Abstract
Background
In the last 20 years, there has been an increase in the incidence of allergic respiratory diseases worldwide and exposure to air pollution has been discussed as one of the factors associated with this increase. The objective of this study was to investigate the effects of air pollution on peak expiratory flow (PEF) and FEV1 in children with and without allergic sensitization.
Methods
Ninety-six children were followed from April to July, 2004 with spirometry measurements. They were tested for allergic sensitization (IgE, skin prick test, eosinophilia) and asked about allergic symptoms. Air pollution, temperature and relative humidity data were available.
Results
Decrements in PEF were observed with previous 24-h average exposure to air pollution, as well as with 3 to 10 day average exposure and were associated mainly with PM10, NO2 and O3. in all three categories of allergic sensitization. Even though allergic sensitized children tended to present larger decrements in the PEF measurements they were not statistically different from the non-allergic sensitized. Decrements in FEV1 were observed mainly with previous 24-h average exposure and 3-day moving average.
Conclusions
Decrements in PEF associated with air pollution were observed in children independent from their allergic sensitization status. Their daily exposure to air pollution can be responsible for a chronic inflammatory process that might impair their lung growth and later their lung function in adulthood.
Keywords: Air pollution/adverse effects, peak expiratory flow, children, allergic sensitization, linear models, PM10, O3, NO2
INTRODUCTION
The incidence of allergic respiratory diseases has been increasing worldwide in the last few decades, in urban areas as well as rural areas [Wyler, et al. 2000, Nicolai 2002, Etzel 2003, D’Amato, et al. 2010]. Allergic respiratory diseases are determined by a combination of genetic and environmental factors [Johnson, et al. 2002]. As it is not probable that the gene pool has changed significantly during these last decades [Bracken, et al. 2002], environmental factors as exposure to aeroallergens, tobacco smoke and air pollution may be associated with the observed increase [Kramer, et al. 2000, Schwartz, et al. 2000, Kay 2001, Salvi 2001, Wamboldt, et al. 2002, Nicolai, et al. 2003, Jerrett, et al. 2008].
Exposure to indoor aeroallergens (dust mite, cat allergen, mold) and tobacco smoke has been associated with allergic sensitization, the development of allergic respiratory diseases and worsening of asthma symptoms [Mannino, et al. 2002, Morkjaroenpong, et al. 2002, Viegi, et al. 2004]. Experimental and epidemiological studies have also shown that there is a significant association between urban levels of air pollution and the increase in allergic respiratory diseases [Davies, et al. 1998, Trasande and Thurston 2005]. One explanation is that air pollution is associated with airway inflammation [Holguin, et al. 2007] and can enhance the airway allergic response to aeroallergens [Salvi 2001].
Panel studies of lung function have been extensively used to investigate the health effects of air pollution. It has been described that lung function in children decreases with exposure to PM10, SO2, NO2, and O3, considering the 24-h average concentration before the Peak Expiratory Flow (PEF) measurements as well as moving averages from 2 to 10 previous days [Gielen, et al. 1997, van der Zee, et al. 1999, Pope and Dockery 1992, Pope, et al. 1991].
The city of Sao Paulo, Brazil, has 10 million inhabitants, and the main source of air pollution is traffic related, with a light motor vehicle fleet of 7 million. Since 1979, with the implementation of the Alcohol National Program (PROALCOOL) in Brazil, hydrated ethanol has been used as a fuel for vehicles. Later, it was also added to pure gasoline forming a mixture named “gasohol”. This mixture consists of 20–25% anhydrous ethyl alcohol and 78% pure gasoline [CETESB, 2011]. In 2004, vehicles that used hydrated ethanol comprised 14.5% of the vehicle fleet in Sao Paulo Metropolitan Region; the ones that used gasohol represented 69.5%. Flex-fuel vehicles, the ones that can use either gasohol or hydrated ethanol, represented only 1%. Motorcycles represented 9.3% and diesel run vehicles, 5.8% [CETESB, 2011].
Air pollution is monitored by the State of Sao Paulo Environmental Agency (CETESB) in 20 air monitoring stations located throughout the metropolitan area of São Paulo. The pollutants that are continuously monitored are: PM10, SO2, NO2, NO, CO, and O3. One of the monitoring stations is located in the backyard of a public school. In this study we chose to investigate the effects of air pollution on peak expiratory flow measurements of students, attending that school, who were allergic-sensitized and who were not allergic-sensitized.
MATERIAL AND METHODS
A panel study was conducted from April 7th to July 7th 2004 in Sao Paulo, Brazil. The school chosen for the study has an air pollution monitoring station in its backyard. All 112 students from this school aged 9 to 11 years old were invited to participate. The study protocol was approved both by the Institutional Review Boards at the Faculdade de Medicina da Universidade de São Paulo in Sao Paulo, Brazil and at the Mount Sinai School of Medicine in New York, USA. Once their parents signed the informed consent, children were enrolled in the study.
This study protocol included measurements of height and weight at the beginning and at the end of the study period, daily records of school absences, of respiratory symptoms, and full spirometry. During the study, children answered the ISAAC questionnaire [1998], had their blood collected for dosage of serum IgE and total blood count, skin prick test and nasal smear. Daily air pollution and weather data were obtained during the study.
Spirometry: Students who received authorization from their parents were trained in the use of pneumotachs (SpiroCard – Medgraphics Coorporation, St Paul, MN, USA) to perform lung function measurements for a period of 15 school days. These values were not used in the analyses as they were considered learning period. Spirometry was performed in the morning of every school day in the presence of the researchers. Measurements were performed at the same time of the day for each child. In this report, the results of the peak expiratory flow (PEF) and of the forced expiratory volume in one second (FEV1) were used. Skin prick test: allergy skin prick tests were performed using a positive control (histamine), a negative control (saline solution) and common allergen extracts (mites (Dermatophagoides pteronyssinus, Dermatophagoides farinae and Blomia tropicalis), dog (Canis familiaris), cat (Felis domesticus), cockroach (Periplaneta americana) and a pool of fungus (Aspergillus fumigatus, Alternaria alternata, Cladosporium herbarium, Chaetomium globosum, Mucor mucedo, Pullularia pullulans, Penicillium notatum) from the laboratory IPI ASAC BRASIL). No child had received antihistamines orally for 3 days before the test. Briefly, after cleansing the forearm skin, a drop of each allergen was placed at 3 cm intervals, the skin was then lightly punctured with a 1mm polymethacrylate lancet for about 10 seconds in a way that no bleeding occurred. Reading took place after 15–20 minutes. The mean diameter of a wheal formed by the allergen was calculated by its longest diameter and the largest diameter perpendicular to it. Any reaction greater than 3mm was considered positive. If there was a reaction with the negative control, its mean diameter was subtracted from the other reaction wheal [Dreborg 1993]. Blood samples: blood was collected from a peripheral vein in Vacutainer™ tubes and was taken to the Hospital das Clínicas Laboratory for the dosage of total IgE and total blood count. This last exam was performed on an automated hematology analyzer that quantified the number of erythrocytes, leukocytes, and thrombocytes. Leukocytes count included mononuclear cells (lymphocytes and monocytes) and granulocytic cells (neutropohils, eosinophils, and basophils). The leukocytes results were given as percentage and as an absolute number per cubic milimeter. In this study, blood eosinophilia was defined as an increase in eosinophils to more than 700 cells per cubic milimeter of blood. Nasal smear: nasal smear samples were taken from both nasal cavities by wiping the surface of the inferior turbinate with cotton tipped applicator. The samples were immediately smeared over a standard glass slide, and transported to the Laboratory to be fixed, stained, and examined. Cells were identified and counted (neutrophils, eosinophils, basophils, goblet cells, mastocytes). The result was expressed as percentages. For eosinophils the proportion was expressed as a percentage of the total non-squamous cell count. For this study, eosinophilia was considered when the percentage of eosinophils was higher than 30%. Allergic sensitization categories: Children were categorized in three groups: 0 (children who presented no symptoms of allergies and normal values of total IgE, eosinophils in total blood count and lower than 30% eosinophils in nasal smear, no positive reaction to skin prick test), 1 (any symptoms of allergies or one test positive – IgE, prick test, eosinophilia in blood or nasal smear), 2 (symptoms of allergies and all tests positive - IgE, prick test, eosinophilia in blood and nasal smear). This way, we created 2 main distinct groups: non allergic-sensitized (0) and allergic-sensitized (2), and one intermediate group (1) where the allergic sensitization was not as clear as in group 2, but was not negative as in group 0. Air pollution and weather: The State of Sao Paulo Environmental Agency (CETESB) provided daily hourly concentrations of particulate matter with aerometric diameter less than 10 μm (PM10), nitrogen dioxide (NO2), nitrogen oxide (NO), carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3), temperature, and relative humidity from the air pollution monitoring station located at the school, from April 1st to July 7th, 2004.
Statistical Analysis
The association between air pollutants and the selected lung function parameters was evaluated by regression analysis based on the generalized estimating equations (GEE) proposed by Liang and Zeger (1993), [Zeger, et al. 1988].
Given that data collected sequentially in time are most likely to exhibit serial correlation, the assumption made was that peak flow measurements within individual children were not independent. Therefore, we used a random effects model. All statistical analyses were performed with STATA 8.0 software (2003). A p-value of <0.05 was considered significant.
Within each child, daily measures of PEF and FEV1 were centred by the child-specific mean for the appropriate period of measurement (month), giving a deviation-from-the-mean measure of PEF and FEV1. This allowed for varying baseline lung function between children. We regressed child-specific deviation-from-mean PEF and FEV1 on date (allowing for the possibility of linear growth during the study period), dummies for school absences lagged by one day (1 = present, 2 = without medical reason, 3 = ill (non-respiratory), 4 = ill (respiratory) with 0 as reference (weekends and holidays)), minimum temperature (°C) measure, minimum relative humidity (%), and pollution measure, allowing for first-order serial correlation [Cochrane and Orcutt 1949] and computing robust standard errors. This regression procedure yielded a coefficient for the pollution measure pooled across children. The coefficient was estimated by way of generalised least squares, as estimation of the parameter of a first-order autoregressive process was performed, the estimate of which was subsequently required for the estimation of the variance-covariance matrix of the errors.
RESULTS
Children characteristics and exams
The collection of data from the children comprised 60 school days (from April 7th to July 7th, 2004). Of the 112 eligible children, 96 (85.7%) were authorized by their parents to participate in the study. Sixty-three (65.6%) of them were males. Fifty children were 9 years old (52.1%), 39 were 10 (40.6%) and only seven were 11 years old (7.3%). Mean height and weight increased by 1.2 cm and 1.8 kg respectively during the study (Table I). Twenty-one (22.1%) [95 tested] children presented eosinophils above 30% of the total non-squamous cell count in the nasal smear exam. Of the 93 children who had their blood collected, eleven (11.8%) presented eosinophils in total blood count above normal limits. Forty-seven children presented high levels of IgE, 17 (18.3%) above 1000 UI/mL. Fifty-six children (58.9%) [94 tested] had a least one positive reaction to the skin prick test. The most common reactions were to house mites (56.8%) and to cat (18.9%).
Table I.
Baseline characteristics of the 96 schoolchildren.
| Characteristic | Data | Min | Max |
|---|---|---|---|
| Sex (M/F)1 | 63 (M) 33 (F) | ||
| Age (years) (m±SD)2 | 9.6 ± 0.63 | 9 | 11 |
| Height 13 (cm) (m±SD) | 140.6 ± 8.31 | 125.5 | 169.0 |
| Height 24 (cm) (m±SD) | 141.8 ± 8.38 | 126.5 | 170.5 |
| Weight 13 (kg) (m±SD) | 36.1 ± 9.10 | 21.5 | 63.3 |
| Weight 24 (kg) (m±SD) | 37.9 ± 9.55 | 22.0 | 66.0 |
| BMI 13 (m±SD) | 18.0 ± 3.07 | 13.2 | 30.7 |
| BMI 24 (m±SD) | 18.6 ± 3.24 | 13.3 | 31.8 |
| IgE (IU/mL) (m±SD) | 684.9 ± 1282.47 | 6 | 8950 |
| Eosinophils (thousand/mm3) (blood) (m±SD) | 0.36 ± 0.45 | 0 | 2.6 |
| Eosinophils (nasal smear) (%) (m±SD) | 17.7 ± 27.57 | 0 | 98 |
| Prick test (N/P)5 | 38/56 | ||
| PEF (L/min) (m±SD) | |||
| April | 270.4 ± 45.46 | 181.8 | 393.9 |
| May | 283.6 ± 49.36 | 171.5 | 441.5 |
| June | 291.9 ± 53.11 | 161.4 | 478.3 |
| July | 284.7 ± 55.95 | 163.1 | 452.7 |
| FEV1 (L) | |||
| April | 1.92 ± 0.36 | 1.01 | 3.25 |
| May | 1.86 ± 0.38 | 0.70 | 3.29 |
| June | 1.84 ± 0.39 | 0.45 | 3.27 |
| July | 1.83 ± 0.37 | 0.74 | 2.85 |
| Allergen exposure: | |||
| Tobacco smoke (Y/N)6 | 47/49 | ||
| Dog (Y/N) | 45/51 | ||
| Cat (Y/N) | 9/87 | ||
| Mold (Y/N) | 22/74 | ||
| Allergy Symptoms: | |||
| Runny or Blocked Nose (Y/N) | 27/69 | ||
| Cough (Y/N) | 62/34 | ||
| Sneeze (Y/N) | 66/30 | ||
| Pruritus in the eye (Y/N) | 55/41 | ||
| Eczema (Y/N) | 4/92 | ||
| Rhinitis (Y/N) | 6/90 | ||
| Wheeze (Y/N) | 16/80 | ||
| Doctor diagnosed asthma (Y/N) | 6/90 | ||
M-male, F-female
m±SD mean±Standard Deviation
1-first day of the study,
2-Last day of the study
N/P Negative/Positive
Y/N Yes/No
Seventy-four children (77.1%) were living in the same house for more than 2 years. There was no stationary source of air pollution near any of the children’s houses. The presence of mold on the wall of the bedroom was referred by 22 children (22.9%). Forty-nine (51.04%) children owned either dog or cat. Also, 49 children were exposed to second-hand tobacco smoke at home.
When asked about respiratory and allergic symptoms without the presence of any respiratory disease, sixty-six children cited sneeze (68.8%), sixty-two cough (64.6%), and fifty-five pruritus in the eye (57.3%). Sixteen children (16.7%) said they had had wheeze in the last year (Table I).
Spirometry
Peak expiratory flow average was 276.4 L/min (SD 40.7) for 9 years old children, 284.4 L/min (SD 49.3) for 10 years old, and 316.0 L/min (SD 77.9) for 11 years old. There was no statistical difference between sexes (males= 283.3 L/min (SD 48.0), females 281.0 L/min (SD 49.0). Mean PEF increased during the study from 270.4 L/min to 284.7 L/min.
Forced expiratory volume in one second average was 1.82 L (SD 0.32) for 9 years old children, 1.90 L (SD 0.40) for 10 years old, and 2.14 L (SD 0.50) for 11 years old. Also, there was no statistical difference between sexes (males= 1.89 L (SD 0.38), females 1.84 L (SD 0.37). Mean FEV decreased during the study from 1.92 L in April to 1.83 L in July.
Allergic sensitization categories
Thirty-one children were classified as Category 0 (%), thirty-six as Category 1 (%), and twenty-eight as Category 2 (%). In Category 0, total IgE levels averaged 57.4 UI/mL, mean eosinophils in blood were 143.3/mm3, and mean percentage of eosinophils in nasal smear was 1.5%. In Category 1, following the same order, the results were 481.5 UI/mL, 322.9/mm3, and 10.9%. In Category 2, they were 1611.5 UI/mL, 559.3/mm3, and 41.2%.
Two children did not collect blood, so their total IgE and complete blood count were unavailable. But they could be categorized according to the nasal smear eosinophil percentage, skin prick test and questionnaire.
It is important to notice that there was no eosinophilia, either in the blood or nasal smear, which occurred without the presence of high IgE levels or at least one positive skin prick test.
Air pollution and weather variables
Data from pollutants and weather variables were obtained from April 1st to July 7th, 2004, six days before the beginning of the spirometry tests as we needed prior days pollutants concentrations to calculate moving averages. Ozone, PM10, and NO2 exceeded air quality primary standards during the study. Temperature was mild, with an average of 18.5°C (lowest 9.9°C). Average relative humidity was 78.2%. A summary of air pollution concentrations and weather during the period studied is shown in Table II. All pollutants measurements were significantly correlated (Table III).
Table II.
Twenty-four hour average (standard deviation) and percentiles of the pollutants, temperature, and relative humidity in the period of study.
| Variable | days | Average (SD) | 10% | 25% | 50% | 75% | 90% |
|---|---|---|---|---|---|---|---|
| CO (ppm) | 99 | 3.0 (0.95) | 2.1 | 2.4 | 2.9 | 3.3 | 4.35 |
| PM10 (μg/m3) | 95 | 48.8 (17.21) | 29.4 | 35.9 | 45.6 | 58.8 | 73.9 |
| SO2 (μg/m3) | 99 | 23.0 (8.56) | 12.9 | 14.9 | 23.1 | 29.4 | 34.4 |
| O3 (μg/m3) | 99 | 63.2 (38.21) | 18.6 | 34.9 | 60.6 | 83.7 | 108.3 |
| NO2 (μg/m3) | 97 | 131.4 (47.90) | 77.5 | 90.9 | 126.7 | 158.9 | 192.0 |
| NO (μg/m3) | 97 | 183.4 (86.68) | 87.0 | 118.6 | 161.4 | 247.6 | 308.9 |
| Temp. (°C) | 99 | 18.5 (3.06) | 14.2 | 16.2 | 18.7 | 20.9 | 22.4 |
| Rel.Humid (%) | 99 | 78.4 (7.67) | 66.0 | 73.6 | 79.4 | 84.4 | 86.9 |
Table III.
Pearson correlation between the 24 hr concentration of the pollutants, temperature and relative humidity in the period of study.
| CO | PM10 | NO | NO2 | SO2 | O3 | TEMP | |
|---|---|---|---|---|---|---|---|
| PM10 | 0.73** | ||||||
| NO | 0.69** | 0.62** | |||||
| NO2 | 0.51** | 0.59** | 0.51** | ||||
| SO2 | 0.62** | 0.75** | 0.87** | 0.60** | |||
| O3 | 0.01 | 0.31** | −0.07 | 0.40** | 0.07 | ||
| Temp. | 0.02 | 0.05 | −0.14 | 0.02 | −0.14 | 0.22* | |
| Rel. Humid. | −0.16 | −0.36** | 0.10 | −0.22* | −0.02 | −0.57** | −0.01 |
p <0.05,
p <0.01
Air pollution, PEF and FEV1 – Regression analyses
Considering the previous 2-h and 24-h exposure, all pollutants were associated with declines in PEF and in FEV1 measurements in the study period (Tables IV and V). School absence related to respiratory symptoms and exposure to tobacco smoke could be confounders to the effects of air pollution, in the regression analysis we controlled for school absence and tobacco smoke exposure, both were not associated with PEF or FEV1 variations (results not shown). Other time lags as three-day, five-day, seven-day and ten-day moving average of PM10, NO2 and O3 were also negatively associated with PEF, but not with the other pollutants - CO, SO2 and NO – (results not shown). Using distributed lag models up to 30 days we verified that exposure to NO2 was significantly associated with declines in PEF until a lag of 21 days. The effects of O3 and PM10 began to diminish after 18 days and 10 days, respectively (Figures 1–3). Percent variations in PEF and FEV1 measurements for the 25%–75% interquartile range in PM10, NO2, NO, SO2, CO, and O3 were calculated as they represent a more realistic scenario for that monitoring station and thus the air the children were exposed to.
Table IV.
Estimates of the association between air pollutant exposures and PEF in 96 schoolchildren.
| Pollutant and time lag | Coef.* (95% CI) | Coef** (95% CI) | p-value |
|---|---|---|---|
| Previous 2-h average | |||
| CO | −1.50 (−2.35 to −0.64) | −0.48 (−0.75 to −0.20) | 0.001 |
| PM10 | −0.11 (−0.14 to −0.07) | −0.89 (−1.13 to −0.57) | <0.0001 |
| SO2 | −0.08 (−0.15 to −0.02) | −0.41 (−0.77 to −0.10) | 0.009 |
| NO | −0.01 (−0.01 to 0.00) | −0.17 (−0.17 to 0.00) | 0.005 |
| NO2 | −0.09 (−0.13 to −0.05) | −2.16 (−3.12 to −1.20) | <0.0001 |
| Previous 24-h average | |||
| CO | −1.89 (−3.24 to −0.53) | −0.60 (−1.03 to −0.17) | 0.006 |
| PM10 | −0.05 (−0.11 to 0.00) | −0.40 (−0.89 to 0.00) | 0.065 |
| SO2 | −0.03 (−0.12 to 0.05) | −0.15 (−0.61 to 0.26) | 0.443 |
| NO | −0.01 (−0.02 to 0.00) | −0.17 (−0.34 to 0.00) | 0.044 |
| NO2 | −0.07 (−0.12 to −0.02) | −1.68 (−2.88 to −0.48) | 0.003 |
| O3 | −0.10 (−0.18 to −0.01) | −4.56 (−8.21 to −0.46) | 0.024 |
| 3-day moving average | |||
| PM10 | −0.11 (−0.19 to −0.03) | −0.89 (−1.54 to −0.24) | 0.008 |
| NO2 | −0.13 (−0.19 to −0.07) | −3.12 (−4.57 to −1.68) | <0.0001 |
| O3 | −0.17 (−0.23 to −0.11) | −2.93 (−3.97 to −1.90) | <0.0001 |
| 5-day moving average | |||
| PM10 | −0.32 (−0.46 to −0.17) | −2.59 (−3.72 to −1.38) | <0.0001 |
| NO2 | −0.26 (−0.36 to −0.16) | −6.25 (−8.65 to −3.84) | <0.0001 |
| O3 | −0.21 (−0.29 to −0.14) | −3.62 (−5.00 to −2.41) | <0.0001 |
| 7-day moving average | |||
| PM10 | −0.26 (−0.43 to −0.10) | −2.10 (−3.48 to −0.81) | 0.002 |
| NO2 | −0.29 (−0.39 to −0.18) | −6.97 (−9.37 to −4.33) | <0.0001 |
| O3 | −0.29 (−0.38 to −0.21) | −5.00 (−6.55 to −3.62) | <0.0001 |
| 10-day moving average | |||
| PM10 | −0.17 (−0.37 to 0.02) | −1.38 (−2.99 to 0.16) | 0.081 |
| NO2 | −0.28 (−0.40 to −0.16) | −6.73 (−9.61 to −3.84) | <0.0001 |
| O3 | −0.29 (−0.41 to −0.16) | −5.00 (−7.07 to −2.76) | <0.0001 |
Coefficients represent the expected change in PEF associated with one μg/m3 or one ppm increase in each air pollutant level, adjusted for temperature, relative humidity, school absences.
Coefficients represent the expected percent change in PEF associated with 25–75% IQR of each air pollutant, adjusted for temperature, relative humidity, school absences.
Table V.
Estimates of the association between air pollutant exposures and FEV1 in 96 schoolchildren.
| Pollutant and time lag | Coef.* (95% CI) | Coef** (95% CI) | p-value |
|---|---|---|---|
| Previous 2-h average | |||
| CO | −0.007 (−0.013 to −0.005) | −0.33 (−0.64 to −0.23) | 0.035 |
| PM10 | −0.001 (−0.001 to 0.000) | −0.65 (−1.08 to −0.22) | 0.003 |
| SO2 | 0.000 (0.000 to 0.000) | −0.08 (−0.36 to 0.19) | 0.541 |
| NO | 0.000 (0.000 to 0.000) | 0.07 (−0.21 to 0.28) | 0.704 |
| NO2 | 0.000 (−0.001 to 0.000) | −1.64 (−2.80 to −0.44) | 0.008 |
| O3 | −0.001 (−0.001 to 0.000) | −1.90 (−3.31 to −0.49) | 0.008 |
| Previous 24-h average | |||
| CO | −0.034 (−0.051 to −0.018) | −1.65 (−2.44 to −0.86) | <0.0001 |
| PM10 | −0.001 (−0.002 to −0.001) | −1.70 (−2.61 to −0.81) | <0.0001 |
| SO2 | −0.001 (−0.002 to 0.000) | −0.85 (−1.54 to −0.16) | 0.016 |
| NO | 0.000 (0.000 to 0.000) | −0.76 (−1.45 to 0.00) | 0.040 |
| NO2 | −0.001 (−0.001 to 0.000) | −2.18 (−3.67 to −0.69) | 0.004 |
| O3 | −0.002 (−0.003 to −0.001) | −4.65 (−6.81 to −2.48) | <0.0001 |
| 3-day moving average | |||
| PM10 | −0.002 (−0.003 to −0.001) | −2.49 (−3.59 to −1.38) | <0.0001 |
| NO2 | −0.001 (−0.002 to −0.001) | −5.27 (−7.53 to −3.02) | <0.0001 |
| O3 | −0.001 (−0.002 to −0.001) | −3.84 (−5.45 to −2.22) | <0.0001 |
| 5-day moving average | |||
| PM10 | −0.001 (−0.002 to 0.000) | −1.63 (−2.84 to −0.42) | 0.008 |
| NO2 | −0.001 (−0.001 to 0.000) | −2.98 (−5.35 to −0.58) | 0.014 |
| O3 | 0.000 (−0.001 to 0.000) | −0.57 (−2.06 to −0.91) | 0.452 |
| 7-day moving average | |||
| PM10 | −0.003 (−0.005 to −0.001) | −3.48 (−5.58 to −1.37) | 0.001 |
| NO2 | −0.002 (−0.003 to −0.001) | −5.82 (−9.13 to −2.51) | 0.001 |
| O3 | 0.000 (−0.001 to 0.001) | −0.73 (−2.79 to 1.30) | 0.481 |
Coefficients represent the expected change in FEV1 associated with one μg/m3 or one ppm increase in each air pollutant level, adjusted for temperature, relative humidity, school absences.
Coefficients represent the expected percent change in FEV1 associated with 25–75% IQR of each air pollutant, adjusted for temperature, relative humidity, school absences.
FIGURE 1.
Mean (95% confidence interval) peak expiratory flow percent change associated with O3 lagged from 1 to 30 days.
FIGURE 3.
Mean (95% confidence interval) peak expiratory flow percent changeassociated with NO2 lagged from 1 to 30 days.
We used two-pollutant models to investigate whether the larger effect of ozone could be explained by its association with other pollutants. For short exposure (previous 2-hr mean), two-pollutant models indicated that PM10 had the most robust effect on lung function, remaining statistically significant with SO2, NO2, NO and CO (p<0.001). Ozone was not used in two-pollutant model for short exposure as its mean values were below 6 μg/m3 and the GEE regression showed non-significant coefficients. For previous 24-hr exposure, PM10 did not remain significant with any pollutant in two-pollutant models. NO2, O3, and CO maintained statistical significance at p<0.05, with smaller effects on PEF than in one pollutant model. When two-pollutant models were used for three to ten-day moving average, NO2 and O3 remained statistically significant with robust effects on PEF. PM10 was not significant with NO2 or O3 (these results are available as supporting material online). We also regressed O3 with absolute high levels of O3 in earlier periods to test for interaction; however no interaction was seen with O3 and O3 absolute levels in earlier periods (results not shown).
Declines in FEV1 (Table V) were similar to PEF but smaller in magnitude, however the percentage variations associated with the pollutants 25–75% IQR were higher for previous 24-hr exposure and 3-day moving average than for PEF (Table V). Considering 5-day and 7-day moving averages, the association with PM10 and NO2 was significant but not with ozone. After this period, all associations were not statistically significant (results not shown).
Air pollution, PEF variations and allergic sensitization
As the coefficients generated in the regression analyses for FEV1 were very small and the effect was not as long as we observed with PEF, we analyzed the association of allergic sensitization only with PEF variations.
Children of all three categories of allergic sensitization exhibited declines in peak expiratory flow measurements associated with increases in pollutant concentration in the regression analysis (Table VI).
Table VI.
Estimates of the association between percent change in PEF associated with 25–75% IQR of each air pollutant in 96 schoolchildren stratified by allergic sensitization categories.
| Pollutant and time lag | Category 0 Coeff.** (95% CI) |
Category 1 Coeff. (95% CI) |
Category 2 Coeff. (95% CI) |
|---|---|---|---|
| Previous 2-h average | |||
| PM10 | −1.62 (−2.73 to −0.52)* | −1.76 (−2.55 to −0.98)* | −2.01 (−3.63 to −0.40)* |
| NO2 | −0.77 (−1.95 to 0.40) | −1.80 (−2.80 to −0.80)* | −2.05 (−4.11 to 0.01) |
| CO | −0.26 (−0.70 to 0.19) | −0.57 (−0.89 to −0.26)* | −0.51 (−1.21 to 0.18) |
| SO2 | −0.13 (−0.53 to 0.27) | −0.24 (−0.58 to 0.10) | −0.45 (−1.06 to 0.17) |
| Previous 24-h average | |||
| PM10 | −1.72 (−3.12 to −0.33)* | −0.52 (−2.06 to 1.02) | −0.70 (−2.95 to 1.55) |
| NO2 | −1.83 (−3.01 to −0.65)* | −1.56 (−3.02 to −0.10)* | −0.55 (−3.02 to 1.92) |
| O3 | −0.30 (−1.18 to 0.58) | −0.14 (−0.83 to 0.55) | −1.74 (−2.98 to −0.50)* |
| CO | −0.59 (−1.23 to 0.06) | −0.93 (−1.49 to −0.38)* | −0.12 (−1.20 to 0.96) |
| SO2 | −0.11 (−0.76 to 0.53) | −0.21 (−0.72 to 0.30) | −0.23 (−1.40 to 0.93) |
| 3-day moving average | |||
| PM10 | −2.98 (−5.62 to −0.33)* | −2.20 (−4.08 to −0.33)* | −2.35 (−5.59 to 0.89) |
| NO2 | −4.91 (−7.16 to −2.66)* | −2.25 (−4.36 to −0.14)* | −2.43 (−6.18 to 1.31) |
| O3 | −2.69 (−4.37 to −1.01)* | −1.88 (−3.25 to −0.51)* | −4.80 (−6.83 to −2.77)* |
| 5-day moving average | |||
| PM10 | −4.18 (−7.56 to −0.80)* | −3.16 (−6.62 to 0.30) | −5.74 (−10.98 to −0.49)* |
| NO2 | −8.41 (−11.63 to −5.19)* | −5.02 (−8.52 to −1.52)* | −6.17 (−10.77 to −1.58)* |
| O3 | −3.95 (−5.88 to −2.02)* | −3.11 (−4.73 to −1.50)* | −4.26 (−7.35 to −1.18)* |
| 7-day moving average | |||
| PM10 | −5.79 (−10.48 to −1.09)* | −2.79 (−7.38 to 1.79) | −6.50 (−12.08 to −0.91)* |
| NO2 | −9.79 (−13.81 to −5.76)* | −5.67 (−9.92 to −1.41)* | −5.77 (−10.82 to −0.72)* |
| O3 | −6.17 (−8.76 to −3.58)* | −3.67 (−5.84 to −1.49)* | −6.12 (−9.26 to −2.99)* |
| 10-day moving average | |||
| PM10 | −3.74 (−10.82 to 3.34) | −0.92 (−5.43 to 3.59) | −5.96 (−12.42 to 0.48) |
| NO2 | −9.30 (−14.99 to −3.61)* | −5.32 (−9.41 to −1.24)* | −6.31 (−11.85 to −0.76)* |
| O3 | −5.88 (−9.23 to −2.52)* | −2.39 (−5.81 to 1.03) | −7.71 (−11.92 to −3.50)* |
p<0.05
Coefficients represent the expected percent change in PEF associated with 25–75% IQR of each air pollutant, adjusted for temperature, relative humidity, school absences.
Percent variation in peak expiratory flow measurements for a 10 μg/m3 increase in PM10, O3 and NO2 concentration for category 0 ranged from −1.44% to − 0.06%. For categories 1 and 2 the declines ranged from −0.83% to −0.03%, and from −1.58% to −0.08% respectively.
It is important to notice that decrements in PEF were larger when the pollutant concentrations were averaged over 3, 5, 7, and 10 days, independent from the children allergic sensitization status.
Considering the 25–75% interquartile range in O3 in the period studied, children in the non-sensitized category presented declines in PEF measurements of 2.8%, 4.0%, 6.2%, 5.9% for exposures to 3-day, 5-day, 7-day, and 10-day moving average respectively. For the same moving averages and pollutant, children in the category 2 (most sensitized) presented declines in PEF measurements of 4.8%, 4.3%, 6.2%, and 7.8%.
DISCUSSION
The results of our study show that exposure to urban air pollution was associated with PEF decrements in children regardless their allergic sensitization status. This effect occurred within hours or days of exposure. The association was strong for all pollutants except for PM10 24-hour average that reached borderline significance and for SO2 where the association was not significant. Also the strongest associations were seen with 3 to 10 day moving averages.
We hypothesized that children who were classified in the third group (high total IgE concentrations, high blood and nasal smear eosinophils, at least one positive reaction to the skin prick test, and symptoms of allergy) would present larger decrements in the peak expiratory flow measurements associated with exposure to air pollution. The results did not confirm our hypothesis even though those children tended to present larger decrements in the PEF measurements, they were not statistically different than those of the non allergic sensitized. This result is in agreement with a review of panel air pollution studies [Ward and Ayres 2004] that did not find children diagnosed with asthma to be more susceptible to the effects of air pollution than children who were not asthmatic as some studies suggest.
Our results on peak expiratory flow variations are also similar to studies published previously [Gielen, et al. 1997, van der Zee, et al. 1999, Pope and Dockery 1992, Pope, et al. 1991, Epton, et al. 2008, Gold, et al. 1999, Neas, et al. 1995], bearing in mind that comparisons with other studies are difficult due to differences in the study design and in the way the outcomes are defined and measured.
The number of children with medical diagnosed asthma in this study was too small (6 children) to compare their results with children without medical diagnosed asthma. But only 31 (32.6%) children could be considered non allergic sensitized, the other children presented signs and/or exams results that pointed to some level of sensitization, enabling us to categorize them in 3 groups (non-sensitized, less sensitized and more sensitized). Several studies show that air pollution has a larger effect in children with allergic symptoms or children diagnosed with asthma. A panel study conducted with 459 children showed that children with bronchial hyperresponsiveness and relatively high levels of total IgE were more susceptible to air pollution [Boezen, et al. 1999]. Also in children with a history of wheeze, there is a significant association between PEF decrements and same day ozone concentration [Jalaludin, et al. 2000]. In this study, children who were not allergic sensitized presented similar PEF decrements as the ones with any degree of allergic sensitization.
When we look at panel studies of children who had medical diagnosed asthma, we find that ozone seems to be the pollutant that is more associated with declines in the peak expiratory flow measurements, the occurrence of asthma attacks, respiratory infections, medication use, and shortness of breath [Romieu, et al. 1997, Just, et al. 2002, Mortimer, et al. 2002, Villeneuve, et al. 2006, Dales, et al. 2009]. In the present study, ozone was the pollutant associated more consistently with larger decrements in PEF and even though there was no statistical difference among the categories, the children categorized in the “more sensitized group” presented even larger decrements. Percent variation in peak expiratory flow measurements for category 0 for a 10 μg/m3 increase in PM10, O3 and NO2 concentration ranged from −1.44% to −0.06%. For categories 1 and 2 the declines ranged from −0.83% to −0.03%, and from −1.58% to −0.08% respectively.
In 2004, when the study was performed, ozone exceeded the national air quality standard (160 μg/m3) in 62 days. It is important to notice that Sao Paulo has a significant fleet of light duty vehicles using hydrated ethanol and “gasohol” (22% anhydrous ethyl alcohol and 78% pure gasoline). Ethanol, a cleaner-burning fuel than gasoline, is responsible for the increase in the emissions of aldehydes, especially acetaldehyde and formaldehyde, which are among the main precursors of ozone in Sao Paulo [Orlando, et al. 2010]. In the last 10 years, SP has experienced an improvement in the levels of most air pollutants, but levels of ozone have not improved as the others. As its formation depends on a chain of reactions that include the presence of volatile organic compounds (VOCs), NOx, and sunlight, it is unlikely that that this picture will change in a near future.
Even though the decrements in lung function measurements found in most studies are modest enough not to produce respiratory symptoms, there is evidence that exposure to urban air pollution contributes to retard lung growth in children [He, et al. 1993, Gauderman, et al. 2007, He, et al. 2010] and that ozone may be an important factor [Frischer, et al. 1999] as it has been demonstrated experimentally that airway inflammation persists after repeated ozone exposure [Jorres, et al. 1996].
In conclusion, our results add more evidence that air pollution has a deleterious effect on children’s lung function. Decrements in PEF and FEV1 were observed with previous 24-h average exposure to air pollution, as well as with 3 to 10 day average exposure and were associated mainly with PM10, NO2, and O3. Even though allergic sensitized children tended to present larger decrements in the PEF measurements when exposure to O3 and PM10 were considered, these decrements were not statistically different than the decrements of the non-allergic sensitized. Given that children are exposed to this air on a daily basis, this exposure can lead to a chronic inflammatory process that might impair their lung growth and further their lung function in adulthood.
Supplementary Material
FIGURE 2.
Mean (95% confidence interval) peak expiratory flow percent change associated with PM10 lagged from 1 to 30 days.
Acknowledgments
We are grateful to the schoolchildren, parents, teachers, and school principal for their help and cooperation in carrying out this study. We would also like to thank the Fogarty International Center for supporting this study. Dr. Joya Emilie de M. Correia-Deur is an Irving J. Selikoff International Fellow of the Mount Sinai School of Medicine ITREOH Program. Her work was supported in part by Grant Number 1 D43 TW000640-05S1, D43 TW000640-06, and D43 ES018745-01.
Footnotes
The Content is solely the responsibility of the authors and does not necessarily represent the official views of the Fogarty International Center or the National Institutes of Health.
Conflict of Interest Statement: None
References
- 1.Worldwide variation in prevalence of symptoms of asthma, allergic rhinoconjunctivitis, and atopic eczema: ISAAC. The International Study of Asthma and Allergies in Childhood (ISAAC) Steering Committee. Lancet. 1998;351:1225–1232. [PubMed] [Google Scholar]
- 2.Boezen HM, van der Zee SC, Postma DS, Vonk JM, Gerritsen J, Hoek G, Brunekreef B, Rijcken B, Schouten JP. Effects of ambient air pollution on upper and lower respiratory symptoms and peak expiratory flow in children. Lancet. 1999;353:874–878. doi: 10.1016/S0140-6736(98)06311-9. [DOI] [PubMed] [Google Scholar]
- 3.Bracken MB, Belanger K, Cookson WO, Triche E, Christiani DC, Leaderer BP. Genetic and perinatal risk factors for asthma onset and severity: a review and theoretical analysis. Epidemiologic reviews. 2002;24:176–189. doi: 10.1093/epirev/mxf012. [DOI] [PubMed] [Google Scholar]
- 4.CETESB (São Paulo) Qualidade do ar no estado de São Paulo 2010. Quality of air in the state of São Paulo. 2011 Report in Portuguese available at: < http://www.cetesb.sp.gov.br/ar/qualidade-doar/31-publicacoes-e-relatorios>.
- 5.Dales R, Chen L, Frescura AM, Liu L, Villeneuve PJ. Acute effects of outdoor air pollution on forced expiratory volume in 1 s: a panel study of schoolchildren with asthma. The European respiratory journal: official journal of the European Society for Clinical Respiratory Physiology. 2009;34:316–323. doi: 10.1183/09031936.00138908. [DOI] [PubMed] [Google Scholar]
- 6.D’Amato G, Cecchi L, D’Amato M, Liccardi G. Urban air pollution and climate change as environmental risk factors of respiratory allergy: an update. Journal of investigational allergology & clinical immunology: official organ of the International Association of Asthmology. 2010;20:95–102. quiz following 102. [PubMed] [Google Scholar]
- 7.Davies RJ, Rusznak C, Devalia JL. Why is allergy increasing?--environmental factors. Clinical and experimental allergy: journal of the British Society for Allergy and Clinical Immunology. 1998;28(Suppl 6):8–14. doi: 10.1046/j.1365-2222.1998.0280s6008.x. [DOI] [PubMed] [Google Scholar]
- 8.Dreborg S. Skin testing. The safety of skin tests and the information obtained from using different methods and concentrations of allergen. Allergy. 1993;48:473–475. doi: 10.1111/j.1398-9995.1993.tb01102.x. [DOI] [PubMed] [Google Scholar]
- 9.Epton MJ, Dawson RD, Brooks WM, Kingham S, Aberkane T, Cavanagh JA, Frampton CM, Hewitt T, Cook JM, McLeod S, McCartin F, Trought K, Brown L. The effect of ambient air pollution on respiratory health of school children: a panel study. Environmental health : a global access science source. 2008;7:16. doi: 10.1186/1476-069X-7-16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Etzel RA. How environmental exposures influence the development and exacerbation of asthma. Pediatrics. 2003;112:233–239. [PubMed] [Google Scholar]
- 11.Frischer T, Studnicka M, Gartner C, Tauber E, Horak F, Veiter A, Spengler J, Kuhr J, Urbanek R. Lung function growth and ambient ozone: a three-year population study in school children. American journal of respiratory and critical care medicine. 1999;160:390–396. doi: 10.1164/ajrccm.160.2.9809075. [DOI] [PubMed] [Google Scholar]
- 12.Gauderman WJ, Vora H, McConnell R, Berhane K, Gilliland F, Thomas D, Lurmann F, Avol E, Kunzli N, Jerrett M, Peters J. Effect of exposure to traffic on lung development from 10 to 18 years of age: a cohort study. Lancet. 2007;369:571–577. doi: 10.1016/S0140-6736(07)60037-3. [DOI] [PubMed] [Google Scholar]
- 13.Gielen MH, van der Zee SC, van Wijnen JH, van Steen CJ, Brunekreef B. Acute effects of summer air pollution on respiratory health of asthmatic children. American journal of respiratory and critical care medicine. 1997;155:2105–2108. doi: 10.1164/ajrccm.155.6.9196122. [DOI] [PubMed] [Google Scholar]
- 14.Gold DR, Damokosh AI, Pope CA, 3rd, Dockery DW, McDonnell WF, Serrano P, Retama A, Castillejos M. Particulate and ozone pollutant effects on the respiratory function of children in southwest Mexico City. Epidemiology. 1999;10:8–16. [PubMed] [Google Scholar]
- 15.He QC, Lioy PJ, Wilson WE, Chapman RS. Effects of air pollution on children’s pulmonary function in urban and suburban areas of Wuhan, People’s Republic of China. Archives of environmental health. 1993;48:382–391. doi: 10.1080/00039896.1993.10545959. [DOI] [PubMed] [Google Scholar]
- 16.He QQ, Wong TW, Du L, Jiang ZQ, Gao Y, Qiu H, Liu WJ, Wu JG, Wong A, Yu TS. Effects of ambient air pollution on lung function growth in Chinese schoolchildren. Respiratory medicine. 2010;104:1512–1520. doi: 10.1016/j.rmed.2010.04.016. [DOI] [PubMed] [Google Scholar]
- 17.Holguin F, Flores S, Ross Z, Cortez M, Molina M, Molina L, Rincon C, Jerrett M, Berhane K, Granados A, Romieu I. Traffic-related exposures, airway function, inflammation, and respiratory symptoms in children. American journal of respiratory and critical care medicine. 2007;176:1236–1242. doi: 10.1164/rccm.200611-1616OC. [DOI] [PubMed] [Google Scholar]
- 18.Jalaludin BB, Chey T, O’Toole BI, Smith WT, Capon AG, Leeder SR. Acute effects of low levels of ambient ozone on peak expiratory flow rate in a cohort of Australian children. International journal of epidemiology. 2000;29:549–557. [PubMed] [Google Scholar]
- 19.Jerrett M, Shankardass K, Berhane K, Gauderman WJ, Kunzli N, Avol E, Gilliland F, Lurmann F, Molitor JN, Molitor JT, Thomas DC, Peters J, McConnell R. Traffic-related air pollution and asthma onset in children: a prospective cohort study with individual exposure measurement. Environmental health perspectives. 2008;116:1433–1438. doi: 10.1289/ehp.10968. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Johnson CC, Ownby DR, Zoratti EM, Alford SH, Williams LK, Joseph CL. Environmental epidemiology of pediatric asthma and allergy. Epidemiologic reviews. 2002;24:154–175. doi: 10.1093/epirev/mxf013. [DOI] [PubMed] [Google Scholar]
- 21.Jorres R, Nowak D, Magnussen H. The effect of ozone exposure on allergen responsiveness in subjects with asthma or rhinitis. American journal of respiratory and critical care medicine. 1996;153:56–64. doi: 10.1164/ajrccm.153.1.8542163. [DOI] [PubMed] [Google Scholar]
- 22.Just J, Segala C, Sahraoui F, Priol G, Grimfeld A, Neukirch F. Short-term health effects of particulate and photochemical air pollution in asthmatic children. The European respiratory journal: official journal of the European Society for Clinical Respiratory Physiology. 2002;20:899–906. doi: 10.1183/09031936.02.00236902. [DOI] [PubMed] [Google Scholar]
- 23.Kay AB. Allergy and allergic diseases. First of two parts. The New England journal of medicine. 2001;344:30–37. doi: 10.1056/NEJM200101043440106. [DOI] [PubMed] [Google Scholar]
- 24.Kramer U, Koch T, Ranft U, Ring J, Behrendt H. Traffic-related air pollution is associated with atopy in children living in urban areas. Epidemiology. 2000;11:64–70. doi: 10.1097/00001648-200001000-00014. [DOI] [PubMed] [Google Scholar]
- 25.Mannino DM, Homa DM, Redd SC. Involuntary smoking and asthma severity in children: data from the Third National Health and Nutrition Examination Survey. Chest. 2002;122:409–415. doi: 10.1378/chest.122.2.409. [DOI] [PubMed] [Google Scholar]
- 26.Morkjaroenpong V, Rand CS, Butz AM, Huss K, Eggleston P, Malveaux FJ, Bartlett SJ. Environmental tobacco smoke exposure and nocturnal symptoms among inner-city children with asthma. The Journal of allergy and clinical immunology. 2002;110:147–153. doi: 10.1067/mai.2002.125832. [DOI] [PubMed] [Google Scholar]
- 27.Mortimer KM, Neas LM, Dockery DW, Redline S, Tager IB. The effect of air pollution on inner-city children with asthma. The European respiratory journal: official journal of the European Society for Clinical Respiratory Physiology. 2002;19:699–705. doi: 10.1183/09031936.02.00247102. [DOI] [PubMed] [Google Scholar]
- 28.Neas LM, Dockery DW, Koutrakis P, Tollerud DJ, Speizer FE. The association of ambient air pollution with twice daily peak expiratory flow rate measurements in children. American journal of epidemiology. 1995;141:111–122. doi: 10.1093/oxfordjournals.aje.a117399. [DOI] [PubMed] [Google Scholar]
- 29.Nicolai T, Carr D, Weiland SK, Duhme H, von Ehrenstein O, Wagner C, von Mutius E. Urban traffic and pollutant exposure related to respiratory outcomes and atopy in a large sample of children. The European respiratory journal : official journal of the European Society for Clinical Respiratory Physiology. 2003;21:956–963. doi: 10.1183/09031936.03.00041103a. [DOI] [PubMed] [Google Scholar]
- 30.Nicolai T. Pollution, environmental factors and childhood respiratory allergic disease. Toxicology. 2002;181–182:317–321. doi: 10.1016/s0300-483x(02)00300-1. [DOI] [PubMed] [Google Scholar]
- 31.Orlando JP, Alvim DS, Yamazaki A, Corrêa SM, Gatti LV. Ozone precursors for the São Paulo Metropolitan Area. Science of the total environment. 2010;408:1612–1620. doi: 10.1016/j.scitotenv.2009.11.060. [DOI] [PubMed] [Google Scholar]
- 32.Pope CA, 3rd, Dockery DW, Spengler JD, Raizenne ME. Respiratory health and PM10 pollution. A daily time series analysis. The American review of respiratory disease. 1991;144:668–674. doi: 10.1164/ajrccm/144.3_Pt_1.668. [DOI] [PubMed] [Google Scholar]
- 33.Pope CA, 3rd, Dockery DW. Acute health effects of PM10 pollution on symptomatic and asymptomatic children. The American review of respiratory disease. 1992;145:1123–1128. doi: 10.1164/ajrccm/145.5.1123. [DOI] [PubMed] [Google Scholar]
- 34.Romieu I, Meneses F, Ruiz S, Huerta J, Sienra JJ, White M, Etzel R, Hernandez M. Effects of intermittent ozone exposure on peak expiratory flow and respiratory symptoms among asthmatic children in Mexico City. Archives of environmental health. 1997;52:368–376. doi: 10.1080/00039899709602213. [DOI] [PubMed] [Google Scholar]
- 35.Salvi S. Pollution and allergic airways disease. Current opinion in allergy and clinical immunology. 2001;1:35–41. doi: 10.1097/01.all.0000010982.31993.84. [DOI] [PubMed] [Google Scholar]
- 36.Schwartz J, Timonen KL, Pekkanen J. Respiratory effects of environmental tobacco smoke in a panel study of asthmatic and symptomatic children. American journal of respiratory and critical care medicine. 2000;161:802–806. doi: 10.1164/ajrccm.161.3.9901002. [DOI] [PubMed] [Google Scholar]
- 37.Trasande L, Thurston GD. The role of air pollution in asthma and other pediatric morbidities. The Journal of allergy and clinical immunology. 2005;115:689–699. doi: 10.1016/j.jaci.2005.01.056. [DOI] [PubMed] [Google Scholar]
- 38.van der Zee S, Hoek G, Boezen HM, Schouten JP, van Wijnen JH, Brunekreef B. Acute effects of urban air pollution on respiratory health of children with and without chronic respiratory symptoms. Occupational and environmental medicine. 1999;56:802–812. doi: 10.1136/oem.56.12.802. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Viegi G, Simoni M, Scognamiglio A, Baldacci S, Pistelli F, Carrozzi L, Annesi-Maesano I. Indoor air pollution and airway disease. The international journal of tuberculosis and lung disease: the official journal of the International Union against Tuberculosis and Lung Disease. 2004;8:1401–1415. [PubMed] [Google Scholar]
- 40.Villeneuve PJ, Doiron MS, Stieb D, Dales R, Burnett RT, Dugandzic R. Is outdoor air pollution associated with physician visits for allergic rhinitis among the elderly in Toronto, Canada? Allergy. 2006;61:750–758. doi: 10.1111/j.1398-9995.2006.01070.x. [DOI] [PubMed] [Google Scholar]
- 41.Wamboldt FS, Ho J, Milgrom H, Wamboldt MZ, Sanders B, Szefler SJ, Bender BG. Prevalence and correlates of household exposures to tobacco smoke and pets in children with asthma. The Journal of pediatrics. 2002;141:109–115. doi: 10.1067/mpd.2002.125490. [DOI] [PubMed] [Google Scholar]
- 42.Ward DJ, Ayres JG. Particulate air pollution and panel studies in children: a systematic review. Occupational and environmental medicine. 2004;61:e13. doi: 10.1136/oem.2003.007088. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Wyler C, Braun-Fahrlander C, Kunzli N, Schindler C, Ackermann-Liebrich U, Perruchoud AP, Leuenberger P, Wuthrich B. Exposure to motor vehicle traffic and allergic sensitization. The Swiss Study on Air Pollution and Lung Diseases in Adults (SAPALDIA) Team. Epidemiology. 2000;11:450–456. doi: 10.1097/00001648-200007000-00015. [DOI] [PubMed] [Google Scholar]
- 44.Zeger SL, Liang KY, Albert PS. Models for longitudinal data: a generalized estimating equation approach. Biometrics. 1988;44:1049–1060. [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.