Secondhand Smoke Exposure Measured in Urinary Cotinine Levels and Severity of Pediatric Sleep Apnea

Key Points

Question

Is there an association between secondhand smoke exposure assessed by urinary cotinine and severity of sleep apnea in children?

Findings

This cohort study including 116 patients found that urinary cotinine was not associated with more severe pediatric obstructive sleep apnea. However, the small number of children with positive urine cotinine levels included in this study limit definitive conclusions regarding obstructive sleep apnea severity and secondhand smoke exposure.

Meaning

This cohort study did not identify clinically meaningful associations between secondhand smoke exposure as measured by urinary cotinine and sleep apnea severity in children.

Abstract

Importance

Exposure to secondhand smoke has been associated with numerous health problems in children, including obstructive sleep apnea. Secondhand smoke exposure may be a risk factor for increased pediatric sleep apnea severity.

Objectives

To assess the association of secondhand smoke exposure (SHSe), quantified by urinary cotinine levels, with severity of obstructive sleep apnea (OSA) in children.

Design, Setting, and Participants

This was a prospective cohort trial including pediatric patients from 3 to 16 years of age with sleep-disordered breathing who underwent a polysomnogram at a tertiary-level children’s hospital in the US in either March 2014 to October 2016 or March 2020 to March 2021. Urine specimens were analyzed for cotinine, an important metabolite of nicotine. Each child’s caregiver completed a validated SHSe questionnaire. Data were analyzed from February to June 2023.

Exposure

OSA and secondhand smoke.

Main Outcome and Measures

SHSe and severity of pediatric OSA, quantified by urinary cotinine levels and obstructive apnea hypopnea index (AHI) scores. Secondary outcomes were association of urinary cotinine levels with nadir oxygen saturation, sleep-related quality of life measured by the OSA-18 questionnaire, and caregiver-reported smoking habits (collected through a questionnaire).

Results

The study included 116 patients with a median (IQR) age of 6 (5-9) years, among whom 51 (45%) had obesity. The median (IQR) AHI was 3.0 (1.2-8.0), with 28 children (30.0%) having severe disease (AHI >10). Thirty-four children (29.0%) were found to have a positive result for urine cotinine screening, with a mean (SD) level of 11.7 (9.4) ng/mL. The percentage of children with SHSe was less than anticipated. There was no association identified between urinary cotinine levels and either AHI (ρ = −0.04; 95% CI, −0.22 to 0.15) or nadir oxygen saturation (ρ = −0.07; 95% CI, −0.26 to 0.11). Furthermore, SHSe was not associated with the presence of severe OSA (odds ratio, 0.70; 95% CI, 0.26 to 1.90). Children whose caregivers reported indoor SHSe were more likely to have a detectable urinary cotinine level (odds ratio, 20.3; 95% CI, 6.67 to 61.8).

Conclusions and Relevance

This cohort study did not identify any clinically meaningful association between SHSe, quantified by urinary cotinine level, and pediatric OSA severity. Future research with a larger number of children with SHSe is needed to confirm these findings and determine whether SHSe affects OSA treatment outcomes in children.

This cohort study assesses urinary cotinine levels in children exposed to secondhand smoke to identify any association with severity of pediatric obstructive sleep apnea.

Introduction

Obstructive sleep apnea (OSA) is a sleep-related breathing disorder that is characterized by intermittent episodes of upper airway collapse during sleep. Pediatric OSA affects 2% to 3% of healthy school-aged children in the US. The severity of OSA in children is typically defined according to full-night polysomnogram (PSG) parameters, such as the obstructive apnea hypopnea index (AHI) and nadir oxygen saturation. Although adenotonsillar hypertrophy has been associated with OSA in children, the pathophysiology of this disorder is often multifactorial.^1,2,3 Obesity and allergic rhinitis are risk factors for pediatric OSA.^4,5 Another recent study⁶ also identified secondhand smoke exposure (SHSe) and Black race being associated with severe OSA in children.

SHSe has been associated with numerous health problems in children; it is typically characterized by a mixture of exhaled mainstream smoke and sidestream smoke from the burning end of a lit cigarette.⁷ According to the US Centers for Disease Control and Prevention, approximately 50% of children between the ages of 3 and 18 years in the US have regular SHSe.⁸ SHSe has been associated with increased frequency of asthma attacks, otitis media, sensorineural hearing loss, and sudden infant death syndrome.^9,10,11 A recent review by Jara et al¹² also highlighted a possible association between sleep disordered breathing (SDB) and SHSe.

Parental reporting of SHSe can be unreliable, making it challenging to study the relationship between SHSe and other pediatric health problems such as OSA. Thus, biomarkers, such as urinary cotinine, have been identified as a way to quantify SHSe more objectively in children.¹³ Cotinine is the major proximate metabolite of nicotine that is found in blood, saliva, and/or urine. Cotinine levels have been utilized to better define the relationship between SHSe and other pediatric medical disorders, such as middle-ear disease and hypertension.^14,15,16,17

Although recent publications^12,18,19 have suggested a relationship between SHSe and pediatric OSA, high-quality prospective studies in this field are lacking. The current literature is limited by the lack of objective data on SHSe and OSA severity; prior studies did not use PSG or cotinine to quantify the degree of disease and exposure. Thus, the primary aim of the present study was to assess whether secondhand smoke exposure measured in urinary cotinine levels was associated with pediatric OSA severity per the AHI on PSG. We hypothesized that children with high urinary cotinine levels would be at risk for severe OSA. The secondary aim was to determine whether parental report of smoking habits was associated with cotinine levels in children undergoing PSG for sleep-disordered breathing (SDB). Lastly, we sought to evaluate the association between sleep-related quality of life (QoL) measures and cotinine levels in children with SDB.

Methods

The study was approved by the institutional review board of the Eastern Virginia Medical School. Informed consent was obtained in writing from a parent or guardian; participants 7 years and older also provided consent.

Study Participants

From March 2014 through October 2016, and then again from March 2020 through March 2021, participants were recruited from the outpatient pediatric otolaryngology clinics at the Children’s Hospital of The King’s Daughters, a tertiary care pediatric hospital in Norfolk, Virginia. The gap in enrollment was due to the principal investigator’s (C.M.B.) parental leave and a need to secure additional funding.

Children eligible for inclusion were from 3 to 16 years of age, had a sleep-disordered breathing (SDB) diagnosis, and had been referred for PSG; developmental delay and a history of prematurity did not exclude these patients. However, patients with cardiovascular, neuromuscular, or craniofacial disorders; a history of adenoidectomy and/or tonsillectomy; and/or a known history of OSA were excluded along with those who did not complete the PSG (<4 hours of total sleep time) or who were unable to provide a urine specimen.

Clinical Evaluation

Patients presenting with SDB were evaluated and recruited during their initial clinic visit. Self-reported demographic information was recorded, including age, sex, and race and ethnicity. Weight and height were measured and the body mass index (BMI, calculated as weight in kilograms divided by height in meters squared) percentile was calculated according to the US Centers for Disease Control and Prevention guidelines.²⁰ A detailed history was obtained, including parental report of the patient’s medication use and a clinical history of asthma or allergic rhinitis. Participants also underwent a comprehensive physical examination. Tonsil size was graded from 1 to 4 according to the scale established by Brodsky.²¹

A validated SHSe questionnaire was completed by the caregiver at the time of enrollment.²² The questionnaire asked caregivers to report frequency of smoking in 7 potential home locations, with responses ranging from several times a day to never or more seldom than once a month. For data analysis, responses were grouped as either (1) indoor exposure or (2) modified indoor/outdoor or no indoor/outdoor exposure, according to the SHSe locations reported by caregivers. For example, “at the TV set” qualified as group 1 (indoor exposure) whereas “near an open door” and “outside” were categorized as group 2 (modified indoor/outdoor and no indoor/outdoor exposure) (Figure).

Figure. Secondhand Smoke Exposure Questionnaire.

Validated survey to assess secondhand smoke exposure, with questions arranged by type of exposure.

To assess QoL, caregivers (with input from the child when appropriate) completed the OSA-18 QoL survey.²³ The OSA-18 is a validated 18-item questionnaire with a Likert scale that is used to assess the association of SDB on a child’s health. Scores of 60 and greater indicate that the child’s QoL is at least moderately affected.

Polysomnography Findings

All PSGs were performed at our institution’s dedicated pediatric sleep laboratories. The average wait time for a PSG at our institution is 4 weeks. PSGs were scored in accordance with the guidelines of the American Academy of Sleep Medicine²⁴ and interpreted by physicians with expertise in pediatric sleep medicine. PSG parameters, such as AHI, nadir oxygen saturation, and peak end-tidal carbon dioxide level, were recorded. OSA was defined as an AHI greater than 1.0, and the degree of OSA was further classified as mild (1.0-5.0), moderate (5.1-10.0), or severe (>10.0).

Urinary Cotinine Levels

On the morning after the PSG, a urine specimen was collected from each patient. Because our institutional laboratory does not perform urinary cotinine testing, the sample was sent to Quest Diagnostics to obtain urinary creatinine and cotinine levels; urinary cotinine levels were adjusted for urinary creatinine. Patients were considered to have SHSe when a detectable level of cotinine was found in the urine specimen.

Statistical Analysis

A total sample size of 116 participants with a significance level α = .05 and an anticipated odds ratio (OR) of 1.5 yields a power equal to 0.80 to detect a statistically significant difference in the urinary cotinine levels in children with OSA using logistic regression. Based on prior research, we anticipated that 50% of the participants would have SHSe.⁸

Descriptive statistics were reported as mean (SD) for normally distributed continuous variables, median (range) for nonnormally distributed variables, and frequency and percentage for categorical variables. The distribution of continuous variables was evaluated with normality tests and graphical plots. Cliff δ (true sampling error) was used to compare the difference between the medians of AHI between severity levels of OSA. The interpretation of Cliff δ values was defined as: small effect, δ less than 0.147; medium effect, δ equal to or greater than 0.147 and less than 0.33; and large effect, δ of 0.33 or greater.

Logistic regression was used to explore associations of urinary cotinine levels with severe OSA. Odds ratios (ORs) and 95% CIs were calculated as measures of effect size and precision. Spearman ρ was used to assess the strength of the associations between cotinine and PSG parameters. Spearman coefficient ρ can have a value from 1 to −1, where ρ value of 1 is a perfect association of rank; of 0 is no association of ranks; and ρ value of −1 is a perfect negative association between ranks. The closer the ρ value is to 0, the weaker the association between the 2 ranks. All analyses were performed using SAS, version 9.4 (SAS Institute). Data analyses were conducted from February to June 2023.

Results

The total study population included 116 pediatric patients (mean [SD] age, 7.2 [2.8] years; 55 [47.4%] female and 61 [52.6%] male; 64 [55.2%] were Black, 46 [39.7%] were White, and 6 [5.2%] did not report race or ethnicity). Demographic information was reported by the parent or guardian who completed the survey. Table 1 depicts the baseline demographic information of the patient population.

Table 1. Participant Characteristics.

Characteristic	Total	No. (%)		Difference (95% CI)a
		Cotinine negative	Cotinine positive
Participant, No.	116	82	34	NA
Age, median (IQR), y	6.0 (5.0 to 9.0)	6.0 (5.0 to 9.0)	7.5 (6.0 to 10.0)	0.26 (0.04 to 0.37)
Sex
Female	55	37 (67)	18 (33)	7% (−10.1% to 23.1%)
Male	61	45 (74)	16 (26)
Obesity
Yes, BMI ≥95%	52	34 (65)	18 (35)	10% (−6.6% to 27.2%)
No, BMI <95%	64	48 (75)	16 (25)
Race and ethnicity
Black	64	43 (67)	21 (33)	18% (−15.5% to 21.1%)
White	46	34 (74)	12 (26)
Otherb	6	5 (83)	1 (17)
Asthma
Yes	34	23 (68)	11 (32)	5% (−22.8% to 14.2%)
No	82	59 (72)	23 (28)
Allergic rhinitis
Yes	50	38 (76)	12 (24)	9% (−7.1% to 25.7%)
No	66	44 (67)	22 (33)
Indoor SHSe
Yes	26	6 (23)	20 (77)	63 (44.9% to 80.8%)
No	78	67 (86)	11 (14)
SHSe survey total score, median (IQR)	8 (8 to 13)	8 (8 to 10)	12.5 (10 to 18)	0.57 (0.34 to 0.73)
OSA-18 score, median (IQR)	59 (45 to 74)	58.0 (43.0 to 72.0)	61.0 (48.5 to 76.5)	0.15 (0.10 to 0.38)

Abbreviations: BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); NA, not applicable; OSA-18, obstructive sleep apnea 18 question quality of life survey²³; SHSe, secondhand smoke exposure.

^a Cliff δ values were interpreted as: small effect, δ less than 0.147; medium effect, δ equal to or greater than 0.147 and less than 0.33; and large effect, δ of 0.33 or greater.

^b Not reported.

Of the 116 patients, 52 (44.8%) had obesity (BMI >95th percentile). Common comorbid medical conditions reported by caregivers included allergic rhinitis (50 patients; 43.1%) and asthma (34 patients; 29.3%). Urinary cotinine levels and PSG data were available for analysis for all 116 child-participants.

In all, 93 children (80.9%) in the study population had OSA (defined as AHI >1). The median (IQR) AHI for all of the included children was 3.0 (1.2-8.0). Table 2 depicts the PSG data for the patients; 46 children (45.0%) had mild disease and 28 (30.1%) had severe disease (AHI >10). The median (IQR) total OSA-18 score was 59 (45-74). Forty-eight children (48.5%) had OSA-18 QoL scores greater than 60, indicating an association of SDB with QoL in these patients. Total OSA-18 QoL scores were not associated (ρ = 0.17; 95% CI, −0.04 to 0.36) with AHI; however, imprecision of the estimate prevented definitive conclusion.

Table 2. Participants’ Polysomnogram Findings.

Polysomnogram findings	Total	Cotinine negative	Cotinine positive	Difference, OR (95% CI)a
AHI score, median (IQR)	3 (1.2 to 8.0)	2.8 (1.5 to 7.6)	3.3 (0.8 to 8.1)	0.06 (−0.18 to 0.29)
OSA classification (AHI score), No. (%)
None, mild, moderate (≤10)	87	60 (68)	27 (32)	0.74 (0.24 to 2.10)
Severe (>10)	28	21 (75)	7 (25)	NA
NOS, mean (SD)	89.4 (6.0)	90.2 (4.4)	87.6 (8.49)	0.43 (0.03 to 0.84)

Abbreviations: AHI, obstructive apnea hypopnea index; NA, not applicable; NOS, nadir oxygen saturation; OR, odds ratio; OSA, obstructive sleep apnea.

^a Cliff δ values were interpreted as: small, δ less than 0.147; medium, δ of 0.147 or greater to less than 0.33; and large effect, δ of 0.33 or greater.

Based on parental responses from the validated SHSe survey, 26 children (22.4%) were at high risk for SHSe due to exposure to indoor smoking; 34 (29.3%) were found to have positive urine cotinine (range, undetectable to 32 ng/mL) (Table 3). The mean (SD) urinary cotinine level in children with detectable cotinine was 11.7 (9.4) ng/mL. Children with detectable urinary cotinine were similar demographically (except age) to those who did not have SHSe, with children in the positive urinary cotinine group having a higher median age (Cliff δ = 0.26; 95% CI, 0.04 to 0.37).

Table 3. Urinary Cotinine Level and Secondhand Smoke Exposure (SHSe) Questionnaire Data.

Variable	No. (%)
Positive cotinine screening result	34 (29)
Cotinine level in patients with positive result
Mean (SD), ng/mL	12.0 (9.4)
Median (IQR), ng/mL	8.5 (4.0-17.0)
Positive cotinine with report of indoor SHSe	21 (81)
Positive cotinine with no reported SHSe	5 (16)

In children with SHSe and OSA, there was no association identified between urinary cotinine levels and AHI (ρ = −0.04; 95% CI, −0.22 to 0.15) and nadir oxygen saturation (ρ = −0.07; 95% CI, −0.26 to 0.11). The strength of the associations between cotinine and AHI and nadir oxygen are represented by Spearman ρ. Furthermore, total OSA-18 scores were not associated (ρ = 0.20; 95% CI, −0.07 to 0.54) with cotinine levels in children with SHSe; however, imprecision of the estimate prevented definitive conclusion. There was no association between positive cotinine and the presence of OSA (AHI >1; OR, 1.15; 95% CI, 0.41 to 3.24).

Urinary cotinine levels were not meaningfully associated with severe OSA as assessed by AHI >10 when compared with children who had a negative cotinine screening result (OR, 0.70, 95% CI, 0.26 to 1.90). Total OSA-18 scores were similar between the positive urinary cotinine group and the group without SHSe (Cliff δ = 0.15; 95% CI, 0.10 to 0.38).

Of the 26 children whose caregivers reported indoor smoke exposure on the questionnaire, 21 children (80.8%) had detectable urinary cotinine levels. Children whose caregivers reported indoor SHSe on the questionnaire were more likely to have detectable urinary cotinine (OR, 20.3; 95% CI, 6.67 to 61.8) than the modified indoor/outdoor/neither indoor nor outdoor group. Five additional children (16.0%) had positive urinary cotinine levels despite their caregivers reporting no SHSe. Children with detectable urinary cotinine levels had higher total scores on the SHSe questionnaires compared with children with negative urinary cotinine (Cliff δ = 0.57; 95% CI, 0.34 to 0.73).

Discussion

In this prospective evaluation of the association between SHSe and pediatric OSA, PSG and urinary cotinine levels were utilized to objectively quantify SHSe exposure and disease severity. Urinary cotinine levels were not meaningfully associated with OSA severity per the PSG parameters of AHI and nadir oxygen saturation. Furthermore, a positive result on cotinine screening was not found to increase the likelihood of having OSA.

Thus, this study did not find a strong association between SHSe measured by urine cotinine levels and OSA in children. This finding counters those of Jara et al,¹² Yolton et al,¹⁹ and Beebe et al,¹⁹ as noted previously. One possible explanation for this discrepancy may be related to differences between the study populations. Most participants in our study were diagnosed with OSA, whereas most children assessed by the other authors had habitual snoring or SDB. Perhaps the association between SHSe and OSA is different from its association with habitual snoring. Additionally, we utilized PSG to diagnose OSA, whereas previous studies have largely relied on parental reports to assess pediatric SDB. Surveys and questionnaires that rely on caregiver reports to make OSA diagnoses in children may have only a moderate specificity and sensitivity.²⁵ Finally, most of the children in our study had mild OSA, so it is unclear whether a stronger association between SHSe and OSA may exist in a population of children with more severe OSA.

SHSe has been found to be associated with numerous health problems in children.^9,10,11 Emerging evidence suggests that a relationship may exist between SHSe and pediatric SDB.^12,18,19 A review of the literature by Jara et al¹² identified 18 studies evaluating the association between SDB and SHSe in children. All of the articles included in that analysis were evidence level 3b case-control studies. Habitual snoring was the most common form of SDB studied in the review and was evaluated by questionnaires completed by caregivers. Only 4 of the 18 studies (22%) used PSG to diagnose OSA. Most (83%) of the studies (15 of 18) showed a statistically significant association between SHSe and SDB, and most (89%; 16 of 18) determined SHSe via caregiver reporting.

Due to concerns regarding the accuracy of parental reporting, objective and quantitative measures of SHSe (eg, cotinine) are being increasingly used to evaluate the degree of SHSe in children. Prior studies have validated that urinary cotinine is an objective measure of SHSe in the pediatric population and can be used as biomarker to assess the degree of SHSe.¹³ Cotinine levels have also been used to differentiate active from passive smoking and to monitor tobacco use in adolescents.

Recent studies have used cotinine levels^8,9,10 among the pediatric population to establish health risks related to SHSe. For example, the association between SHSe and middle-ear disease has been strengthened by studies measuring cotinine levels in children. Strachan et al¹⁴ conducted a cross-sectional study of salivary cotinine concentrations and tympanometry in 6.5- to 7.5-year-old children. The authors determined that one-third of the cases of middle-ear effusion diagnosed by tympanometry were attributable to SHSe. This study also found that doubling the cotinine concentration significantly increased the odds of an effusion.

Cotinine levels have also been utilized to better define the associations between SHSe and certain cardiovascular parameters in children.^15,16,17 Cotinine levels were associated in a dose-response manner, with increased waist circumference, elevated apolipoprotein B, high blood pressure, increased triglycerides, low high-density lipoprotein cholesterol, and metabolic syndrome rates. Effects on arterial structure and function have also been observed.

Only 2 studies (11%) have utilized cotinine levels to objectively quantify SHSe in children with SDB.^19,26 Yolton et al¹⁹ analyzed the relationship between SHSe and sleep patterns among 219 children with asthma. The authors used the Children’s Sleep Habits Questionnaire¹⁹ to assess sleep. They determined that higher cotinine levels were associated with prolonged sleep onset latency, SDB, parasomnia, daytime sleepiness, and overall sleep disturbance. Beebe et al²⁶ conducted a prospective cohort study to assess predictors of snoring in 249 preschool-aged children. The cotinine levels of participating children were measured at 2 and 3 years of age, with caregivers completing the Children’s Sleep Habits Questionnaire at the same time intervals. Higher mean cotinine levels substantially increased the odds of snoring. These authors concluded that cotinine levels are stable over time and may predict SDB in children. The participants in these studies did not routinely undergo PSG; therefore, the authors were not able to comment on the association between SHSe and PSG parameters.

Previous research has shown that studies that rely on parental reporting of SHSe may substantially underestimate the amount of SHSe in children.²⁵ Accuracy of SHSe as assessed by parental reports can be variable, with a sensitivity in the literature of less than 70%. Caregivers often fail to acknowledge smoking in the car or home.²⁷ Seasonal variation, daycare attendance, and the age of a child can also affect the degree of SHSe.²⁸ In our study, there were 5 children who had a positive cotinine screening result despite that their caregivers denied SHSe. We also found that children were more likely to have a positive urinary cotinine if their parents reported indoor smoking without any modifications. These findings may be due to the parents being more honest in their reporting given their awareness of our intent to quantify SHSe in their child.

Future research should explore whether the validated SHSe survey used in our study may be used as a screening tool to identify children as risk for SHSe. Furthermore, additional studies are needed to confirm that modifications in smoking behaviors, such as smoking outside, consistently reduce cotinine levels in children with OSA. This evidence could improve outcomes in patients with medical conditions associated with SHSe.

The precise pathophysiologic process involved in the connection between SHSe and pediatric SDB is unknown. SHSe may contribute to mucosal inflammation and tonsil hypertrophy.²⁹ Wang et al³⁰ studied adenoid specimens from pediatric patients undergoing adenotonsillectomy for SDB and found impaired ciliary beat frequency in samples from children with SHSe. The authors postulated that inflammatory cytokines may play a role in the association between SHSe and SDB. SHSe may be a contributing factor to the inflammatory changes in the upper airway of children with SDB. Further research is necessary to better identify the connection between SHSe and SDB.

Strengths and Limitations

Our study used objective quantitative testing—PSG and urinary cotinine level screening—to assess the association between SHSe and OSA severity in children. Another strength was the use of validated surveys to assess parental reporting of SHSe and QoL outcomes.

Limitations of this study include small sample size that undermined the precision of the estimates and ability to make meaningful conclusions. The study encountered lower-than-expected rates of positive urinary cotinine screening results. Based on our review of prior studies, we postulated that 50% of children would have SHSe. However, fewer than 30% of the participants were found to have a positive result for urinary cotinine. Perhaps educational campaigns to reduce SHSe are affecting the number of children exposed.

Future study protocols should focus on obtaining cotinine levels in children deemed to be at high risk for SHSe. Using a validated SHSe survey may be useful in identifying these children. Another limitation of the study involved the timing of the urinary specimen collection. The lead author (C.M.B.) has conducted a prior study evaluating biomarkers in pediatric OSA; therefore, there were well-established protocols for urine sample collection at the conclusion of the sleep study.³¹ When considering our study design, we noted that the half-life of cotinine is 16 to 19 hours³²; therefore, the timing of urine collection may possibly have attenuated cotinine levels because the participant had been in a smoke-free environment for 10 or more hours before collection. Finally, we did not assess for vaping or e-cigarette smoking in this study; therefore, further research is needed to determine whether exposure to vaping aerosol is associated with pediatric OSA severity.

Although our study added to the body of literature on SHSe and OSA severity, data regarding the association of SHSe with pediatric OSA treatment outcomes remain limited. Additional research is needed to determine whether SHSe affects outcomes in children undergoing adenotonsillectomy for OSA. Furthermore, it remains unknown whether children with SHSe are more likely to have persistent disease after surgery and whether modifying exposure reduces this risk.

Conclusion

This cohort study found that urinary cotinine levels were not associated with OSA severity quantified using PSG parameters (AHI level and nadir oxygen saturation). Furthermore, among this study population, SHSe assessed by urinary cotinine levels was not found to be associated with an increase in the likelihood of having severe OSA. The small sample size limited the ability to identify meaningful associations. Additional research with a larger sample size is needed to better understand the potential relationship between SHSe and SDB in children and to determine whether SHSe affects OSA treatment outcomes.

Supplement.

Data Sharing Statement