.893
Northeast vs. others region	1.36 (0.25–7.26)	0.722
Age started smoking	0.97 (0.92–1.02)	0.204
Cigarettes per day	0.98 (0.95–1.02)	0.388
Smoking ≤30 minutes after waking up vs. >30	3.38 (1.53–7.47)	0.003
Self-perceived poor health vs. other	0.41 (0.20–0.86)	0.018

Discussion

Findings and comparison with other studies

The quit rate of current smokers in 2011 was 16.6% at the 4-year follow up in 2015, which corresponded to an annualized quit rate of 4.4%. This estimate of annualized quit rate was surprisingly similar to findings from previous large-scale longitudinal studies in China and in other countries, given the considerable differences in smokers’ characteristics, socioeconomic and health care circumstances, and definition of smoking cessation outcomes. For example, a study in six cities in China found an annualized quit rate of 4.1% [10]. A study in the United States reported that 30.2% of the 5127 current smokers in 1993 stopped smoking by 2001, with an annualized quit rate of 4.4% [12]. A 1-year follow-up study in 1996 in the UK reported a quit rate of 5% in smokers [13]. In a longitudinal study of 4636 smokers from seven centers in Northern Europe, the crude quit rate was 4.5 per 100 person-years [14]. In a Japanese study, the quit rate in 1358 smokers after 1-year follow-up was 4.6% (≥3-months’ abstinence) or 3.1% (≥6 months’ abstinence) [15].

The current study found that smoking cessation was associated with age, region, perception of health status, and nicotine dependence, which are similar to previous studies [10, 16]. Older smokers are more likely to quit smoking, which may be partially due to increased concerns about health [10], and the inverse U-shaped relationship of nicotine dependence with age [15]. Smokers in northeast China had a lower smoking cessation rate, which is similar to the findings of the ITC study and likely due to differences in tobacco culture across regions in China [10].

The smoking cessation rate was relatively high in smokers with self-perceived poor health. According to the previous studies, concern about personal health was a common reason for smokers to quit smoking [17]. When smokers realize that smoking is affecting their health, they may be more motivated to quit smoking [18]. Therefore, doctors and family members should take the opportunity to promote smoking cessation in patients with smoking related diseases.

Our results confirm findings from previous studies that smoking cessation was associated with tobacco dependence, including the number of cigarettes smoked daily, age of smoking initiation and time to the first cigarette in the morning [7]. Compared with participants who were less addicted, smokers with more severe nicotine dependence were less likely to quit [19, 20]. Adult smokers with more health problems may be more motivated to quit smoking than smokers with fewer health problems [21].

There are various interventions to help smokers quit smoking, as specified by the World Health Organization Framework Convention on Tobacco Control (FCTC) and MPOWER (Monitor, Protect, Offer, Warn, Enforce, Raise) [13]. Strong evidence revealed that policies of tobacco control intervention reduced the smoking rate of general population [22]. However, the implementation of the FCTC in China must be further strengthened, including political leadership, government oversight of the tobacco industry, and advocacy and support [23]. Smoking cessation support in China is rarely available, and only 5.6% smokers in China used smoking cessation medications [24]. Therefore, smoking cessation in China was predominantly unassisted compared to quitters in other countries [25]. Therefore, cessation treatments were unlikely to be an important factor that directly affected the quit rate or motivation to quit in the study participants.

We also found that smoking relapse was lower in older adults, smokers with less severe tobacco dependence, and who had perceived poor health. In addition to the self-perception of poor health, previous studies found that smoking relapse was associated with perceived stress, anxiety or depression [9, 26]. Further research is required to improve our understanding of smoking relapse and related factors.

Strengths and limitations

The present study was based on data from a large, nationally representative longitudinal study that included urban and rural areas. Another strength of the study is the longitudinal data from two-waves of follow-ups, which provided rare opportunity for us to estimate quit rates and related factors in current smokers in China. However, this study has some limitations. First, the study relied on self-reported data and excluded participants with missing follow-up data, which may result in recall bias and errors. Second, we measured point prevalence of smoking status at the two follow-ups in 2013 and 2015, and did not assess possible changes in smoking status between the assessments. Third, we conducted multiple statistical comparisons, and did not correct for possibly inflated type I errors. Furthermore, we did not consider the impacts of smoking cessation interventions (e.g., tobacco control policies, smoking cessation therapy) on smoking and quitting. These limitations should be appropriately addressed in further studies.

Conclusion

The overall quit rate was 8.5% at the 2-year follow up, and 16.6% at the 4-year follow up in the middle-aged and older smokers, although 13% of quitters returned to smoking in two years. Successful smoking cessation was associated with older age, lower nicotine dependence, and self-perceived poor health.

2 Sep 2020

PONE-D-20-20662

Smoking cessation and related factors in middle-aged and older Chinese adults: evidence from a longitudinal study

PLOS ONE

Dear Dr. Song,

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Reviewer #1: Qiu et al report smoking cessation and relapse data from a large, longitudinal study of middle-aged and older smokers in China. The authors evaluated a wide array of demographic, clinical and other variables that might potentially influence cessation and relapse rates. Factors that predicted cessation included geographic location, number of cigarettes smoked per day and time to first cigarette after waking; relapse prediction included age and poor overall health. Although the paper is difficult to follow at times due to language limitations, the study design is straightforward and impressively longitudinal, has a high power sample size and conveys valuable information regarding characterization of the smoking population in China. These data, in turn, may aid public health officials design and manage smoking cessation strategies.

Abstract

Please indicate in the abstract introduction what percentage of total smokers are those that are middle-aged and older.

The abstract (and entire paper) requires a thorough editing to correct word choice, grammar and punctuation. For example, the current word choice in the abstract makes it difficult to ascertain what are exactly the independent predictors for relapse: “being late smoking after waking up every day”?

Methods

“Ever smokers” are defined as greater than 100. This value seems excessively high.

How were health status and self-perceived poor health information defined/extracted?

Were data analyses corrected for multiple comparisons?

Results

Please include at the beginning of table 1 the overall quit rates for 2013 and 2015.

The tables and figure are all very informative.

The Discussion is comprehensive and appropriate, although again, the paper requires a writing service to correct the language problems.

Reviewer #2: This manuscript reports on a secondary analysis with the aim of identifying predictors of quitting and relapse in a sample of 3708 Chinese smokers who participated in a parent study (a general health epidemiology survey) in which they provided data on smoking status at baseline (2011), 2-year(2013), and 4-year (2015) follow ups.

This study makes a contribution by focusing on middle-aged smokers (> 45 yrs), which is useful since the over 40 yr old group represents increased health risk of continued smoking. Another novel aspect of the study is the representation of participants from rural China, which adds to the work that has been done in this area. Quit rates were approx. 10% at the 2 year follow up and 17% at the 4-year follow up with 86% of quitters at the 2 year follow up also reporting abstinence at the 4-year follow up. Predictors of quitting in multivariate analysis included older age, higher levels of nicotine dependence, and poorer self-rated health. This replicates other work done internationally as well as work done in China.

Suggestions:

As reported, I found the quit rates somewhat confusing. Since the authors say they will calculate quit rates as follows: “The quit rate was estimated by dividing the number of quitters at the follow-up survey by the number of smokers at baseline,” I think it would be useful to report the quit rate at 2013 as well as the quit rate at 2015 in terms of the entire sample, (617 self-reported being quit at the 2015 follow up, which would be a quit rate of 16.6% ) rather than just reporting the % quit at 2015 of those who were smoking at 2013.

An overall quit rate of 16.6% is reported in the manuscript from 2011-2015, is this meant to be the point prevalence self-reported quit rate at the 2015/4-year follow up? If yes would suggest changing the terminology to reflect this.

Between the 2 and 4 year follow-ups, a continuous quit rate of 7.4% (275/3708) was reported, however, was continuous quit rate actually assessed or could this be capturing people who happen to report being quit at the two assessments, but have not been continuously quit? If the latter is true, needs to be mentioned as a limitation that could be overestimating continuous quit rates. Also, although this does represent the “continuous” quit rate in the total sample, would suggest the authors emphasize the high % of quitters who maintained abstinence (86.7%) as well as noting, as they do, the relapse rate of 13.1% .

I do not understand what is meant here: “To facilitate the comparison with findings from different studies, the annual quit rate was estimated based on the quit rates for different follow-up years and an assumption of constant rates of still smoking during the follow-up.”

Two limitations that should be emphasized are the reliance on self-report data and on a completer analysis. Reliance on self-report data is mentioned in the discussion, but deserves more attention, particularly whether there are reasons people might lie about their smoking status (in the US, for example, people lie about this to health insurers because they do not want their rates to be higher). Statistical tests should be done to evaluate differences between those who completed both follow-ups to those who did not.

An alternative to age started smoking and number of cigs/day as separate predictor variables would be to create a composite variable “pack years.”

Did any unique predictors emerge to predict “continuous quit?” (Or are these all just the opposite of the predictors of relapse?)

Minor comments:

Throughout manuscript, when citing predictors of quitting, specify direction (e.g., age) of the effect

“The starting smoking age” should be rephrased “Age started smoking”

A few grammatical errors in this statement in the Abstract:

having less smoking number use, longer time to first cigarette upon waking. Independent predictors of staying quit in 2015 included being younger, being late smoking after waking up every day, having shorter smoking age

Reviewer #3: This is a study that examined smoking cessation, smoking relapse and related factors among middle-aged and older(≧45) smokers based on a secondary analysis of the dataset of the the China Health and Retirement Longitudinal Study (CHARLS). Here are some comments that may be helpful to improve the quality of this manuscript.

-The language needs to be further edited by native English speakers.

Abstract:

-The statistics of independent correlates of smoking quit, and staying quit should be provided in both the Abstract and the main text, such as p values, and ORs with 95%CI.

Methods:

-Statistics: Please specify which type of multiple logistic regression anayses were performed. Stepwise or other types? One- or two-sided tests? Clarify how to select the independent variables?

Results:

-Table 1: add (years) for Age.

Discussion:

-Strengths/Limitations: This is not a national survey.

-All Table/Figures should appear after the ref list, rather than in the text. This is general knowledge when drafting a manuscript.

**********

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Reviewer #1: No

Reviewer #2: Yes: Corinne Cather

Reviewer #3: No

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14 Sep 2020

Thanks for helpful comments from editors and peer reviewers. Please find our point-to-point response below.

---------------------------

Editorial comments

---------------------------

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Authors’ response: We have checked and revised the manuscript according to PLOS ONE's style requirements.

2. Your ethics statement must appear in the Methods section of your manuscript. If your ethics statement is written in any section besides the Methods, please move it to the Methods section and delete it from any other section. Please also ensure that your ethics statement is included in your manuscript, as the ethics section of your online submission will not be published alongside your manuscript.

Authors’ response: ethics statement added in the methods on page 5, paragraph 3. “The Biomedical Ethics Review Committee of Peking University approved the CHARLS study in January 2011. All participants were informed and provided informed consents before interviews. For this study, we applied to the CHARLS team and obtained data that maintained the participant anonymity.”

----------------------------

Review comments

----------------------------

Reviewer #1: Qiu et al report smoking cessation and relapse data from a large, longitudinal study of middle-aged and older smokers in China. The authors evaluated a wide array of demographic, clinical and other variables that might potentially influence cessation and relapse rates. Factors that predicted cessation included geographic location, number of cigarettes smoked per day and time to first cigarette after waking; relapse prediction included age and poor overall health. Although the paper is difficult to follow at times due to language limitations, the study design is straightforward and impressively longitudinal, has a high power sample size and conveys valuable information regarding characterization of the smoking population in China. These data, in turn, may aid public health officials design and manage smoking cessation strategies.

Authors’ response: Thank you very much for your positive comments. We have revised the manuscript and hope it is now satisfactory for being published in PLOS ONE.

Abstract

Please indicate in the abstract introduction what percentage of total smokers are those that are middle-aged and older.

Authors’ response: The CHARLS study recruited only participants aged 45 and older, so all smokers were middle-aged and older. We have clarified this in the manuscript’s Method section: “CHARLS is a longitudinal study that assessed the social, economic, and health status of a nationally representative sample of Chinese adults aged ≥45 years”(page 5, paragraph 2).

The abstract (and entire paper) requires a thorough editing to correct word choice, grammar and punctuation. For example, the current word choice in the abstract makes it difficult to ascertain what are exactly the independent predictors for relapse: “being late smoking after waking up every day”?

Authors’ response: Thank for your comments. We have checked and clarified terms carefully, and used American Journal Experts (AJE) service to edit the English language. For example, we amended “being late smoking after waking up every day” to “longer time to first cigarette in the morning”.

Methods

(1)“Ever smokers” are defined as greater than 100. This value seems excessively high. (2) How were health status and self-perceived poor health information defined/extracted? (3) Were data analyses corrected for multiple comparisons?

Authors’ response: Thanks for your comments. (1) We have clarified that “Ever smokers in the CHARLS study were defined as participants who smoked more than 100 cigarettes in their lifetime, according to answers to the following question: “have you ever chewed tobacco, smoked a pipe, smoked self-rolled cigarettes, or smoked cigarettes/cigars?” (page 6, para 2). (2) The perception of health by participants was measured by asking the following question: “Would you say your health is very good, good, fair, poor or very poor?” (page 7, para 1). (3) We did not attempt to correct for multiple comparisons, which has now been mentioned as a limitation in Discussion: “Third, we conducted multiple statistical comparisons, and did not correct for possibly inflated type I errors.”

Results

Please include at the beginning of table 1 the overall quit rates for 2013 and 2015. The tables and figure are all very informative.

Authors’ response: Thank for this suggestion. We have added the total quit rates for 2013 and 2015 in Table 1.

The Discussion is comprehensive and appropriate, although again, the paper requires a writing service to correct the language problems.

Authors’ response: We have revised the discussion by focusing on some unclear points, and received help on language problems from American Journal (AJE) service team.

Reviewer #2: This manuscript reports on a secondary analysis with the aim of identifying predictors of quitting and relapse in a sample of 3708 Chinese smokers who participated in a parent study (a general health epidemiology survey) in which they provided data on smoking status at baseline (2011), 2-year(2013), and 4-year (2015) follow ups. This study makes a contribution by focusing on middle-aged smokers (> 45 yrs), which is useful since the over 40 yr old group represents increased health risk of continued smoking. Another novel aspect of the study is the representation of participants from rural China, which adds to the work that has been done in this area. Quit rates were approx. 10% at the 2 year follow up and 17% at the 4-year follow up with 86% of quitters at the 2 year follow up also reporting abstinence at the 4-year follow up. Predictors of quitting in multivariate analysis included older age, higher levels of nicotine dependence, and poorer self-rated health. This replicates other work done internationally as well as work done in China.

Authors’ response: Thanks for reviewer’s positive comments.

Suggestions:

As reported, I found the quit rates somewhat confusing. Since the authors say they will calculate quit rates as follows: “The quit rate was estimated by dividing the number of quitters at the follow-up survey by the number of smokers at baseline,” I think it would be useful to report the quit rate at 2013 as well as the quit rate at 2015 in terms of the entire sample, (617 self-reported being quit at the 2015 follow up, which would be a quit rate of 16.6% ) rather than just reporting the % quit at 2015 of those who were smoking at 2013.

Authors’ response: Thanks for this suggestion. We have now revised the manuscript to improve the understandability and corrected language errors. In the method section, we clarified the method to calculate the quit rate: “The quit rate was estimated by dividing the number of quitters at the end of the follow-ups in 2013 and 2015 by the number of current smokers before the follow-up in 2011 and 2013 (see Fig 1).” In Result section, quit rates at the two follow-ups were reported: “The overall quit rate was 8.5% (95% CI 7.7%-9.5%) at the 2-year follow-up in 2013, and 16.6% (95% CI 15.5%-17.9%) at the 4-year follow up.”

An overall quit rate of 16.6% is reported in the manuscript from 2011-2015, is this meant to be the point prevalence self-reported quit rate at the 2015/4-year follow up? If yes would suggest changing the terminology to reflect this.

Authors’ response: Indeed, the quit rates were based on point prevalence self-reported quit. We have clarified this in Method section: “The quit rate was estimated by dividing the number of quitters at the end of the follow-ups in 2013 and 2015 by the number of current smokers before the follow-up in 2011 and 2013 (Fig 1) . Therefore, quit rates in this study were point self-reported quit rates.”

Between the 2 and 4 year follow-ups, a continuous quit rate of 7.4% (275/3708) was reported, however, was continuous quit rate actually assessed or could this be capturing people who happen to report being quit at the two assessments, but have not been continuously quit? If the latter is true, needs to be mentioned as a limitation that could be overestimating continuous quit rates. Also, although this does represent the “continuous” quit rate in the total sample, would suggest the authors emphasize the high % of quitters who maintained abstinence (86.7%) as well as noting, as they do, the relapse rate of 13.1% .

Authors’ response: Thanks for this comment. We have clarified this as a limitation: “Second, we measured point smoking cessation status at the two follow-ups in 2013 and 2015, and did not assess possible changes in smoking status between the assessments”. (page16, para 3)

I do not understand what is meant here: “To facilitate the comparison with findings from different studies, the annual quit rate was estimated based on the quit rates for different follow-up years and an assumption of constant rates of still smoking during the follow-up.”

Authors’ response: Reported quit rates in different studies were often based on different follow-up periods, and were not directly comparable. In order to compare results of different studies, an estimate of annual quit rate as a common outcome is helpful. We have now added more details on method and the equation used to convert 2-year or 4-year quit rates to an annual quit rate in the manuscript: “To facilitate comparisons with different studies, we estimate the annual quit rate (q) based on an assumption of constant quit rate during the follow-up: q=1-exp⁡{ln⁡(1-nq/ns)/yr}, in which ns refers smokers at the beginning of follow-ups, nq refers the number of quitters at the end of follow-ups, and yr refers the number of follow-up years.” (page 7, para 2).

Two limitations that should be emphasized are the reliance on self-report data and on a completer analysis. Reliance on self-report data is mentioned in the discussion, but deserves more attention, particularly whether there are reasons people might lie about their smoking status (in the US, for example, people lie about this to health insurers because they do not want their rates to be higher). Statistical tests should be done to evaluate differences between those who completed both follow-ups to those who did not.

Authors’ response: We agree with these helpful comments. We have amended “limitation” section, with more explanations: “First, the study relied on self-reported data and excluded participants with missing data on follow-ups, which may result in recall bias and errors.” (page16, para 3). Because of resource and time restrictions, we are unable to conduct further analyses to compare participants included and those excluded for missing data reasons. We mentioned that “these limitations should be appropriately addressed in further studies.” (page 15, para 2)

An alternative to age started smoking and number of cigs/day as separate predictor variables would be to create a composite variable “pack years.”

Authors’ response: Thanks for suggesting the use of a composite variable as an alternative index of tobacco dependence. Because the effects of age of smoking initiation and cigs/day tended to be in the same direction, the use of a composite variable may not materially change the results of this study.

Did any unique predictors emerge to predict “continuous quit?” (Or are these all just the opposite of the predictors of relapse?)

Authors’ response: Thanks for this interesting question. In deeded, continued smoking and relapse tended to be associated with similar predictors (such as age, tobacco dependence, and self-perceived health), although the statistical significances may change due to different sample/even sizes.

Minor comments:

Throughout manuscript, when citing predictors of quitting, specify direction (e.g., age) of the effect

Authors’ response: Thanks for this helpful advice. We have revised the manuscript to indicate the direction of the effects, for example: “Smoking cessation in 2013 was associated with not living in the northeast region (P=0.003), fewer cigarettes smoked daily (P<0.001), and longer time to first cigarette in the morning (P<0.001)”. (Abstract)

“The starting smoking age” should be rephrased “Age started smoking”

Authors’ response: Thanks for this advice. We have now used “age started smoking” or “age at starring smoking in the manuscript.

A few grammatical errors in this statement in the Abstract: having less smoking number use, longer time to first cigarette upon waking. Independent predictors of staying quit in 2015 included being younger, being late smoking after waking up every day, having shorter smoking age

Authors’ response: Thanks for pointing out these errors. We have revised the abstract (and the whole manuscript) to correct any identified grammatical errors. The revised abstract result section: “The overall quit rate was 8.5% (95% CI 7.7% - 9.5%) at the 2-year follow-up in 2013, and 16.6% (95% CI 15.5% - 17.9%) at the 4-year follow up. Smoking cessation in 2013 was associated with not living in the northeast region (P=0.003), fewer cigarettes smoked daily (P<0.001), and longer time to first cigarette in the morning (P<0.001). Smoking cessation in 2015 was associated with older age (P=0.049), smoking initiation at age ≥20 years (P<0.001), longer time to first cigarette in the morning (P<0.001), and self-perceived poor health (P<0.001). Of the 317 participants who stopped smoking in 2013, 13.3% (95% CI 9.9% - 17.5%) relapsed by 2015. Smoking relapse was associated with younger age (P=0.025), shorter time to first cigarette in the morning (P=0.003), and self-perception of good health (P=0.018).”

Reviewer #3: This is a study that examined smoking cessation, smoking relapse and related factors among middle-aged and older(≧45) smokers based on a secondary analysis of the dataset of the the China Health and Retirement Longitudinal Study (CHARLS). Here are some comments that may be helpful to improve the quality of this manuscript. -The language needs to be further edited by native English speakers.

Authors’ response: Thank Reviewer #3 for helpful comments. We have revised the manuscript carefully. In addition, the manuscript has been proofread by a writing service by American Journal Experts (AJE).

Abstract:

-The statistics of independent correlates of smoking quit, and staying quit should be provided in both the Abstract and the main text, such as p values, and ORs with 95%CI.

Authors’ response: Thanks for these suggestions. We have revised the abstract and manuscript to provide details on p values, OR and 95% Cis where possible. Due to word count limitation, we reported 95%Cis for quit and relapse rates and p values for significant predictors in the Abstract. As an example, here is the revised abstract result: “The overall quit rate was 8.5% (95% CI 7.7% - 9.5%) at the 2-year follow-up in 2013, and 16.6% (95% CI 15.5% - 17.9%) at the 4-year follow up. Smoking cessation in 2013 was associated with not living in the northeast region (P=0.003), fewer cigarettes smoked daily (P<0.001), and longer time to first cigarette in the morning (P<0.001). Smoking cessation in 2015 was associated with older age (P=0.049), smoking initiation at age ≥20 years (P<0.001), longer time to first cigarette in the morning (P<0.001), and self-perceived poor health (P<0.001). Of the 317 participants who stopped smoking in 2013, 13.3% (95% CI 9.9% - 17.5%) relapsed by 2015. Smoking relapse was associated with younger age (P=0.025), shorter time to first cigarette in the morning (P=0.003), and self-perception of good health (P=0.018).”

Methods:

-Statistics: Please specify which type of multiple logistic regression analyses were performed. Stepwise or other types? One- or two-sided tests? Clarify how to select the independent variables?

Authors’ response: We did not use stepwise analyses to select independent variables in multiple logistic regression analyses, and have fully reported the analysis results (statistically significant or not). We have clarified variable selection in Method section: “The selection of independent variables was restricted by data availability, and according to our understanding of factors that may be possibly associated with the dependent variable.”

Results:

-Table 1: add (years) for Age.

Authors’ response: Thanks and we have added “years” for age in Table 1. (page10)

Discussion:

-Strengths/Limitations: This is not a national survey.

Authors’ response: CHARLS covers 28 provinces, 150 county-level units and 450 village-level units in China. The sampling methods aimed to have a nationally representative sample of Chinese adults aged ≥45 years.

-All Table/Figures should appear after the ref list, rather than in the text. This is general knowledge when drafting a manuscript.

Authors’ response: Thanks for your comments. According to PLOS ONE’s style requirements, we have now removed Fig 1 from the manuscript (submitted separately as an independent image file). However, tables remain “directly after the paragraph in which they are first cited”.

22 Sep 2020

PONE-D-20-20662R1

Smoking cessation and related factors in middle-aged and older Chinese adults: Evidence from a longitudinal study

PLOS ONE

Dear Dr. Song,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please make the following changes so that your manuscript will be acceptable for publication. Note – that the comments below are with reference to the tracked changes resubmitted version

Abstract and throughout the manuscript – P for probability should be “p” and not “P”

Abstract page 2 – line 31 and line 33: correct grammar to state – “longer time to the first cigarette” - note addition of “the”

Page 3 line 38: Conclusions: Because data were only collected every two years, it is not feasible to state what % of persons quit smoking annually.  Please remove this statement from the abstract and also from other parts of the manuscript.

Page 5 line 71 – delete “the”

Page 6 line 86- change to “ participants’ “

Page 6 – line 89 – delete the word “respectively”

Page 6 line 91 – Please clarify – Participants were those who were followed up in BOTH 2013 and 2015?  If so, please state

Page 7 – line 107 – persons who were quitters at both time points were not necessarily “continuous” quitters as the data were point prevalences.   They were quitters at both time points.

Page 7 line 115 – here and elsewhere use the term “sex” and not “gender”

Page 8 – please delete text about how the annual quit rate was calculated.  If persons were queried every 2 years, it is not known what is the annual quit rate.

Page 8 line 128 and 129 – if this text is to be retained note that the correct grammar is “…refers to the number of ….”

Page 8 line 133 – should be “Logistic regressions” (note plural)

Page 8 line 138 – last word on page and going to next page– note that “health status” is cited twice

Page 9 – line 155 – please delete results about the annual quit rate

Page 10 – line 158 – I don’t think you can say that the quit rate was “continuous” – just that quit status was reported at both time points

Page 10 – line 162 – remove the word “ statistically” and just say “was significantly associated”

Page 10 – line 164 – the same issue as above

Page 10 – line 168.  Is the variable “deterioration of self-perceived health” or just “self perceived health”

Page 10 – line 170 – replace “gender” with “sex”

Page 12 – line 175 – should be “logistic regressions” (plural)

Page 14 – line 193 – is the variable “good health” or is it “not poor health”?

Page 14 – line 196, should be “or” not “and”

Page 14 – line 204 and continuing to next page – remove reference to annual quit rate and also the discussion of the annual quit rate

Page 16 – line 233, insert a “the” – should read “time to the first cigarette in the morning”

Page 17 – line 245 – delete “to” – should read “must be further strengthened”

Page 17 – line 248 – typo- note spelling - should be “medications”

Page 19 – line 286 – eliminate statement about annual quit rate

Please submit your revised manuscript by November 1, 2020. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

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Faith B. Dickerson

Academic Editor

PLOS ONE

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26 Sep 2020

Response to comments (R2)

Dear Dr Dickerson

Thank you very much for your swiftly completing the review of our revised manuscript (R1), with very detailed comments. We have revised the manuscript accordingly, and hope it is now satisfactory for being published in PLOS ONE. Please see our point-by-point response to the comments below.

1. Abstract and throughout the manuscript – P for probability should be “p” and not “P”

Authors’ response: We have used “p” to replace “P” in abstract and throughout the manuscript.

2. Abstract page 2 – line 31 and line 33: correct grammar to state – “longer time to the first cigarette” - note addition of “the”

Authors’ response: Thanks for pointing out this error. We have added “the” where required.

3. Page 3 line 38: Conclusions: Because data were only collected every two years, it is not feasible to state what % of persons quit smoking annually. Please remove this statement from the abstract and also from other parts of the manuscript.

Authors’ response: We have removed annual quit rate from the abstract. Please see our detailed response to comment point #10 about “annual quit rate”.

4. Page 5 line 71 – delete “the”

Authors’ response: Thanks for this advice. We have deleted “the” from this sentence.

5. Page 6 line 86- change to “participants”

Authors’ response: Thanks, we have revised “participant” as “participants”.

6. Page 6 – line 89 – delete the word “respectively”

Authors’ response: Thanks for this suggestion. We have deleted the word “respectively”.

7. Page 6 line 91 – Please clarify – Participants were those who were followed up in BOTH 2013 and 2015? If so, please state

Authors’ response: Thanks for your comments. We have added “both” before “2013 and 2015”.

8. Page 7 – line 107 – persons who were quitters at both time points were not necessarily “continuous” quitters as the data were point prevalences. They were quitters at both time points.

Authors’ response: Thanks for this suggestion. We have deleted “continuous” before “quitters”.

9. Page 7 line 115 – here and elsewhere use the term “sex” and not “gender”

Authors’ response: We have adopted the recommendation and changed “gender” to “sex” in the manuscript.

10. Page 8 – please delete text about how the annual quit rate was calculated. If persons were queried every 2 years, it is not known what is the annual quit rate.

Authors’ response: Thanks but we disagree with this suggestion. Reported quit rates in different studies were often based on different follow-up periods. In order to compare results of different studies, we used survival analysis method to estimate annual quit rate as a common outcome. Otherwise, it would be impossible to compare results of different epidemiological studies. We believe our analysis approach is methodological sound and practically helpful. Your reason for removing the annual quit rate was that participants were followed up every 2 years. Actually the annual rate could be estimated based on 2 or more follow-up years. For example, a previous study had estimated “the annualized quit rate” in a cohort of current and former smokers followed over 13 years (reference: Hyland, et al. Predictors of cessation in a cohort of current and former smokers followed over 13 years. Nicotine Tob Res 2004 Dec;6 Suppl 3:S363-9.). Therefore, we would like to retain contents related to annual quit rate in the manuscript. We have added Hyland et al’s study as a reference in Method section, and used term “annualized quit rate” to avoid possible misunderstanding of “annual quit rate” in the manuscript. We have used the annualized quite rate mainly in the discussion to compare with results of other studies, and have deleted it from the abstract and conclusion.

11. Page 8 line 128 and 129 – if this text is to be retained note that the correct grammar is “…refers to the number of ….”

Authors’ response: Thanks for pointing out this error. We have added “to” after “refers”.

12. Page 8 line 133 – should be “Logistic regressions” (note plural)

Authors’ response: Thank you, we have amended “logistic regression” to “logistic regressions” throughout the manuscript.

13. Page 8 line 138 – last word on page and going to next page– note that “health status” is cited twice

Authors’ response: Thanks for this suggestion. We have deleted the repeated "health status"

14. Page 9 – line 155 – please delete results about the annual quit rate

Authors’ response: Thanks but please see our response to comment point #10.

15. Page 10 – line 158 – I don’t think you can say that the quit rate was “continuous” – just that quit status was reported at both time points

Authors’ response: Thanks for this suggestion. We amended “continuous quit rate” to “quit rate”.

16. Page 10 – line 162 – remove the word “ statistically” and just say “was significantly associated”

Authors’ response: Thanks for this suggestion. We amended to “was significantly associated”.

17. Page 10 – line 164 – the same issue as above

Authors’ response: Thanks, and we amended to “was significantly associated”.

18. Page 10 – line 168. Is the variable “deterioration of self-perceived health” or just “self- perceived health”

Authors’ response: It was the variable “deterioration of self-perceived health”. When smokers perceived the deterioration of their health，they are more likely to change their smoking behavior.

19. Page 10 – line 170 – replace “gender” with “sex”

Authors’ response: Thanks, and we have used “sex” to replace “gender” in the manuscript.

20. Page 12 – line 175 – should be “logistic regressions” (plural)

Authors’ response: Thanks, and we have amended “logistic regression” to “logistic regressions” throughout the manuscript.

21. Page 14 – line 193 – is the variable “good health” or is it “not poor health”?

Authors’ response: Thanks for this suggestion. We have amended to "not poor health".

22. Page 14 – line 196, should be “or” not “and”

Authors’ response: Thanks for pointing out these errors. We have amended to “or”

23. Page 14 – line 204 and continuing to next page – remove reference to annual quit rate and also the discussion of the annual quit rate

Authors’ response: Thanks, but we disagree with this suggestion. Please see our response to comment point #10.

24. Page 16 – line 233, insert a “the” – should read “time to the first cigarette in the morning”

Authors’ response: Thanks, and we have added “the” in the relevant place in the manuscript.

25. Page 17 – line 245 – delete “to” – should read “must be further strengthened”

Authors’ response: Thanks for this helpful advice. We have deleted “to” here.

26. Page 17 – line 248 – typo- note spelling - should be “medications”

Authors’ response: Thanks for this suggestion. We have amended the spelling.

27. Page 19 – line 286 – eliminate statement about annual quit rate

Authors’ response: Thanks, and we have deleted “annual quit rate” from the conclusion.

30 Sep 2020

PONE-D-20-20662R2

Smoking cessation and related factors in middle-aged and older Chinese adults: Evidence from a longitudinal study

PLOS ONE

Dear Dr. Song,

Thank you for submitting your manuscript to PLOS ONE and for the detailed changes that you have made. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please note below a few minor revisions, most related to English-language usage, that are required before the manuscript is fully acceptable.  These notes are with reference to the CLEAN version of the resubmitted manuscript.

Page 5, line 81 – should be “participants”

Page 7 – line 110 – should be “point prevalence” [add “prevalence”].  Here and elsewhere - page 16 - line 227.

Page 8, line 135 – delete “the” near end of the line

Page 11, row 152 – add “the” to “not living in the northeast region”

Page 12, line 162 – add “the: to “relapse in the 317 quitters”

Page 15, line 206 – typo at end of line – “the”

Page 15 – line 216 – add “had” to “…and who had perceived poor health”

Page 16 – line 223 – isn’t it two waves of follow-up not three?  Please correct here and elsewhere

Please submit your revised manuscript by November 1, 2020. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Faith B. Dickerson

Academic Editor

PLOS ONE

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1 Oct 2020

Please note below a few minor revisions, most related to English-language usage, that are required before the manuscript is fully acceptable. These notes are with reference to the CLEAN version of the resubmitted manuscript.

1 Page 5, line 81 – should be “participants”

Authors’ response: Thanks, we have revised to “anonymous data of participants” and amended “participant” to “participants”.

2 Page 7 – line 110 – should be “point prevalence” [add “prevalence”]. Here and elsewhere - page 16 - line 227.

Authors’ response: We agree with these helpful comments. We have made changes in Page 7 – line 110, and in page 16 - line 227.

3 Page 8, line 135 – delete “the” near end of the line

Authors’ response: Thanks for this advice. We have deleted “the” from this sentence.

4 Page 11, row 152 – add “the” to “not living in the northeast region”

Authors’ response: Thanks for pointing out this error. We have added “the” where required.

5 Page 12, line 162 – add “the: to “relapse in the 317 quitters”

Authors’ response: Thanks, and we have added “the” in the manuscript.

6 Page 15, line 206 – typo at end of line – “the”

Authors’ response: Thanks for this helpful advice. We have amended to “the smoking rate of general population”.

7 Page 15 – line 216 – add “had” to “…and who had perceived poor health”

Authors’ response: We have adopted the recommendation and changed to “…and who had perceived poor health” in the manuscript.

8 Page 16 – line 223 – isn’t it two waves of follow-up not three? Please correct here and elsewhere

Authors’ response: Thanks for this suggestion. We have amended to “two waves of follow-up”.

5 Oct 2020

Smoking cessation and related factors in middle-aged and older Chinese adults: Evidence from a longitudinal study

PONE-D-20-20662R3

Dear Dr. Song,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Faith B. Dickerson

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

7 Oct 2020

PONE-D-20-20662R3

Smoking cessation and related factors in middle-aged and older Chinese adults: Evidence from a longitudinal study

Dear Dr. Song:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Faith B. Dickerson

Academic Editor

PLOS ONE