Characterization of urinary cotinine concentrations among non-smoking adults in smoking and smoke-free homes in the Korean national environmental health survey (KoNEHS) cycle 3 (2015–2017)

This study used data from KoNEHS cycle 3 (2015–2017), a representative cross-sectional sample of the population of Korea. KoNEHS cycle 3 was conducted between August 2015 to June 2017 to ensure homogeneity of the sample composition for each year considering regional and seasonal distribution. The study population of KoNEHS cycle 3 comprised 6167 individuals, including 2380 children (≥3 years old) and 3787 adults (≥19 years old). Different multi-stage stratified cluster sampling methods were used between children and adults. In the present study, data from adults were used. For adults, the first stratification was centered on local administrative districts and coastal areas based on the Population and Household Census 2015 provided by Statistics Korea. The second stratification was based on the proportion of residential-complex districts, as well as location within 5 km of the east, south, and west coast, which related to socio-economic status. Ultimately, KoNEHS cycle 3 included 233 districts nationwide, including 20 areas that had national air quality monitoring stations.

The KoNEHS cycle 3 collected questionnaires and urine and blood samples for 16 clinical tests and performed analyses for 26 harmful environmental substances (e.g., phthalates and VOC metabolites). In the present study, the urinary cotinine concentration, the primary metabolite of nicotine, was used as a biomarker for SHS exposure. Cotinine is a specific and sensitive biomarker for SHS exposure with an average half-life of 17 h [15]. This study was approved by the institutional review board of the NIER in Korea (NIER-2016-BR-003-01, NIER-2016-BR-003-03).

Based on questionnaires, the sample of 3787 adult participants was limited to never and former smokers (n = 3183). Next, our sample was limited to those who lived in apartments, attached housing, or detached housing (n = 3168) because participants who answered “others” could not be distinguished. We then limited our samples to participants whose proportion of daily time spent indoors at home, at the workplace, indoors somewhere other than home or workplace, on transportation, and outdoors as recorded by the questionnaires was more than 80% (i.e., 1152 of 1440 min) (n = 3094). Finally, our samples were limited to participants whose urinary creatinine concentrations were between 0.3 and 3.0 g/L [16] (n = 2701). Among the 2701 qualified participants, 126 were excluded because their urinary cotinine concentration was higher than the cut-off point of 53 μg/L [17], and they were suspected of being smokers. The cut-off point of 53 μg/L for distinguishing true smokers from true nonsmokers was previously determined in KoNEHS cycle 1 (2009–2011) using the receiver operating characteristic curve, which was 97.1% for sensitivity and 95.1% for specificity [17]. Ultimately, a total of 2575 non-smoking adults were included in the final analysis.

Non-smokers were classified as participants who answered “I have never smoked” and “I used to smoke in the past but not anymore.” Smokers were defined as participants who answered “I smoke now” and were excluded from this study. Among the non-smokers, those who lived in smoking homes were defined as those who responded “yes” and those lived in smoke-free homes were defined as those who responded “no” to the question “Do you live with any smokers at home?”

Socio-demographic characteristics such as sex (male or female), age (19–39, 40–59, or ≥ 60 years), type of housing (apartment, attached housing, or detached housing), household income (< 1000, 1000–1999, 2000–2999, or ≥ 3000 USD/month), ventilation duration at home (< 30, 30–59, 60–599, ≥600 min/day), self-reported weekly SHS exposure (no or yes), time spent in residential indoors (< 780, 780–1079, ≥1080 min/day) and outdoors (< 10, 10–69, ≥70 min/day), and job classification (unemployed, manual occupation, non-manual occupation, or hospitality venue worker). In Korea, an apartment was defined as a high-rise multifamily building more than or equal to five stories and an attached house was a multi-family house less than or equal to four stories. A detached house included single-family and multifamily houses less than or equal to three stories. These housing type is applied in the KoNEHS because it was based on legal classification in Korea.

For this study, jobs were classified into unemployed (unemployed, students, or stay-at-home parents), non-manual occupations (manager, office/service/sales workers, or expert/related workers), manual occupations (skilled/functional workers, machine operators, assembly/simple labor workers, or agricultural/forestry/fishery workers), and hospitality venues (restaurant, bar, cafe, fast-food franchise, or bakery workers) based on the job-classification code or written job title in the raw data from KoNEHS cycle 3. Similar classifications have been used in a previous study [18]. Jobs in hospitality venues in this study were classified separately from non-manual occupation to examine differences in urinary cotinine concentrations by job type after the implementation of smoke-free regulations in these places started from 1st January, 2015 [5].

Spot urine samples were collected at survey centers situated throughout the country and frozen at − 20 °C until laboratory analysis following the standard procedure by the NIER [19]. For urinary cotinine analysis, we added 250 μL of internal standard, 50 μL of 0.1 M sodium hydroxide, and 0.5 mL of chloroform to 3-mL urine samples. The solution was centrifuged, and the upper layer was removed. Next, 0.2 g of sodium sulfate was added to remove residual water and 3 μL of solution was injected into a gas chromatograph/mass spectrometer (Clarus 600 T, PerkinElmer, USA) to estimate urinary cotinine concentrations. The detail analytical methods for urinary cotinine have been described in elsewhere [14, 20]. The method detection limit (MDL) for urinary cotinine was 0.3 μg/L. Urinary cotinine concentrations below the MDL were assigned a value of 0.2 μg/L (MDL/ \( \sqrt{2} \)).

Statistical analyses were conducted using SAS 9.4 (SAS Institute, Inc., Cary, NC, USA). In the analysis, the domain option in SAS was used to control for subgroups with full clusters in reducing the dataset of interest. The PROC SURVEYFREQ function was used to calculate weighted percentages of socio-demographic variables and determine the differences in these percentages between those living in smoking and smoke-free homes.

Natural log (ln)-transformed urinary cotinine and creatinine concentrations were used in statistical analyses due to the skewness of the distribution of the untransformed data. SAS PROC SURVEYMEAN was used to calculate the weighted overall geometric means (GMs) and 95% confidence intervals (CIs) of the urinary cotinine concentrations. The same procedure was used to determine the weighted GM and 95% CI of the urinary cotinine concentrations of non-smoking adults living in smoking and smoke-free homes. Using SAS PROC SURVEYREG, covariate-adjusted least-square geometric means (LSGMs) and 95% CIs of urinary cotinine concentrations of non-smoking adults by subgroup living in smoking and smoke-free homes were estimated in weighted multivariable linear regression models. Socio-demographic variables, including sex, age, type of housing, household income, ventilation duration at home, weekly SHS exposure, time spent at residential indoors and outdoors, and job classification, were included as covariates. Furthermore, ln-transformed urinary creatinine concentrations included in the weighted linear regression models as an independent variable to adjust for hydration. A previous study reported that urinary creatinine concentrations is not only factor that affects hydration but also factor that affects age and several other factors [21]. Model based normalization to adjust for hydration and other factor have been used previously [22]. We also included interactions of the home smoking status with each socio-demographic variables to estimate weighted LSGMs and 95% CIs and to test the differences in urinary cotinine concentrations of non-smoking adults between living in smoking homes and smoke-free homes. The difference test of urinary cotinine concentrations were examined by including the home smoking status variable as a continuous variable in the weighted linear regression models. The false discovery rate (FDR) was controlled at the levels of 0.05 with the Benjamini–Hochberg’s correction for multiple comparison. Among the socio-demographic characteristics, we did not use education level, former smoker status, or time spent at the workplace because of potential collinearity between age and education level (Spearman’s rho = − 0.62), sex and former smoker status (Spearman’s rho = − 0.69), and time spent at residential indoors and that at the workplace (Spearman’s rho = − 0.72). Similar criteria have been used in previous studies [18]. The same SAS procedure was used to conduct weighted multivariable linear regression analysis to examine relationships between urinary cotinine concentrations and socio-demographic characteristics in non-smoking adults who lived in smoking and smoke-free homes. A p-value of less than 0.05 was considered statistically significant.

The characteristics of the non-smoking adults who lived in smoking and smoke-free homes are shown in Table 1. Female non-smoking adults were more likely to live in smoking homes (p < 0.001). Non-smoking adults who were younger (p < 0.001), those who had higher household income (p = 0.004), and those who were exposed to SHS weekly (p = 0.002) were more likely to live in smoking homes. Non-smoking adults who were unemployed were more likely to live in smoking homes (p < 0.001). However, the type of housing, ventilation duration at home, time spent at residential indoors, and time spent outdoors were not associated with living in a smoking or smoke-free home.

Urinary cotinine was detected in 2422 of 2575 samples (94.1%). The overall GM of the urinary cotinine concentrations of 2575 non-smoking adults was 1.5 μg/L (95% CI = 1.4–1.6). Urinary cotinine was detected in 95.5% of non-smokers living in smoking homes and 93.6% of non-smokers living in smoke-free homes. The distribution of urinary cotinine concentrations among non-smoking adults living in smoking and smoke-free home are shown in Fig. 1. The GM of urinary cotinine concentration for non-smoking adults living in smoking homes was 2.1 μg/L (95% CI = 1.8–2.4) and that for those in smoke-free homes was 1.3 μg/L (95% CI = 1.2–1.4), which was significantly different (p < 0.001).

The LSGM of urinary cotinine of non-smoking adults by living in smoking and smoke-free homes are shown in Table 2. Cotinine concentrations in the non-smoking-adult population were different significantly between living in smoking homes and smoke-free homes (p < 0.001). The concentrations in subgroups, including those based on sex, age, type of housing, household income, ventilation duration at home, weekly SHS exposure, time spent at residential indoor, and time spent at outdoor were different significantly by home smoking status. Regarding job classification, all levels except the hospitality venue employees, showed significant differences by home smoking status.

Among non-smoking adults living in smoking homes, urinary cotinine concentrations were significantly lower in non-smoking adults who worked in non-manual occupations than those who were unemployed (p = 0.002, Table 3). However, sex, age, type of housing, household income, ventilation duration at home, weekly SHS exposure, time spent at residential indoors, and time spent outdoors were not associated with urinary cotinine concentrations.

For smoke-free homes, urinary cotinine concentrations were significantly higher in non-smoking adults who lived at home with ventilation duration < 30 min/day than those with ventilation duration 60–599 min/day (p = 0.016). Urinary cotinine concentrations were significantly higher in non-smoking adults who spent ≥1080 min/day indoors at home than those who spent 780–1079 min/day indoors at home (p = 0.003). Urinary cotinine concentrations were significantly lower in non-smoking adults who spent ≥70 min/day outdoors than those who spent 10–69 min/day outdoors (p = 0.005). Urinary cotinine concentrations were significantly higher in non-smoking adults who worked in non-manual (p = 0.011) and manual occupations than those who were unemployed (p = 0.041). However, sex, age, type of housing, household income, and weekly SHS exposure were not significantly associated with urinary cotinine.