Exposure to air pollution concentrations of various intensities in early life and allergic sensitisation later in childhood

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Gao, Amanda J. Wheeler, Graeme R. Zosky, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3045254/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Dec, 2023 Read the published version in BMC Pulmonary Medicine → Version 1 posted 9 You are reading this latest preprint version Abstract BACKGROUND Evidence on the relationship between air pollution and allergic sensitisation in childhood is inconsistent, and this relationship has not been investigated in the context of smoke events that are predicted to increase with climate change. Thus, we aimed to evaluate associations between exposure in two early life periods to severe levels of particulate matter with an aerodynamic diameter < 2.5µm (PM 2.5 ) from a mine fire, background PM 2.5 , and allergic sensitisation later in childhood. METHODS We measured specific immunoglobulin E (IgE) levels for seven common aeroallergens as well as total IgE levels in a cohort of children who had been exposed to the Hazelwood coal mine fire, either in utero or during their first two years of life, in a regional area of Australia where ambient levels of PM 2.5 are generally low. We estimated personal exposure to fire-specific emissions of PM 2.5 based on a high-resolution meteorological and pollutant dispersion model and detailed reported movements of pregnant mothers and young children during the fire. We also estimated the usual background exposure to PM 2.5 at the residential address at birth using a national satellite-based land-use regression model. Associations between both sources of PM 2.5 and sensitisation to dust, cat, fungi, and grass seven years after the fire were estimated with logistic regression, while associations with total IgE levels were estimated with linear regression. RESULTS No association was found between the levels of exposure at either developmental stage to fire-related PM 2.5 and allergic sensitisation seven years after the event. However, levels of background exposure were positively associated with sensitisation to dust (OR = 1.89, 95%CI = 1.11,3.20 per 1 µg/m 3 ). CONCLUSIONS Chronic but low exposure to PM 2.5 in early life could be more strongly associated with allergic sensitisation in childhood than time-limited high exposure levels, such as the ones experienced during landscape fires. Allergic sensitisation Immunoglobulin E Landscape fires Child health Particulate air pollution Early life Long-term effects Figures Figure 1 Figure 2 Figure 3 Introduction Allergies have been increasing in prevalence globally over the last few decades. 1 Allergic conditions have been shown to directly impact quality of life, including the psychological, educational, professional and social domains of those affected. 2,3 Allergies are known to be caused by a combination of genetic, lifestyle and environmental factors, although the relative importance of these, and their interactions, is not fully understood. 4 There is robust evidence showing that exposure to air pollution is associated with the development and exacerbation of allergic conditions such as asthma 5,6 and allergic rhinitis. 7 However, the relationship with allergic sensitisation is inconsistent and relatively scarce. 8 Children have been consistently identified as a subgroup vulnerable to the effects of air pollution for both physiological and behavioural reasons. 9 A systematic review and meta-analysis of birth cohorts found that early childhood exposure to traffic-related air pollutants was related to increased sensitisation to aeroallergens and food allergens. 10 More recently, one study conducted in Puerto Rican school-aged children found that long-term exposure to SO 2 , a component of air pollution linked to fossil fuel combustion, and living in proximity to a major highway were associated with sensitisation to common allergens. 11 However, several other recent studies have found no association, especially with aeroallergens. 12-14 Such knowledge is important to understand the implications of increasing air pollution levels globally and to develop specific intervention strategies. The field is further limited by the fact that chronic exposures to ambient air pollutants in urban areas have been the focus of research, with no study investigating how air pollution from episodic major pollution events, such as landscape fires, relates to allergic sensitisation in children. In the summer of 2014, a spot fire started in the Hazelwood open-cut brown coal mine in the Latrobe Valley, Victoria, Australia (Figure 1) leading to an underground fire that burnt for 45 days. Morwell, a town located in the immediate vicinity of the mine, and other localities within the Latrobe Valley, experienced extreme levels of air pollution for several weeks, including elevated particulate matter, carbon monoxide and benzene. 15 The Hazelwood Health Study (HHS) 16,17 was established to monitor the short- and long-term health and social repercussions in people impacted by the event. As pregnant mothers and young children were identified as a vulnerable subpopulation, the Latrobe Early Life Follow-Up (ELF) Study cohort was established as a stream of the HHS, to evaluate possible long term health and developmental outcomes in children who were either in utero or in their first two years of life at the time of the fire. 16 In this analysis, we leverage the ELF cohort to address gaps in knowledge regarding the link between short-term, high-intensity air pollution in early life and the subsequent development of allergic sensitisation. Specifically, we aimed to determine whether exposure in prenatal and postnatal periods to air pollution from background sources and the severe smoke event, including long-term exposure to ambient PM 2.5 (particulate matter with an aerodynamic diameter <2.5µm), was associated with subsequent allergic sensitisation determined by serum total Immunoglobulin E (IgE) and allergen-specific IgE production. Methods Study design The ELF cohort was recruited in 2016 and consisted of 571 children born between 1 st March 2012 and 31 st December 2015 and residing in the Latrobe Valley at the time of the mine fire. Details regarding the recruitment and characteristics of the ambidirectional cohort have been published elsewhere. 16 Of the 571 children, 438 had a parent or caregiver agreeing at the time of enrolment to participate in longitudinal clinical assessments (three, seven and nine years following the fire) to evaluate respiratory and vascular function. At the second follow-up clinic, held between April and July 2021, the 167 attending participants were also invited to provide a blood sample for markers of allergic sensitisation. Blood samples were collected for 103 children (Figure 2), with the consent of the participant and the parent or caregiver who provided signed informed consent. This study was approved by the Tasmanian Health and Medical Human Research Ethics Committee (reference H14875). Allergic sensitisation Blood samples were analysed for IgE to seven aeroallergens using radio-allergosorbent testing (RAST) by a commercial laboratory (Australian Clinical Labs, Victoria, Australia). Results were provided in kU/L and participants were classified as sensitised to an allergen if levels of specific IgE to that allergen were ≥ 0.35 kU A /L. 18 Aeroallergens assayed included Alternaria tenuis , cat epithelium, Cladosporium herbarum , Dermatophagoides pteronyssinus (house dust mites; HDM), a dust panel (mix of D. pteronyssinus , D. farinae , house dust, and cockroach), perennial rye grass (Lolium perenne) pollen and a grass pollen panel (mix of Bermuda, perennial rye, Timothy, Kentucky blue, Johnson, and Bahia grasses). For analysis purposes, A. tenuis and Cl. herbarum were combined under a fungi category, D. pteronyssinus and the dust panel under a dust category, and perennial rye grass pollen and the grass pollen panel under a grass category. Combination of allergens within these categories was motivated by (1) the presence of HDM in the dust panel and of perennial rye grass in the grass panel, due to availability of the tests, and (2) the very small number of children sensitised to Cl. herbarum within the cohort. Total IgE was measured using a Human IgE ELISA Kit (Catalog Numbers BMS2097, Invitrogen) according to manufacturer’s instructions. Results were reported as ng/ml and converted to kU/L (1 kU/L=2.4 ng/ml). The number of analyses successfully completed for each child (4-8) was dependent on the number of aliquots collected, which varied with their degree of cooperation. Exposure assessment For the purpose of this study, the Hazelwood open-cut coal mine fire was defined as lasting from the 9 th February to 28 th March 2014, as some low levels of residual smoke remained over certain areas after the fire was declared safe on 26 th March 2014. Hourly concentrations of PM 2.5 emitted specifically by the fire were estimated at a 1-km 2 resolution by a meteorological and dispersion model incorporating wind data and a plume rise process. 19 Detailed diary reporting 12-hourly locations of the pregnant mother or infant were retrospectively obtained throughout the fire and were used to assign daily average and peak 24-hour average exposures for each child. Prenatal and postnatal exposures were estimated separately for each child depending on their estimated dates of conception and delivery. Only days following the estimated date of conception were considered for children conceived during the fire while those conceived following the fire were assigned average and peak fire-related PM 2.5 concentrations of 0 µg/m 3 . To estimate ambient (‘background’) PM 2.5 exposure, validated satellite-informed land-use regression models were used. The models were constructed with different spatial predictors ( e.g., the proportion of households using wood heaters, commercial areas, wind speed), and explained 63% of spatial variation in measured annual PM 2.5 (RMSE: 1.0 µg/m 3 ) across Australia. Details on methodology and validation of the models have been reported elsewhere. 20 Annual averages for the years 2011-2015 of both pollutants were estimated for ∼347,000 census mesh blocks, the smallest geographic areas defined by the Australian Bureau of Statistics, 21 throughout the country. Early life background PM 2.5 exposure was assigned at the mesh block of the birth address by averaging exposure of the years of conception and birth of each child to account for prenatal and early postnatal exposure. There was little year-to-year variation, either within the state of Victoria or within the cohort (Pearson’s r > 0.95 for all pairwise correlations between years). Spatial distribution was different for fire-related and background ambient PM 2.5 (Figure 3), which was motivation to investigate them as co-exposures. Covariates During the recruitment in 2016, enrolled participants completed an extensive baseline questionnaire, that included a wide range of characteristics about the child, parents and housing environment. 16 Potential confounding variables were proposed based on the existing literature on air pollution and/or allergic sensitisation or diseases. 23-30 Two minimal sufficient adjustment sets of confounders were identified based on a directed acyclic graph (DAG) using DAGitty v3.0 (Figure S1). 31 The first one, selected for our primary analysis, included three variables: maternal education (≤ year 12 vs. > year 12), child age in months at the time of the blood collection, and Index of Relative Socio-economic Disadvantage (IRSD) decile of the household. The second included seven variables: breastfeeding ( year 12), pregnancy stress (no/hardly vs. sometimes/mostly), presence of a smoker in the house (yes vs. no), parent with history of asthma or allergic rhinitis, and premature birth. IRSD is an area-level indicator of social and economic disadvantage developed by the Australian Bureau of Statistics incorporating 16 familial, educational, occupational, and financial measurements. 32 It was assigned at the SA1 level, the smallest geographical area defined by the Australian Bureau of Statistics for which census data are released, 22 of the home address. Statistical analysis We fitted single- and multi-pollutant logistic regression models to evaluate the exposure-response relationship between the three exposures (prenatal fire-related PM 2.5 , postnatal fire-related PM 2.5 , background PM 2.5 ) and (1) sensitisation to each of the allergen categories (fungi, dust, cat, grass), and (2) sensitisation to at least one allergen category. We additionally fitted single- and multi-pollutant linear regression models to estimate the relationship between the air pollutant exposures and total IgE levels. Odds ratios (OR) and β coefficients were estimated per interquartile range (IQR) increase in each pollutant. Models assessing average and peak fire-related PM 2.5 exposures were fitted separately. As estimates for background PM 2.5 were similar in the multi-pollutant models including average and peak metrics, we only presented the estimates with average fire-related PM 2.5 as a co-exposure. All models fitted were adjusted for the first minimal sufficient adjustment set of confounders. Multiple imputation by chained equations was performed to handle missing data using the mice package (v 3.15.0). 33 A total of twenty imputed datasets, including all exposure, confounding and outcome variables, was created with a random forest algorithm for continuous and categorical variables. Confounding or outcome variables derived from collected or measured data were imputed following the Impute, then transform approach, where primary data is imputed and then derived into the final variables while following the same rules as non-imputed data. 34 All analyses were performed using R (v 4.2.1). Sensitivity analysis To examine the robustness of the results, sensitivity analyses were performed. We repeated the primary analyses adjusting for the alternative larger minimal sufficient adjustment set identified using the DAG. We also repeated the primary analyses using complete data without missing values (also known as listwise deletion) in place of multiple imputation. Results Participant characteristics A total of 103 children presenting to the 2021 clinical follow-up agreed to provide a blood sample. Children born overseas (n=2) were excluded from the analyses as their background exposure at birth could not be estimated accurately, which led to the inclusion of 101 children (Figure 2). Among them, 50 were born before the start of the fire, four were born during the fire, 29 were in utero during the whole fire period and 18 were conceived after the fire. Baseline and exposure characteristics are presented in Table 1. Within the participants, evidence of collinearity amongst the three exposures was low, with all -0.3 < Pearson’s r < 0.1, with the exception from mean and peak values for fire-related PM 2.5 at each stage (prenatal, postnatal), which were related, but included in separate models (Figure S2). Allergen-specific sensitisation Prevalence rates of sensitisation to each allergen in the cohort are presented in Table 2. Sensitisation to D. pteronyssinus had the highest prevalence (35.4%), while Cl. herbarum had the lowest (2.0%). We found no association between the levels of fire-related PM 2.5 and the odds of sensitisation to any of the distinct allergen categories, for both peak and cumulative exposure (Table 3). Exposure to fire-related PM 2.5 was not linked with sensitisation to any category either. Early life background exposure to PM 2.5 was positively associated with the odds of being sensitised to dust (adjusted OR=1.89, 95%CI=1.11,3.20), but not with cat, grass, fungi, or overall sensitisation (Table 3). The two sensitivity analyses adjusting for the larger set of possible confounders and including only complete cases without performing imputation obtained similar results for all the exposures (Tables S1-S2). Total IgE The median total IgE levels of children in our cohort was of 161.4 kU/L (Table 2). We did not observe evidence of a relationship between exposure to fire-related or background PM 2.5 and overall total IgE in the blood. Both sensitivity analyses were consistent with the primary results (Tables S1-S2). Table 1 – Baseline and exposure characteristics of the study participants. Participants (N=101) n (%) Sex, female 48 (47.5%) Maternal education, > Year 12 71 (70.3%) Breastfeeding > 6 months a 56 (55.4%) Main heater releasing combustion emissions into living space b 35 (34.7%) Stress during pregnancy No/hardly Sometimes/mostly 29 (28.7%) 72 (71.3%) Premature birth 6 (5.9%) Any smoker in the house c 17 (16.8%) Mother with history of asthma/allergic rhinitis d 34 (33.7%) Father with history of asthma/allergic rhinitis e 35 (34.7%) Mean (SD) Median [Q1-Q3] Range Age (years) 6.8 (1.0) 7 [6-8] 5-9 IRSD decile 3.9 (3.0) 3 [1-6] 1-10 Average prenatal fire-related PM 2.5 (µg/m 3 ) 2.7 (7.1) 0.0 [0.0-1.7] 0.0-44.2 Average postnatal fire-related PM 2.5 (µg/m 3 ) 3.7 (7.0) 0.2 [0.0-2.5] 0.0-30.2 Peak prenatal fire-related PM 2.5 (µg/m 3 ) 45.4 (107.2) 0.0 [0.0-35.7] 0.0-593.5 Peak postnatal fire-related PM 2.5 (µg/m 3 ) 59.5 (91.8) 1.1 [0.0-97.0] 0.0-447.1 Background PM 2.5 (µg/m 3 ) 6.0 (1.0) 6.0 [5.6-6.6] 0.8-8.4 a Missing data: n = 1. b Missing data: n = 3. c Missing data: n = 2. d Missing data: n = 1. e Missing data: n = 8. Table 2 – Prevalence of sensitisation for each allergen and descriptive total IgE levels within the 101 participating children. Allergen Number of children tested Prevalence of sensitisation, n (%) Dust Dermatophagoides pteronyssinus 99 35 (35.4%) Dust panel 101 35 (34.7%) Cat Cat epithelium 101 14 (13.9%) Grass Perennial rye grass 100 34 (34.0%) Grass panel 100 32 (32.0%) Fungi Alternaria tenuis 100 10 (10.0%) Cladosporium herbarum 101 2 (2.0%) Number of children tested Median (Q1─Q3) Total IgE (kU/L) 82 161.4 (35.0-340.2) Note: Prevalences of sensitisation were calculated with the number of children tested to that specific allergen as a denominator. Table 3 – Association between exposure to the various sources of PM 2.5 , and sensitisation to various allergen categories and total IgE levels. Average fire-related PM 2.5 Allergen categories Prenatal Postnatal Crude Adjusted Crude Adjusted OR [95% CI] OR adj [95% CI] OR [95% CI] OR adj [95% CI] Dust 0.91 [0.79,1.05] 0.93 [0.81,1.08] 1.02 [0.88,1.18] 0.95 [0.80,1.12] Cat 1.00 [0.88,1.14] 1.03 [0.90,1.18] 1.12 [0.95,1.33] 1.12 [0.93,1.35] Grass 0.87 [0.72,1.04] 0.91 [0.76,1.09] 1.13 [0.97,1.31] 1.06 [0.90,1.24] Fungi 0.95 [0.78,1.17] 1.01 [0.84,1.22] 1.14 [0.95,1.36] 1.09 [0.89,1.32] Any 0.88 [0.77,1.01] 0.90 [0.78,1.03] 1.04 [0.90,1.20] 0.96 [0.82,1.13] β [95% CI] β [95% CI] β [95% CI] β [95% CI] Total IgE -1.1 [-10.1,8.0] -0.5 [-9.9,8.9] -6.2 [-19.6,7.3] -10.2 [-25.0,4.6] B. Peak fire-related PM 2.5 Allergen categories Prenatal Postnatal Crude Adjusted Crude Adjusted OR [95% CI] OR adj [95% CI] OR [95% CI] OR adj [95% CI] Dust 0.93 [0.79,1.09] 0.96 [0.82,1.13] 1.10 [0.72,1.69] 0.87 [0.52,1.45] Cat 1.05 [0.89,1.24] 1.11 [0.93,1.32] 1.47 [0.89,2.44] 1.59 [0.89,2.83] Grass 0.83 [0.65,1.05] 0.89 [0.71,1.13] 1.48 [0.95,2.30] 1.19 [0.72,1.96] Fungi 1.02 [0.84,1.24] 1.11 [0.90,1.36] 1.31 [0.74,2.32] 1.14 [0.58,2.24] Any 0.89 [0.76,1.04] 0.91 [0.78,1.07] 1.17 [0.76,1.79] 0.90 [0.55,1.48] β [95% CI] β [95% CI] β [95% CI] β [95% CI] Total IgE -2.3 [-15.7,11.2] -2.5 [-16.8,11.8] -17.9 [-57.6,21.8] -34.8 [-81.6,11.9] B. Background PM 2.5 Allergen categories Background Crude Adjusted OR [95% CI] OR adj [95% CI] Dust 1.95 [1.15,3.28] 1.89 [1.11,3.20] Cat 1.39 [0.74,2.62] 1.38 [0.70,2.73] Grass 1.68 [1.02,2.75] 1.53 [0.92,2.52] Fungi 1.57 [0.76,3.24] 1.41 [0.70,2.82] Any 1.44 [0.94,2.21] 1.42 [0.92,2.18] β [95% CI] β [95% CI] Total IgE 20.3 [-17.9,58.4] 22.0 [-16.6,60.5] Note: Odds ratios and 95%CI from the crude models were estimated with univariable logistic regression/linear regression models incorporating only the outcome (logistic: sensitisation to allergen category, linear: total IgE levels) and a single pollutant. Odds ratios and 95%CI from the adjusted models incorporated all three exposures and maternal education, age in months, and IRSD. All estimates were scaled by IQR increase of the relevant pollutant. IgE: Immunoglobulin E. Abbreviations DAG Directed acyclic graph DEP Diesel exhaust particles HDM House dust mites IgE Immunoglobulin E IQR Interquartile range IRSD Index of Relative Socio-economic Disadvantage NO 2 Nitrogen dioxide NO x Nitrogen oxides OR Odds ratio PM 2.5 Particulate matter with an aerodynamic diameter of less than 2.5 μm RMSE Root mean square error SO 2 Sulphur dioxide Declarations Ethics approval and consent to participate This study was approved by the Tasmanian Health and Medical Human Research Ethics Committee (reference H0014875) and performed in accordance with the requirements of the Australian National Statement on Ethical Conduct in Human Research. Additional approval was received from the Human Research Ethics Committees of Monash University and the University of Melbourne. All parents and caregivers of the participants provided signed informed consent. Consent for publication Not applicable. Availability of data and materials The data that support the findings of this study are available from the corresponding author upon reasonable request. Competing interests No conflicts of interest to declare for the following authors: MZ, CG, AW, GZ, NS, LK, GW or MD. SD reports a relationship with AstraZeneca plc that includes: funding grants. SD reports a relationship with GSK plc that includes: funding grants. FJ reports financial support from the Victoria Department of Health. Funding This study is funded by the Victorian Department of Health (Australia). This paper represents the views of the authors and does not represent the views of the Department. Authors' contributions MZ was involved in the conceptualization, methodology, software and formal analysis, writing - original draft, writing - Review & Editing. Caroline Gao was involved in the methodology, writing - review & editing. AW was involved in the conceptualization, methodology, writing - review & editing, project administration. Graeme Zosky was involved in the conceptualization, methodology, writing - review & editing, funding acquisition. Nicola Stephens was involved in the conceptualization, methodology, writing - review & editing. Luke Knibbs was involved in the software, formal analysis, writing - review & editing. Grant Williamson was involved in the software, formal analysis, writing - review & editing. Marita Dalton was involved in the writing - review & editing, project administration. Shyamali Dharmage was involved in the methodology, writing - review & editing. Fay Johnston was involved in the conceptualization, methodology, writing - review & editing, supervision, project administration, funding acquisition. Acknowledgements The Latrobe Early Life Follow-up (ELF) Study constitutes the child health and development stream of the Hazelwood Health Study (HHS). 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Gergen PJ, Arbes SJ, Jr., Calatroni A, Mitchell HE, Zeldin DC. Total IgE levels and asthma prevalence in the US population: results from the National Health and Nutrition Examination Survey 2005-2006. J Allergy Clin Immunol 2009;124(3):447-53. Textor J, van der Zander B, Gilthorpe MS, Liskiewicz M, Ellison GT. Robust causal inference using directed acyclic graphs: the R package 'dagitty'. Int J Epidemiol 2016;45(6):1887-94. Australian Bureau of Statistics. IRSD. ABS; 2018 [cited 2022 29 June]; Available from: https://www.abs.gov.au/ausstats/ [email protected] /Lookup/by%20Subject/2033.0.55.001~2011~Main%20Features~IRSD~10005 Buuren Sv, Groothuis-Oudshoorn K. mice: Multivariate Imputation by Chained Equations inR. Journal of Statistical Software 2011;45(3). von Hippel PT. 8. How to Impute Interactions, Squares, and other Transformed Variables. Sociological Methodology 2009;39(1):265-91. Lopez DJ, Lodge CJ, Bui DS, Waidyatillake NT, Su JC, Perret JL, et al. 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Codispoti CD, LeMasters GK, Levin L, Reponen T, Ryan PH, Biagini Myers JM, et al. Traffic pollution is associated with early childhood aeroallergen sensitization. Ann Allergy Asthma Immunol 2015;114(2):126-33. Jung HJ, Ko YK, Shim WS, Kim HJ, Kim DY, Rhee CS, et al. Diesel exhaust particles increase nasal symptoms and IL-17A in house dust mite-induced allergic mice. Sci Rep 2021;11(1):16300. Brandt EB, Kovacic MB, Lee GB, Gibson AM, Acciani TH, Le Cras TD, et al. Diesel exhaust particle induction of IL-17A contributes to severe asthma. J Allergy Clin Immunol 2013;132(5):1194-204 e2. Melén E, Nyberg F, Lindgren CM, Berglind N, Zucchelli M, Nordling E, et al. Interactions between Glutathione S-Transferase P1, Tumor Necrosis Factor, and Traffic-Related Air Pollution for Development of Childhood Allergic Disease. Environmental Health Perspectives 2008;116(8):1077-84. Hansell AL, Bakolis I, Cowie CT, Belousova EG, Ng K, Weber-Chrysochoou C, et al. Childhood fish oil supplementation modifies associations between traffic related air pollution and allergic sensitisation. Environ Health 2018;17(1):27. Hu Y, Liu S, Liu P, Mu Z, Zhang J. Clinical relevance of eosinophils, basophils, serum total IgE level, allergen-specific IgE, and clinical features in atopic dermatitis. J Clin Lab Anal 2020;34(6):e23214. Awan NU, Sohail SK, Naumeri F, Niazi S, Cheema K, Qamar S, et al. Association of Serum Vitamin D and Immunoglobulin E Levels With Severity of Allergic Rhinitis. Cureus 2021;13(1):e12911. Haselkorn T, Szefler SJ, Simons FE, Zeiger RS, Mink DR, Chipps BE, et al. Allergy, total serum immunoglobulin E, and airflow in children and adolescents in TENOR. Pediatr Allergy Immunol 2010;21(8):1157-65. Nickel R, Illi S, Lau S, Sommerfeld C, Bergmann R, Kamin W, et al. Variability of total serum immunoglobulin E levels from birth to the age of 10 years. A prospective evaluation in a large birth cohort (German Multicenter Allergy Study). Clin Exp Allergy 2005;35(5):619-23. Santiago Hda C, Ribeiro-Gomes FL, Bennuru S, Nutman TB. Helminth infection alters IgE responses to allergens structurally related to parasite proteins. J Immunol 2015;194(1):93-100. Zahedi A, Hassanvand MS, Jaafarzadeh N, Ghadiri A, Shamsipour M, Dehcheshmeh MG. Effect of ambient air PM(2.5)-bound heavy metals on blood metal(loid)s and children's asthma and allergy pro-inflammatory (IgE, IL-4 and IL-13) biomarkers. J Trace Elem Med Biol 2021;68:126826. Dunea D, Liu HY, Iordache S, Buruleanu L, Pohoata A. Liaison between exposure to sub-micrometric particulate matter and allergic response in children from a petrochemical industry city. Sci Total Environ 2020;745:141170. Brauer M, Hoek G, Smit HA, de Jongste JC, Gerritsen J, Postma DS, et al. Air pollution and development of asthma, allergy and infections in a birth cohort. Eur Respir J 2007;29(5):879-88. Sordillo JE, Switkowski KM, Coull BA, Schwartz J, Kloog I, Gibson H, et al. Relation of Prenatal Air Pollutant and Nutritional Exposures with Biomarkers of Allergic Disease in Adolescence. Sci Rep 2018;8(1):10578. Rothman KJ. No adjustments are needed for multiple comparisons. Epidemiology 1990;1(1):43-6. Jepsen P, Johnsen SP, Gillman MW, Sorensen HT. Interpretation of observational studies. Heart 2004;90(8):956-60. Additional Declarations Competing interest reported. No conflicts of interest to declare for the following authors: MZ, CG, AW, GZ, NS, LK, GW or MD. SD reports a relationship with AstraZeneca plc that includes: funding grants. SD reports a relationship with GSK plc that includes: funding grants. FJ reports financial support from the Victoria Department of Health. Supplementary Files DraftManuscriptSuppMaterialELFAllergiesV1submission.docx Cite Share Download PDF Status: Published Journal Publication published 21 Dec, 2023 Read the published version in BMC Pulmonary Medicine → Version 1 posted Editorial decision: Major revision 31 Jul, 2023 Reviews received at journal 17 Jul, 2023 Reviewers agreed at journal 13 Jul, 2023 Reviewers agreed at journal 07 Jul, 2023 Reviewers invited by journal 03 Jul, 2023 Editor assigned by journal 03 Jul, 2023 Editor invited by journal 15 Jun, 2023 Submission checks completed at journal 15 Jun, 2023 First submitted to journal 09 Jun, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3045254","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":210223914,"identity":"50176baf-3a40-4704-81fb-a29ae23158e3","order_by":0,"name":"Myriam Ziou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYJCCAyCCjYGB8QFUwIBoLcwGDAlEaoEBNgmitOjOyH144GcOgz0fe49ZNe8PO3sG9uZtEgw1h3FqMbuRbnCwdxsDMxvPGbPbPAnJiQ08x8okGI7h05LGcIB3GwMbm0QOSAtzAgOQIcHAhl/Lwb/bGHhAWop5EurtGeTfALX8w6/lMNAWCZAWZp6Ew4wNEjxmEoxteLScecZwWHabhAEbz7FiyTlpxxPbeNKKLRL70nFrOZ7G/PHtNht7+fbmjR/e2FTb87Mf3njjwzdrnFoYBBJApARCgA1EJODWwMDAfwCf7CgYBaNgFIwCIAAAabJJgYUMfzgAAAAASUVORK5CYII=","orcid":"","institution":"University of Tasmania","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Myriam","middleName":"","lastName":"Ziou","suffix":""},{"id":210223915,"identity":"79007eba-8999-42b1-adb8-e3cf585d2ded","order_by":1,"name":"Caroline X. 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Zosky","email":"","orcid":"","institution":"University of Tasmania","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Graeme","middleName":"R.","lastName":"Zosky","suffix":""},{"id":210223921,"identity":"38d7cdc0-c74c-4c4d-b0e7-1859089b8f4c","order_by":4,"name":"Nicola Stephens","email":"","orcid":"","institution":"University of Tasmania","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nicola","middleName":"","lastName":"Stephens","suffix":""},{"id":210223922,"identity":"843d7144-9524-41b6-bb88-807d7ec95547","order_by":5,"name":"Luke D. Knibbs","email":"","orcid":"","institution":"The University of Sydney","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Luke","middleName":"D.","lastName":"Knibbs","suffix":""},{"id":210223923,"identity":"91fe1516-4837-426b-b07b-1aac9b54a3ad","order_by":6,"name":"Grant J. 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Johnston","email":"","orcid":"","institution":"University of Tasmania","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fay","middleName":"H.","lastName":"Johnston","suffix":""}],"badges":[],"createdAt":"2023-06-10 03:29:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3045254/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3045254/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12890-023-02815-8","type":"published","date":"2023-12-21T15:00:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":38992563,"identity":"cd6fb860-3aa9-474e-bcda-c56814d7e0bc","added_by":"auto","created_at":"2023-06-23 19:27:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":27431,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLocation of the Latrobe Valley and the Hazelwood mine. \u003c/strong\u003eLatrobe Valley borders are in bold and the mine location is illustrated as a red dot.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3045254/v1/158aec72a87e0e4c9cf24284.png"},{"id":38992749,"identity":"2282dbd7-b577-45fb-96e9-646f315201f9","added_by":"auto","created_at":"2023-06-23 19:35:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":7122,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of the study participants.\u003c/strong\u003e ELF: Early Life Follow-Up.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3045254/v1/d562a26ec982d8208cc468bf.png"},{"id":38992561,"identity":"6498cdff-ed27-4ada-b86a-3119d694d97c","added_by":"auto","created_at":"2023-06-23 19:27:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":20417,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePM\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2.5 \u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003econcentrations (µg/m\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e) from both sources mapped to Statistical Area level 1 (SA1).\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e22\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3045254/v1/21bbe9a3a693e5721769cbdb.png"},{"id":48776747,"identity":"f566e0b0-d260-48a9-bde0-aabfca829eaa","added_by":"auto","created_at":"2023-12-25 15:07:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":896069,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3045254/v1/b0a3cca0-1e7d-4cc2-9a87-7e7f537f9915.pdf"},{"id":38992564,"identity":"4be829f7-4df0-479b-a5d1-ff9daa3ddd8d","added_by":"auto","created_at":"2023-06-23 19:27:22","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1186420,"visible":true,"origin":"","legend":"","description":"","filename":"DraftManuscriptSuppMaterialELFAllergiesV1submission.docx","url":"https://assets-eu.researchsquare.com/files/rs-3045254/v1/5aff7cad85b71f1cae83ce6c.docx"}],"financialInterests":"Competing interest reported. No conflicts of interest to declare for the following authors: MZ, CG, AW, GZ, NS, LK, GW or MD. SD reports a relationship with AstraZeneca plc that includes: funding grants. SD reports a relationship with GSK plc that includes: funding grants. FJ reports financial support from the Victoria Department of Health.","formattedTitle":"Exposure to air pollution concentrations of various intensities in early life and allergic sensitisation later in childhood","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAllergies have been increasing in prevalence globally over the last few decades.\u003csup\u003e1\u003c/sup\u003e Allergic conditions have been shown to directly impact quality of life, including the psychological, educational, professional and social domains of those affected.\u003csup\u003e2,3\u003c/sup\u003e Allergies are known to be caused by a combination of genetic, lifestyle and environmental factors, although the relative importance of these, and their interactions, is not fully understood.\u003csup\u003e4\u003c/sup\u003e There is robust evidence showing that exposure to air pollution is associated with the development and exacerbation of allergic conditions such as asthma\u003csup\u003e5,6\u003c/sup\u003e and allergic rhinitis.\u003csup\u003e7\u003c/sup\u003e However, the relationship with allergic sensitisation is inconsistent and relatively scarce.\u003csup\u003e8\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eChildren have been consistently identified as a subgroup vulnerable to the effects of air pollution for both physiological and behavioural reasons.\u003csup\u003e9\u003c/sup\u003e A systematic review and meta-analysis of birth cohorts found that early childhood exposure to traffic-related air pollutants was related to increased sensitisation to aeroallergens and food allergens.\u003csup\u003e10\u003c/sup\u003e More recently, one study conducted in Puerto Rican school-aged children found that long-term exposure to SO\u003csub\u003e2\u003c/sub\u003e, a component of air pollution linked to fossil fuel combustion, and living in proximity to a major highway were associated with sensitisation to common allergens.\u003csup\u003e11\u003c/sup\u003e However, several other recent studies have found no association, especially with aeroallergens.\u003csup\u003e12-14\u003c/sup\u003e Such knowledge is important to understand the implications of increasing air pollution levels globally and to develop specific intervention strategies. The field is further limited by the fact that chronic exposures to ambient air pollutants in urban areas have been the focus of research, with no study investigating how air pollution from episodic major pollution events, such as landscape fires, relates to allergic sensitisation in children.\u003c/p\u003e\n\u003cp\u003eIn the summer of 2014, a spot fire started in the Hazelwood open-cut brown coal mine in the Latrobe Valley, Victoria, Australia (Figure 1) leading to an underground fire that burnt for 45 days. Morwell, a town located in the immediate vicinity of the mine, and other localities within the Latrobe Valley, experienced extreme levels of air pollution for several weeks, including elevated particulate matter, carbon monoxide and benzene.\u003csup\u003e15\u003c/sup\u003e The Hazelwood Health Study (HHS)\u003csup\u003e16,17\u003c/sup\u003e was established to monitor the short- and long-term health and social repercussions in people impacted by the event. As pregnant mothers and young children were identified as a vulnerable subpopulation, the Latrobe Early Life Follow-Up (ELF) Study cohort was established as a stream of the HHS, to evaluate possible long term health and developmental outcomes in children who were either \u003cem\u003ein utero\u003c/em\u003e or in their first two years of life at the time of the fire.\u003csup\u003e16\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eIn this analysis, we leverage the ELF cohort to address gaps in knowledge regarding the link between short-term, high-intensity air pollution in early life and the subsequent development of allergic sensitisation. Specifically, we aimed to determine whether exposure in prenatal and postnatal periods to air pollution from background sources and the severe smoke event, including long-term exposure to ambient PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003e(particulate matter with an aerodynamic diameter \u0026lt;2.5\u0026micro;m), was associated with subsequent allergic sensitisation determined by serum total Immunoglobulin E (IgE) and allergen-specific IgE production.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eStudy design\u003c/h2\u003e\n\u003cp\u003eThe ELF cohort was recruited in 2016 and consisted of 571 children born between 1\u003csup\u003est\u003c/sup\u003e March 2012 and 31\u003csup\u003est\u003c/sup\u003e December 2015 and residing in the Latrobe Valley at the time of the mine fire. Details regarding the recruitment and characteristics of the ambidirectional cohort have been published elsewhere.\u003csup\u003e16\u003c/sup\u003e Of the 571 children, 438 had a parent or caregiver agreeing at the time of enrolment to participate in longitudinal clinical assessments (three, seven and nine years following the fire) to evaluate respiratory and vascular function. At the second follow-up clinic, held between April and July 2021, the 167 attending participants were also invited to provide a blood sample for markers of allergic sensitisation. Blood samples were collected for 103 children (Figure 2), with the consent of the participant and the parent or caregiver who provided signed informed consent. This study was approved by the Tasmanian Health and Medical Human Research Ethics Committee (reference H14875).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eAllergic sensitisation\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eBlood samples were analysed for IgE to seven aeroallergens using radio-allergosorbent testing (RAST) by a commercial laboratory (Australian Clinical Labs, Victoria, Australia). Results were provided in kU/L and participants were classified as sensitised to an allergen if levels of specific IgE to that allergen were \u0026ge; 0.35 kU\u003csub\u003eA\u003c/sub\u003e/L.\u003csup\u003e18\u003c/sup\u003e Aeroallergens assayed included \u003cem\u003eAlternaria tenuis\u003c/em\u003e, cat epithelium, \u003cem\u003eCladosporium herbarum\u003c/em\u003e, \u003cem\u003eDermatophagoides pteronyssinus\u0026nbsp;\u003c/em\u003e(house dust mites; HDM), a dust panel (mix of \u003cem\u003eD. pteronyssinus\u003c/em\u003e, \u003cem\u003eD. farinae\u003c/em\u003e, house dust, and cockroach), perennial rye grass\u003cem\u003e\u0026nbsp;(Lolium perenne)\u0026nbsp;\u003c/em\u003epollen and a grass pollen panel (mix of Bermuda, perennial rye, Timothy, Kentucky blue, Johnson, and Bahia grasses). For analysis purposes, \u003cem\u003eA. tenuis\u003c/em\u003e and \u003cem\u003eCl. herbarum\u0026nbsp;\u003c/em\u003ewere combined under a fungi category, \u003cem\u003eD. pteronyssinus\u0026nbsp;\u003c/em\u003eand the dust panel under a dust category, and\u003cem\u003e\u0026nbsp;\u003c/em\u003eperennial rye grass pollen and the grass pollen panel under a grass category.\u0026nbsp;Combination of allergens within these categories was motivated by (1) the presence of HDM in the dust panel and of perennial rye grass in the grass panel, due to availability of the tests, and (2) the very small number of children sensitised to\u0026nbsp;\u003cem\u003eCl. herbarum\u003c/em\u003e within the cohort.\u0026nbsp;Total\u003cem\u003e\u0026nbsp;\u003c/em\u003eIgE was measured using a Human IgE ELISA Kit (Catalog Numbers BMS2097, Invitrogen) according to manufacturer\u0026rsquo;s instructions. Results were reported as ng/ml and converted to kU/L (1 kU/L=2.4 ng/ml). The number of analyses successfully completed for each child (4-8) was dependent on the number of aliquots collected, which varied with their degree of cooperation.\u003c/p\u003e\n\u003ch2\u003eExposure assessment\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eFor the purpose of this study, the Hazelwood open-cut coal mine fire was defined as lasting from the 9\u003csup\u003eth\u003c/sup\u003e February to 28\u003csup\u003eth\u003c/sup\u003e March 2014, as some low levels of residual smoke remained over certain areas after the fire was declared safe on 26\u003csup\u003eth\u003c/sup\u003e March 2014. Hourly concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e emitted specifically by the fire were estimated at a 1-km\u003csup\u003e2\u003c/sup\u003e resolution by a meteorological and dispersion model incorporating wind data and a plume rise process.\u003csup\u003e19\u003c/sup\u003e Detailed diary reporting 12-hourly locations of the pregnant mother or infant were retrospectively obtained throughout the fire and were used to assign daily average and peak 24-hour average exposures for each child. Prenatal and postnatal exposures were estimated separately for each child depending on their estimated dates of conception and delivery. Only days following the estimated date of conception were considered for children conceived during the fire while those conceived following the fire were assigned average and peak fire-related PM\u003csub\u003e2.5\u003c/sub\u003e concentrations of 0 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo estimate ambient (\u0026lsquo;background\u0026rsquo;) PM\u003csub\u003e2.5\u003c/sub\u003e exposure, validated satellite-informed land-use regression models were used. The models were constructed with different spatial predictors (\u003cem\u003ee.g.,\u003c/em\u003e the proportion of households using wood heaters, commercial areas, wind speed), and explained 63% of spatial variation in measured annual PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003e(RMSE: 1.0 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) across Australia. Details on methodology and validation of the models have been reported elsewhere.\u003csup\u003e20\u003c/sup\u003e Annual averages for the years 2011-2015 of both pollutants were estimated for\u0026nbsp;\u0026sim;347,000 census mesh blocks, the smallest geographic areas defined by the Australian Bureau of Statistics,\u003csup\u003e21\u003c/sup\u003e throughout the country. Early life background PM\u003csub\u003e2.5\u003c/sub\u003e exposure was assigned at the mesh block of the birth address by averaging exposure of the years of conception and birth of each child to account for prenatal and early postnatal exposure. There was little year-to-year variation, either within the state of Victoria or within the cohort (Pearson\u0026rsquo;s \u003cem\u003er\u003c/em\u003e \u0026gt; 0.95 for all pairwise correlations between years).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSpatial distribution was different for fire-related and background ambient PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003e(Figure 3), which was motivation to investigate them as co-exposures.\u003c/p\u003e\n\u003ch2\u003eCovariates\u003c/h2\u003e\n\u003cp\u003eDuring the recruitment in 2016, enrolled participants completed an extensive baseline questionnaire, that included a wide range of characteristics about the child, parents and housing environment.\u003csup\u003e16\u003c/sup\u003e Potential confounding variables were proposed based on the existing literature on air pollution and/or allergic sensitisation or diseases.\u003csup\u003e23-30\u003c/sup\u003e Two minimal sufficient adjustment sets of confounders were identified based on a directed acyclic graph (DAG) using DAGitty v3.0 (Figure S1).\u003csup\u003e31\u003c/sup\u003e The first one, selected for our primary analysis, included three variables: maternal education (\u0026le; year 12 vs. \u0026gt; year 12), child age in months at the time of the blood collection, and Index of Relative Socio-economic Disadvantage (IRSD) decile of the household. The second included seven variables: breastfeeding (\u0026lt;6 months vs. \u0026ge; 6 months), main heating type in the house (combustion emissions released vs. not released into the living space), maternal education (\u0026le; year 12 vs. \u0026gt; year 12), pregnancy stress (no/hardly vs. sometimes/mostly), presence of a smoker in the house (yes vs. no), parent with history of asthma or allergic rhinitis, and premature birth. IRSD is an area-level indicator of social and economic disadvantage developed by the Australian Bureau of Statistics incorporating 16 familial, educational, occupational, and financial measurements.\u003csup\u003e32\u003c/sup\u003e It was assigned at the\u0026nbsp;SA1 level, the smallest geographical area defined by the Australian Bureau of Statistics for which census data are released,\u003csup\u003e22\u003c/sup\u003e of the home address.\u003c/p\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eWe fitted single- and multi-pollutant logistic regression models to evaluate the exposure-response relationship between the three exposures (prenatal fire-related PM\u003csub\u003e2.5\u003c/sub\u003e, postnatal fire-related PM\u003csub\u003e2.5\u003c/sub\u003e, background PM\u003csub\u003e2.5\u003c/sub\u003e) and (1) sensitisation to each of the allergen categories (fungi, dust, cat, grass), and (2) sensitisation to at least one allergen category. We additionally fitted single- and multi-pollutant linear regression models to estimate the relationship between the air pollutant exposures and total IgE levels. Odds ratios (OR) and \u0026beta; coefficients were estimated per interquartile range (IQR) increase in each pollutant.\u003c/p\u003e\n\u003cp\u003eModels assessing average and peak fire-related PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003eexposures were fitted separately. As estimates for background PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003ewere similar in the multi-pollutant models including average and peak metrics, we only presented the estimates with average fire-related PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003eas a co-exposure. All models fitted were adjusted for the first minimal sufficient adjustment set of confounders.\u003c/p\u003e\n\u003cp\u003eMultiple imputation by chained equations was performed to handle missing data using the \u003cem\u003emice\u003c/em\u003e package (v 3.15.0).\u003csup\u003e33\u003c/sup\u003e A total of twenty imputed datasets, including all exposure, confounding and outcome variables, was created with a random forest algorithm for continuous and categorical variables. Confounding or outcome variables derived from collected or measured data were imputed following the \u003cem\u003eImpute, then transform\u003c/em\u003e approach, where primary data is imputed and then derived into the final variables while following the same rules as non-imputed data.\u003csup\u003e34\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eAll analyses were performed using R (v 4.2.1).\u003c/p\u003e\n\u003ch2\u003eSensitivity analysis\u003c/h2\u003e\n\u003cp\u003eTo examine the robustness of the results, sensitivity analyses were performed. We repeated the primary analyses adjusting for the alternative larger minimal sufficient adjustment set identified using the DAG. We also repeated the primary analyses using complete data without missing values (also known as listwise deletion) in place of multiple imputation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eParticipant characteristics\u003c/h2\u003e\n\u003cp\u003eA total of 103 children presenting to the 2021 clinical follow-up agreed to provide a blood sample. Children born overseas (n=2) were excluded from the analyses as their background exposure at birth could not be estimated accurately, which led to the inclusion of 101 children (Figure 2). Among them, 50 were born before the start of the fire, four were born during the fire, 29 were \u003cem\u003ein utero\u003c/em\u003e during the whole fire period and 18 were conceived after the fire. Baseline and exposure characteristics are presented in Table 1. Within the participants, evidence of collinearity amongst the three exposures was low, with all -0.3 \u0026lt; Pearson\u0026rsquo;s \u003cem\u003er\u0026nbsp;\u003c/em\u003e\u0026lt; 0.1, with the exception from mean and peak values for fire-related PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003eat each stage (prenatal, postnatal), which were related, but included in separate models (Figure S2).\u003c/p\u003e\n\u003ch2\u003eAllergen-specific sensitisation\u003c/h2\u003e\n\u003cp\u003ePrevalence rates of sensitisation to each allergen in the cohort are presented in Table 2. Sensitisation to \u003cem\u003eD. pteronyssinus\u003c/em\u003e had the highest prevalence (35.4%), while \u003cem\u003eCl. herbarum\u003c/em\u003e had the lowest (2.0%). We found no association between the levels of fire-related PM\u003csub\u003e2.5\u003c/sub\u003e and the odds of sensitisation to any of the distinct allergen categories, for both peak and cumulative exposure (Table 3). Exposure to fire-related PM\u003csub\u003e2.5\u003c/sub\u003e was not linked with sensitisation to any category either.\u003c/p\u003e\n\u003cp\u003eEarly life background exposure to PM\u003csub\u003e2.5\u003c/sub\u003e was positively associated with the odds of being sensitised to dust (adjusted OR=1.89, 95%CI=1.11,3.20), but not with cat, grass, fungi, or overall sensitisation (Table 3). The two sensitivity analyses adjusting for the larger set of possible confounders and including only complete cases without performing imputation obtained similar results for all the exposures (Tables S1-S2).\u003c/p\u003e\n\u003ch2\u003eTotal IgE\u003c/h2\u003e\n\u003cp\u003eThe median total IgE levels of children in our cohort was of 161.4 kU/L (Table 2). We did not observe evidence of a relationship between exposure to fire-related or background PM\u003csub\u003e2.5\u003c/sub\u003e and overall total IgE in the blood. Both sensitivity analyses were consistent with the primary results (Tables S1-S2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 \u0026ndash; Baseline and exposure characteristics of the study participants.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"83%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eParticipants (N=101)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex, female\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e48 (47.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal education, \u0026gt; Year 12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e71 (70.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBreastfeeding \u0026gt; 6 months \u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e56 (55.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMain heater releasing combustion emissions into living space \u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e35 (34.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStress during pregnancy\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; No/hardly\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Sometimes/mostly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e29 (28.7%)\u003c/p\u003e\n \u003cp\u003e72 (71.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePremature birth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e6 (5.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAny smoker in the house \u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e17 (16.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMother with history of asthma/allergic rhinitis \u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e34 (33.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFather with history of asthma/allergic rhinitis \u003csup\u003ee\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e35 (34.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMedian [Q1-Q3]\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eRange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e6.8 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e7 [6-8]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e5-9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIRSD decile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e3.9 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e3 [1-6]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e1-10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage prenatal fire-related PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003e(\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e2.7 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e0.0 [0.0-1.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e0.0-44.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage postnatal fire-related PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003e(\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e3.7 (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e0.2 [0.0-2.5]\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e0.0-30.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeak prenatal fire-related PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003e(\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e45.4 (107.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e0.0 [0.0-35.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e0.0-593.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeak postnatal fire-related PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003e(\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e59.5 (91.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e1.1 [0.0-97.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e0.0-447.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBackground PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003e(\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e6.0 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e6.0 [5.6-6.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.67676767676768%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.323232323232325%\" valign=\"top\"\u003e\n \u003cp\u003e0.8-8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Missing data: n = 1. \u0026nbsp; \u0026nbsp;\u003csup\u003eb\u003c/sup\u003e Missing data: n = 3. \u0026nbsp; \u0026nbsp;\u003csup\u003ec\u003c/sup\u003e Missing data: n = 2. \u0026nbsp; \u0026nbsp; \u003csup\u003ed\u003c/sup\u003e Missing data: n = 1. \u0026nbsp; \u0026nbsp;\u003csup\u003ee\u003c/sup\u003e Missing data: n = 8.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 \u0026ndash; Prevalence of sensitisation for each allergen and descriptive total IgE levels within the 101 participating children.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.1764705882353%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAllergen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of children tested\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15126050420168%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrevalence of sensitisation,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.1764705882353%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDust\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.8235294117647%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.1764705882353%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; Dermatophagoides pteronyssinus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15126050420168%\" valign=\"top\"\u003e\n \u003cp\u003e35 (35.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.1764705882353%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Dust panel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15126050420168%\" valign=\"top\"\u003e\n \u003cp\u003e35 (34.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.1764705882353%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.8235294117647%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.1764705882353%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Cat epithelium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15126050420168%\" valign=\"top\"\u003e\n \u003cp\u003e14 (13.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.1764705882353%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrass\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.8235294117647%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.1764705882353%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Perennial rye grass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15126050420168%\" valign=\"top\"\u003e\n \u003cp\u003e34 (34.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.1764705882353%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; Grass panel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15126050420168%\" valign=\"top\"\u003e\n \u003cp\u003e32 (32.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.1764705882353%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFungi\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.8235294117647%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.1764705882353%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; Alternaria tenuis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15126050420168%\" valign=\"top\"\u003e\n \u003cp\u003e10 (10.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.1764705882353%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; Cladosporium herbarum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15126050420168%\" valign=\"top\"\u003e\n \u003cp\u003e2 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.1764705882353%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of children tested\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15126050420168%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian (Q1─Q3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.1764705882353%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal IgE (kU/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.672268907563026%\" valign=\"top\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.15126050420168%\" valign=\"top\"\u003e\n \u003cp\u003e161.4 (35.0-340.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Prevalences of sensitisation were calculated with the number of children tested to that specific allergen as a denominator.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 \u0026ndash; Association between exposure to the various sources of PM\u003csub\u003e2.5\u003c/sub\u003e, and sensitisation to various allergen categories and total IgE levels.\u003c/strong\u003e\u003c/p\u003e\n\u003col style=\"list-style-type: upper-alpha;\"\u003e\n \u003cli\u003eAverage fire-related PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"671\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.865671641791046%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAllergen categories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.8955223880597%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrenatal\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.23880597014925%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePostnatal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.448028673835125%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.448028673835125%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.448028673835125%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.448028673835125%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR [95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003eadj\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;[95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.448028673835125%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR [95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.448028673835125%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003eadj\u0026nbsp;\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e[95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.84053651266766%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDust\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\"\u003e\n \u003cp\u003e0.91 [0.79,1.05]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.672131147540984%\" valign=\"top\"\u003e\n \u003cp\u003e0.93 [0.81,1.08]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\"\u003e\n \u003cp\u003e1.02 [0.88,1.18]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\"\u003e\n \u003cp\u003e0.95 [0.80,1.12]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.84053651266766%\" valign=\"top\"\u003e\n 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[0.76,1.09]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\"\u003e\n \u003cp\u003e1.13 [0.97,1.31]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\"\u003e\n \u003cp\u003e1.06 [0.90,1.24]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.84053651266766%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFungi\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\"\u003e\n \u003cp\u003e0.95 [0.78,1.17]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.672131147540984%\" valign=\"top\"\u003e\n \u003cp\u003e1.01 [0.84,1.22]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\"\u003e\n \u003cp\u003e1.14 [0.95,1.36]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\"\u003e\n \u003cp\u003e1.09 [0.89,1.32]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.84053651266766%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAny\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\"\u003e\n \u003cp\u003e0.88 [0.77,1.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.672131147540984%\" valign=\"top\"\u003e\n \u003cp\u003e0.90 [0.78,1.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\"\u003e\n \u003cp\u003e1.04 [0.90,1.20]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\"\u003e\n \u003cp\u003e0.96 [0.82,1.13]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.84053651266766%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta; [95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.672131147540984%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta; [95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta; [95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta; [95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.84053651266766%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal IgE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\"\u003e\n \u003cp\u003e-1.1 [-10.1,8.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.672131147540984%\" valign=\"top\"\u003e\n \u003cp\u003e-0.5 [-9.9,8.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\"\u003e\n \u003cp\u003e-6.2 [-19.6,7.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\"\u003e\n \u003cp\u003e-10.2 [-25.0,4.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;B. Peak fire-related PM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"671\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"null;width: 16.6667%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.865671641791046%\" rowspan=\"3\" valign=\"top\" style=\"width: 13.537%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAllergen categories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.8955223880597%\" colspan=\"2\" valign=\"top\" style=\"width: 38.9965%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrenatal\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.23880597014925%\" colspan=\"2\" valign=\"top\" style=\"width: 30.6756%;\"\u003e\n 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16.6667%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"25.448028673835125%\" valign=\"top\" style=\"width: 20.1192%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR [95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.655913978494624%\" valign=\"top\" style=\"width: 18.8773%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003eadj\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;[95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.448028673835125%\" valign=\"top\" style=\"width: 20.1192%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR [95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.448028673835125%\" valign=\"top\" style=\"width: 10.5564%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003eadj\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;[95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"null;width: 16.6667%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.84053651266766%\" valign=\"top\" style=\"width: 13.537%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDust\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\" style=\"width: 20.1192%;\"\u003e\n \u003cp\u003e0.93 [0.79,1.09]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.672131147540984%\" valign=\"top\" style=\"width: 18.8773%;\"\u003e\n \u003cp\u003e0.96 [0.82,1.13]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\" style=\"width: 20.1192%;\"\u003e\n \u003cp\u003e1.10 [0.72,1.69]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\" style=\"width: 10.5564%;\"\u003e\n \u003cp\u003e0.87 [0.52,1.45]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"null;width: 16.6667%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.84053651266766%\" valign=\"top\" style=\"width: 13.537%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\" style=\"width: 20.1192%;\"\u003e\n \u003cp\u003e1.05 [0.89,1.24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.672131147540984%\" valign=\"top\" style=\"width: 18.8773%;\"\u003e\n \u003cp\u003e1.11 [0.93,1.32]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\" style=\"width: 20.1192%;\"\u003e\n \u003cp\u003e1.47 [0.89,2.44]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\" style=\"width: 10.5564%;\"\u003e\n \u003cp\u003e1.59 [0.89,2.83]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"null;width: 16.6667%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.84053651266766%\" valign=\"top\" style=\"width: 13.537%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrass\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\" style=\"width: 20.1192%;\"\u003e\n \u003cp\u003e0.83 [0.65,1.05]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.672131147540984%\" valign=\"top\" style=\"width: 18.8773%;\"\u003e\n \u003cp\u003e0.89 [0.71,1.13]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\" style=\"width: 20.1192%;\"\u003e\n \u003cp\u003e1.48 [0.95,2.30]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\" style=\"width: 10.5564%;\"\u003e\n \u003cp\u003e1.19 [0.72,1.96]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"null;width: 16.6667%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.84053651266766%\" valign=\"top\" style=\"width: 13.537%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFungi\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\" style=\"width: 20.1192%;\"\u003e\n \u003cp\u003e1.02 [0.84,1.24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.672131147540984%\" valign=\"top\" style=\"width: 18.8773%;\"\u003e\n \u003cp\u003e1.11 [0.90,1.36]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\" style=\"width: 20.1192%;\"\u003e\n \u003cp\u003e1.31 [0.74,2.32]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\" style=\"width: 10.5564%;\"\u003e\n \u003cp\u003e1.14 [0.58,2.24]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"null;width: 16.6667%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.84053651266766%\" valign=\"top\" style=\"width: 13.537%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAny\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\" style=\"width: 20.1192%;\"\u003e\n \u003cp\u003e0.89 [0.76,1.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.672131147540984%\" valign=\"top\" style=\"width: 18.8773%;\"\u003e\n \u003cp\u003e0.91 [0.78,1.07]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\" style=\"width: 20.1192%;\"\u003e\n \u003cp\u003e1.17 [0.76,1.79]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\" style=\"width: 10.5564%;\"\u003e\n \u003cp\u003e0.90 [0.55,1.48]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"null;width: 16.6667%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\" style=\"width: 83.2091%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"null;width: 16.6667%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.84053651266766%\" valign=\"top\" style=\"width: 13.537%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\" style=\"width: 20.1192%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta; [95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.672131147540984%\" valign=\"top\" style=\"width: 18.8773%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta; [95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\" style=\"width: 20.1192%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta; [95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\" style=\"width: 10.5564%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta; [95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"null;width: 16.6667%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.84053651266766%\" valign=\"top\" style=\"width: 13.537%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal IgE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\" style=\"width: 20.1192%;\"\u003e\n \u003cp\u003e-2.3 [-15.7,11.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.672131147540984%\" valign=\"top\" style=\"width: 18.8773%;\"\u003e\n \u003cp\u003e-2.5 [-16.8,11.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\" style=\"width: 20.1192%;\"\u003e\n \u003cp\u003e-17.9 [-57.6,21.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.162444113263785%\" valign=\"top\" style=\"width: 10.5564%;\"\u003e\n \u003cp\u003e-34.8 [-81.6,11.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eB. Background PM\u003c/em\u003e\u003csub\u003e\u003cem\u003e2.5\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"388\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.198966408268735%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAllergen categories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.80103359173127%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.824817518248175%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.175182481751825%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.824817518248175%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR [95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.175182481751825%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003eadj\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;[95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.198966408268735%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDust\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.69250645994832%\" valign=\"top\"\u003e\n \u003cp\u003e1.95 [1.15,3.28]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.10852713178294%\" valign=\"top\"\u003e\n \u003cp\u003e1.89 [1.11,3.20]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.198966408268735%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.69250645994832%\" valign=\"top\"\u003e\n \u003cp\u003e1.39 [0.74,2.62]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.10852713178294%\" valign=\"top\"\u003e\n \u003cp\u003e1.38 [0.70,2.73]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.198966408268735%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrass\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.69250645994832%\" valign=\"top\"\u003e\n \u003cp\u003e1.68 [1.02,2.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.10852713178294%\" valign=\"top\"\u003e\n \u003cp\u003e1.53 [0.92,2.52]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.198966408268735%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFungi\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.69250645994832%\" valign=\"top\"\u003e\n \u003cp\u003e1.57 [0.76,3.24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.10852713178294%\" valign=\"top\"\u003e\n \u003cp\u003e1.41 [0.70,2.82]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.198966408268735%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAny\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.69250645994832%\" valign=\"top\"\u003e\n \u003cp\u003e1.44 [0.94,2.21]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.10852713178294%\" valign=\"top\"\u003e\n \u003cp\u003e1.42 [0.92,2.18]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.198966408268735%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.69250645994832%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta; [95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.10852713178294%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta; [95% CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.198966408268735%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal IgE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.69250645994832%\" valign=\"top\"\u003e\n \u003cp\u003e20.3 [-17.9,58.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.10852713178294%\" valign=\"top\"\u003e\n \u003cp\u003e22.0 [-16.6,60.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Odds ratios and 95%CI from the crude models were estimated with univariable logistic regression/linear regression models incorporating only the outcome (logistic: sensitisation to allergen category, linear: total IgE levels) and a single pollutant. Odds ratios and 95%CI from the adjusted models incorporated all three exposures and maternal education, age in months, and IRSD. All estimates were scaled by IQR increase of the relevant pollutant. IgE: Immunoglobulin E.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.3044925124792%\" valign=\"top\"\u003e\n \u003cp\u003eDAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.6955074875208%\" valign=\"top\"\u003e\n \u003cp\u003eDirected acyclic graph\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.3044925124792%\" valign=\"top\"\u003e\n \u003cp\u003eDEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.6955074875208%\" valign=\"top\"\u003e\n \u003cp\u003eDiesel exhaust particles\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.3044925124792%\" valign=\"top\"\u003e\n \u003cp\u003eHDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.6955074875208%\" valign=\"top\"\u003e\n \u003cp\u003eHouse dust mites\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.3044925124792%\" valign=\"top\"\u003e\n \u003cp\u003eIgE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.6955074875208%\" valign=\"top\"\u003e\n \u003cp\u003eImmunoglobulin E\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.3044925124792%\" valign=\"top\"\u003e\n \u003cp\u003eIQR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.6955074875208%\" valign=\"top\"\u003e\n \u003cp\u003eInterquartile range\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.3044925124792%\" valign=\"top\"\u003e\n \u003cp\u003eIRSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.6955074875208%\" valign=\"top\"\u003e\n \u003cp\u003eIndex of Relative Socio-economic Disadvantage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.3044925124792%\" valign=\"top\"\u003e\n \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.6955074875208%\" valign=\"top\"\u003e\n \u003cp\u003eNitrogen dioxide\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.3044925124792%\" valign=\"top\"\u003e\n \u003cp\u003eNO\u003csub\u003ex\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.6955074875208%\" valign=\"top\"\u003e\n \u003cp\u003eNitrogen oxides\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.3044925124792%\" valign=\"top\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.6955074875208%\" valign=\"top\"\u003e\n \u003cp\u003eOdds ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.3044925124792%\" valign=\"top\"\u003e\n \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.6955074875208%\" valign=\"top\"\u003e\n \u003cp\u003eParticulate matter with an aerodynamic diameter of less than 2.5 \u0026mu;m\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.3044925124792%\" valign=\"top\"\u003e\n \u003cp\u003eRMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.6955074875208%\" valign=\"top\"\u003e\n \u003cp\u003eRoot mean square error\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.3044925124792%\" valign=\"top\"\u003e\n \u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"82.6955074875208%\" valign=\"top\"\u003e\n \u003cp\u003eSulphur dioxide\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThis study was approved by the Tasmanian Health and Medical Human Research Ethics Committee (reference H0014875) and performed in accordance with the requirements of the Australian National Statement on Ethical Conduct in Human Research. Additional approval was received from the Human Research Ethics Committees of Monash University and the University of Melbourne. All parents and caregivers of the participants provided signed informed consent.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eNo conflicts of interest to declare for the following authors: MZ, CG, AW, GZ, NS, LK, GW or MD. SD reports a relationship with AstraZeneca plc that includes: funding grants. SD reports a relationship with GSK plc that includes: funding grants. FJ reports financial support from the Victoria Department of Health.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study is funded by the Victorian Department of Health (Australia). This paper represents the views of the authors and does not represent the views of the Department.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eMZ was involved in the conceptualization, methodology, software and formal analysis, writing - original draft, writing - Review \u0026amp; Editing. Caroline Gao was involved in the methodology, writing - review \u0026amp; editing. AW was involved in the conceptualization, methodology, writing - review \u0026amp; editing, project administration. Graeme Zosky was involved in the conceptualization, methodology, writing - review \u0026amp; editing, funding acquisition. Nicola Stephens was involved in the conceptualization, methodology, writing - review \u0026amp; editing. Luke Knibbs was involved in the software, formal analysis, writing - review \u0026amp; editing. Grant Williamson was involved in the software, formal analysis, writing - review \u0026amp; editing. Marita Dalton was involved in the writing - review \u0026amp; editing, project administration. Shyamali Dharmage was involved in the methodology, writing - review \u0026amp; editing. Fay Johnston was involved in the conceptualization, methodology, writing - review \u0026amp; editing, supervision, project administration, funding acquisition.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe Latrobe Early Life Follow-up (ELF) Study constitutes the child health and development stream of the Hazelwood Health Study (HHS). The Latrobe ELF Study forms part of the wider research programme of the HHS and is run by a multidisciplinary group of researchers and administrative staff from the University of Tasmania, Monash University, the University of Melbourne, the University of Sydney and CSIRO. We would like to acknowledge all of these staff for their important contributions. Most of all, the study team would like to acknowledge the contribution of all families and community members who have participated in the study to date. This study is funded by the Victorian Department of Health (Australia). The study represents the views of the authors and does not represent the views of the Department.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBrozek G, Lawson J, Szumilas D, Zejda J. Increasing prevalence of asthma, respiratory symptoms, and allergic diseases: Four repeated surveys from 1993-2014. 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Respir Med 2013;107(11):1763-72.\u003c/li\u003e\n\u003cli\u003eWyler C, Braun-Fahrlander C, Kunzli N, Schindler C, Ackermann-Liebrich U, Perruchoud AP, et al. Exposure to motor vehicle traffic and allergic sensitization. The Swiss Study on Air Pollution and Lung Diseases in Adults (SAPALDIA) Team. Epidemiology 2000;11(4):450-6.\u003c/li\u003e\n\u003cli\u003eTreudler R, Zeynalova S, Kirsten T, Engel C, Loeffler M, Simon JC. Living in the city centre is associated with type 1 sensitization to outdoor allergens in Leipzig, Germany. Clin Respir J 2018;12(12):2686-8.\u003c/li\u003e\n\u003cli\u003eBedada GB, Heinrich J, Gotschi T, Downs SH, Forsberg B, Jarvis D, et al. Urban background particulate matter and allergic sensitization in adults of ECRHS II. Int J Hyg Environ Health 2007;210(6):691-700.\u003c/li\u003e\n\u003cli\u003eHeinrich J, Topp R, Gehring U, Thefeld W. Traffic at residential address, respiratory health, and atopy in adults: the National German Health Survey 1998. 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Ann Allergy Asthma Immunol 2015;114(2):126-33.\u003c/li\u003e\n\u003cli\u003eJung HJ, Ko YK, Shim WS, Kim HJ, Kim DY, Rhee CS, et al. Diesel exhaust particles increase nasal symptoms and IL-17A in house dust mite-induced allergic mice. Sci Rep 2021;11(1):16300.\u003c/li\u003e\n\u003cli\u003eBrandt EB, Kovacic MB, Lee GB, Gibson AM, Acciani TH, Le Cras TD, et al. Diesel exhaust particle induction of IL-17A contributes to severe asthma. J Allergy Clin Immunol 2013;132(5):1194-204 e2.\u003c/li\u003e\n\u003cli\u003eMel\u0026eacute;n E, Nyberg F, Lindgren CM, Berglind N, Zucchelli M, Nordling E, et al. Interactions between Glutathione S-Transferase P1, Tumor Necrosis Factor, and Traffic-Related Air Pollution for Development of Childhood Allergic Disease. Environmental Health Perspectives 2008;116(8):1077-84.\u003c/li\u003e\n\u003cli\u003eHansell AL, Bakolis I, Cowie CT, Belousova EG, Ng K, Weber-Chrysochoou C, et al. Childhood fish oil supplementation modifies associations between traffic related air pollution and allergic sensitisation. Environ Health 2018;17(1):27.\u003c/li\u003e\n\u003cli\u003eHu Y, Liu S, Liu P, Mu Z, Zhang J. Clinical relevance of eosinophils, basophils, serum total IgE level, allergen-specific IgE, and clinical features in atopic dermatitis. J Clin Lab Anal 2020;34(6):e23214.\u003c/li\u003e\n\u003cli\u003eAwan NU, Sohail SK, Naumeri F, Niazi S, Cheema K, Qamar S, et al. Association of Serum Vitamin D and Immunoglobulin E Levels With Severity of Allergic Rhinitis. Cureus 2021;13(1):e12911.\u003c/li\u003e\n\u003cli\u003eHaselkorn T, Szefler SJ, Simons FE, Zeiger RS, Mink DR, Chipps BE, et al. Allergy, total serum immunoglobulin E, and airflow in children and adolescents in TENOR. Pediatr Allergy Immunol 2010;21(8):1157-65.\u003c/li\u003e\n\u003cli\u003eNickel R, Illi S, Lau S, Sommerfeld C, Bergmann R, Kamin W, et al. Variability of total serum immunoglobulin E levels from birth to the age of 10 years. A prospective evaluation in a large birth cohort (German Multicenter Allergy Study). Clin Exp Allergy 2005;35(5):619-23.\u003c/li\u003e\n\u003cli\u003eSantiago Hda C, Ribeiro-Gomes FL, Bennuru S, Nutman TB. Helminth infection alters IgE responses to allergens structurally related to parasite proteins. J Immunol 2015;194(1):93-100.\u003c/li\u003e\n\u003cli\u003eZahedi A, Hassanvand MS, Jaafarzadeh N, Ghadiri A, Shamsipour M, Dehcheshmeh MG. Effect of ambient air PM(2.5)-bound heavy metals on blood metal(loid)s and children\u0026apos;s asthma and allergy pro-inflammatory (IgE, IL-4 and IL-13) biomarkers. J Trace Elem Med Biol 2021;68:126826.\u003c/li\u003e\n\u003cli\u003eDunea D, Liu HY, Iordache S, Buruleanu L, Pohoata A. Liaison between exposure to sub-micrometric particulate matter and allergic response in children from a petrochemical industry city. Sci Total Environ 2020;745:141170.\u003c/li\u003e\n\u003cli\u003eBrauer M, Hoek G, Smit HA, de Jongste JC, Gerritsen J, Postma DS, et al. Air pollution and development of asthma, allergy and infections in a birth cohort. Eur Respir J 2007;29(5):879-88.\u003c/li\u003e\n\u003cli\u003eSordillo JE, Switkowski KM, Coull BA, Schwartz J, Kloog I, Gibson H, et al. Relation of Prenatal Air Pollutant and Nutritional Exposures with Biomarkers of Allergic Disease in Adolescence. Sci Rep 2018;8(1):10578.\u003c/li\u003e\n\u003cli\u003eRothman KJ. No adjustments are needed for multiple comparisons. Epidemiology 1990;1(1):43-6.\u003c/li\u003e\n\u003cli\u003eJepsen P, Johnsen SP, Gillman MW, Sorensen HT. Interpretation of observational studies. Heart 2004;90(8):956-60.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Allergic sensitisation, Immunoglobulin E, Landscape fires, Child health, Particulate air pollution, Early life, Long-term effects","lastPublishedDoi":"10.21203/rs.3.rs-3045254/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3045254/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBACKGROUND\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEvidence on the relationship between air pollution and allergic sensitisation in childhood is inconsistent, and this relationship has not been investigated in the context of smoke events that are predicted to increase with climate change. Thus, we aimed to evaluate associations between exposure in two early life periods to severe levels of particulate matter with an aerodynamic diameter \u0026lt; 2.5µm (PM\u003csub\u003e2.5\u003c/sub\u003e) from a mine fire, background PM\u003csub\u003e2.5\u003c/sub\u003e, and allergic sensitisation later in childhood.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMETHODS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe measured specific immunoglobulin E (IgE) levels for seven common aeroallergens as well as total IgE levels in a cohort of children who had been exposed to the Hazelwood coal mine fire, either \u003cem\u003ein utero\u003c/em\u003e or during their first two years of life, in a regional area of Australia where ambient levels of PM\u003csub\u003e2.5\u003c/sub\u003e are generally low. We estimated personal exposure to fire-specific emissions of PM\u003csub\u003e2.5\u003c/sub\u003e based on a high-resolution meteorological and pollutant dispersion model and detailed reported movements of pregnant mothers and young children during the fire. We also estimated the usual background exposure to PM\u003csub\u003e2.5\u003c/sub\u003e at the residential address at birth using a national satellite-based land-use regression model. Associations between both sources of PM\u003csub\u003e2.5\u003c/sub\u003e and sensitisation to dust, cat, fungi, and grass seven years after the fire were estimated with logistic regression, while associations with total IgE levels were estimated with linear regression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRESULTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo association was found between the levels of exposure at either developmental stage to fire-related PM\u003csub\u003e2.5\u003c/sub\u003e and allergic sensitisation seven years after the event. However, levels of background exposure were positively associated with sensitisation to dust (OR = 1.89, 95%CI = 1.11,3.20 per 1 µg/m\u003csup\u003e3\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONCLUSIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChronic but low exposure to PM\u003csub\u003e2.5\u003c/sub\u003e in early life could be more strongly associated with allergic sensitisation in childhood than time-limited high exposure levels, such as the ones experienced during landscape fires.\u003c/p\u003e","manuscriptTitle":"Exposure to air pollution concentrations of various intensities in early life and allergic sensitisation later in childhood","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-23 19:27:17","doi":"10.21203/rs.3.rs-3045254/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-08-01T03:47:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-07-17T11:15:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6f951d9b-3c01-4f35-a3b1-2f6cc310c0de","date":"2023-07-13T15:40:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"ab0d6fdc-c771-42d7-a736-f28d0d3ceea7","date":"2023-07-07T08:16:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-07-03T12:39:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-07-03T12:16:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-06-15T13:34:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-06-15T13:30:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2023-06-10T03:25:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ebdb4e2a-0b07-4a6b-83e2-1e92bef5d6fc","owner":[],"postedDate":"June 23rd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-12-25T15:04:35+00:00","versionOfRecord":{"articleIdentity":"rs-3045254","link":"https://doi.org/10.1186/s12890-023-02815-8","journal":{"identity":"bmc-pulmonary-medicine","isVorOnly":false,"title":"BMC Pulmonary Medicine"},"publishedOn":"2023-12-21 15:00:51","publishedOnDateReadable":"December 21st, 2023"},"versionCreatedAt":"2023-06-23 19:27:17","video":"","vorDoi":"10.1186/s12890-023-02815-8","vorDoiUrl":"https://doi.org/10.1186/s12890-023-02815-8","workflowStages":[]},"version":"v1","identity":"rs-3045254","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3045254","identity":"rs-3045254","version":["v1"]},"buildId":"omnImTCwR2MFx8CMYfrG7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}