The geographical distribution of pediatric sleep respiratory diseases is believed to be influenced by air pollution, and consequently by the presence of industries, railway stations, vehicular congestion, and high intensity of transportation modes. In particular, the role of environmental air pollution among the pediatric population has frequently been investigated as a causal factor for respiratory diseases (Bates, 1995; Bedeschi et al., 2007; Brauer et al., 2002; Dockery et al., 1996; Nicolai et al., 2003; Orazzo et al., 2009; Sestini et al., 2005; Thurston, Lippmann, Scott, & Fine, 1997; Vigotti, Chiaverini, Biagiola, & Rossi, 2007) and for respiratory infections (Prieto, Mancilla, Astudillo, Reyes, & Roman, 2007).
Moreover, air pollution has been shown to be associated with the number of hospital admissions for respiratory diseases in children and adolescents (Jasinski, Pereira, & Braga, 2011), chronic respiratory diseases, acute respiratory symptoms frequency in children (Kukec, Farkas, Erzen, & Zaletel-Kragelj, 2013), and asthma symptom exacerbation or development (D'Amato et al., 2013; Esposito et al., 2014). Yet few authors have focused their attention on the relationship between environmental pollution and sleep-disordered breathing (SDB) in children.
A cross-sectional study by Abou-Khadra (2013) analyzed the possible associations between exposure to [PM.sub.10] and sleep disturbances in school children 6-13 years who were recruited from four elementary schools in Egypt located in two districts with great differences in [PM.sub.10]. A significant association was observed, namely between [PM.sub.10] exposure and disorders of initiating and maintaining sleep. In the study, the relationship between SDB and air pollution was not specifically investigated and only some of the examined patients had SDB. The proven association of poor sleep quality with environmental pollution, however, is noteworthy.
Zanobetti and coauthors (2010) studied the relationship between [PM.sub.10] air levels and SDB in adults in seven U.S. urban areas, and reported that increasing levels of daily particulate matter in summer are associated with increases in SDB and decreases in the percentage of sleep efficiency. Some authors suggest an influence from air pollutants on the central nervous system. It has previously been reported that particles can translocate from the nose up to the olfactory nerve and to the brain (Elder et al., 2006; Wang et al., 2007), causing an inflammatory response and changes in neurotransmitter levels. Such consequences could be related to adverse effects on sleep and its duration and architecture, as well as on SDB (Kleinman et al., 2008).
Other authors hypothesize that pollution can influence the ventilatory control centers of the central nervous system and, moreover, that particulate matter can trigger a nasal and pharyngeal inflammatory response, causing an increase in upper airway resistance and a reduction in airway patency (DeMeo et al., 2004; Mehra & Redline, 2008). Kuehni and coauthors (2008) conducted a population survey of 6,811 children ages 1-4 years from Leicestershire, UK, to determine prevalence, severity, and risk factors for snoring; they found habitual snoring to be associated with exposure to air pollutants.
Particularly noteworthy is the study performed by Kheirandish-Gozal and coauthors (2014) exploring the relationship between air quality and the prevalence of habitual snoring in school-age children in five distinct neighborhoods of Teheran. The neighborhoods were characterized by considerable differences in air composition, and consequently in air pollutant concentration. A statistically significant association between the prevalence of habitual snoring and environmental air pollution was found, even when considering the influence of other factors such as age, sex, clinical history, and familial history components.
In school-age children, SDB can lead to important consequences, including impacting school performance. This aspect was studied by Gozal (1998), who analyzed the prevalence of sleep-associated gas exchange abnormalities (SAGEA) among children attending elementary school whose educational performance was in the lowest 10th percentile of their class. SAGEA was found to frequently be present in poorly performing first-grade students, in whom it is assumed to have adversely affected learning performance.
In light of the aforementioned considerations, an analysis of the geographical distribution of SDB could help better identify and label geographical areas with higher risk, namely local areas where an unusually higher frequency of children and adolescents are observed to be affected by SDB. These areas could then be more closely investigated in search of possible sources of environmental pollution. A match between unusual SDB intensity/severity peaks and areas where specific sources of air pollution are reported would highlight a positive association.
The aim of the present ecological study was to analyze the geographical distribution of pediatric SDB in the Italian province of Varese using data collected in the provincial reference hospital center for children with SDB. To highlight possible associations between SDB and exposure to combustion-related pollutants, these results were compared with the spatial pattern of nitrogen dioxide (NO2), which is regarded as a marker for such pollutants, as it is a significant constituent of emissions and is highly correlated with other combustion products, including fine particles (World Health Organization [WHO], 2013).
We used data provided by the Sleep Disordered Breathing Center of the Pediatric Unit Insubria University-Filippo del Ponte Hospital of Varese, which is the largest hospital in the province of Varese and a specialized center for SDB in Northern Italy. Data were collected from 2010-2014 and focused on children who resided in 112 municipalities in the province of Varese, were over 1 year of age, and who were admitted to the hospital because of recurrent respiratory disturbances during sleep.
The total number of children analyzed was 754; for each patient, we gathered information about the child's municipality of residence, sex, and the value of the apnea-hypopnea index (AHI). AHI is based on polysomnographic recordings conducted overnight by means of Embla's Embletta Gold sleep system, a recording system that can discriminate the SDB severity level. All of the children were diagnosed with respect to SDB based on their AHI index.
In comparison with adults, for whom AHI-based classification of SDB is consolidated, there currently are no universally accepted guidelines as to when SDB is sufficiently severe in children to warrant treatment. Considering that most pediatric sleep specialists regard values of AHI >1 as already abnormal (American Academy of Sleep Medicine, 2006; Loughlin & Eigen, 1994; Scholle, Wiater, & Scholle, 2011; Uliel, Tauman, Greenfeld, & Sivan, 2004), this cutoff point was taken to identify SDB.
For the purpose of this study, each patient was assigned to a municipality according to the place of residence at the time of hospital admission. The parent(s) of each child gave written informed consent for...