Study design and population
This study used data from Beijing Chronic Disease and Risk Factors Surveillance (BCDRFS) in 2017, a cross-sectional study conducted by Beijing Center for Disease Prevention and Control (Beijing CDC). The surveillance covered all 16 districts, 55 township streets, 165 villages or neighborhood committees of Beijing, and 13,240 residents aged 18 to 79 years old were investigated. Among them, 12,798 valid samples were analyzed. The study protocol was approved by the Ethical Review Board of BJCDC (The number is no. 5 of 2017). All participants signed written informed consent.
The contents of the surveillance included face-to-face questionnaire interviews, physical measurements and laboratory tests. The questionnaire collected information on demography, status of having chronic non-communicable diseases (NCD) and risk factors, including tobacco use, alcohol consumption, physical activity, food consumption, etc.
Anthropometric And Laboratory Measurements
Height, weight, waist circumference, and blood pressure were measured. Height was measured in the standing position to the nearest 0.1cm without shoes. Weight was measured to the nearest 0.1kg while the subjects were minimally clothed without shoes. Body mass index (BMI) was calculated as weight (kg) divided by square of height (m2). Overweight and obesity were defined based on BMI of 24 and 28 Kg/m2 as cutoff points recommended by the Working Group on Obesity in China13. Resting blood pressure (BP) and heart rate were measured 3 times with 1-minute intervals in the sitting position after 15 min of rest on the left arm by standard methods using electronic sphygmomanometer (HBP-1300, OMRON). Hypertension was defined as a systolic blood pressure (SBP) ≥ 140 mmHg, and/or diastolic BP (DBP) ≥ 90 mmHg, or previous medical diagnosis and the use of antihypertensive medications in the last two weeks 14. Diabetes mellitus (DM) was defined as either previous medical diagnosis of DM, or fulfillment of the diagnostic criteria for diagnosis based on fasting ≥ 126 mg/dl (7.0 mmol/l) in this survey 15. Fasting blood samples were obtained from participants after a 10 hours’ overnight fast. TC and HDL-C were measured by an enzymatic method using Hitachi auto-analyzer (Tokyo, Japan). We calculated non-HDL-C by subtracting HDL-C from TC.
Ascertainment Of Tse
The question on tobacco use included frequency, amount of smoking currently and in the past, and smoking cessation. Smoker was defined as a documented lifetime use of more than 100 cigarettes16, 17. The primary TSE variables were tobacco exposure status (unexposed, passively exposed, actively exposed), smoking intensity (number of cigarettes smoked per week among current smokers) and smoking burden (pack-years of smoking).
Based on the self-reported smoking patterns, tobacco exposure status was divided into three subgroups: tobacco unexposed (non-smoking and no exposure to secondhand smoke), passively exposed (non-current smokers but exposed to secondhand smoke), actively exposed (current smokers).
Smoking intensity and burden were two measures of tobacco exposure levels among current smokers, who were defined as smoking within the previous 30 days. Smoking intensity could reflect short-term exposure levels and it was divided into three categories: current light smoker (< 70 cigarettes per week), moderate smoker (≥ 70 and < 140 cigarettes per week) and heavy smoker (≥ 140 cigarettes per week)18. Smoking burden which was a comprehensive indicator of smoking amount and duration, was divided into two categories according to the median level: lower burden (< 20 pack-years) and higher burden (≥ 20 pack-years)19. Pack-years of smoking was defined as the product of the average number of packs of cigarette smoked per day multiplied by the average days of smoking in one year.
Covariates
We selected covariates into the analysis,which were age, gender, education status, alcohol drinking within 30 days, hypertension, type 2 diabetes, physical inactivity and overweight and obesity. Education status was self-reported as primary school and below, secondary school, or university degree and above. According to whether they had consumed alcohol within 30 days, participants were divided into two groups as current drinkers or not. Physical inactivity was defined as less than 150 minutes of moderate intensity activity or equivalent quantity per week according to WHO classification criteria20.
Statistical analysis
Statistical analyses were performed in complex sampling module which considering sampling weight and post-weight using SPSS software (ver. 21.0 for Windows; SPSS, Chicago, IL, USA) for all analyses. All tests were two-sided, and P < 0.05 was considered statistically significant.
Differences between covariates (i.e. age, education status, current drinking, physical inactivity, hypertension, diabetes, BMI, non-HDL-C) and tobacco exposed status (i.e., actively exposed, passively exposed, unexposed) were examined by performing logistic regression models adjusting age and sex (Table 1). Moreover, subgroup analysis was performed according to sex to explore the distribution of population characteristics among different genders.
Table 1
Age-adjusted demographic characteristics by tobacco exposure status for Beijing participants, aged 18–79 years old
Variable
|
Total subjects
|
Overall (N = 12798)
|
Men (N = 5994)
|
Women (N = 6804)
|
Actively exposed
(n = 3286)
|
Passively exposed
(n = 4573)
|
Unexposed
(n = 4939)
|
P
|
Actively exposed
(n = 3068)
|
Passively exposed
(n = 1414)
|
Unexposed
(n = 1512)
|
P
|
Actively exposed
(n = 218)
|
Passively exposed
(n = 3159)
|
Unexposed
(n = 3427)
|
P
|
Age (χ̅ ± SD, y)a
|
47.8 ± 0.6
|
46.3 ± 0.5
|
45.5 ± 0.6
|
51.2 ± 0.9
|
< 0.01
|
46.2 ± 0.5
|
45.1 ± 0.7
|
52.5 ± 1.2
|
< 0.01
|
49.2 ± 1.4
|
46.0 ± 0.6
|
49.6 ± 0.7
|
< 0.01
|
Education status (%)b
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Primary school
|
9.2
|
9.3
|
7.6
|
10.5
|
< 0.01
|
9.0
|
6.3
|
10.0
|
< 0.01
|
18.4
|
8.9
|
11.0
|
< 0.01
|
Secondary school
|
64.7
|
71.0
|
64.1
|
59.4
|
|
71.1
|
67.0
|
60.7
|
|
67.3
|
61.3
|
57.9
|
|
College degree
|
26.0
|
19.7
|
28.3
|
30.2
|
|
19.8
|
26.7
|
29.3
|
|
14.4
|
29.8
|
31.1
|
|
Current drinking within 30d (%)b
|
37.3
|
57.4
|
33.3
|
21.7
|
< 0.01
|
58.3
|
49.8
|
32.4
|
< 0.01
|
31.5
|
17.1
|
9.7
|
< 0.01
|
Physical inactivity (%)b
|
54.3
|
50.9
|
60.0
|
52.7
|
< 0.01
|
50.4
|
54.3
|
44.2
|
< 0.01
|
66.7
|
65.6
|
62.2
|
0.09
|
Hypertension (%)b
|
35.4
|
37.1
|
31.0
|
37.6
|
0.88
|
37.1
|
34.9
|
44.3
|
0.56
|
38.0
|
27.3
|
30.1
|
0.02
|
Diabetes (%)b
|
13.5
|
13.7
|
12.1
|
14.4
|
0.81
|
13.7
|
12.2
|
16.9
|
0.55
|
14.2
|
11.9
|
11.7
|
< 0.01
|
BMI (IQR, kg/m2)c
|
25.4 (23.1,27.9)
|
25.6(23.3,28.2)
|
25.3(23.0,28.1)
|
25.4(23.1,27.7)
|
0.01
|
25.6(23.4,28.2)
|
25.7(23.4,28.3)
|
25.9(23.7,27.9)
|
0.44
|
26.3(23.0,28.4)
|
25.1(22.5,27.6)
|
24.7(22.4,27.3)
|
< 0.01
|
Non-HDL-C (IQR,mmol/l)c
|
3.3(2.8,3.9)
|
3.4 (2.8, 4.0)
|
3.3(2.7, 3.8)
|
3.3(2.7, 3.9)
|
< 0.01
|
3.4 (2.8, 4.0)
|
3.3 (2.8, 3.8)
|
3.3 (2.7, 3.8)
|
< 0.01
|
3.4(2.8, 4.0)
|
3.3(2.7, 3.9)
|
3.3 (2.8, 4.0)
|
0.78
|
Note: aData are presented as mean (SD). bData are presented as number (percentage). c Data are presented as median (IQR). The estimates for age are not adjusted. |
Distribution of tobacco exposure status among male and female were showed Table 2. Secondhand smokers (tobacco passively exposed) were divided into 4 subgroups (0, 1 ~ 3, 4 ~ 6 and 7) according to days per week passively exposed to tobacco. Current smokers (tobacco actively exposed) were divided into 5 subgroups according to numbers of cigarettes smoked per week (< 40, 40~, 80~, 120~, and ≥ 160). Moreover, smoking intensity (light, moderate, heavy) and smoking burden (lower, higher) among current smokers were showed in Table 2. Logistic regression models were used to explore the different distribution in the same gender groups.
Table 2
Associated variables of tobacco exposure status among male and female
Variable
|
Total
(n, weighted %)a
|
Subgroup analysis (n, weighted %)b
|
Male
|
Female
|
Passively exposure among non-smokers (day/week, %)
|
|
|
|
0
|
4939 (54.1)
|
1512 (55.7)
|
3427 (52.4)
|
1–3
|
1754 (18.5)
|
643 (20.6)
|
1111 (16.3)
|
4–6
|
528 (5.7)
|
225 (6.9)
|
303 (4.4)
|
7
|
2291 (21.7)
|
546 (6.8)
|
1745 (27.0)
|
χ2
|
684.7
|
410.4
|
785.3
|
P value for trend
|
< 0.01
|
< 0.01
|
< 0.01
|
Actively exposure among current smokers (n/week, %)
|
|
|
|
< 40
|
629 (18.4)
|
545 (17.8)
|
84 (38.1)
|
40~
|
985 (28.4)
|
915 (28.1)
|
70 (34.8)
|
80~
|
366 (12.0)
|
351 (12.2)
|
15 (5.6)
|
120~
|
1026 (32.8)
|
985 (33.3)
|
41 (18.7)
|
≥ 160
|
268 (8.4)
|
261 (8.6)
|
7 (2.7)
|
χ2
|
322.8
|
302.2
|
50.9
|
P value for trend
|
< 0.01
|
< 0.01
|
< 0.01
|
Smoking intensity status (n, %)b
|
|
|
|
Light
|
878 (25.7)
|
772 (24.9)
|
106 (49.1)
|
Moderate
|
1137 (33.7)
|
1073 (33.8)
|
64 (29.5)
|
Heavy
|
1259 (40.6)
|
1212 (41.3)
|
47 (21.4)
|
χ2
|
43.2
|
48.0
|
13.8
|
P value for trend
|
< 0.01
|
< 0.01
|
0.001
|
Smoking burden status (n, %)c
|
|
|
|
Lower burden
|
1387 (43.4)
|
1278 (42.7)
|
109 (65.3)
|
Higher burden
|
1378 (56.6)
|
1316 (57.3)
|
62 (34.7)
|
χ2
|
20.7
|
24.7
|
7.6
|
P
|
< 0.01
|
< 0.01
|
0.01
|
Note: a n was expressed as un-weighted number and proportion was weighted. b Smoking intensity was divided into three categories: current light smoker (< 70 cigarettes per week), moderate smoker (≥ 70 and < 140 cigarettes per week) and heavy smoker (≥ 140 cigarettes per week). c Smoking burden was divided into two categories according to the median level: lower burden (< 20 pack-years) and higher burden (≥ 20 pack-years). |
Average non-HDL-C levels of subjects in each subgroup was calculated and plotted. The ANOVA analysis was conducted to compare the differences between groups (Fig A-B).
Participants were divided into two groups according to the mean value of serum non-HDL-C level with the cut-off value as 3.35mmol/l. Multivariate logistic regression analysis controlling for confounding factors including age, education status, alcohol drinking within 30 days, hypertension, type 2 diabetes and BMI was also performed to examine the association between non-HDL-C level and tobacco exposed status, smoking intensity or burden. Data are presented as odds ratio (OR) with 95% confidence interval (CI) (Table 3–4).
Table 3
Logistic regression results to estimate of association between higher non-HDL-C levels (cut-off value: 3.35mmol/l) and tobacco exposed status
Participants
|
Tobacco exposed status
|
higher non-HDL-C
|
χ2
|
OR (95%CI)
|
P
|
Total
|
Unexposed
|
Reference
|
|
|
Passively exposed
|
0.03
|
0.98 (0.78,1.23)
|
0.87
|
Actively exposed
|
11.34
|
1.34(1.20,1.59)
|
0.001
|
Male
|
Unexposed
|
Reference
|
|
|
Passively exposed
|
0.04
|
0.96 (0.82,1.13)
|
0.84
|
Actively exposed
|
8.00
|
1.30 (1.08,1.56)
|
0.005
|
Female
|
Unexposed
|
Reference
|
|
|
Passively exposed
|
0.21
|
0.97 (0.82,1.14)
|
0.65
|
Actively exposed
|
0.51
|
1.13 (0.79,1.62)
|
0.48
|
Note: Adjusted for age, education status, alcohol drinking within 30days, hypertension, type 2 diabetes, physical inactivity and BMI. |
Table 4
Logistic regression results between higher non-HDL-C levels (cut-off value: 3.43mmol/l) and smoking intensity and burden among current smokers
Participants
|
Total
|
Male
|
Female
|
χ2
|
OR (95%CI)
|
P
|
χ2
|
OR (95%CI)
|
P
|
χ2
|
OR (95%CI)
|
P
|
Smoking intensity: Light smoker as reference
|
Moderate smoker
|
0.96
|
1.09 (0.92,1.29)
|
0.33
|
1.16
|
1.10 (0.92,1.33)
|
0.28
|
0.44
|
0.76(0.33,1.77)
|
0.51
|
Heavy smoker
|
4.7
|
1.30 (1.02,1.67)
|
0.03
|
4.11
|
1.30 (1.00,1.70)
|
0.04
|
0.62
|
1.60 (0.48,5.35)
|
0.43
|
Smoking burden: Lower burden as reference
|
Higher burden
|
27.48
|
1.85 (1.46,2.35)
|
< 0.001
|
25.03
|
1.88 (1.46,2.43)
|
< 0.001
|
0.34
|
1.43 (0.41,4.98)
|
0.57
|
Note: Adjusted for age, education status, alcohol drinking within 30 days, hypertension, type 2 diabetes, physical inactivity and BMI. |