The Journal of Allergy and Clinical Immunology
Volume 127, Issue 4 , Pages 875-882, April 2011

Effects of distance from a heavily transited avenue on asthma and atopy in a periurban shantytown in Lima, Peru

  • Lauren M. Baumann, MHS

      Affiliations

    • Division of Pulmonary and Critical Care, Johns Hopkins University, Baltimore, Md
    • A.B. PRISMA, Lima, Peru
    • ,
  • Colin L. Robinson, BA

      Affiliations

    • Division of Pulmonary and Critical Care, Johns Hopkins University, Baltimore, Md
    • A.B. PRISMA, Lima, Peru
    • ,
  • Juan M. Combe, MD

      Affiliations

    • A.B. PRISMA, Lima, Peru
    • ,
  • Alfonso Gomez, MD

      Affiliations

    • A.B. PRISMA, Lima, Peru
    • ,
  • Karina Romero, MD

      Affiliations

    • A.B. PRISMA, Lima, Peru
    • ,
  • Robert H. Gilman, MD

      Affiliations

    • A.B. PRISMA, Lima, Peru
    • Program in Global Disease Epidemiology and Control, Department of International Health, Johns Hopkins University, Baltimore, Md
    • Departamento de Microbiología, Facultad de Ciencias y Filosofía, and the CRONICAS Center of Excellence for Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru
    • ,
  • Lilia Cabrera, RN

      Affiliations

    • A.B. PRISMA, Lima, Peru
    • ,
  • Nadia N. Hansel, MD, MPH

      Affiliations

    • Division of Pulmonary and Critical Care, Johns Hopkins University, Baltimore, Md
    • Division of Allergy and Clinical Immunology, Johns Hopkins School of Medicine, Baltimore, Md
    • Department of Environmental Health Sciences, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md
    • ,
  • Robert A. Wise, MD

      Affiliations

    • Division of Pulmonary and Critical Care, Johns Hopkins University, Baltimore, Md
    • Department of Environmental Health Sciences, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md
    • ,
  • Patrick N. Breysse, PhD, MHS

      Affiliations

    • Department of Environmental Health Sciences, Johns Hopkins Bloomberg School of Public Health, Baltimore, Md
    • ,
  • Kathleen Barnes, PhD

      Affiliations

    • Division of Pulmonary and Critical Care, Johns Hopkins University, Baltimore, Md
    • Division of Allergy and Clinical Immunology, Johns Hopkins School of Medicine, Baltimore, Md
    • ,
  • Juan E. Hernandez, PhD

      Affiliations

    • Centro de Información para Decisiones en Salud Pública, Instituto Nacional de Salud Publica, Cuernavaca, Mexico
    • ,
  • William Checkley, MD, PhD

      Affiliations

    • Division of Pulmonary and Critical Care, Johns Hopkins University, Baltimore, Md
    • A.B. PRISMA, Lima, Peru
    • Departamento de Microbiología, Facultad de Ciencias y Filosofía, and the CRONICAS Center of Excellence for Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru
    • Corresponding Author InformationReprint requests: William Checkley, MD, PhD, Johns Hopkins University, Division of Pulmonary and Critical Care, 1830 Monument St, Fifth Floor, Baltimore, MD 21205.

Received 13 July 2010; received in revised form 19 November 2010; accepted 23 November 2010. published online 18 January 2011.

Article Outline

Background

Proximity to roadways increases the risk of asthma in developed countries; however, relatively little is known about this relationship in developing countries, where rapid and uncontrolled growth of cities has resulted in urban sprawl and heavy traffic volumes.

Objective

We sought to determine the effect of distance from a heavily transited avenue on asthma symptoms and quantitative respiratory outcome measures in a periurban shantytown in Lima, Peru.

Methods

We enrolled 725 adolescents aged 13 to 15 years who were administered a survey on asthma symptoms and measured spirometry, response to allergy skin testing, and exhaled nitric oxide (eNO). We calculated distances from the main avenue for all households and measured indoor particulate matter in 100 households. We used multivariable regression to model the risk of asthma symptoms, risk of atopy, eNO levels, and FEV1/forced vital capacity ratio as a function of distance.

Results

Compared against 384 meters, the odds of current asthma symptoms in households living within 100 meters increased by a factor of 2 (P < .05). The odds of atopy increased by a factor of 1.07 for every 100-meter difference in the distance from the avenue (P = .03). We found an inverse relationship in prebronchodilator FEV1/forced vital capacity and distance to the avenue in female subjects (P = .01) but not in male subjects. We did not find an association between eNO or household particulate matter levels and distance.

Conclusion

Living in close proximity to a high-traffic-density avenue in a periurban community in Peru was associated with a greater risk of asthma symptoms and atopy. Regulation of mobile-source pollutants in periurban areas of developing countries might help reduce the burden of asthma symptoms and atopy.

Key words: Asthma symptoms, atopy, distance, traffic, particulate matter, spirometry

Abbreviations used: BMI, Body mass index, eNO, Exhaled nitric oxide, FVC, Forced vital capacity, ISAAC, International Study on Asthma and Allergies in Childhood, NO2, Nitrogen dioxide, PM, Particulate matter, PM2.5, Particulate matter 2.5 μm in diameter, PM10, Particulate matter 10 μm in diameter

 

Discuss this article on the JACI Journal Club blog: www.jaci-online.blogspot.com.

There is increasing evidence that living within close proximity to a major roadway increases the prevalence and severity of asthma. However, nearly all research into the effects of traffic-related pollution exposure on asthma risk and severity has been done in developed countries,1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 where high traffic density is observed from multiple large intersecting roadways. Relatively little is known about the association between traffic exposure and the risk of asthma in developing countries,12, 13 where rapid and uncontrolled growth of cities has resulted in urban sprawl and heavy traffic volumes over the last 2 decades.

The majority of studies look at self- or parent-reported asthma symptoms and their association with distance to major roadways. Venn et al1 documented an increase in wheeze for patients living within 150 meters of a major roadway in the United Kingdom. More recent studies, also in developed countries, found similar increases in parent-reported lifetime asthma, current asthma, and current wheeze for those living within 50 or 75 meters of the road compared with greater distances.2, 3, 4, 5 These findings, however, have not been consistently replicated.6, 7 Studies evaluating the proximity to roadways and asthma in developed countries are complicated by the complex nature of urban transportation networks, where it is difficult to isolate the effect of individual roads. Moreover, there are only limited data on quantitative respiratory outcome measures and traffic exposure, all of which have been predominantly collected in developed countries. In The Netherlands, investigators did not find an association between traffic and spirometric outcomes despite having reported an association between high-density truck traffic and self-reported wheeze.8 A similar trend was seen in a large study from the United Kingdom in which a smaller FEV1 was seen for children living within 150 meters of main roadways, but these findings did not achieve statistical significance.9

Furthermore, mobile source–related pollutant exposures such as particulate matter 2.5 μm in size (PM2.5), nitrogen dioxide (NO2), and sulfur dioxide might directly increase the risk of asthma symptoms.14, 15 In asthmatic children ambient PM2.5 levels were directly associated with an increase in exhaled nitric oxide (eNO) values for up to 12 hours.16 Epidemiologic studies have shown higher atopy rates in urban areas with higher pollution levels.17 However, little is known about the direct relationship between mobile source–related pollutant exposures and risk of atopy.

With a 26% prevalence, the International Study on Asthma and Allergies in Childhood (ISAAC) found Peru to have the highest prevalence of childhood asthma symptoms in Latin America,18 ranking it among the top quintile worldwide. Lima, the capital of Peru, is a rapidly growing city with high population density, periurban sprawl, and heavy, unregulated automotive traffic. We sought to determine the effect of distance from a heavily transited avenue on asthma symptoms, lung function, atopy, airways inflammation, and household particulate matter (PM) concentrations in a cohort of adolescents living in a periurban shantytown in Lima, Peru. In contrast to previous research conducted in developed countries, our community only has 1 major roadway with high traffic density as the source of exposure, which simplifies the evaluation of the effect of traffic. Because our community is relatively homogenous in sociodemographic composition, it provides a naturally controlled environment to study the effects of distance from a major roadway on the risk of asthma and atopy. The results of this study were presented in part previously in an abstract.19

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Methods 

Study setting 

We conducted our study in Pampas de San Juan de Miraflores, a periurban shantytown located 25 km south of the center of Lima.20, 21 This community has grown rapidly (population 60,000) as a result of urban sprawl, and homes are tightly packed with a mixture of paved and unpaved roads. It is cut in half by a highly trafficked avenue that serves as a main commuter route travelled mainly by unregulated commuter buses. There are no point sources of industrial pollution nearby. Our research team has participated in several community-based research projects at Pampas for more than 2 decades. As a result, we have conducted community-wide censuses and updates thereof over the years in which we visited every house in the community and asked about the number of persons per household and the age and sex of each household member.

Study design 

We recruited an age- and sex-stratified random sample of 725 adolescents aged 13 to 15 years by home visitation. Adolescents were eligible to participate if they were capable of understanding or performing procedures, if their parents or guardians were capable of providing written informed consent and they were capable of providing assent, and if they had no ocular, abdominal, or thoracic surgery or were hospitalized for cardiac reasons in the last 3 months. They were ineligible if they had a chronic respiratory condition other than asthma, such as cystic fibrosis or chronic lung disease of prematurity; if they were pregnant; or if they had pulmonary tuberculosis or were currently receiving treatment for pulmonary tuberculosis. We recruited only 1 adolescent per household. After obtaining written informed consent, we asked participants to complete a survey on asthma symptoms, sociodemographics, and exposure data. We used a Spanish version of the ISAAC questionnaire previously validated in Peru.22

During a second home visit, we asked participants to undergo eNO testing, allergy skin testing, and spirometry before and after bronchodilators. We measured eNO levels by using the NIOX MINO portable eNO monitor (Aerocrine, New Providence, NJ). We applied allergy skin prick tests for cockroach, dust mite mix, cat hair, dog epithelium, mouse epithelium, and mixed molds along with a positive histamine control and negative glycerol control in the inner forearm by using the Multi-Test II allergen applicator (Lincoln Diagnostics, Decatur, Ill). An allergy skin test response was considered positive if the sum of the vertical and horizontal dimensions of the induration was greater than 3 mm larger than that elicited by the negative control and if the sum of the vertical and horizontal dimensions of erythema was greater than 5 mm larger than that elicited the negative control.

We performed spirometry with a handheld spirometer (SpiroPro; Jaeger, Hochberg, Germany). SpiroPro uses disposable, single-use, factory-calibrated pneumotachometer tubes. We asked each participant to perform up to a maximum of 8 prebronchodilator tests to attain 3 acceptable and reproducible tests as per standard quality criteria.23 We then administered 4 doses of inhaled salbutamol (100 μg each) and repeated spirometry 15 minutes later. All maneuvers were performed seated upright and with a nose clip. Participants who did not meet quality criteria were revisited up to 2 more times on a different day and asked to repeat spirometry. We asked participants to withhold any short-acting bronchodilators for 8 hours and long-acting bronchodilators for 24 to 48 hours of testing unless clinically necessary; however, we did not have instances in which this occurred. We revisited participants who reported having a respiratory tract infection in the last 2 weeks on a later date.

We assessed indoor PM concentrations in 100 homes that were randomly selected from our study sample according to 11 geographic zones determined by distance from the main avenue. We measured PM concentrations over 48-hour periods on weekdays only by using the pDR-1000 (Thermo Scientific, Franklin, Mass). We also measured relative humidity concurrently using a HOBO Data Logger (Onset Corp, Bourne, Mass). We adjusted PM concentrations to relative humidity levels, as previously specified.24 Outdoor pollution data were provided by DIGESA (www.digesa.sld.pe). We obtained all 24-hour ambient measurements of PM2.5 and particulate matter 10 μm in diameter (PM10) for the years 2008 and 2009 collected from a governmental post located within 3 km of Pampas. Global positioning system coordinates were determined for every participating household, and the perpendicular distance from the avenue was calculated with ArcGis 9.3 (ESRI Corp, Redlands, Calif).

This study was approved by the Institutional Review Boards of the Johns Hopkins Bloomberg School of Public Health in Baltimore, Maryland, and A.B. PRISMA in Lima, Peru. We obtained written informed consent from parents or guardians and assent from adolescent participants.

Definitions 

We defined subjects with current asthma symptoms as those who reported wheezing or used any asthma medications in the past 12 months. We defined atopy as a positive test response to 1 or more skin allergens. We defined bronchodilator-induced reversibility as a change of 12% or greater in FEV1 between premeasurement and postmeasurement values.23

Biostatistical methods 

We used a multivariable logistic additive model to model the effect of distance to the main avenue on the risk of current asthma symptoms and atopy.25 We regressed the log odds of current asthma on a smooth (spline) function of distance from the main avenue, age, sex, maternal education, and household income. Fewer participants had data on body mass index (BMI) or smoking status; however, adding BMI and current smoking in the subset analysis did not alter findings (data not shown). We obtained a 95% CI by using a percentile-based bootstrap approach.26 Because only 84 participants had current asthma symptoms, we did not have a sufficient sample size to adequately explore for differences by sex.

In exploratory analysis we found that the relationship between the log odds of atopy and distance from the main avenue was approximately linear. We regressed the log odds of atopy on distance from the avenue, sex, age, height, BMI, smoking, passive tobacco exposure, maternal education, and household income. We did not find a significant interaction between distance from the avenue and sex (data not shown). To model lung function, we used linear regression stratified by sex. We regressed FEV1/forced vital capacity (FVC) ratios on quartile of distance from the avenue, calendar quarter, age, BMI, history of personal smoking, household income, and maternal education. We regressed FEV1 on quartile of distance from the avenue, height, BMI, personal history of smoking, household income, and maternal education.

To model eNO levels and indoor PM concentration, we used a generalized linear model with a log-normal distribution. We regressed eNO levels on distance from the avenue, sex, age, height, BMI, wheeze in the past 12 months, lifetime diagnosis of asthma, use of asthma medication in the past 12 months, atopy, calendar quarter, personal history of smoking, passive tobacco exposure, maternal education, and household income. We regressed indoor PM on distance from the road, calendar quarter, outdoor PM2.5 and PM10, personal history of smoking, and household tobacco smoke exposure.

We conducted our analyses in R (www.r-project.org) and STATA (StataCorp, College Station, Tex).

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Results 

Baseline characteristics 

Of 1056 adolescents who were identified from census data, we enrolled 725 (69%) in our study. During the first home visit, all 725 children completed the survey. Of those recruited, 646 (89%) completed at least 1 or more of the clinical tests. Four (1%) participants did not complete testing because they moved out of the community, 3 (<1%) became pregnant and thus were ineligible to continue in the study, and 60 (8%) declined to continue with the study. Six hundred twenty-five (86%) of the participants completed spirometry, 604 (83%) completed eNO testing, and 614 (85%) completed an allergy skin test. We did not find differences in the distribution of sex, age, demographics, or socioeconomic status by categories of asthma symptoms and atopy (Table I). Six hundred ninety-eight (96%) participants lived in Lima since birth, and more than 99% have lived in the study community for 5 years or longer. Seven hundred sixteen (99%) participants reported that propane gas was the predominant type of fuel used for cooking at home.

Table I. Personal characteristics and sociodemographics according to the presence or absence asthma symptoms and atopy: Lima, Peru, 2009-2010
Asthma and atopyAsthma and no atopyNo asthma and atopyNo asthma and no atopyP value
No. of adolescents5717285255
No. of boys35 (61%)7 (41%)144 (51%)120 (47%).22
Average age (y [SD])14.7 (1.0)14.8 (0.9)14.8 (0.9)14.9 (0.8).07
Born in Lima56 (98%)16 (94%)272 (95%)249 (98%).33
Parents from Lima20 (35%)9 (53%)99 (35%)102 (40%).31
Parents from highlands45 (79%)14 (82%)221 (78%)194 (76%).95
Parents from rainforest2 (4%)0 (0%)10 (4%)4 (2%).42
Income <175 USD15 (26%)4 (24%)68 (24%)54 (21%).80
Maternal education (y), mean (SD)8.5 (4.0)8.3 (4.2)8.4 (3.8)8.0 (3.6).76
Paternal education (y), mean (SD)8.9 (3.2)9.6 (4.0)9.6 (3.2)9.9 (2.8).21
Electricity 24 h57 (100%)17 (100%)282 (99%)254 (100%).78
Water 24 h53 (93%)16 (94%)259 (91%)238 (93%).77
Hygienic services in home55 (96%)17 (100%)274 (96%)246 (96%)1.00
Rooms in household (SD)4.9 (1.4)4.7 (1.8)5.0 (1.8)4.8 (1.7).74
Persons per room (SD)1.3 (0.7)1.3 (1.0)1.3 (0.7)1.4 (0.8).81
Concrete floor18 (32%)6 (35%)113 (40%)92 (36%).65
Unprocessed concrete floor26 (46%)7 (41%)106 (37%)106 (42%).58
Computer11 (19%)6 (35%)75 (26%)62 (24%).52
Smoking in home7 (12%)3 (18%)51 (18%)37 (15%).60
Landline34 (60%)11 (65%)95 (33%)107 (42%).21
Cellular telephone50 (88%)15 (88%)246 (86%)224 (88%).96
Owns dogs32 (56%)11 (65%)159 (56%)156 (61%).57
Owns cats22 (39%)8 (47%)122 (43%)125 (49%).36
Cockroaches present in home in past month32 (56%)11 (65%)174 (61%)161 (63%).76
Time spent in house (h/d)
0-60 (0%)0 (0%)0 (0%)2 (1%).40
7-100 (0%)0 (0%)7 (2%)8 (3%).70
11-159 (16%)5 (29%)88 (31%)68 (27%).12
≥1648 (84%)12 (71%)190 (67%)177 (69%).08
Height (cm), mean (SD)
Male subjects162.5 (6.8)165.0 (6.7)162.6 (7.4)161.3 (7.5).36
Female subjects151.3 (5.0)152.3 (6.1)152.9 (5.7)153.1 (5.4).54
Weight (kg), mean (SD)
Male subjects60.6 (13.5)59.8 (10.8)56.7 (10.7)55.1 (11.4).07
Female subjects52.8 (12.4)56.6 (6.5)52.5 (9.3)51.2 (8.5).25
BMI (kg/m2), mean (SD)
Male subjects22.8 (3.9)21.9 (3.4)21.3 (3.2)21.0 (3.2).05
Female subjects23.0 (4.7)24.4 (2.9)22.4 (3.4)21.8 (3.3).07

USD, US dollars.

Our study community is divided in half by the main avenue (Fig 1). Participating houses were located adjacent to the road up to a maximum distance of 1063 meters. One hundred ninety-six (27%) participants lived within 200 meters of the avenue. We did not find differences in the distribution of sex, age, or demographics according to distance from the avenue and only minor differences in household characteristics (Table II).

  • View full-size image.
  • Fig 1. 

    Distribution of the 725 study households in Pampas de San Juan around the main avenue of Lima, Peru. The thick black line represents the main avenue (Avenida Miguel Iglesias) that intersects our study community. The black circles represent households in our study. The thin white lines represent distances from the avenue in 100-meter intervals.

Table II. Personal characteristics and sociodemographics according to distance from the main avenue: Lima, Peru, 2009-2010
Distance from road (meters)
0-199.99 (n = 196)200-399.99 (n = 176)400-649.99 (n = 171)≥650 (n = 182)Trend
P value
No. of adolescents196176171182
No. of boys95 (48%)88 (50%)76 (44%)98 (54%).511
Average age (y [SD])14.8 (0.9)14.9 (0.9)14.9 (0.9)14.8 (0.8).586
Born in Lima192 (98%)170 (97%)167 (98%)169 (93%).021
Parents from Lima87 (44%)66 (38%)63 (37%)53 (29%).003
Parents from highlands143 (73%)134 (76%)132 (77%)147 (81%).07
Parents from rainforest5 (3%)7 (4%)3 (2%)6 (3%).97
Income <175 USD56 (29%)38 (22%)27 (16%)52 (29%).703
Maternal education (y), mean (SD)8.6 (3.6)8.2 (3.8)8.5 (3.7)7.5 (3.6).013
Paternal education (y), mean (SD)9.8 (3.1)9.9 (3.1)10.1 (3.0)9.5 (3.0).601
Electricity 24 h195 (99%)175 (99%)169 (99%)182 (100%).812
Water 24 h179 (91%)175 (99%)160 (94%)153 (84%).063
Hygienic services in home185 (94%)176 (100%)167 (98%)170 (93%).451
Rooms in household (SD)4.8 (1.7)5.2 (1.9)4.8 (1.7)4.6 (1.5).054
Persons per room (SD)1.3 (0.9)1.3 (0.7)1.4 (0.9)1.4 (0.9).491
Concrete floor77 (39%)70 (40%)63 (37%)62 (34%).246
Unprocessed concrete floor81 (42%)61 (35%)73 (43%)71 (39%).986
Computer51 (26%)49 (28%)53 (31%)27 (15%).033
Smoking in home29 (15%)28 (16%)29 (17%)24 (13%).296
Landline130 (66%)107 (61%)116 (68%)93 (51%).014
Cellular telephone168 (86%)149 (85%)154 (90%)157 (86%).542
Owns dogs112 (57%)104 (59%)93 (54%)104 (57%).786
Owns cats87 (44%)64 (36%)73 (43%)96 (53%).062
Cockroaches present in home in past month117 (60%)98 (56%)110 (64%)121 (66%).086
Time spent in house (h/d)
0-60 (0%)0 (0%)1 (1%)1 (1%).199
7-102 (1%)2 (1%)7 (4%)7 (4%).026
11-1546 (23%)47 (27%)49 (29%)56 (31%).1
≥16148 (76%)127 (72%)114 (67%)118 (65%).13
Height (cm), mean (SD)
Male subjects162.9 (6.9)161.4 (7.6)163.5 (7.7)160.5 (7.1).077
Female subjects152.5 (5.9)152.7 (5.0)153.3 (5.8)152.8 (5.1).463
Weight (kg), mean (SD)
Male subjects58.3 (13.1)55.9 (9.3)57.3 (11.4)55.4 (10.9).179
Female subjects51.5 (8.5)53.1 (10.3)52.6 (11.1)51.1 (7.0).916
BMI (kg/m2), mean (SD)
Male subjects21.8 (3.6)21.1 (2.8)21.3 (3.0)21.4 (3.6).555
Female subjects22.1 (3.4)22.7 (3.8)22.3 (4.1)21.9 (2.8).672

USD, US dollars.

Asthma symptoms 

The prevalence of current asthma symptoms was 12% (84/725). Ninety-four (13%) participants reported ever having a physician’s diagnosis of asthma, and 44 (6%) had used inhaled or oral corticosteroids or bronchodilators (β-agonists) for asthma in the past year. The odds of current asthma symptoms increased with closer proximity to the main avenue (Fig 2). Compared against 384 meters, the odds of current asthma symptoms in households living within 100 meters increased by a factor of 2 and remained significantly greater until about 250 meters (P < .05). We did not find a difference in the odds of current asthma symptoms at distances beyond 384 meters.

  • View full-size image.
  • Fig 2. 

    Odds ratio of current asthma symptoms and distance from the main avenue (reference distance, 384 meters) of Lima, Peru (2009-2010). The thick black line represents the odds ratio of current asthma symptoms by using 384 meters as the reference distance. The gray area represents the 95% bootstrap CIs. The vertical segments in the x-axis represent the distribution of household distances from the main avenue.

Spirometry 

Of the 625 participants who completed spirometry, 24 (3%) demonstrated postbronchodilator reversibility. The proportion of cases with postbronchodilator reversibility appeared to be greater with closer proximity to the main avenue; however, this increase was not statistically significant (Table III). We observed a decrease in prebronchodilator FEV1/FVC ratio with closer proximity to the avenue in girls (P = .01) but not in boys. We also observed a marginally significant decrease in postbronchodilator FEV1/FVC ratio with closer proximity to the avenue in girls. We did not find important differences in prebronchodilator or postbronchodilator FEV1 and distance from the main avenue (Table III).

Table III. Spirometric outcomes according to distance from the main avenue: Lima, Peru, 2009-2010
Distance from road (meters)
0-199.99 (n = 172)200-399.99 (n = 143)400-649.99 (n = 154)≥650 (n = 156)P value
Postbronchodilator reversibility9 (5.2%)6 (4.2%)4 (2.6%)5 (3.2%).25

Mean prebronchodilator FEV1 (L), mean (SD)
Male subjects3.87 (0.6)3.84 (0.7)3.92 (0.6)3.72 (0.6).80∗
Female subjects3.05 (0.4)3.08 (0.4)3.11 (0.4)3.10 (0.4).20∗

Mean prebronchodilator FEV1/FVC (%), mean (SD)
Male subjects88.2 (5.7)88.8 (6.4)88.0 (6.3)87.9 (6.5).74†
Female subjects88.0 (7.1)89.5 (5.3)90.4 (5.8)89.8 (6.3).01†

Mean postbronchodilator FEV1 (L), mean (SD)
Male subjects4.02 (0.6)3.96 (0.7)4.04 (0.6)3.80 (0.6).46∗
Female subjects3.16 (0.4)3.18 (0.4)3.20 (0.4)3.18 (0.4).67∗

Mean postbronchodilator FEV1/FVC (%), mean (SD)
Male subjects90.7 (5.3)90.6 (6.0)89.8 (5.9)89.8 (6.9).44†
Female subjects91.1 (5.3)91.6 (5.5)92.2 (5.0)92.0 (5.8).08†

∗Adjusted by calendar quarter, age, height, BMI, personal history of current smoking, household income, and maternal education.

†Adjusted for calendar quarter, age, household income, maternal education, personal history of current smoking, and BMI.

Atopy 

Of 725 total participants, 614 (85%) underwent allergy skin testing, and 342 (56%) were atopic. One hundred eight (18%) had positive results to 1 allergen, 90 (15%) had positive results to 2 allergens, and 144 (23%) had positive results to 3 or more allergens. The log odds of atopy increased approximately linearly with closer proximity to the main avenue (Fig 3). The odds of atopy increased multiplicatively by a factor of 1.07 for every 100-meter difference in the distance from the main avenue (Table IV). We did not find an interaction between distance from the main avenue and sex on risk of atopy (P = .42).

  • View full-size image.
  • Fig 3. 

    Log odds of atopy and distance from the main avenue of Lima, Peru (2009-2010). The circles represent the log odds of atopy calculated by 100-meter intervals of distance from the main avenue. The size of the circles is proportional to the square root of the number of participants at each interval. The dashed red line represents a fitted line of the log odds of atopy by 100-meter intervals of distance from the main avenue.

Table IV. Multivariable logistic regression of odds of atopy on distance from the main avenue: Lima, Peru, 2009-2010
PredictorCoefficient (as log odds ratio)SEP value
Intercept0.872.47.73
Distance (per 100 meters)−0.070.03.03
Sex (male is reference)−0.160.22.47
Age (y)−0.090.10.34
Height (cm)0.010.01.62
BMI (kg/m2)0.000.00.83
Current smoker−0.110.38.77
Passive tobacco exposure0.330.25.18
Maternal education (6-8 y completed)0.080.26.75
Maternal education (9-10 y completed)−0.460.31.14
Maternal education (≥11 y completed)0.180.24.45
Income 175-280 USD−0.160.23.48
Income 281-400 USD−0.180.24.45
Income >400 USD0.680.44.12

USD, US dollars.

eNO 

Six hundred four (83%) participants completed eNO testing. Values ranged from less than 5 ppb to 269 ppb, with a mean of 21.7 ppb (SD, 19.6 ppb). We did not find an association between proximity to the main avenue and eNO levels (Table V). Female subjects had a lower mean eNO value than male subjects. Neither a personal history nor passive exposure to tobacco smoke affected eNO levels. We found higher eNO values in participants with current asthma, a history of physician-diagnosed asthma, and atopy.

Table V. Multivariable regression of eNO on distance from the main avenue: Lima, Peru, 2009-2010
PredictorCoefficient (in log scale)SEP value
Intercept−0.831.16.48
Distance (per 100 meters)0.000.01.97
Sex (male is reference)−0.230.11.04
Age (y)0.200.04<.001
Height (cm)0.000.01.89
BMI (kg/m2)0.000.00.57
Current smoker0.070.14.63
Passive tobacco exposure−0.130.11.23
Maternal education (≥11 y completed)0.300.08<.001
Income ≤175 USD−0.190.11.08
April-June0.010.17.93
July-September0.160.12.17
October-December0.150.12.21
Current asthma0.700.09<.001
Asthma diagnosis0.290.09<.01
Atopy0.680.11<.001

USD, US dollars.

Indoor air pollution 

The mean 24-hour PM concentration was 43.4 μg/m3 (SD, 24.3 μg/m3), and the median was 30.9 μg/m3 (interquartile range, 16.9 μg/m3). PM concentrations at 24 hours ranged from 9.0 μg/m3 to 159.1 μg/m3. In 2009, average 24-hour outdoor concentrations were 37.5 μg/m3 (SD, 31.6 μg/m3) and 75.1 μg/m3 (SD, 27.6 μg/m3) for PM2.5 and PM10, respectively. We did not find an association between indoor PM levels and distance to the main avenue (P = .48). However, indoor PM levels were positively correlated with outdoor PM2.5 levels.

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Discussion 

The risk of both asthma symptoms and atopy increased with closer proximity to a high-traffic-density avenue in a developing country. Measures of airflow limitation27 decreased with closer proximity to the avenue in girls but not in boys. We did not find higher levels of airways inflammation or higher levels of indoor PM with closer proximity to the main avenue.

We were surprised to find that the risk of atopy increased linearly with closer proximity to the main avenue. We have not found any other epidemiologic studies that report a significant association between distance from a major roadway and risk of atopy. The study in Jimma, Ethiopia, found a trend in the relationship between distance from major roadways and skin sensitization to dust mites, but this increase did not achieve statistical significance.12 Evidence of this relationship can be supported with data from animal experiments. For example, guinea pigs exposed to high concentrations of NO2 after immunization and antigen challenge showed significantly higher levels of specific IgG and IgE than control animals.28 Increased levels of total IgG have been shown when ovalbumin was coupled with sulfur dioxide exposure in rats.29 In human populations increased levels of CD4+ and CD8+ lymphocytes have been documented with increased exposure to PM2.5 and PM10, and increases in total IgG levels were linked with higher levels of PM2.5,30 demonstrating that PM activates the immunologic response.31, 32 The increased risk of atopy with closer proximity to major roadways provides mechanistic insights into the increased risk of asthma symptoms.

Our findings on asthma symptoms and proximity to high-traffic-density roads are similar to those observed by other studies conducted in developed countries.1, 2, 3, 4, 5, 11, 12 It is widely accepted that environmental pollutants have an adverse effect on asthma symptoms,14, 15, 33 which supports findings that areas of increased exposure, such as the roadside, lead to increased symptoms. The majority of studies have found these effects to be strongest within 150 meters from the road,1, 2, 3, 4, 5, 11, 12 and this is substantiated by research that shows ambient PM and NO2 concentrations significantly decrease around 100 to 200 meters from a traffic source.34, 35, 36 Many studies have also found these effects to be stronger in girls than in boys, showing higher odds of wheeze and asthma symptoms for girls living in close proximity to high traffic densities compared with their male counterparts.1, 37, 38

Despite the strong evidence of an association between traffic exposures and asthma symptoms in the developed world, very little is known about this relationship in developing countries. In the developing world many cities are experiencing fast population growth with relatively unregulated sprawl, providing a setting with high traffic flow and pollution in highly crowded communities. A study conducted on the United States–Mexico border represents one of only 2 studies we identified outside of a developed country.11 In this study investigators found that asthmatic children living within 200 meters of the road had a lower FEV1 associated with increasing traffic density and a positive association between eNO values and closer distances. The other study was conducted in Jimma, Ethiopia, in which investigators found that the risk of wheeze increased inversely with distance within 150 meters of a main road.12 Jimma is a town of about 80,000 persons with no major industry and very little motorized transport, and main roads were defined as any roadway with a traffic density of 55 or more vehicles per hour.12, 13 However, asthma rates in both Mexico and Ethiopia are relatively low when compared with those in other developing countries with large urban cities, such as Peru or Brazil.18 The prevalence of wheeze in the past 12 months in 13- to 14-year-olds is approximately 7% in Mexico,18 and in Jimma the reported prevalence of asthma symptoms is less than 4%.13 The prevalence of asthma symptoms in the last 12 months in our study community was 12%. This estimate is lower than that found by ISAAC in Peru,18 and the reasons for this difference need to be explored further. Pollution rates were also much lower in the United States–Mexico border community than in Lima, with a mean PM2.5 of 17.5 μg/m3 during the study.11

Statistical modeling of environmental exposures and disease susceptibility can be very complex. Therefore we explored alternative models to establish the robustness of our findings. Specifically, we found similar results when we used a probit link instead of a logit link to model the effects of distance from the road on either risk of asthma symptoms or atopy. When we used quantile regression to model effects on median FEV1/FVC ratios, we found that both the prebronchodilator and postbronchodilator ratios in girls were inversely associated with distance from the main avenue at the .05 level of significance. One limitation of our study is that we did not collect quantitative measures of allergen exposure because there appear to be some slight differences in household characteristics and distance from the road. However, we collected qualitative data on exposure to cockroaches, dogs, and cats and did not observe any differences by distance from the main avenue. We were also not able to collect other pollutants associated with traffic, such as NO2 and ozone. Another limitation is that about 70% of the adolescents agreed to participate in our study. Although this might limit the generalizability of the study findings to the population, this participation rate is similar to that of other studies. Furthermore, we did not offer any incentives or payments for participation in our study, and our study sample was similar in age and sex to the overall eligible population.

Another potential shortcoming is that passive sampling of PM does not include a specific particle size selection inlet; however, passive PM measurements with the pDR-1000 have been shown to be a good proxy for PM2.5 concentrations.24 Furthermore, in our study we found that outdoor PM2.5 concentrations were significantly associated with indoor PM using our PM monitor. Additionally, we did not assess bronchial hyperresponsiveness, which is an additional quantitative marker of asthma. However, this would have been unfeasible because all physical testing was done by means of home visitation. Finally, the cross-sectional nature of our study design, particularly in the case of the effect of PM on asthma symptoms, is a limitation.

A strength of our study is that it is population based with a random selection of participants from a high-density periurban community. Moreover, there is only 1 main source of heavy traffic in our community; however, there are roads with lower traffic density that might nonetheless contribute to traffic pollution for which we have not accounted in our analysis.

Lima is undergoing strong economic development in some areas of the city, whereas other areas are experiencing rapid, uncontrolled urban sprawl. Periurban communities located in the outskirts of the city are home to poor populations living in communities with high population density and are exposed to heavy pollution from traffic that is largely unregulated. Indoor and outdoor levels of PM in our community exceed current safety recommendations of the World Health Organization by a factor of 4.39 As the city continues to grow, policy changes will be necessary to regulate mobile-source emissions and control traffic volumes. Policy makers in Lima might use Santiago, Chile, as a lead to follow, where air pollution has significantly decreased in recent years as a result of successful traffic-control campaigns. Over the period of 1989 to 2000, PM2.5 concentrations decreased 52% in Santiago, and this is attributable to the removal of old buses, the addition of catalytic converters to vehicles, and the cleaning and paving of streets.40

In summary, we found a link between asthma symptoms and proximity to traffic, and our findings add new evidence to associations of living near a major road and risk of atopy. Policies to regulate traffic control in periurban communities with high traffic density will have a direct health benefit for the population.

Key messages


•Proximity to multiple intersecting roadways has been shown to increase the risk of asthma in developed countries; however, relatively little is known about the association between traffic exposure and the risk of asthma in developing countries, where rapid and uncontrolled growth of cities has resulted in urban sprawl and heavy traffic volumes.

•Living in close proximity to a high-traffic-density avenue in a periurban community in Peru is associated with an increased risk of asthma symptoms. We also show for the first time that proximity to the main avenue is associated with an increased risk of atopy.

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References 

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 Supported by a Johns Hopkins Center for Global Health Award (PI: Hansel) and the Fogarty International Center Training Grant (Grant R24 TW007988). W.C. was supported by a Clinician Scientist Award from the Johns Hopkins University, a K99/R00 Pathway to Independence Award (K99HL096955) from the National Heart, Lung, and Blood Institute, National Institutes of Health, and by a contract (HHSN268200900033C) with the National Heart, Lung, and Blood Institute, National Institutes of Health. K.B. was supported in part by the Mary Beryl Patch Turnbull Scholar Program. C.L.R. was a Fogarty International Clinical Research Scholar during the time of this work and was further supported by Tufts University School of Medicine. L.M.B. was supported by a predoctoral NIH T35 Training Grant (T35AI065385). Support for exposure measurements were provided by National Institute for Environmental Health Sciences grants ES015903 and ES03819. Additional support came from A.B. PRISMA and collaborators at JHU. Lincoln Diagnostics (Decatur, Ill) and ALK-Abelló (Round Rock, Tex) generously donated all skin prick atopy kits and antigens, respectively, used in this in this study. Aerocrine (New Providence, NJ) provided us, at discount, materials for eNO testing.

 Disclosure of potential conflict of interest: K. Barnes has received research support from the National Institutes of Health and Sanofi-Aventis. The rest of the authors have declared that they have no conflict of interest.

PII: S0091-6749(10)01852-X

doi:10.1016/j.jaci.2010.11.031

The Journal of Allergy and Clinical Immunology
Volume 127, Issue 4 , Pages 875-882, April 2011

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