Effect of parental smoking on their children's urine cotinine level in Korea: A population-based study

Neighborhood Disadvantage, Quality of Life, and Symptom Burden in Children with Mild Sleep-disordered Breathing

Rationale: Neighborhood disadvantage (ND) has been associated with sleep-disordered breathing (SDB) in children. However, the association between ND and SDB symptom burden and quality of life (QOL) has not yet been studied.Objectives: To evaluate associations between ND with SDB symptom burden and QOL.Methods: Cross-sectional analyses were performed on 453 children, ages 3-12.9 years, with mild SDB (habitual snoring and apnea-hypopnea index < 3/h) enrolled in the PATS (Pediatric Adenotonsillectomy Trial for Snoring) multicenter study. The primary exposure, neighborhood disadvantage, was characterized by the Child Opportunity Index (COI) (range, 0-100), in which lower values (specifically COI ⩽ 40) signify less advantageous neighborhoods. The primary outcomes were QOL assessed by the obstructive sleep apnea (OSA)-18 questionnaire (range, 18-126) and SDB symptom burden assessed by the Pediatric Sleep Questionnaire-Sleep-related Breathing Disorder (PSQ-SRBD) scale (range, 0-1). The primary model was adjusted for age, sex, race, ethnicity, maternal education, recruitment site, and season. In addition, we explored the role of body mass index (BMI) percentile, environmental tobacco smoke (ETS), and asthma in these associations.Results: The sample included 453 children (16% Hispanic, 26% Black or African American, 52% White, and 6% other). COI mean (standard deviation [SD]) was 50.3 (29.4), and 37% (n = 169) of participants lived in disadvantaged neighborhoods. Poor SDB-related QOL (OSA-18 ⩾ 60) and high symptom burden (PSQ-SRBD ⩾ 0.33) were found in 30% (n = 134) and 75% (n = 341) of participants, respectively. In adjusted models, a COI increase by 1 SD (i.e., more advantageous neighborhood) was associated with an improvement in OSA-18 score by 2.5 points (95% confidence interval [CI], -4.34 to -0.62) and in PSQ-SRBD score by 0.03 points (95% CI, -0.05 to -0.01). These associations remained significant after adjusting for BMI percentile, ETS, or asthma; however, associations between COI and SDB-related QOL attenuated by 23% and 10% after adjusting for ETS or asthma, respectively.Conclusions: Neighborhood disadvantage was associated with poorer SDB-related QOL and greater SDB symptoms. Associations were partially attenuated after considering the effects of ETS or asthma. The findings support efforts to reduce ETS and neighborhood-level asthma-related risk factors and identify other neighborhood-level factors that contribute to SDB symptom burden as strategies to address sleep-health disparities.Clinical trial registered with www.clinicaltrials.gov (NCT02562040).

Association between parental smoking and child exposure to environmental tobacco smoke in Israel

BACKGROUND

Environmental tobacco smoke (ETS) exposure in children can cause delayed lung development and lifelong cardiovascular damage. The aim of this study was to measure ETS exposure in children in Israel in 2020-2021 using urinary cotinine (UC) measurements and to assess correlates of ETS exposure, including parental smoking.

METHODS

In the framework of the National Human Biomonitoring Program, spot urine samples and questionnaire data were collected from 166 children aged 4-12 years, during the years 2020-2021. We collected urine samples in 233 adults, 69 of whom were parents of children included in the study. Parents of participating children were asked about parental smoking, child's exposure to ETS and smoking policy at home. Cotinine and creatinine were measured in urine. Creatinine-adjusted and unadjusted urine cotinine (UC) geometric means were calculated. Associations between potential correlates and UC concentrations were analyzed in univariate and multivariate analyses. For 69 child-parent pairs, correlation between child and parental UC was analyzed.

RESULTS

Based on urinary cotinine measurement, 65.2% of children of smokers are exposed to ETS, compared to 20.7% of children in non-smoking families. Greater numbers of smokers living in the home (beta = 1.27, p < 0.01), and low maternal education (beta = - 2.32, p < 0.01) were associated with higher levels of UC in a multivariate analysis. Spearman correlations showed a positive moderate correlation between UC in 69 child-parent pairs (r = 0.52, p < 0.01).

CONCLUSIONS

In order to reduce child exposure to ETS, smoking parents should be urgently targeted for smoking cessation and smoke-free home interventions. Further interventions are needed to protect all children from ETS.

Impact of health risk factors on healthcare resource utilization, work-related outcomes and health-related quality of life of Australians: a population-based longitudinal data analysis

Background

Health risk factors, including smoking, excessive alcohol consumption, overweight, obesity, and insufficient physical activity, are major contributors to many poor health conditions. This study aimed to assess the impact of health risk factors on healthcare resource utilization, work-related outcomes and health-related quality of life (HRQoL) in Australia.

Methods

We used two waves of the nationally representative Household, Income, and Labor Dynamics in Australia (HILDA) Survey from 2013 and 2017 for the analysis. Healthcare resource utilization included outpatient visits, hospitalisations, and prescribed medication use. Work-related outcomes were assessed through employment status and sick leave. HRQoL was assessed using the SF-6D scores. Generalized estimating equation (GEE) with logit or log link function and random-effects regression models were used to analyse the longitudinal data on the relationship between health risk factors and the outcomes. The models were adjusted for age, sex, marital status, education background, employment status, equilibrium household income, residential area, country of birth, indigenous status, and socio-economic status.

Results

After adjusting for all other health risk factors covariates, physical inactivity had the greatest impact on healthcare resource utilization, work-related outcomes, and HRQoL. Physical inactivity increased the likelihood of outpatient visits (AOR = 1.60, 95% CI = 1.45, 1.76 p < 0.001), hospitalization (AOR = 1.83, 95% CI = 1.66-2.01, p < 0.001), and the probability of taking sick leave (AOR = 1.31, 95% CI = 1.21-1.41, p < 0.001), and decreased the odds of having an above population median HRQoL (AOR = 0.48, 95% CI = 0.45-0.51, p < 0.001) after adjusting for all other health risk factors and covariates. Obesity had the greatest impact on medication use (AOR = 2.02, 95% CI = 1.97-2.29, p < 0.001) after adjusting for all other health risk factors and covariates.

Conclusion

Our study contributed to the growing body of literature on the relative impact of health risk factors for healthcare resource utilization, work-related outcomes and HRQoL. Our results suggested that public health interventions aim at improving these risk factors, particularly physical inactivity and obesity, can offer substantial benefits, not only for healthcare resource utilization but also for productivity.