Abstract
Background
Indoor environmental factors, such as pet ownership, presence of cockroaches, mattress quality, fuel usage (gas or electricity), use of biomass for cooking and heating, exposure to tobacco smoke or household molds can significantly affect the sleep quality of children with chronic cough. However, data regarding the effects of indoor environmental conditions on sleep in this population are limited. This study aimed to assess the prevalence of abnormal sleep behaviors and to establish associations between indoor environmental factors and sleep behaviors among children with chronic cough in Wuxi, China.
Methods
A cross-sectional design was employed in this study, involving children aged 3–18 years. Data on sociodemographic factors, allergies, home environmental exposures, and sleep characteristics of the participants were collected using paper-based questionnaires. The association between indoor environmental factors and sleep behaviors in children with chronic cough was analyzed using logistic regression models.
Results
The findings demonstrated that the prevalence of chronic cough among children in Wuxi was 15.50%. The chronic cough group exhibited a significantly higher incidence of eczema, wheezing, rhinitis, food allergy, and nasosinusitis than the non-chronic cough group. In addition, children with chronic cough also tended to have a family history of sleep disorders and adenoid hypertrophy (P < 0.01). After adjusting for confounding factors, a significant association was observed between bruxism (teeth grinding) and chronic cough (sometimes: odds ratio [OR] = 1.04; confidence interval [CI] = 1.01–1.08; always: OR = 1.11; CI = 1.04–1.19; P < 0.01). Among children with chronic cough, recent home decoration was associated with sleepwalking (OR = 1.04; CI = 1.00–1.07; P < 0.05), mold exposure was associated with bruxism (OR = 1.15; CI = 1.0–1.31; P < 0.05), and carpet use at home was associated with apnea (OR = 1.09; CI = 1.02–1.17; P < 0.05), twitching during sleep (OR = 1.13; CI = 1.00–1.27; P < 0.01) and morning headache (OR = 1.14; CI = 1.05–1.23; P < 0.01).
Conclusion
Children with chronic cough are more prone to some abnormal sleep behaviors than children without chronic cough. Household decoration within a year, household mold exposure, and carpet use were all significantly positively associated with abnormal sleep behaviors in children with chronic cough. Our study provides novel insights into the impact of the indoor environment on children’s sleep and the occurrence of chronic cough, offering guidance for tailored health promotion programs for families.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12887-024-04876-y.
Keywords: Chronic cough, Abnormal sleep behavior, Indoor environment, Children
Background
The Clinical Practice Guidelines for the Diagnosis and Management of Children with Cough in China (v2021) define chronic cough as persistent cough lasting > 4 weeks [1]. Unfortunately, many children with chronic cough and their families still make unnecessary medical visits, leading to significant financial and societal costs. In addition, chronic cough can cause various adverse effects, such as disrupting sleep quality, limiting participation in activities, causing emotional distress, and reducing overall quality of life [2]. The prevalence of chronic cough among children in China varies across different regions, with incidences ranging from 6 to 27% [3–5].
Quality sleep is crucial for a healthy childhood, but sleep-disordered breathing (SDB) can interfere with proper ventilation and breathing patterns during sleep, leading to a variety of physical and behavioral problems [6]. The International Classification of Sleep Disorders (ICSD), 3rd edition, categorizes sleep disorders into seven groups: insomnia disorders, sleep-related breathing disorders, central disorders of hypersomnolence, circadian rhythm sleep-wake disorders, sleep-related movement disorders, parasomnias, and other sleep disorders [7]. Sleep breathing disorders are relatively common, with approximately one in ten healthy children habitually snoring during sleep. One in four children who snore have obstructive sleep apnea syndrome (OSAS) [8]. Previous studies have explored the relationship between OSAS and chronic cough, reporting an association between chronic cough and obstructive sleep apnea (OSA) in adults. These studies suggested that treating OSA with continuous positive airway pressure can alleviate cough symptoms [9]. However, there is no evidence to endorse the application of OSA-specific therapies for managing chronic cough in children [10]. Furthermore, many children without a clear diagnosis of OSAS are often assumed to have sleep-breathing disorders. This assumption arises because parents and guardians may not fully understand the medical terminology, and instead describe various abnormal sleep behaviors, including somnolence, insomnia, snoring, open-mouth breathing, dyspnea, apnea, sleep disturbance, teething disorder, sleep talking, sleepwalking, bedwetting, and hyperhidrosis. As such, the association between chronic cough and sleep disorders may have been underestimated.
In addition to the above, previous studies have highlighted the influence of indoor environments on childhood sleep. A systematic review exploring the relationships between air pollution exposure and sleep measures across different age groups revealed that children and adolescents exposed to higher environmental and indoor pollutants had a higher risk of experiencing respiratory sleep problems and other adverse sleep outcomes [11]. Moreover, studies have indicated that various indoor environmental factors, such as pet ownership, exposure to environmental tobacco smoke [12], exposure to household molds [13], the presence of cockroaches, mattress quality [14], fuel usage (gas or electricity) [15], and usage of biomass for cooking and heating [16], can increase the risk of persistent cough and intractable sputum in children. As such, it is reasonable to speculate that indoor environmental factors may significantly affect the sleep quality of children with chronic cough. However, data regarding the effects of indoor environmental conditions on sleep in this population are limited. Consequently, this research aimed to assess the prevalence of abnormal sleep behaviors and the associations between indoor environmental factors and sleep behaviors in children with chronic cough in Wuxi, China.
Methods
Setting, sampling, and participants
A cross-sectional design was employed in this study. Based on statistical calculations (
), a sample size of 1,920 was determined, assuming a chronic cough prevalence of approximately 15% in children, a significance level of α = 0.05, a 
value of 1.96, and an acceptable error of 0.1 p. Considering a 20% non-response rate, the sample size was fixed at 2,400. This study was conducted between October 2020 and April 2022 across five districts in Wuxi, Jiangsu, China. Schools and grade levels were randomly selected using computer-generated random numbers, considering the scale and distribution of schools in each region. Specifically, children aged 3–18 years were recruited as participants. School teachers distributed questionnaires to parents or guardians, and collected them after completion. The questionnaires were completed by the primary carers for children under 16 years old, and by both the primary carers and the children themselves for those aged 16–18 years. Before participant recruitment, approval for this study was obtained from the Affiliated Wuxi Children’s Hospital of Nanjing Medical University’s ethics committee (approval number WXCH2019-8-002).
Questionnaires
Demographic data/characteristics
Demographic data encompassed a variety of factors, including age, sex, weight (in kilograms), and height (in centimeters). Moreover, information was gathered regarding their history of asthma, sleep disturbances, exposure to maternal smoking during pregnancy, and current smoking status within the family. Body mass index (BMI) was calculated as the weight (kg)/ height (m2), using values reported in the questionnaires.
Disease assessment
The definition of chronic cough used in this study aligned with the Clinical Practice Guidelines for the Diagnosis and Management of Children with Cough in China [1], which has achieved consensus in the country. It was characterized by a positive response to the question, “Has your child ever experienced a cough lasting longer than 4 weeks in the last year?” An experienced physician re-confirmed the diagnosis of chronic cough after excluding the presence of respiratory illnesses, such as pneumonia and bronchitis, during chronic cough by checking electronic or paper medical records. Another interesting aspect of this study was the assessment of allergic comorbidities based on self-reported information provided by guardians regarding physician-diagnosed conditions at any point in the child’s life. The definitions for various comorbidities were as follows. Eczema: Affirmative response to the query, “Has your child ever received a physician’s diagnosis of eczema?” Wheezing: Positive response to the question, “Has your child ever experienced whistling or wheezing in the chest at any time in the past?” Rhinitis: Confirmation of physician-diagnosed rhinitis or recurring symptoms of sneezing, blocked or runny nose not associated with a cold or flu. Food allergy: Validation of a clinician’s diagnosis of food allergy. Nasosinusitis: Physician-confirmed diagnosis of nasosinusitis. Urticaria: Physician-confirmed diagnosis of urticaria. Adenoid hypertrophy: Diagnosis of adenoid hypertrophy through nasopharyngeal lateral radiographs or electronic nasopharyngoscopy. Please refer to Additional file 1 for further details.
Home environmental exposure
The inquiries on home environmental exposure in this study were classified into three distinct groups: (1) Household renovation: These encompassed aspects such as recent decoration within a year, floor level, wall materials, floor materials, wall painting materials, furniture material, door material, pillow materials, and quilt materials. (2) Cooling method, heating method, and cooking fuel: This category focused on the specific techniques employed for cooling and heating, as well as the type of fuel used for cooking within the participants’ residences. (3) Household exposure to molds, pet ownership, presence of carpets, plants, and tobacco: This group of questions explored factors such as the presence of household molds, keeping of pets, utilization of carpets, presence of indoor plants, and exposure to tobacco smoke within the home environment. For further details, please refer to Additional file 1.
Pediatric sleep questionnaire
A retrospective parental report was used as the sleep questionnaire to assess sleep problems in children. The questionnaire evaluated various sleep behaviors and difficulties, including sleep gestures, duration, and abnormal sleep behaviors. Abnormal sleep behaviors encompassed several categories: (1) Night waking: waking during sleep and sleepwalking. (2) Parasomnias: This category encompassed twitching, bedwetting, restless sleep, sleep talking, bruxism (teeth grinding), and sweating. (3) Sleep-disordered breathing: Behaviors such as snorting, mouth opening, and dyspnea (difficulty breathing) fell under this category. (4) Daytime sleepiness: This included taking a long time to become alert, experiencing headaches, morning thirst, and napping. The participants’ parents were requested to indicate the frequency of their children’s sleep behaviors for each item over a typical recent week, using a three-point scale (never = 0 times/week; sometimes = 1–2 times/week; and always = 3–7 times/week). The Children’s Sleep Habits Questionnaire (CSHQ), which served as the foundation for this questionnaire, was initially designed to screen problematic sleep behaviors in children aged 4–10 years [17], and was later validated for its applicability in toddlers and preschool-aged children, irrespective of their medical condition [18]. The CSHQ has been commonly applied in China, and has demonstrated acceptable internal consistency, reliability, and validity. The Cronbach’s α coefficient for the Chinese version of the full scale was 0.73, while the sub-scale coefficients ranged from 0.42 to 0.69 [19].
Statistical analysis
Statistical tests were conducted using the R program v3.6.2 (http://www.r-project.org/). Normally distributed data are presented as the mean ± standard deviation, whereas non-normally distributed data are expressed as the median and interquartile range. The t-test and Mann–Whitney U test were used for continuous variables. The Chi-squared or Fisher’s exact test was employed for categorical variables. Logistic regression models were constructed to explore the association between each abnormal sleep behavior, chronic cough and indoor environmental factors. In order to reduce the confounding bias, the logistic regression models were adjusted for confounding factors (such as age, sex, BMI, former diseases, family history of asthma, etc.). Estimated odds ratios (eOR) and adjusted OR with corresponding 95% confidence intervals (CI) were computed to identify these associations. Statistical significance was set at P < 0.05.
Results
Questionnaire and participant characteristics
In total, 2,310 parents/caregivers completed the questionnaires for this study. Some questionnaires were considered invalid due to incomplete data, duplicates, or false information, leaving 2,026 valid responses (response rate, 87.7%).
Overall, as shown in Tables 1 and 316 valid questionnaires were completed by the chronic cough group, whereas 1,710 were completed by children without chronic cough. The survey-weighted prevalence of children with chronic cough was 15.50% (n = 316). Overall, the average ages of chronic and non-chronic cough groups were 8.15 and 9.10 years (P < 0.01), respectively. However, there were no significant differences in terms of sex (56.60% vs. 51.90%, P = 0.14) or BMI (17.49 vs. 17.67, P = 0.59) between the chronic and non-chronic cough groups.
Table 1.
Characteristics of study participants (n = 2,026)
| Chronic cough (n = 316) | Non-chronic cough (n = 1,710) | P-value | |
|---|---|---|---|
| Age | 8.15 ± 3.01 | 9.10 ± 3.15 | < 0.001*** |
| Sex, female (%) | 179 (56.65) | 889 (51.99) | 0.14 |
| BMI (kg/m2) | 17.49 ± 5.38 | 17.67 ± 4.76 | 0.59 |
| Diseases, n/N (%) | |||
| Eczema, yes | 143 (45.25) | 518 (30.29) | < 0.001*** |
| Wheeze, yes | 77 (24.37) | 143 (8.36) | < 0.001*** |
| Rhinitis, yes | 133 (42.09) | 416 (24.32) | < 0.001*** |
| Food allergy, yes | 40 (12.66) | 118 (6.90) | < 0.001*** |
| Nasosinusitis, yes | 28 (8.87) | 80 (4.67) | 0.0036** |
| Urticaria, yes | 51 (16.14) | 210 (12.28) | 0.070 |
| Family asthma history, yes | 6 (1.90) | 50 (2.92) | 0.40 |
| Family history of sleep disturbance, yes | 89 (28.16) | 330 (19.29) | 0.0014** |
| Adenoid hypertrophy, yes | 25 (7.91) | 67 (3.92) | 0.0027** |
| Sleep duration | 8.94 ± 0.73 | 8.84± 0.95 | 0.037* |
| Sleep behaviors | |||
| Snorting | < 0.001*** | ||
| Never | 146 (46.20) | 982 (57.4) | |
| Sometimes | 145 (45.89) | 672 (39.30) | |
| Always | 25 (7.91) | 56 (3.27) | |
| Mouth opening | 0.0022** | ||
| Never | 147 (46.52) | 952 (55.67) | |
| Sometimes | 131 (46.46) | 629 (36.78) | |
| Always | 38 (12.03) | 129 (7.54) | |
| Restless sleep | < 0.001*** | ||
| Never | 144 (45.57) | 1,053 (61.58) | |
| Sometimes | 148 (46.84) | 584 (34.15) | |
| Always | 24 (7.59) | 73 (4.27) | |
| Twitching | < 0.001*** | ||
| Never | 266 (84.18) | 1,560 (91.22) | |
| Sometimes | 45 (14.24) | 139 (8.13) | |
| Always | 5 (1.58) | 11 (0.64) | |
| Sleep talking | < 0.001*** | ||
| Never | 108 (34.18) | 788 (46.08) | |
| Sometimes | 200 (63.29) | 892 (52.16) | |
| Always | 8 (2.53) | 30 (1.75) | |
| Bruxism | < 0.001*** | ||
| Never | 138 (43.67) | 987 (57.72) | |
| Sometimes | 151 (47.78) | 646 (37.78) | |
| Always | 27 (8.54) | 77 (4.50) | |
| Sweating | < 0.001*** | ||
| Never | 121 (38.29) | 908 (53.10) | |
| Sometimes | 132 (41.77) | 629 (36.78) | |
| Always | 63 (19.94) | 173 (10.12) | |
| Waking during sleep | < 0.001*** | ||
| Never | 230 (72.78) | 1,401 (81.93) | |
| Sometimes | 84 (26.58) | 302 (17.66) | |
| Always | 2 (0.63) | 7 (0.41) |
never = 0 times/week; sometimes = 1 to 2 times/week; usually = 3 to 7 times/week. *P value < 0.05, **P value < 0.01, ***P value < 0.001
The chronic cough group exhibited significantly higher incidences of eczema (45.25% vs. 30.29%), wheezing (24.37% vs. 143%), rhinitis (42.09% vs. 24.32%), food allergy (12.66% vs. 6.90%) and nasosinusitis (8.87% vs. 4.67%) than controls. Children in the chronic cough group were also more likely to have a family history of sleep disorders (28.16% vs. 19.29%) and adenoid hypertrophy (7.91% vs. 3.92%) (P < 0.01) compared with the non-chronic cough group. The average sleep duration was 8.95 h for children with chronic cough, and 8.85 h for controls (P = 0.03).
No significant differences were detected between the two groups in terms of home environmental exposure variables, including floor type, household renovations (walls, furniture, doors, pillows, and quilt materials), use of air conditioners, fuel burning for cooking and heating, tobacco smoke exposure, carpet, flower, and pet exposure at home (Additional file 2). However, a higher percentage of children with chronic cough reported living in environments with a perception of moldy air compared with controls (21.2% vs. 15.1%; P < 0.001).
Sleep behavior and indoor environment of children with and without chronic cough
Table 2 presents the relationship between chronic cough and sleep behavior. In the unadjusted models, snoring (sometimes: OR = 1.05; CI = 1.02–1.08; P < 0.01; always: OR = 1.20; CI = 1.10–1.30; P < 0.001), restless sleep (sometimes: OR = 1.08; CI = 1.05–1.12; P < 0.001; always: OR = 1.13; CI = 1.05–1.22; P < 0.001), bruxism (sometimes: OR = 1.07; CI = 1.03–1.10; P < 0.001; always: OR = 1.15; CI = 1.07–1.23; P < 0.001), and sweating (sometimes: OR = 1.06; CI = 1.02–1.09; P < 0.01; always: OR = 1.16; CI = 1.10–1.22; P < 0.001) were significantly associated with a high risk of chronic cough. After adjusting for confounding factors, chronic cough was still significantly associated with bruxism (sometimes: OR = 1.04; CI = 1.01–1.08; always: OR = 1.11; CI = 1.04–1.19; P < 0.01). In the unadjusted analysis, a family history of sleep disturbance was significantly associated with the risk of chronic cough (OR = 1.07; CI = 1.03–1.11; P < 0.001). This significant difference was still detected even after adjusting for age and other covariates (OR = 1.05; CI = 1.01–1.09; P < 0.01). However, the adjusted analysis found no notable difference for the adenoid hypertrophy subset (OR = 1.02; CI = 0.95–1.10).
Table 2.
Association between chronic cough and sleep behaviors
| Unadjusted model | Adjusted model | |||
|---|---|---|---|---|
| Sleep status | OR (95% CI) | P-value | OR (95% CI) | P-value |
| Sleep duration | 1.01 (0.99, 1.03) | 0.080 | 1.00 (0.98, 1.02) | 0.90 |
| Snorting | ||||
| Never | Ref. | Ref. | ||
| Sometimes | 1.05 (1.02, 1.08) | 0.0038** | 1.01 (0.98, 1.05) | 0.37 |
| Always | 1.20 (1.10, 1.30) | < 0.001*** | 1.12 (1.04, 1.21) | 0.0037** |
| Apnea | ||||
| Never | Ref. | Ref. | ||
| Sometimes | 1.06 (0.93, 1.21) | 0.38 | 1.02 (0.90, 1.16) | 0.70 |
| Always | 1.67 (1.11, 2.52) | 0.015* | 1.69 (1.15, 2.50) | 0.0078** |
| Restless sleep | ||||
| Never | Ref. | Ref. | ||
| Sometimes | 1.08 (1.05, 1.12) | < 0.001*** | 1.06 (1.02, 1.09) | < 0.001*** |
| Always | 1.13 (1.05, 1.22) | < 0.001*** | 1.07 (0.99, 1.15) | 0.05 |
| Twitching | ||||
| Never | Ref. | Ref. | ||
| Sometimes | 1.10 (1.04, 1.17) | < 0.001*** | 1.09 (1.03, 1.15) | 0.0013** |
| Always | 1.18 (0.99, 1.41) | 0.06 | 1.17 (0.99, 1.39) | 0.06 |
| Sleep talking | ||||
| Never | Ref. | Ref. | ||
| Sometimes | 1.06 (1.03, 1.10) | < 0.001*** | 1.03 (1.00, 1.06) | 0.044* |
| Always | 1.09 (0.97, 1.23) | 0.13 | 1.04 (0.93, 1.16) | 0.49 |
| Bruxism | ||||
| Never | Ref. | Ref. | ||
| Sometimes | 1.07 (1.03, 1.10) | < 0.001*** | 1.04 (1.01, 1.08) | 0.0058** |
| Always | 1.15 (1.07, 1.23) | < 0.001*** | 1.11 (1.04, 1.19) | 0.0022** |
| Sweating | ||||
| Never | Ref. | Ref. | ||
| Sometimes | 1.06 (1.02, 1.09) | 0.0012** | 1.02 (0.99, 1.05) | 0.25 |
| Always | 1.16 (1.10, 1.22) | < 0.001*** | 1.10 (1.05, 1.16) | < 0.001*** |
| Waking during sleep | ||||
| Never | Ref. | Ref. | ||
| Sometimes | 1.08 (1.04, 1.12) | < 0.001*** | 1.05 (1.01, 1.10) | 0.0068** |
| Always | 1.08 (0.86, 1.37) | 0.50 | 1.17 (0.93, 1.46) | 0.17 |
| Long to waking | ||||
| Never | Ref. | Ref. | ||
| Sometimes | 1.03 (0.99, 1.06) | 0.12 | 1.02 (0.99, 1.06) | 0.18 |
| Always | 1.13 (1.03,1.25) | 0.013* | 1.10 (1.01, 1.21) | 0.034* |
| Family history of sleep disturbance, yes | 1.07 (1.03, 1.11) | < 0.001*** | 1.05 (1.01, 1.09) | 0.0084** |
| Adenoid hypertrophy | 1.13 (1.05, 1.22) | < 0.001*** | 1.02 (0.95, 1.10) | 0.63 |
Models were adjusted for age, sex, BMI, former diseases, family history of asthma, eczema, wheeze, rhinitis, food allergy, nasosinusitis and urticaria. never = 0 times/week; sometimes = 1 to 2 times/week; always = 3 to 7 times/week. *P value < 0.05, **P value < 0.01, ***P value < 0.001
Table 3 shows the association between chronic cough and indoor environment. Mold exposure (OR = 1.40; CI = 1.02– 1.90; P 0.05) was found to be a high-risk factor for chronic cough after adjusting for age, sex, BMI, eczema, wheezing, rhinitis, food allergies, nasosinusitis, and urticaria. Furthermore, carpet use at home (OR = 1.07; CI = 0.74–1.55), growing flowers at home (OR = 1.19; CI = 0.90–1.56), pregnancy and perinatal smoke exposure (OR = 1.24; CI = 0.97–1.59), and current smoke exposure (OR = 1.04; CI = 0.81–1.33) were all positively associated with chronic cough, but the differences were not statistically significant.
Table 3.
Association between chronic cough and indoor environment
| Unadjusted model | Adjusted model | |||
|---|---|---|---|---|
| Indoor environment | OR (95% CI) | P-value | OR (95% CI) | P-value |
| Decoration | ||||
| No | Ref. | Ref. | ||
| Yes | 0.94 (0.73, 1.21) | 0.62 | 0.78 (0.60, 1.01) | 0.060 |
| Mold exposure | ||||
| No | Ref. | Ref. | ||
| Yes | 0.68 (0.50, 0.92) | 0.010* | 1.40 (1.02, 1.90) | 0.030* |
| Carpet | ||||
| No | Ref. | Ref. | ||
| Yes | 0.88 (0.61, 1.26) | 0.49 | 1.07 (0.74, 1.55) | 0.71 |
| Flower | ||||
| No | Ref. | Ref. | ||
| Yes | 0.91 (0.69, 1.19) | 0.49 | 1.19 (0.90, 1.56) | 0.22 |
| Pet | ||||
| No | Ref. | Ref. | ||
| Yes | 1.14 (0.80, 1.63) | 0.47 | 0.86 (0.60, 1.24) | 0.42 |
| Former smoker | ||||
| No | Ref. | Ref. | ||
| Yes | 0.73 (0.53, 1.00) | 0.050 | 1.24 (0.97, 1.59) | 0.090 |
| Current smoker | ||||
| No | Ref. | Ref. | ||
| Yes | 1.33 (0.97, 1.84) | 0.080 | 1.04 (0.81, 1.33) | 0.78 |
Models were adjusted for age, sex, BMI, former diseases, family history of asthma, eczema, wheeze, rhinitis, food allergy, nasosinusitis and urticaria. *P value < 0.05, **P value < 0.01, ***P value < 0.001
Associations between the indoor environment and abnormal sleep behavior among children with chronic cough
Logistic regression analysis conducted among children with chronic cough, before and after adjusting for confounders (Tables S3 and 4), identified several associations. Home decoration within a year was positively associated with sleepwalking (OR = 1.04; CI = 1.00–1.07; P < 0.05); mold exposure was positively associated with bruxism (OR = 1.15; CI = 1.0–1.31; P < 0.05); and carpet use at home was significantly positively associated with apnea (OR = 1.09; CI = 1.02–1.17; P < 0.05), twitching during sleep (OR = 1.13; CI = 1.00–1.27; P < 0.01), and morning headache (OR = 1.14; CI = 1.05–1.23; P < 0.01).
Table 4.
Associations between the indoor environment and abnormal sleep behavior after adjusted analysis among chronic cough children (n = 316, multivariate)
| Variable | Decoration | Mold exposure | Carpet | Flower | Pet | Former smoker | Current smoker |
|---|---|---|---|---|---|---|---|
| OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | |
| Sleep duration | 0.89 (0.76, 1.06) | 1.16 (0.95, 1.41) | 1.08 (0.85, 1.37) | 1.04 (0.87, 1.24) | 1.06 (0.84, 1.34) | 1.08 (0.92, 1.28) | 0.96 (0.82, 1.13) |
| Snorting | 0.92 (0.82, 1.03) | 1.16 (1.02, 1.33)* | 1.10 (0.93, 1.29) | 0.96 (0.85, 1.08) | 1.08 (0.92, 1.27) | 1.11 (0.99, 1.24) | 1.01 (0.90, 1.12) |
| Mouth opening | 1.04 (0.93, 1.17) | 0.93 (0.81, 1.06) | 1.10 (0.94, 1.30) | 1.03 (0.91, 1.07) | 1.08 (0.92, 1.27) | 1.12 (1.00, 1.25) | 0.97 (0.87, 1.09) |
| Dyspnea | 0.99 (0.95, 1.04) | 0.98 (0.93, 1.04) | 1.09 (1.02, 1.16)* | 1.00 (0.95, 1.00) | 0.98 (0.91, 1.04) | 0.98 (0.93, 1.02) | 0.96 (0.91, 1.00) |
| Apnea | 0.98 (0.92, 1.02) | 1.02 (0.98, 1.07) | 1.05 (1.00, 1.11) | 1.02 (0.98, 1.06) | 1.02 (0.97, 1.08) | 1.00 (0.97, 1.04) | 1.00 (0.97, 1.04) |
| Restless sleep | 0.92 (0.82, 1.03) | 1.13 (0.99, 1.29) | 0.98 (0.83, 1.15) | 1.05 (0.93, 1.18) | 1.04 (0.89, 1.22) | 1.11 (0.99, 1.24) | 1.02 (0.91, 1.14) |
| Twitching | 0.99 (0.91, 1.08) | 1.03 (0.93, 1.13) | 1.13 (1.00, 1.27)* | 1.07 (0.98, 1.17) | 1.18 (1.05, 1.33) | 1.03 (0.95, 1.11) | 1.01 (0.94, 1.10) |
| Sleep talking | 0.97 (0.87, 1.08) | 1.02 (0.89, 1.60) | 1.04 (0.89, 1.21) | 1.00 (0.89, 1.13) | 1.02 (0.87, 1.19) | 1.17 (1.06, 1.30) | 1.11 (1.00, 1.23) |
| Sleep walking | 1.04 (1.00, 1.07)* | 0.98 (0.94, 1.01) | 1.01 (0.96, 1.05) | 1.01 (0.98, 1.04) | 1.00 (0.96, 1.05) | 1.00 (0.97, 1.03) | 0.99 (0.96, 1.02) |
| Bruxism | 1.01 (0.90, 1.13) | 1.15 (1.00, 1.31)* | 0.95 (0.81, 1.12) | 1.11 (0.98, 1.25) | 0.94 (0.80, 1.10) | 0.98 (0.88, 1.10) | 0.97 (0.87, 1.09) |
| Sweating | 0.96 (0.86, 1.07) | 0.92 (0.81, 1.05) | 1.15 (0.98, 1.34) | 1.01 (0.89, 1.14) | 1.01 (0.87, 1.18) | 1.04 (0.93, 1.16) | 0.98 (0.88, 1.09) |
| Bed wetting | 0.91 (0.83, 0.99) | 1.05 (0.94, 1.16) | 0.95 (0.84, 1.08) | 0.92 (0.84, 1.01) | 1.08 (0.96, 1.23) | 1.01 (0.92, 1.10) | 0.99 (0.91, 1.08) |
| Waking during sleep | 0.95 (0.86, 1.06) | 0.98 (0.87, 1.10) | 1.10 (0.85, 1.27) | 0.93 (0.83, 1.04) | 1.01 (0.87, 1.16) | 1.03 (0.93, 1.13) | 1.00 (0.90, 1.10) |
| Long to waking | 0.99 (0.89, 1.10) | 0.97 (0.85, 1.10) | 1.15 (0.98, 1.34) | 1.17 (1.04, 1.31) | 1.02 (0.87, 1.19) | 1.11 (1.00, 1.23) | 1.06 (0.96, 1.18) |
| Headache in morning | 1.02 (0.97, 1.07) | 1.05 (0.99, 1.12) | 1.13 (1.04, 1.22)* | 1.06 (0.99, 1.12) | 0.98 (0.91, 1.06) | 1.03 (0.97, 1.08) | 0.97 (0.92, 1.03) |
| Thirsting | 0.96 (0.86, 1.07) | 0.98 (0.86, 1.11) | 1.03 (0.88, 1.21) | 1.06 (0.94, 1.20) | 1.11 (0.95, 1.29) | 1.01 (0.91, 1.12) | 1.00 (0.90, 1.11) |
| Napping | 1.07 (0.97, 1.19) | 0.96 (0.85, 1.09) | 1.08 (0.93, 1.26) | 1.12 (1.00, 1.25) | 1.07 (0.92, 1.24) | 1.04 (0.93, 1.15) | 0.97 (0.88, 1.08) |
Models were adjusted for age, sex, BMI, eczema, wheeze, rhinitis, food allergy, nasosinusitis and urticaria. *P value < 0.05, **P value < 0.01, ***P value < 0.001
In the non–chronic cough group, pet ownership was found to be significantly positively associated with several sleep-related factors, such as open-mouth breathing (OR = 1.48; CI = 1.10–1,98; P < 0.01), dyspnea (OR = 2.64; CI = 1.38–5.56; P < 0.01), apnea (OR = 2.90; CI = 1.07–7.87; P < 0.05), sleepwalking (OR = 2.77; CI = 1.37–5.60; P < 0.01), morning headache (OR = 2.01; CI = 1.38–3.40; P < 0.05), and thirst (OR = 1.90; CI = 1.41–2.57; P < 0.001) both before and after adjusting for variables (Additional file 3 and 4).
Discussion
This study was the first to investigate the prevalence of chronic cough among children aged 3–18 years in Wuxi, finding an overall incidence of 15.5%. Moreover, certain factors, such as household decoration within a year, household mold exposure, and carpet use, were identified as risk factors for abnormal sleep behaviors in children with chronic cough. Coughing serves as a defensive mechanism to prevent the intake of excessive fluids and foreign objects [20]; however, it can also significantly reduce quality of life and impose a substantial social and financial burden on society. In children, cough that persists for ≥ 4 weeks is classified as chronic [21, 22]. Chronic cough incurs an annual expenditure of approximately $360 million on over-the-counter drugs in the United States alone, with global costs exceeding $10 billion [23]. The economic burden of chronic cough has not been specifically investigated in China; however, we speculate that the situation is likely similar to that in the United States.
One meta-analysis conducted in 2022 indicated that the prevalence of chronic cough in southern cities was lower than that in northern cities [24], which suggests that regional factors and unique environmental and economic conditions may influence the prevalence of chronic cough. The higher prevalence observed in our study could be attributed to these factors. Wuxi, located in southern China, has experienced rapid industrialization, and has emerged as a major economic center in the Yangtze River Delta, with Shanghai at its core, since the 1980s. In 2022, Wuxi recorded a per capita GDP of RMB 199,015.7 billion, ranking third highest in Jiangsu province. Moreover, the city has undergone substantial urbanization, which could contribute to the increase in chronic cough diagnoses [25].
In our study, we observed that children with chronic cough were more likely to experience wheezing, eczema, rhinitis, or nasosinusitis than those in the control group, which aligns with previous studies [26, 27]. This increase may largely be attributed to shared underlying conditions such as airway hyper-responsiveness and atopy. A chart study focusing on children with sinusitis and chronic cough revealed that 65% of children with persistent cough displayed sinus abnormalities on radiography [28]. It is believed that nasal secretions, whether reaching the pharynx alone or in conjunction with the larynx, can stimulate nerve endings and trigger coughing [29]. The pathogenesis of chronic cough is complex and involves a variety of factors, including nasobronchial reflex, upper airway inflammation, exposure to cold and dry air, and the propagation of inflammatory mediators through the systemic circulation [30]. In addition, other etiological factors, such as central and peripheral neuroplasticity, may also contribute [31]. However, a definitive explanation has not yet been provided. Allergic diseases rarely occur in isolation, and must be considered alongside various comorbid conditions, including sinusitis, asthma, disrupted sleep, lymphoid hypertrophy causing obstructive sleep apnea, and subsequent behavioral effects [32].
Sleep is vital to children’s health and overall well-being. Previous research has confirmed that patients with chronic cough often complain of sleep disorders [33]. Moreover, another study found that the relationship between psychological distress and allergic disease is mediated by sleep disruption, highlighting the importance of assessing the sleep health of children with allergic conditions [34]. In the present study, sleep disturbances such as snoring, restless sleep, bruxism, and sweating were all found to besignificantly associated with a high risk of chronic cough, although some associations did not reach statistical significance. Most of these symptoms are consistent with the clinical presentation of OSA. Notably, multiple adult studies have demonstrated the association between OAS and chronic cough, with mechanisms including airway inflammatory responses, esophageal-tracheobronchial reflexes, airway wall edema, and sleep deprivation [35]. The muscular movements associated with coughing can lead to teeth grinding and arousal from sleep. Nocturnal hyperhidrosis can contribute to sympathetic nerves and acetylcholine neurotransmitters [36]. However, investigations into the association between chronic cough and sleep disorders in children are limited, and further investigations are require to assess if the underlying processes differ between children and adults.
Despite the well-documented impact of the indoor environment on children’s respiratory systems in numerous countries [37–39], only a few studies have examined the relationships between indoor environments and sleep disorders. Our study found that the use of carpets at home was associated with morning apnea, convulsions, and headaches in children with chronic cough. Carpets are known to be repositories of allergens, and considering that modern societies spend a significant amount of time indoors [40], it is crucial to examine their potential impact. Feline allergen I (feld I) [41], a common and significant feline allergen, has an 11-fold higher average concentration per unit area on carpeted floors than on other surfaces. This allergen can attach to surfaces, including fur, and tends to persist on carpets for extended periods. It can also bind to small dust particles (1–20 μm in size), enabling allergens to remain suspended in the air for longer durations [42]. A cross-sectional study conducted in four Swedish cities found a higher prevalence of snoring among individuals who reported humidity and air pollution in their homes [43]. However, in our study, no statistical significance was observed after adjustment. Tiesler et al. previously reported that visible mold or moisture in the home might have a negative impact on children’s sleep, although the specific sleep problems were not categorized. This study suggested an association between home moisture and bruxism [44]. Generally, indoor air pollutants, such as total volatile organic compounds, formaldehyde, plasticizers, carbon monoxide, carbon dioxide, particulate matter (PM2.5, PM10 etc.), and ultrafine particles, are significantly higher in bedrooms that have undergone home decoration or are exposed to smoke [45, 46]. Considering that decoration materials, indoor humidity, and smoking exposure are associated with allergic diseases [47], we hypothesized that indoor environments could negatively affect sleep through allergic inflammation. Nevertheless, further research is necessary to validate this finding and explore its underlying biological mechanisms.
This study identified the prevalence of chronic cough in Wuxi for the first time, and it concentrated on indoor environmental factors, which are relatively easy to modify among the variables which affect children’s life and health and have more guiding significance in the daily care of children by their guardians. The present study has several limitations that should be acknowledged. First, the assessment of clinical symptoms relied on questionnaires completed by parents and guardians. This subjective reporting of whether children snore, experience difficulty breathing, grind their teeth, or experience night-time convulsions may introduce biases influenced by parental anxiety and lack of professional judgment, potentially impacting the reported prevalence of chronic cough and sleep disorders. As the variables required in our study were all provided by the children themselves and/or the primary caregivers, recall bias may have existed. However; we asked older children and their primary caregivers to complete the questionnaires together to minimize the risk of recall bias. Second, due to the study’s cross-sectional design, establishing causal relationships between variables was not possible. Finally, the influence of residual confounding factors cannot be entirely ruled out.
Conclusion
This study revealed a prevalence of 15.5% for chronic cough among children aged 3–18 years in Wuxi. Children with chronic cough are more prone to exhibiting some abnormal sleep behaviors than those without chronic cough. Household decoration within a year, household mold exposure, and carpet use were significantly positively associated with abnormal sleep behaviors in children with chronic cough. These results suggest that clinicians and parents should carefully consider these factors. Overall, our study provides novel insights into the impact of the indoor environment on children’s sleep and the occurrence of chronic cough, offering guidance for tailored health promotion programs for families.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
We thank the Medical Innovation Team of Jiangsu Province and the Wuxi Municipal Bureau on Science and Technology for their generous funding.
Abbreviations
- BMI
Body mass index
- CI
Confidence interval
- CSHQ
Children’s Sleep Habits Questionnaire
- Fel d I
Felis domesticus allergen I
- GER
Gastroesophageal reflux disease
- ICSD
International Classification of Sleep Disorders
- OR
Odds ratio
- OSA
Obstructive sleep apnea
- OSAS
Obstructive sleep apnea syndrome
- SDB
Sleep-disordered breathing
Author contributions
LL designed and supervised the research project. SYX drafted the initial manuscript and contributed to its writing. ZZP meticulously revised the paper. ZZP and YG analyzed the participants’ data. SSP, QW, and QZ diligently collected and organized the data. The implementation of this study involved contributions from all authors. All authors thoroughly reviewed and gave their approval for the final manuscript.
Funding
This study was funded by Wuxi Taihu Lake Talent Plan [grant number DJTD202304], Major Program of Wuxi Health and Family Planning Commission [grant number Z202016], General project of Wuxi Health and Family Planning Commission [grant number M202305], Youth Project of Wuxi Health and Family Planning Commission [grant number Q201837], Wuxi Medical Talents [grant number QNRC071], Maternal and child health project of Wuxi Health Commission [grant number FYKY202204], and Top Talent Support Program for Young and Middle-aged People of the Wuxi Health Committee [grant number HB2020088 and HB2023092].
Data availability
The datasets generated and/or analyzed in this study are not publicly available due to the lack of consent from all participants to share their information online. However, interested parties can obtain the datasets from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
Before participant recruitment, this research obtained approval from the Ethics Committee of the Affiliated Wuxi Children’s Hospital of Nanjing Medical University (Approval number. WXCH2019-8-002). The parents or guardians of all children were informed regarding the purpose of this research and signed written informed consent before the study’s commencement. All information obtained was treated as confidential.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Shiyao Xu and Zhenzhen Pan contributed equally to this work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets generated and/or analyzed in this study are not publicly available due to the lack of consent from all participants to share their information online. However, interested parties can obtain the datasets from the corresponding author upon reasonable request.