A Comparison of Measured Airborne and Self-Reported Secondhand Smoke Exposure in the MADRES Pregnancy Cohort Study

Karl O’Sharkey; Yan Xu; Jane Cabison; Marisela Rosales; Thomas Chavez; Mark Johnson; Tingyu Yang; Seung-Hyun Cho; Ryan Chartier; Deborah Lerner; Nathana Lurvey; Claudia M Toledo Corral; Myles Cockburn; Meredith Franklin; Shohreh F Farzan; Theresa M Bastain; Carrie V Breton; Rima Habre

doi:10.1093/ntr/ntad202

. 2023 Oct 17;26(6):669–677. doi: 10.1093/ntr/ntad202

A Comparison of Measured Airborne and Self-Reported Secondhand Smoke Exposure in the MADRES Pregnancy Cohort Study

Karl O’Sharkey ^1,^2,^✉, Yan Xu ³, Jane Cabison ⁴, Marisela Rosales ⁵, Thomas Chavez ⁶, Mark Johnson ⁷, Tingyu Yang ⁸, Seung-Hyun Cho ⁹, Ryan Chartier ¹⁰, Deborah Lerner ¹¹, Nathana Lurvey ¹², Claudia M Toledo Corral ^13,¹⁴, Myles Cockburn ¹⁵, Meredith Franklin ^16,¹⁷, Shohreh F Farzan ¹⁸, Theresa M Bastain ¹⁹, Carrie V Breton ²⁰, Rima Habre ^21,²²

¹ Department of Epidemiology, University of California, Los Angeles, Los Angeles, CA, USA

² Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA

³ Spatial Sciences Institute, University of Southern California, Los Angeles, CA, USA

⁴ Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA

⁵ Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA

⁶ Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA

⁷ Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA

⁸ Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA

⁹ Research Triangle Institute, Research Triangle Park, NC, USA

¹⁰ Research Triangle Institute, Research Triangle Park, NC, USA

¹¹ Eisner Health, Los Angeles, CA, USA

¹² Eisner Health, Los Angeles, CA, USA

¹³ Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA

¹⁴ Department of Health Sciences, California State University Northridge, Northridge, CA, USA

¹⁵ Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA

¹⁶ Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA

¹⁷ Department of Statistical Sciences, School of the Environment, University of Toronto, Toronto, Ontario, Canada

¹⁸ Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA

¹⁹ Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA

²⁰ Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA

²¹ Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA

²² Spatial Sciences Institute, University of Southern California, Los Angeles, CA, USA

^✉

Corresponding Author: Karl O’Sharkey, PhD, MPH, Department of Epidemiology, University of California, Los Angeles, 650 Charles E. Young Dr. South, Los Angeles, CA 90095, USA. Telephone: +(310) 913-7323; E-mail: kosharkey@ucla.edu.

PMCID: PMC11109487 PMID: 37846518

Abstract

Introduction

Secondhand smoke (SHS) exposure during pregnancy is linked to adverse birth outcomes, such as low birth weight and preterm birth. While questionnaires are commonly used to assess SHS exposure, their ability to capture true exposure can vary, making it difficult for researchers to harmonize SHS measures. This study aimed to compare self-reported SHS exposure with measurements of airborne SHS in personal samples of pregnant women.

Methods

SHS was measured on 48-hour integrated personal PM_2.5 Teflon filters collected from 204 pregnant women, and self-reported SHS exposure measures were obtained via questionnaires. Descriptive statistics were calculated for airborne SHS measures, and analysis of variance tests assessed group differences in airborne SHS concentrations by self-reported SHS exposure.

Results

Participants were 81% Hispanic, with a mean (standard deviation [SD]) age of 28.2 (6.0) years. Geometric mean (SD) personal airborne SHS concentrations were 0.14 (9.41) µg/m³. Participants reporting lower education have significantly higher airborne SHS exposure (p = .015). Mean airborne SHS concentrations were greater in those reporting longer duration with windows open in the home. There was no association between airborne SHS and self-reported SHS exposure; however, asking about the number of smokers nearby in the 48-hour monitoring period was most correlated with measured airborne SHS (Two + smokers: 0.30 µg/m³ vs. One: 0.12 µg/m³ and Zero: 0.15 µg/m³; p = .230).

Conclusions

Self-reported SHS exposure was not associated with measured airborne SHS in personal PM_2.5 samples. This suggests exposure misclassification using SHS questionnaires and the need for harmonized and validated questions to characterize this exposure in health studies.

Implications

This study adds to the growing body of evidence that measurement error is a major concern in pregnancy research, particularly in studies that rely on self-report questionnaires to measure SHS exposure. The study introduces an alternative method of SHS exposure assessment using objective optical measurements, which can help improve the accuracy of exposure assessment. The findings emphasize the importance of using harmonized and validated SHS questionnaires in pregnancy health research to avoid biased effect estimates. This study can inform future research, practice, and policy development to reduce SHS exposure and its adverse health effects.

Introduction

Secondhand smoke (SHS) exposure causes over 600,000 yearly global deaths¹ and contributes to several health risks including reduced birthweight,^2,3 preterm births,⁴ and developmental effects and respiratory issues in infants and children.⁵ SHS during pregnancy can affect fetal health via oxidative stress,⁶ metabolic and endocrine disruption,⁷ and increased secretion of inflammatory cytokines such as IL-1β and TNF-α,⁸ leading to lower placental weight. While an estimated 33%–35% of nonsmokers worldwide are exposed to SHS,^1,9 children’s exposure rates are often much higher at 40%–50%.^1,9–11 SHS exposure is impacted by factors such as the number of smokers, patterns, location of smoking, and household ventilation and infiltration rates.^12,13 Self-reported exposure tends to be lower than more objective biomarker and monitor-based exposure assessments,^6,14 leading to questions regarding the actual prevalence of SHS exposure in different populations.

The primary method of assessing SHS exposure is through questionnaires, which inquire about the presence of smokers and the duration and intensity of exposure.¹⁵ However, response heterogeneity is high, with only about 17.9% (~35% in pregnancy studies) of questions being validated, and variation in question type and wording makes it challenging to harmonize questionnaire data across studies.¹⁶ Despite generally supporting the validity of self-reported assessment,^17,18 there is evidence of exposure misclassification,^19,20 particularly in pregnancy studies due to recall bias and social stigma.^6,21

Biomarkers of exposure, such as nicotine and cotinine levels in blood, urine, saliva, and hair, are considered the “gold standard” of measure of internal exposure.²² However, they can be invasive and have short half-lives, and cannot determine when and where exposure occurred.¹³ Air sampling can also be used to measure smoke-related particles or vapor phase chemicals in air through stationary^23,24 and personal monitoring.^25,26 Air pollutants commonly used to measure SHS concentration include particulate matter,²⁷ nicotine,²⁸ various metals and volatile organic carbons,²⁹ and elemental carbon fractions of PM_2.5.³⁰

Personal air pollution monitoring is a highly effective way of measuring individual-level exposures, but its use is limited due to being costly and short in duration.^29,30 The nondestructive multiwavelength approach used in this study has been shown to measure airborne SHS, Brown Carbon (BrC), and Black Carbon (BC) concentrations from personal PM_2.5 and has been successfully validated against other optical and thermal–optical approaches.³⁰ This study aimed to characterize personal PM_2.5-bound airborne SHS concentration as a surrogate of SHS exposure in the third trimester of pregnancy in the personal monitoring substudy of the Maternal and Developmental Risks from Environmental and Social Stressors (MADRES) pregnancy cohort and to evaluate the association between optical SHS measurements and self-reported SHS exposure from various questionnaires across pregnancy to determine the best formulation of questions that capture the variation in measured, personal PM_2.5-bound airborne SHS.

Methods and Materials

Study Population

The MADRES study is a longitudinal cohort study that focuses on the impact of environmental pollutants and other risk factors on maternal and infant health in more than 1000 primarily Hispanic, low-income participants in Los Angeles County, CA.³¹ The study began in November 2015, and recruited participants from four prenatal care provider partnerships who were at least 18 years old, fluent in Spanish or English, and less than 30 weeks pregnant at the time of recruitment. Exclusion criteria included physical, mental, or cognitive disabilities that would prevent informed consent, HIV positivity, multiple gestations, and current incarceration.

The present study analyzed data from a substudy within the MADRES cohort that included 214 pregnant individuals in their third trimester of pregnancy. The third trimester of pregnancy was originally chosen because it is a critical period for fetal development and has the most consistent evidence for increased risk of adverse birth outcomes from environmental exposures. Participants were asked to wear a personal monitoring device housed in a single-strap purse for 48 hours to measure their PM_2.5 exposure. Additional exclusion criteria for the personal monitoring study component included smoking households (where at least one currently active smoker lives permanently) to prevent PM_2.5 sampler overloading. However, this criterion was not consistently applied across the study and was later removed. The distribution of personal PM_2.5 and of SHS did not materially change between when smoking households were excluded (November 2018) and when this criterion was removed. The MADRES cohort and the substudy sample were comparable for demographic characteristics, birth outcomes, and outdoor air pollution metrics. The study was approved by the USC Institutional Review Board and all participants provided written informed consent.

Personal PM_2.5 and Airborne Secondhand Smoke (Airborne SHS) Exposure Monitoring

Personal PM_2.5 Exposure Sampling

Personal PM_2.5 sampling occurred in the integrated 48-hour personal PM_2.5 exposure monitoring substudy between October 2016 and February 2020. These participants were provided with a sampling apparatus enclosed in a purse, which included a Gilian Plus Datalogging Pump (Sensidyne Inc.) and a Harvard PM_2.5 personal environmental monitor with a preweighed 37-mm Pall Teflo filter attached to the shoulder strap, placed near the breathing zone. The pump was set to start actively sampling at midnight on the day following recruitment, with a flowrate of 1.8 L/min and 50% cycle. Trained, bilingual staff members gave instructions on proper usage and demonstrated how to wear the device.

Sampling device instructions emphasized proper usage and protection, including wearing the device during daily activities, ensuring proper placement and avoiding blocked inlets, and protecting it from water, heat, and pets. Exceptions were made for activities such as driving, showering, and sleeping, during which the device could be placed nearby but elevated from the ground and away from walls due to concerns of sampling artifacts. The device was picked up after the 48-hour sampling period and taken to the USC Exposure Analytics lab for processing. Filters were weighed gravimetrically to determine PM_2.5 mass concentrations.

Measured Airborne Secondhand Smoke (Airborne SHS) Concentrations in PM_2.5

Filters were analyzed using a seven-wavelength optical transmittance integrating sphere technique at RTI International, Inc, to determine concentrations of BC, BrC, and airborne SHS in PM_2.5 and are described in more detail in Lawless et al.^30,32 Lawless et al. used a chamber study to capture side-stream smoke of cigarettes at different exposure times to allow different mass loadings before assessing the quantity with the optical approaches. This optical method has been shown to perform well against other carbon measurement approaches.^30,32 It leverages the wavelength-dependent absorption properties of various carbonaceous components, which have different optical densities at differing wavelengths, allowing for the calculation of mass loadings using fitting algorithms.^30,32 For instance, BC highly absorbs light across the analysis spectrum (430–940 nm), while airborne SHS absorbs little infrared light and much more ultraviolet light, making it characterized by a yellowish-brown color.³² Airborne SHS (μg/m³) measured via this approach on the personal PM_2.5 samples was the main concentration of interest in this study and was used as a surrogate for SHS exposure.

Questionnaire and Electronic Medical Record Variables

Self-reported questionnaire data were collected from initial cohort recruitment until infant birth, from a sequence of in-person and telephone interview questionnaires by trained staff in either English or Spanish. This included a post-personal monitoring substudy exit survey (most time aligned with the personal exposure measurements), a third-trimester-specific questionnaire for the entire cohort, and data abstracted from electronic medical records (EMRs).

Maternal Demographic and Environmental/Home Characteristic Variables

Maternal demographics were collected, including age, race/ethnicity, education level, household income, marital status, parity, and US-born status. Additionally, environmental and household characteristics that may impact airborne SHS concentrations were evaluated, including home type, window opening time, air conditioning use, cooking smoke exposure, and candle or incense smoke exposure. Some variables were recategorized based on their distribution.

Self-Report Secondhand Smoke Exposure Questionnaire and EMR Variables

Self-reported smoking and SHS exposure questionnaire variables were accumulated from all possible MADRES questionnaires related to the third trimester to evaluate how different questionnaire type/wording and timepoint correlated with measured airborne SHS concentrations. The personal monitoring exit survey consisted of the following SHS questions: (1) approximately how much of the time were you close to cigarette, cigar, hookah, or pipe smoke from people smoking nearby? (2) If greater than none, how many people were smoking nearby? Within the third-trimester questionnaire, the following questions were asked: (1) Since we last saw you/spoke to you in your first/second trimester, excluding e-cigarettes, has anyone else living in your home smoked cigarettes, cigars or pipes inside the house? (2) Since we last saw you/spoke to you in your first/second trimester, how many hours per day have you been exposed to cigarette, cigar or pipe smoke because of smoking by others? Missing self-reported SHS exposure data in the third trimester were replaced with data from the second followed by the first-trimester questionnaires for <5% of participants. Finally, participants’ EMRs provided the following SHS variable: (1) history of any secondhand smoking exposure? Only self-reported SHS exposure was evaluated in this study since virtually all expectant mothers reported no personal smoking during pregnancy. In keeping with prior studies,^15,16 SHS exposure variables were grouped into three general domains: (1) presence of a smoker nearby, (2) intensity of exposure (number of smokers nearby), and (3) duration of exposure (time exposed to smoking). Several of the self-reported SHS variables underwent recategorization based on the distribution. Due to low cell count, we were not able to assess the impact of use of e-cigarettes in this present study.

Statistical Analysis

PM₂_.5 and Airborne SHS by Key Characteristics

Personal PM_2.5 and airborne SHS concentrations were log-transformed due to right skew, with a small offset added to zero values. Geometric means and standard deviations were calculated and analysis of variance (ANOVA) tests were used to determine whether exposures varied by sample demographic characteristics and environmental factors. Log-transforming airborne SHS allowed for the use of parametric tests, which have greater statistical power on skewed data than nonparametric tests like Kruskal–Wallis.³³

PM₂_.5 and Airborne SHS by Self-Reported SHS Exposure

Measured personal PM_2.5 and airborne SHS concentrations were then compared to self-reported SHS exposure to investigate whether questions related to smoker proximity, smoking intensity, and duration of airborne SHS exposure were useful for capturing the variation in airborne SHS measurements. Similarly, ANOVA tests were used to test whether measured airborne SHS exposure differed by levels of self-reported SHS exposure for all these questions. No statistical test was conducted on the EMR self-reported SHS question due to small cell counts.

Additional airborne SHS metrics were also considered in similar analyses to determine whether results were sensitive to the definition of airborne SHS exposure. The ratio of airborne SHS to personal PM_2.5 concentration was calculated to account or adjust for variable PM_2.5 mass concentrations. Additionally, a binary variable was created based on the 75th percentile airborne SHS cut-point (0.5 μg/m³), which was similar to a prior study that used 0.4 μg/m³ as a cut-point for airborne SHS exposure.²⁵ However, results are not included as both of these analyses did not reveal any additional information about the relationship between measured airborne SHS and self-reported SHS.

Statistical significance was determined with an alpha level of 0.05 for all tests. Models were assessed for violations of model assumptions and influential points. Analyses were conducted using SAS v9.4 and JMP 16 Pro (SAS Institute, Inc, Cary, NC).

Results

Descriptive Statistics

Out of 214 participants in the MADRES personal monitoring substudy, 8 were removed due to errors in personal PM_2.5 measurements, and 2 were removed due to missing airborne SHS measurements. The final sample included 204 mostly Hispanic participants (80.69%) with a mean age of 28.16 (6.01) years. About 63% had a prior pregnancy, and over 55% had completed high school or less. The geometric mean and standard deviation for personal PM_2.5 and airborne SHS concentrations were 17.70 (1.88) and 0.14 (9.41) μg/m³, respectively.

PM₂_.5 and Airborne SHS by Key Characteristics

Differences in personal PM_2.5 and airborne SHS concentrations were observed by demographic characteristics as presented in Table 1. Education level was statistically significantly associated with measured airborne SHS concentrations, with lower educated participants having greater airborne SHS concentrations (<12th grade: 0.16 μg/m³, completed high school: 0.27 μg/m³, some college: 0.08 μg/m³, completed college: 0.10 μg/m³; p = .015). There was also a marginally significant difference in personal PM_2.5 by education level (p = .093), with the lowest level (less than 12th grade) experiencing 19.42 versus 13.69 μg/m³ for the highest education level (completed college). While race/ethnicity was not statistically significantly associated with personal airborne SHS overall (p = .162), non-Hispanic Black participants had higher geometric mean airborne SHS exposures (0.28 μg/m³, compared to 0.14 and 0.07 μg/m³ for Hispanic and non-Hispanic Other participants, respectively).

Table 1.

Summary of Study Participants by Measured Personal PM_2.5 and Airborne SHS Concentrations

Characteristic	Mean (SD) or n (%)	PM_2.5		Airborne SHS
Characteristic	Mean (SD) or n (%)	Geometric mean (SD) (µg/m³)	p ^a	Geometric mean (SD) (µg/m³)	p ^a
Full sample	204	17.70 (1.88)		0.14 (9.41)
Maternal age (years)	28.16 (6.01)	—		—
Race and ethnicity			.244		.162
Hispanic	163 (80.69%)	18.03 (1.86)		0.14 (8.46)
Black, non-Hispanic	22 (10.89%)	18.66 (1.95)		0.28 (13.25)
Other, non-Hispanic	17 (8.42%)	13.85 (1.98)		0.07 (15.89)
Education			.093		.015
<12th grade	49 (24.26%)	19.42 (1.92)		0.16 (6.24)
Completed high school	64 (31.68%)	18.28 (1.77)		0.27 (8.79)
Some college	58 (28.71%)	18.12 (2.04)		0.08 (12.05)
Completed college	31 (15.35%)	13.69 (1.68)		0.10 (9.77)
Maternal income			.105		.615
Less than $15,000	41 (20.30%)	20.73 (1.80)		0.18 (15.52)
$15,000–$29,999	46 (22.77%)	16.98 (1.87)		0.11 (8.95)
$30,000+	41 (20.30%)	15.51 (1.90)		0.14 (6.99)
Don’t know	74 (36.63%)	17.91 (1.92)		0.15 (8.82)
Parity			.117		.749
Yes	128 (62.75%)	18.62 (1.86)		0.15 (8.87)
No	68 (33.33%)	16.08 (1.92)		0.14 (11.00)
Missing	8 (3.92%)	17.93 (1.90)		0.12 (7.63)
Marital status			.731		.654
Married	55 (26.96%)	18.53 (2.03)		0.13 (8.47)
Living together	88 (43.14%)	17.81 (1.65)		0.17 (10.02)
Single	47 (23.04%)	16.77 (2.09)		0.13 (8.47)
Missing	14 (6.86%)	17.09 (2.07)		0.09 (14.75)
Employment status			.812		.662
Employed	86 (42.36%)	16.99 (1.88)		0.12 (9.23)
Student	21 (10.34%)	17.35 (1.89)		0.15 (14.14)
Homemaker	55 (27.09%)	18.82 (1.94)		0.20 (7.60)
Unemployed	41 (20.20%)	18.19 (1.84)		0.13 (10.04)

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PM_2.5 = particulate matter with an aerodynamic diameter less than 2.5 µm; SD = standard deviation.

^aAnalysis of variance test; bolded = statistically significant at p value <.05; p values are for tests without missing or don’t know levels.

Differences in personal PM_2.5 and airborne SHS concentrations were observed by environmental factors such as housing characteristics (Table 2). Airborne SHS concentrations were significantly different by window opening behavior (as reported in the exit survey; p = .047) with higher concentrations in participants that reported opening their windows most and all of the time versus none and a little of the time, which was not observed with personal PM_2.5 mass. Personal PM_2.5 concentrations were marginally significantly different by home type (p = .099), with single-family homes having the lowest concentrations compared to multiunit residences. Self-reported candle or incense smoke exposure was significantly associated with personal PM_2.5 mass concentrations (p = .036) but no differences in airborne SHS concentrations were observed.

Table 2.

Summary of Environment/Household Characteristics by Measured Personal PM_2.5 and Airborne SHS Concentrations

Question	n (%)	PM_2.5		Airborne SHS
Question	n (%)	Geometric Mean (SD) (µg/m³)	p ^a	Geometric mean (SD) (µg/m³)	p ^a
Home type			.099		.059
Which best describes the home in which you currently live most of the time? ^c
Single-family home	71 (34.80%)	15.40 (1.74)		0.10 (9.21)
2-4 units	48 (23.53%)	19.37 (1.86)		0.26 (5.74)
5 + units	67 (32.84%)	18.40 (2.01)		0.13 (12.29)
Missing	18 (8.82%)	20.90 (1.90)		0.16 (9.66)
Window opening			.564		.047
How much of the time were windows (or porch/balcony doors if applicable) open in your home, when you were there with the sampler? ^b
Most and all of the time	125 (61.58%)	17.22 (1.75)		0.19 (8.24)
None and a little of time	78 (38.42%)	18.14 (2.05)		0.10 (10.45)
Air conditioner use			.930		.642
How much of the time was the air conditioner used in your home, when you were there with the sampler? ^b
A little, most, or all of the time	54 (26.60%)	17.45 (1.87)		0.13 (7.75)
None of the time	149 (73.40%)	17.61 (1.87)		0.15 (9.81)
Cooking smoke			.750		.255
How much of the time were you close to smoke or fumes from cooking (yourself, or nearby cooking by someone else), for example, burnt toast, barbeque, stir fry, etc.? ^b
A little, most, or all of the time	80 (39.41%)	17.26 (1.78)		0.12 (9.64)
None of the time	123 (60.59%)	17.77 (1.93)		0.17 (8.90)
Candle/incense smoke			.036		.235
How much of the time were you close to smoke from candles or incense burning nearby? ^b
A little, most, or all of the time	50 (24.63%)	20.62 (1.81)		0.11 (16.35)
None of the time	153 (75.37%)	16.67 (1.87)		0.16 (7.36)
Commute (in car, bus, or train)			.172		.184
Did you commute in a car, bus or train on roadways? ^c
Yes	174 (85.71%)	18.00 (1.84)		0.14 (9.90)
No	29 (14.29%)	15.16 (2.02)		0.24 (5.28)

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PM_2.5 = particulate matter with an aerodynamic diameter less than 2.5 µm; SD = standard deviation.

^aAnalysis of variance test; bolded = statistically significant at p value <.05; p value are for tests without missing or don’t know levels.

^bExit survey.

^cThird-trimester questionnaire.

PM₂_.5 and Airborne SHS Concentrations by Self-Reported SHS Exposure

Table 3 shows the association between self-reported SHS exposure questionnaire variables and personal PM_2.5 and airborne SHS concentrations. No significant associations were observed, but the number of people smoking nearby (referring to intensity of exposure) was found to be most correlated with airborne SHS concentrations. Exposure to two or more smokers (geometric mean (SD) 0.30 (9.72) μg/m³) was associated with higher airborne SHS concentrations compared to one smoker (0.12 (8.95) μg/m³) or zero smokers. (0.15 (9.26) μg/m³). Increasing duration of SHS exposure did not show a trend with higher airborne SHS concentrations.

Table 3.

Summary of Measured Personal PM_2.5 and Airborne SHS Concentrations by Self-Reported SHS Exposure Questionnaire Responses

Question	n (%)	PM_2.5		Airborne SHS
Question	n (%)	Geometric mean (SD) (µg/m³)	p ^a	Geometric mean (SD) (µg/m³)	p ^a
Presence of smoker nearby or in residence
			.445		.942
Approximately how much of the time were you close to cigarette, cigar, hookah or pipe smoke from people smoking nearby?^b
A little, most, or all of the time	80 (39.60%)	16.89 (2.04)		0.15 (9.37)
None of the time	122 (60.40%)	18.09 (1.75)		0.15 (9.26)
			.435		.870
Since we last saw you/spoke to you in your first/second trimester, excluding e-cigarettes, has anyone else living in your home smoked cigarettes, cigars or pipes inside the house?^c
Yes	11 (5.42%)	20.53 (1.67)		0.16 (12.20)
No	192 (94.58%)	17.60 (1.90)		0.14 (9.38)
			—		—
History of any secondhand smoking exposure?^d
Yes	3 (1.47%)	15.69 (1.56)		0.08 (55.93)
No	184 (90.20%)	17.66 (1.90)		0.15 (9.34)
Not Recorded	17 (8.33%)	18.59 (1.84)		0.10 (8.09)
Intensity of SHS exposure
			.432		.230
If greater than none, how many people were smoking nearby?^b
2+	27 (13.37%)	17.00 (1.77)		0.30 (9.72)
1	40 (19.80%)	15.69 (2.14)		0.12 (8.95)
0	122 (60.40%)	18.09 (1.75)		0.15 (9.26)
Don’t know	13 (6.44%)	20.88 (2.31)		0.07 (7.99)
Duration of SHS exposure
			.248		.831
Since we last saw you/spoke to you in your first/second trimester, how many hours per day have you been exposed to cigarette, cigar or pipe smoke because of smoking by others?^c
2+ h	15 (7.43%)	21.23 (1.70)		0.15 (7.27)
1–2 h	17 (8.42%)	14.61 (2.27)		0.20 (14.21)
0–1 h	170 (84.16%)	17.81 (1.86)		0.14 (9.40)

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PM_2.5 = particulate matter with an aerodynamic diameter less than 2.5 µm; SD = standard deviation.

^aanalysis of variance test; bolded = statistically significant at p value <.05; p values are for tests without missing or don’t know levels.

^bExit survey.

^cThird-trimester questionnaire.

^dAbstracted from electronic medical records.

Discussion

In this study, we evaluated airborne SHS exposure levels and their differences across key characteristics in a health disparities population in Los Angeles, CA. We also assessed how well self-reported SHS questions from different timepoints and wording formats/styles explained variations in personal airborne SHS exposure during pregnancy. To the best of our knowledge, this is the first study to compare an optically derived airborne SHS measure from a personal PM_2.5 exposure monitoring study to self-reported SHS questions during pregnancy, which is important due to the persistent effects of SHS and exposure misclassification concerns in health analyses, particularly in pregnancy studies.

Self-reported SHS exposure questions at different timepoints during the third trimester of pregnancy were not associated with measured airborne SHS in personal PM_2.5 samples; however, the question regarding the number of smokers nearby was most associated with airborne SHS measurements. Previous studies have reported that the number of smokers in a household accounted for a significant proportion of variation in serum cotinine levels.¹⁷ This emphasizes the need for further investigation into the sensitivity of specific question wording or types to variations in SHS exposure. In this study of pregnant women largely reporting no SHS exposure, 91% of participants had detectable airborne SHS concentrations, displaying the need to validate self-reported SHS questionnaires for use in health analyses to minimize the risk of biased results. Additionally, this finding has public health significance for it highlights SHS exposure in nonsmoking homes and among individuals that may not be aware they are exposed.

Although studies have found concordance between self-reported and objective SHS exposure measures in pregnancy studies,³⁴ many others have found discordance.^6,21,35 Exposure misclassification and potential bias in these analyses may result from the absence of a standardized battery of SHS exposure questionnaire measures, as well as underreported SHS exposure in validation studies.³⁶

The measured airborne SHS concentration in personal PM_2.5 samples was not associated with other combustion sources in the home reported in the available questionnaires, such as cooking or burning candles or incense, which adds to the confidence in our assumption that the measured airborne SHS concentration is what is being measured. Although, it was not possible to rule out self-report misclassification or a correlation with non-reported indoor exposures. Additionally, the optical method used in this study was optimized for burning cigarettes in chamber studies, so it may be more sensitive to very fresh or nearby SHS exposure.

There were notable discrepancies in responses from participants to similar self-reported SHS exposure questions taken at different time points. For instance, while 80 participants reported being close to people smoking nearby during the 48-hour personal sampling period, only 11 stated they lived with an indoor smoker and 3 had EMR-recorded SHS exposure. This indicates that the type and wording of questions, as well as the time window, can impact exposure misclassification when relying on self-reporting. This suggests that asking a self-report question about SHS exposure in a recent time period (past days vs. months) may be the preferred approach, with the question asking about the number of people smoking nearby the most correlated with exposure. These findings can also help inform clinical care and suggest that smoking-related questions in EMRs may benefit from refinement to more accurately assess SHS exposure in pregnancy as a critical time window.

Exposure to SHS may result in nondifferential misclassification, as participants may be unaware of their exposure. Additionally, underreporting due to social stigma associated with smoking (especially during pregnancy) may weaken health effect estimates. Woodruff et al.²⁰ reported modest associations between parental SHS report and both nicotine and cotinine hair samples of their child in Latinos, indicating there may be racial/ethnic differences in the sensitivity of self-reported questions. However, low sample sizes of Black and non-Hispanic Other participants prevented us from stratifying the analysis by race/ethnicity.

Airborne SHS concentrations in this study differed from previous studies, which ranged from “negligible” to 4.0 µg/m³.^25,26 As a pregnancy study, the participants in our study may be more likely to avoid smoking and being around smokers. Methodological differences in sample integration time and wear mode exist between our study and those mentioned here, with our 48-hour integrated personal exposure monitor located near the shoulder of participants.²⁶ Brook et al. used a monitoring vest over 24 hours, and Sloan et al. attached a personal exposure monitor to a stroller or diaper bag over a 7-day period. ^25,26 It is unclear whether these differences explain the differences in results found in our study.

Participants with lower educational attainment had higher airborne SHS exposures, consistent with previous research.^6,37 Lower parental education was also associated with higher SHS exposure in children.^38,39 Surprisingly, participants who had completed high school had the highest measured airborne SHS exposure. The reason for this remains unclear, and evaluating participants’ country of birth did not provide an explanation. Although no significant differences were observed by race/ethnicity, Black participants had airborne SHS measurements twice and four times higher than Hispanic and non-Hispanic Other participants, respectively, which is consistent with previous research showing Black individuals are exposed to higher levels of SHS.^40–42 Black individuals also have the highest prevalence of SHS exposure,⁴¹ which may be attributed to targeted tobacco product marketing in this community.⁴³ These results add to the literature that highlights disparities in SHS exposure among low-income populations, and the pressing need for further interventions aimed at reducing SHS exposure in the most vulnerable.

While a suspected major contributor to exposure, we did not observe significant differences in SHS exposure between employed and unemployed participants, suggesting that smoke-free public spaces may be effective in reducing exposure to SHS.^44–46 Moreover, we found no significant difference in SHS exposure by day of the week. However, we did observe that homemakers had higher airborne SHS levels compared to employed, students, and unemployed participants, although the reason for this is unclear.

Home characteristics are thought to play a role in SHS exposure.^12,47 In our study, pregnant women living in homes with high window opening had significantly higher airborne SHS exposure, potentially due to nearby units or residents and the impact of ventilation. However, we cannot rule out that window use was not a response to unreported smoking inside the unit. The type of home was also associated with SHS exposure, with multiunit residents experiencing higher levels. Leakage and infiltration from neighboring units may contribute to this.^47,48

Strengths of this study include the use of personal exposure monitoring and a multiwavelength optical absorbance technique to objectively measure SHS exposure in a health disparities population. This study sheds light on SHS exposure among pregnant women in Los Angeles, even in self-reported nonsmoking households. Additionally, the MADRES cohort is a well-characterized study, allowing for the evaluation of the relationship between objective measurements and self-reported SHS exposure data from different timepoints and questionnaires that capture various domains of exposure.

The sample size of this study is a potential limitation, which, while small for a population-based study, is quite large for personal exposure monitoring studies.^49,50 Despite this, significant differences in measured airborne SHS concentrations and several exposure factors were detected. Another limitation is that the optical carbon analysis method only measures the particle phase of airborne SHS in personal PM_2.5 and does not capture gas or volatile phases of SHS or specific chemical tracers like nicotine. However, this method has performed well in comparison to other optical and optical-thermal methods.³⁰ Additionally, the 48-hour sampling period during the third trimester of pregnancy may not reflect typical exposure during pregnancy due to mobility changes, or behavior changes in response to the monitoring phase. However, the study evaluated behavioral patterns across study questionnaires and found reasonable agreement, suggesting that behaviors were generally consistent across time (results not shown). Finally, this study was conducted in a primarily low-income Hispanic pregnancy study in LA County, making extrapolation to different communities and regions potentially difficult.

Overall, this work highlights that SHS exposure is endemic in this group of largely lower-income, Hispanic pregnant women in Los Angeles, CA, even in households that avoid smoking. SHS is a known risk factor for several adverse health outcomes; however, this study shows a poor correlation between self-reported questions and measured “true” exposure, highlighting a pressing need for better-selected questions and greater harmonization of questionnaire-based SHS measures. Additionally, more policies aimed at eliminating smoking in residential and other public locations are needed.

Acknowledgments

The authors gratefully acknowledge the contributions of Ms. Lisa Valencia and the larger MADRES team. We also thank the MADRES participants, their families, and our clinic partners for their time and effort.

Contributor Information

Karl O’Sharkey, Department of Epidemiology, University of California, Los Angeles, Los Angeles, CA, USA; Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.

Yan Xu, Spatial Sciences Institute, University of Southern California, Los Angeles, CA, USA.

Jane Cabison, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.

Marisela Rosales, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.

Thomas Chavez, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.

Mark Johnson, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.

Tingyu Yang, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.

Seung-Hyun Cho, Research Triangle Institute, Research Triangle Park, NC, USA.

Ryan Chartier, Research Triangle Institute, Research Triangle Park, NC, USA.

Deborah Lerner, Eisner Health, Los Angeles, CA, USA.

Nathana Lurvey, Eisner Health, Los Angeles, CA, USA.

Claudia M Toledo Corral, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA; Department of Health Sciences, California State University Northridge, Northridge, CA, USA.

Myles Cockburn, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.

Meredith Franklin, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA; Department of Statistical Sciences, School of the Environment, University of Toronto, Toronto, Ontario, Canada.

Shohreh F Farzan, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.

Theresa M Bastain, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.

Carrie V Breton, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.

Rima Habre, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA; Spatial Sciences Institute, University of Southern California, Los Angeles, CA, USA.

Funding

The study was supported by National Institute of Environmental Health Sciences (NIEHS R01ES027409, NIEHS P30ES007048 pilot funding), and the MADRES Center (NIEHS/National Institute on Minority Health and Health Disparities [NIMHD] P50ES026086, Environmental Protection Agency (EPA) 83615801, NIMHD P50MD015705).

Declaration of Interests

None declared.

Author Contributions

Karl O'Sharkey (Formal analysis [lead], Investigation [lead], Methodology [lead], Visualization [lead], Writing—original draft [lead], Writing—review & editing [lead]), Yan Xu (Data curation [equal], Writing—review & editing [equal]), Jane Cabison (Data curation [equal], Investigation [equal], Methodology [equal], Project administration [equal], Writing—review & editing [equal]), Marisela Rosales (Data curation [equal], Investigation [Equal], Methodology [equal], Project administration [equal], Writing—review & editing [equal]), Thomas Chavez (Data curation [equal], Writing—review & editing [equal]), Mark Johnson (Data curation [equal], Writing—review & editing [equal]), Tingyu Yang (Data curation [equal], Writing—review & editing [equal]), Seung-Hyun Cho (Methodology [equal], Writing—review & editing [equal]), Ryan Chartier (Methodology [equal], Writing—review & editing [equal]), Deborah Lerner (Data curation [equal], Writing—review & editing [equal]), Nathana Lurvey (Data curation [equal], Writing—review & editing [equal]), Claudia M. Toledo Corral (Investigation [equal], Writing—review & editing [equal]), Myles Cockburn (Investigation [equal], Writing—review & editing [equal]), Meredith Franklin (Methodology [equal], Writing—review & editing [equal]), Shohreh F. Farzan (Conceptualization [equal], Data curation [equal], Writing—review & editing [equal]), Theresa M. Bastain (Conceptualization [equal], Data curation [equal], Funding acquisition [equal], Investigation [supporting], Project administration [equal], Resources [supporting], Writing—review & editing [equal]), Carrie Breton (Conceptualization [equal], Data curation [equal], Funding acquisition [equal], Investigation [supporting], Project administration [equal], Resources [equal], Writing—review & editing [equal]), and Rima Habre (Conceptualization [equal], Data curation [equal], Funding acquisition [equal], Investigation [equal], Methodology [equal], Project administration [equal], Resources [equal], Supervision [equal], Visualization [equal], Writing—review & editing [equal])

Ethical Approval

Study procedures were approved by the USC Institutional Review Board (IRB) and all participants completed written informed consent at first study visit (IRB: HS-16-00530).

Data Availability

The datasets generated during and/or analyzed during the current study are not publicly available due human subjects research protections but are available from the corresponding author on reasonable request.

References

1. Oberg M, Jaakkola MS, Woodward A, Peruga A, Prüss-Ustün A.. Worldwide burden of disease from exposure to second-hand smoke: a retrospective analysis of data from 192 countries. Lancet. 2011;377(9760):139–146. [DOI] [PubMed] [Google Scholar]
2. Hawsawi AM, Bryant LO, Goodfellow LT.. Association between exposure to secondhand smoke during pregnancy and low birthweight: a narrative review. Respir Care. 2015;60(1):135–140. [DOI] [PubMed] [Google Scholar]
3. Pogodina C, Brunner Huber LR, Racine EF, Platonova E.. Smoke-free homes for smoke-free babies: the role of residential environmental tobacco smoke on low birth weight. J Community Health. 2009;34(5):376–382. [DOI] [PubMed] [Google Scholar]
4. Hoyt AT, Canfield MA, Romitti PA, et al. Does maternal exposure to secondhand tobacco smoke during pregnancy increase the risk for preterm or small-for-gestational age birth? Matern Child Health J. 2018;22(10):1418–1429. [DOI] [PubMed] [Google Scholar]
5. Office of Environmental Health Hazard Assessment. Environmental Tobacco Smoke as a Toxic Air Contaminant to Which Infants and Children May Be Especially Susceptible. Sacramento, CA: California Office of Environmental Health Hazard Assessment; 2006. [Google Scholar]
6. Argalasova L, Zitnanova I, Vondrova D, et al. Self-reported exposure to ETS (Environmental Tobacco Smoke), urinary cotinine, and oxidative stress parameters in pregnant women—the pilot study. Int J Environ Res Public Health. 2019;16(9):1656. [DOI] [PMC free article] [PubMed] [Google Scholar]
7. Flouris AD, Vardavas CI, Metsios GS, Tsatsakis AM, Koutedakis Y.. Biological evidence for the acute health effects of secondhand smoke exposure. Am J Physiol Lung Cell Mol Physiol. 2010;298(1):L3–L12. [DOI] [PubMed] [Google Scholar]
8. Niu Z, Xie C, Wen X, et al. Potential pathways by which maternal second-hand smoke exposure during pregnancy causes full-term low birth weight. Sci Rep. 2016;6(1):24987. [DOI] [PMC free article] [PubMed] [Google Scholar]
9. Pirkle JL, Flegal KM, Bernert JT, et al. Exposure of the US population to environmental tobacco smoke: the third national health and nutrition examination survey, 1988 to 1991. JAMA. 1996;275(16):1233–1240. [PubMed] [Google Scholar]
10. Fuentes-Leonarte V, Estarlich M, Ballester F, et al. Pre- and postnatal exposure to tobacco smoke and respiratory outcomes during the first year. Indoor Air. 2015;25(1):4–12. [DOI] [PubMed] [Google Scholar]
11. Wang CP, Ma SJ, Xu XF, et al. The prevalence of household second-hand smoke exposure and its correlated factors in six counties of China. Tob Control. 2009;18(2):121–126. [DOI] [PMC free article] [PubMed] [Google Scholar]
12. Apte MG, Gundel LA, Dod RL, et al. Indoor Measurements of Environmental Tobacco Smoke Final Report to the Tobacco Related Disease Research Program [Internet]. s. 2004. [cited 2021] p. LBNL--49148, 823921. Available from: http://www.osti.gov/servlets/purl/823921-qMUxuq/native/
13. Jaakkola MS, Jaakkola JJ.. Assessment of exposure to environmental tobacco smoke. Eur Respir J. 1997;10(10):2384–2397. [DOI] [PubMed] [Google Scholar]
14. Ashford KB, Hahn E, Hall L, et al. Measuring prenatal secondhand smoke exposure in mother–baby couplets. Nicotine Tob Res. 2010;12(2):127–135. [DOI] [PMC free article] [PubMed] [Google Scholar]
15. Avila-Tang E, Elf JL, Cummings KM, et al. Assessing secondhand smoke exposure with reported measures. Tob Control. 2013;22(3):156–163. [DOI] [PMC free article] [PubMed] [Google Scholar]
16. Pérez-Ríos M, Schiaffino A, López MJ, et al. Questionnaire-based second-hand smoke assessment in adults. Eur J Public Health. 2013;23(5):763–767. [DOI] [PubMed] [Google Scholar]
17. Kaufman FL, Kharrazi M, Delorenze GN, Eskenazi B, Bernert JT.. Estimation of environmental tobacco smoke exposure during pregnancy using a single question on household smokers versus serum cotinine. J Expo Anal Environ Epidemiol. 2002;12(4):286–295. [DOI] [PubMed] [Google Scholar]
18. Wipfli H, Avila-Tang E, Navas-Acien A, et al. ; Famri Homes Study Investigators. Secondhand smoke exposure among women and children: evidence from 31 countries. Am J Public Health. 2008;98(4):672–679. [DOI] [PMC free article] [PubMed] [Google Scholar]
19. Mourino N, Pérez-Ríos M, Santiago-Pérez MI, et al. Secondhand tobacco smoke exposure among children under 5 years old: questionnaires versus cotinine biomarkers: a cohort study. BMJ Open. 2021;11(6):e044829. [DOI] [PMC free article] [PubMed] [Google Scholar]
20. Woodruff SI, Conway TL, Edwards CC, Hovell MF.. Acceptability and validity of hair collection from Latino children to assess exposure to environmental tobacco smoke. Nicotine Tob Res. 2003;5(3):375–385. [DOI] [PubMed] [Google Scholar]
21. Xiao X, Li Y, Song X, et al. Discrepancy between self-reported and urine cotinine-verified environmental tobacco smoke exposure among rural pregnant women in China. Int J Environ Res Public Health. 2018;15(7):1499. [DOI] [PMC free article] [PubMed] [Google Scholar]
22. Benowitz NL. Cotinine as a biomarker of environmental tobacco smoke exposure. Epidemiol Rev. 1996;18(2):188–204. [DOI] [PubMed] [Google Scholar]
23. Kaur J, Prasad VM.. Air nicotine monitoring for second hand smoke exposure in public places in India. Indian J Community Med. 2011;36(2):98–103. [DOI] [PMC free article] [PubMed] [Google Scholar]
24. Semple S, Apsley A, Ibrahim TA, Turner SW, Cherrie JW.. Fine particulate matter concentrations in smoking households: just how much secondhand smoke do you breathe in if you live with a smoker who smokes indoors? Tob Control. 2015;24(e3):e205–e211. [DOI] [PubMed] [Google Scholar]
25. Brook RD, Bard RL, Burnett RT, et al. Differences in blood pressure and vascular responses associated with ambient fine particulate matter exposures measured at the personal versus community level. Occup Environ Med. 2011;68(3):224–230. [DOI] [PubMed] [Google Scholar]
26. Sloan CD, Weber FX, Bradshaw RK, et al. Elemental analysis of infant airborne particulate exposures. J Expo Sci Environ Epidemiol. 2017;27(5):526–534. [DOI] [PubMed] [Google Scholar]
27. Ratschen E, Thorley R, Jones L, et al. A randomised controlled trial of a complex intervention to reduce children’s exposure to secondhand smoke in the home. Tob Control. 2018;27(2):155–162. [DOI] [PMC free article] [PubMed] [Google Scholar]
28. Martínez-Sánchez JM, González-Marrón A, Martín-Sánchez JC, et al. Validity of self-reported intensity of exposure to second-hand smoke at home against environmental and personal markers. Gac Sanit. 2018;32(4):393–395. [DOI] [PubMed] [Google Scholar]
29. Apelberg BJ, Hepp LM, Avila-Tang E, et al. Environmental monitoring of secondhand smoke exposure. Tob Control. 2013;22(3):147–155. [DOI] [PMC free article] [PubMed] [Google Scholar]
30. Yan B, Kennedy D, Miller RL, et al. Validating a nondestructive optical method for apportioning colored particulate matter into black carbon and additional components. Atmos Environ. 2011;45(39):7478–7486. [DOI] [PMC free article] [PubMed] [Google Scholar]
31. Bastain TM, Chavez T, Habre R, et al. Study design, protocol and profile of the Maternal and Developmental Risks from Environmental and Social Stressors (MADRES) pregnancy cohort: a prospective cohort study in predominantly low-income Hispanic women in urban Los Angeles. BMC Pregnancy Childbirth. 2019;19(1):189. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6543670/ [DOI] [PMC free article] [PubMed] [Google Scholar]
32. Lawless P. Multiwavelength absorbance of filter deposits for determination of environmental tobacco smoke and black carbon. Atmos Environ. 2004;38(21):3373–3383. [Google Scholar]
33. Rasmussen JL, Dunlap WP.. Dealing with nonnormal data: parametric analysis of transformed data vs nonparametric analysis. Educ Psychol Meas. 1991;51(4):809–820. [Google Scholar]
34. Christensen AE, Tobiassen M, Jensen TK, et al. Repeated validation of parental self-reported smoking during pregnancy and infancy: a prospective cohort study of infants at high risk for allergy development. Paediatr Perinat Epidemiol. 2004;18(1):73–79. [DOI] [PubMed] [Google Scholar]
35. DeLorenze GN, Kharrazi M, Kaufman FL, Eskenazi B, Bernert JT.. Exposure to environmental tobacco smoke in pregnant women: the association between self-report and serum cotinine. Environ Res. 2002;90(1):21–32. [DOI] [PubMed] [Google Scholar]
36. Carroll RJ. Measurement error in epidemiologic studies. In: Encyclopedia of Biostatistics [Internet]. Hoboken, NJ: American Cancer Society; 2005. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/0470011815.b2a03082 [Google Scholar]
37. Levine H, Berman T, Goldsmith R, et al. Exposure to tobacco smoke based on urinary cotinine levels among Israeli smoking and nonsmoking adults: a cross-sectional analysis of the first Israeli human biomonitoring study. BMC Public Health. 2013;13(1):1241. [DOI] [PMC free article] [PubMed] [Google Scholar]
38. Bolte G, Fromme H; for the GME Study Group. Socioeconomic determinants of children’s environmental tobacco smoke exposure and family’s home smoking policy. Eur J Public Health. 2008;19(1):52–58. [DOI] [PubMed] [Google Scholar]
39. Protano C, Cammalleri V, Antonucci A, et al. Further insights on predictors of environmental tobacco smoke exposure during the pediatric age. Int J Environ Res Public Health. 2019;16(21):4062. [DOI] [PMC free article] [PubMed] [Google Scholar]
40. Shastri SS, Talluri R, Shete S.. Disparities in secondhand smoke exposure in the United States: national health and nutrition examination survey 2011-2018. JAMA Intern Med. 2021;181(1):134–137. [DOI] [PMC free article] [PubMed] [Google Scholar]
41. Tsai J. Exposure to secondhand smoke among nonsmokers—United States, 1988–2014. MMWR Morb Mortal Wkly Rep. 2018;67(48):1342–1346. Available from: https://www.cdc.gov/mmwr/volumes/67/wr/mm6748a3.htm [DOI] [PMC free article] [PubMed] [Google Scholar]
42. Wilson SE, Kahn RS, Khoury J, Lanphear BP.. Racial differences in exposure to environmental tobacco smoke among children. Environ Health Perspect. 2005;113(3):362–367. [DOI] [PMC free article] [PubMed] [Google Scholar]
43. Primack BA, Bost JE, Land SR, Fine MJ.. Volume of tobacco advertising in African American markets: systematic review and meta-analysis. Public Health Rep. 2007;122(5):607–615. [DOI] [PMC free article] [PubMed] [Google Scholar]
44. Wynne O, Bonevski B.. Developments in the research base on reducing exposure to second-hand smoke. Int J Environ Res Public Health. 2018;15(9):1873. [DOI] [PMC free article] [PubMed] [Google Scholar]
45. Max W, Sung HY, Shi Y.. Who is exposed to secondhand smoke? Self-reported and serum cotinine measured exposure in the US, 1999–2006. Int J Environ Res Public Health. 2009;6(5):1633–1648. [DOI] [PMC free article] [PubMed] [Google Scholar]
46. Park H, Cho S, Lee C.. Second hand smoke exposure in workplace by job status and occupations. Ann Occup Environ Med. 2019;31(1):3. [DOI] [PMC free article] [PubMed] [Google Scholar]
47. Price PN, Shehabi A, Chan RW, Gadgil AJ.. Indoor-Outdoor Air Leakage of Apartments and Commercial Buildings [Internet]. Sacramento, CA: California Energy Commission; 2006. [cited 2021] p. LBNL-60682. [Google Scholar]
48. Bonevski B, Paul C, Jones A, Bisquera A, Regan T.. Smoky homes: gender, socioeconomic and housing disparities in second hand tobacco smoke (SHS) exposure in a large population-based Australian cohort. Prev Med. 2014;60(1):95–101. [DOI] [PubMed] [Google Scholar]
49. Dadvand P, de Nazella A, Triguero-Mas M, et al. Surrounding greenness and exposure to air pollution during pregnancy: an analysis of personal monitoring data. Environ Health Perspect. 2012;120(9):1286–1290. [DOI] [PMC free article] [PubMed] [Google Scholar]
50. Suh HH, Zanobetti A.. Exposure error masks the relationship between traffic-related air pollution and heart rate variability (HRV). J Occup Environ Med. 2010;52(7):685–692. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

[CIT0001] 1. Oberg M, Jaakkola MS, Woodward A, Peruga A, Prüss-Ustün A.. Worldwide burden of disease from exposure to second-hand smoke: a retrospective analysis of data from 192 countries. Lancet. 2011;377(9760):139–146. [DOI] [PubMed] [Google Scholar]

[CIT0002] 2. Hawsawi AM, Bryant LO, Goodfellow LT.. Association between exposure to secondhand smoke during pregnancy and low birthweight: a narrative review. Respir Care. 2015;60(1):135–140. [DOI] [PubMed] [Google Scholar]

[CIT0003] 3. Pogodina C, Brunner Huber LR, Racine EF, Platonova E.. Smoke-free homes for smoke-free babies: the role of residential environmental tobacco smoke on low birth weight. J Community Health. 2009;34(5):376–382. [DOI] [PubMed] [Google Scholar]

[CIT0004] 4. Hoyt AT, Canfield MA, Romitti PA, et al. Does maternal exposure to secondhand tobacco smoke during pregnancy increase the risk for preterm or small-for-gestational age birth? Matern Child Health J. 2018;22(10):1418–1429. [DOI] [PubMed] [Google Scholar]

[CIT0005] 5. Office of Environmental Health Hazard Assessment. Environmental Tobacco Smoke as a Toxic Air Contaminant to Which Infants and Children May Be Especially Susceptible. Sacramento, CA: California Office of Environmental Health Hazard Assessment; 2006. [Google Scholar]

[CIT0006] 6. Argalasova L, Zitnanova I, Vondrova D, et al. Self-reported exposure to ETS (Environmental Tobacco Smoke), urinary cotinine, and oxidative stress parameters in pregnant women—the pilot study. Int J Environ Res Public Health. 2019;16(9):1656. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0007] 7. Flouris AD, Vardavas CI, Metsios GS, Tsatsakis AM, Koutedakis Y.. Biological evidence for the acute health effects of secondhand smoke exposure. Am J Physiol Lung Cell Mol Physiol. 2010;298(1):L3–L12. [DOI] [PubMed] [Google Scholar]

[CIT0008] 8. Niu Z, Xie C, Wen X, et al. Potential pathways by which maternal second-hand smoke exposure during pregnancy causes full-term low birth weight. Sci Rep. 2016;6(1):24987. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0009] 9. Pirkle JL, Flegal KM, Bernert JT, et al. Exposure of the US population to environmental tobacco smoke: the third national health and nutrition examination survey, 1988 to 1991. JAMA. 1996;275(16):1233–1240. [PubMed] [Google Scholar]

[CIT0010] 10. Fuentes-Leonarte V, Estarlich M, Ballester F, et al. Pre- and postnatal exposure to tobacco smoke and respiratory outcomes during the first year. Indoor Air. 2015;25(1):4–12. [DOI] [PubMed] [Google Scholar]

[CIT0011] 11. Wang CP, Ma SJ, Xu XF, et al. The prevalence of household second-hand smoke exposure and its correlated factors in six counties of China. Tob Control. 2009;18(2):121–126. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0012] 12. Apte MG, Gundel LA, Dod RL, et al. Indoor Measurements of Environmental Tobacco Smoke Final Report to the Tobacco Related Disease Research Program [Internet]. s. 2004. [cited 2021] p. LBNL--49148, 823921. Available from: http://www.osti.gov/servlets/purl/823921-qMUxuq/native/

[CIT0013] 13. Jaakkola MS, Jaakkola JJ.. Assessment of exposure to environmental tobacco smoke. Eur Respir J. 1997;10(10):2384–2397. [DOI] [PubMed] [Google Scholar]

[CIT0014] 14. Ashford KB, Hahn E, Hall L, et al. Measuring prenatal secondhand smoke exposure in mother–baby couplets. Nicotine Tob Res. 2010;12(2):127–135. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0015] 15. Avila-Tang E, Elf JL, Cummings KM, et al. Assessing secondhand smoke exposure with reported measures. Tob Control. 2013;22(3):156–163. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0016] 16. Pérez-Ríos M, Schiaffino A, López MJ, et al. Questionnaire-based second-hand smoke assessment in adults. Eur J Public Health. 2013;23(5):763–767. [DOI] [PubMed] [Google Scholar]

[CIT0017] 17. Kaufman FL, Kharrazi M, Delorenze GN, Eskenazi B, Bernert JT.. Estimation of environmental tobacco smoke exposure during pregnancy using a single question on household smokers versus serum cotinine. J Expo Anal Environ Epidemiol. 2002;12(4):286–295. [DOI] [PubMed] [Google Scholar]

[CIT0018] 18. Wipfli H, Avila-Tang E, Navas-Acien A, et al. ; Famri Homes Study Investigators. Secondhand smoke exposure among women and children: evidence from 31 countries. Am J Public Health. 2008;98(4):672–679. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0019] 19. Mourino N, Pérez-Ríos M, Santiago-Pérez MI, et al. Secondhand tobacco smoke exposure among children under 5 years old: questionnaires versus cotinine biomarkers: a cohort study. BMJ Open. 2021;11(6):e044829. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0020] 20. Woodruff SI, Conway TL, Edwards CC, Hovell MF.. Acceptability and validity of hair collection from Latino children to assess exposure to environmental tobacco smoke. Nicotine Tob Res. 2003;5(3):375–385. [DOI] [PubMed] [Google Scholar]

[CIT0021] 21. Xiao X, Li Y, Song X, et al. Discrepancy between self-reported and urine cotinine-verified environmental tobacco smoke exposure among rural pregnant women in China. Int J Environ Res Public Health. 2018;15(7):1499. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0022] 22. Benowitz NL. Cotinine as a biomarker of environmental tobacco smoke exposure. Epidemiol Rev. 1996;18(2):188–204. [DOI] [PubMed] [Google Scholar]

[CIT0023] 23. Kaur J, Prasad VM.. Air nicotine monitoring for second hand smoke exposure in public places in India. Indian J Community Med. 2011;36(2):98–103. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0024] 24. Semple S, Apsley A, Ibrahim TA, Turner SW, Cherrie JW.. Fine particulate matter concentrations in smoking households: just how much secondhand smoke do you breathe in if you live with a smoker who smokes indoors? Tob Control. 2015;24(e3):e205–e211. [DOI] [PubMed] [Google Scholar]

[CIT0025] 25. Brook RD, Bard RL, Burnett RT, et al. Differences in blood pressure and vascular responses associated with ambient fine particulate matter exposures measured at the personal versus community level. Occup Environ Med. 2011;68(3):224–230. [DOI] [PubMed] [Google Scholar]

[CIT0026] 26. Sloan CD, Weber FX, Bradshaw RK, et al. Elemental analysis of infant airborne particulate exposures. J Expo Sci Environ Epidemiol. 2017;27(5):526–534. [DOI] [PubMed] [Google Scholar]

[CIT0027] 27. Ratschen E, Thorley R, Jones L, et al. A randomised controlled trial of a complex intervention to reduce children’s exposure to secondhand smoke in the home. Tob Control. 2018;27(2):155–162. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0028] 28. Martínez-Sánchez JM, González-Marrón A, Martín-Sánchez JC, et al. Validity of self-reported intensity of exposure to second-hand smoke at home against environmental and personal markers. Gac Sanit. 2018;32(4):393–395. [DOI] [PubMed] [Google Scholar]

[CIT0029] 29. Apelberg BJ, Hepp LM, Avila-Tang E, et al. Environmental monitoring of secondhand smoke exposure. Tob Control. 2013;22(3):147–155. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0030] 30. Yan B, Kennedy D, Miller RL, et al. Validating a nondestructive optical method for apportioning colored particulate matter into black carbon and additional components. Atmos Environ. 2011;45(39):7478–7486. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0031] 31. Bastain TM, Chavez T, Habre R, et al. Study design, protocol and profile of the Maternal and Developmental Risks from Environmental and Social Stressors (MADRES) pregnancy cohort: a prospective cohort study in predominantly low-income Hispanic women in urban Los Angeles. BMC Pregnancy Childbirth. 2019;19(1):189. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6543670/ [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0032] 32. Lawless P. Multiwavelength absorbance of filter deposits for determination of environmental tobacco smoke and black carbon. Atmos Environ. 2004;38(21):3373–3383. [Google Scholar]

[CIT0033] 33. Rasmussen JL, Dunlap WP.. Dealing with nonnormal data: parametric analysis of transformed data vs nonparametric analysis. Educ Psychol Meas. 1991;51(4):809–820. [Google Scholar]

[CIT0034] 34. Christensen AE, Tobiassen M, Jensen TK, et al. Repeated validation of parental self-reported smoking during pregnancy and infancy: a prospective cohort study of infants at high risk for allergy development. Paediatr Perinat Epidemiol. 2004;18(1):73–79. [DOI] [PubMed] [Google Scholar]

[CIT0035] 35. DeLorenze GN, Kharrazi M, Kaufman FL, Eskenazi B, Bernert JT.. Exposure to environmental tobacco smoke in pregnant women: the association between self-report and serum cotinine. Environ Res. 2002;90(1):21–32. [DOI] [PubMed] [Google Scholar]

[CIT0036] 36. Carroll RJ. Measurement error in epidemiologic studies. In: Encyclopedia of Biostatistics [Internet]. Hoboken, NJ: American Cancer Society; 2005. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/0470011815.b2a03082 [Google Scholar]

[CIT0037] 37. Levine H, Berman T, Goldsmith R, et al. Exposure to tobacco smoke based on urinary cotinine levels among Israeli smoking and nonsmoking adults: a cross-sectional analysis of the first Israeli human biomonitoring study. BMC Public Health. 2013;13(1):1241. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0038] 38. Bolte G, Fromme H; for the GME Study Group. Socioeconomic determinants of children’s environmental tobacco smoke exposure and family’s home smoking policy. Eur J Public Health. 2008;19(1):52–58. [DOI] [PubMed] [Google Scholar]

[CIT0039] 39. Protano C, Cammalleri V, Antonucci A, et al. Further insights on predictors of environmental tobacco smoke exposure during the pediatric age. Int J Environ Res Public Health. 2019;16(21):4062. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0040] 40. Shastri SS, Talluri R, Shete S.. Disparities in secondhand smoke exposure in the United States: national health and nutrition examination survey 2011-2018. JAMA Intern Med. 2021;181(1):134–137. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0041] 41. Tsai J. Exposure to secondhand smoke among nonsmokers—United States, 1988–2014. MMWR Morb Mortal Wkly Rep. 2018;67(48):1342–1346. Available from: https://www.cdc.gov/mmwr/volumes/67/wr/mm6748a3.htm [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0042] 42. Wilson SE, Kahn RS, Khoury J, Lanphear BP.. Racial differences in exposure to environmental tobacco smoke among children. Environ Health Perspect. 2005;113(3):362–367. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0043] 43. Primack BA, Bost JE, Land SR, Fine MJ.. Volume of tobacco advertising in African American markets: systematic review and meta-analysis. Public Health Rep. 2007;122(5):607–615. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0044] 44. Wynne O, Bonevski B.. Developments in the research base on reducing exposure to second-hand smoke. Int J Environ Res Public Health. 2018;15(9):1873. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0045] 45. Max W, Sung HY, Shi Y.. Who is exposed to secondhand smoke? Self-reported and serum cotinine measured exposure in the US, 1999–2006. Int J Environ Res Public Health. 2009;6(5):1633–1648. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0046] 46. Park H, Cho S, Lee C.. Second hand smoke exposure in workplace by job status and occupations. Ann Occup Environ Med. 2019;31(1):3. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0047] 47. Price PN, Shehabi A, Chan RW, Gadgil AJ.. Indoor-Outdoor Air Leakage of Apartments and Commercial Buildings [Internet]. Sacramento, CA: California Energy Commission; 2006. [cited 2021] p. LBNL-60682. [Google Scholar]

[CIT0048] 48. Bonevski B, Paul C, Jones A, Bisquera A, Regan T.. Smoky homes: gender, socioeconomic and housing disparities in second hand tobacco smoke (SHS) exposure in a large population-based Australian cohort. Prev Med. 2014;60(1):95–101. [DOI] [PubMed] [Google Scholar]

[CIT0049] 49. Dadvand P, de Nazella A, Triguero-Mas M, et al. Surrounding greenness and exposure to air pollution during pregnancy: an analysis of personal monitoring data. Environ Health Perspect. 2012;120(9):1286–1290. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0050] 50. Suh HH, Zanobetti A.. Exposure error masks the relationship between traffic-related air pollution and heart rate variability (HRV). J Occup Environ Med. 2010;52(7):685–692. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

A Comparison of Measured Airborne and Self-Reported Secondhand Smoke Exposure in the MADRES Pregnancy Cohort Study

Karl O’Sharkey, PhD

Yan Xu, PhD

Jane Cabison, MSPM

Marisela Rosales, BA

Thomas Chavez, MS

Mark Johnson, MPH

Tingyu Yang, MS

Seung-Hyun Cho, PhD

Ryan Chartier, MS

Deborah Lerner, MD

Nathana Lurvey, MD

Claudia M Toledo Corral, PhD

Myles Cockburn, PhD

Meredith Franklin, PhD

Shohreh F Farzan, PhD

Theresa M Bastain, PhD

Carrie V Breton, ScD

Rima Habre, ScD

Abstract

Introduction

Methods

Results

Conclusions

Implications

Introduction

Methods and Materials

Study Population

Personal PM2.5 and Airborne Secondhand Smoke (Airborne SHS) Exposure Monitoring

Personal PM2.5 Exposure Sampling

Measured Airborne Secondhand Smoke (Airborne SHS) Concentrations in PM2.5

Questionnaire and Electronic Medical Record Variables

Maternal Demographic and Environmental/Home Characteristic Variables

Self-Report Secondhand Smoke Exposure Questionnaire and EMR Variables

Statistical Analysis

PM2.5 and Airborne SHS by Key Characteristics

PM2.5 and Airborne SHS by Self-Reported SHS Exposure

Results

Descriptive Statistics

PM2.5 and Airborne SHS by Key Characteristics

Table 1.

Table 2.

PM2.5 and Airborne SHS Concentrations by Self-Reported SHS Exposure

Table 3.

Discussion

Acknowledgments

Contributor Information

Funding

Declaration of Interests

Author Contributions

Ethical Approval

Data Availability

References

Associated Data

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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Links to NCBI Databases

Personal PM_2.5 and Airborne Secondhand Smoke (Airborne SHS) Exposure Monitoring

Personal PM_2.5 Exposure Sampling

Measured Airborne Secondhand Smoke (Airborne SHS) Concentrations in PM_2.5

PM₂_.5 and Airborne SHS by Key Characteristics

PM₂_.5 and Airborne SHS by Self-Reported SHS Exposure

PM₂_.5 and Airborne SHS by Key Characteristics

PM₂_.5 and Airborne SHS Concentrations by Self-Reported SHS Exposure