When we adjusted effects sizes for SES, the positive associations were slightly attenuated (Table 1), but the associations for NH3, NH4+, NHx, and PM2.5 with asthma incidence were negative when adjusting for region. Correspondingly, a similar pattern of association was shown when we performed the analyses stratified on geographical regions (Table e6; http://links.lww.com/EE/A13). Finally, we performed all analyses adjusting for base characteristics (sex, date of birth, age, and calendar year), region, and SES and additionally for any other pollutant by including one pollutant simultaneously in each logistic regression analysis in a two-pollutant model (Figure 2). The negative association for PM2.5, NH3, and NHx with asthma remained, whereas the positive association persisted for NH4+ when adjusting for PM2.5.
The present study is unique as it allowed us to potentially link concentrations of specific components of particulate air pollution with incident cases of asthma, thus, to investigate whether gaseous NH3, particulate NH4+, and PM2.5 are risk factors for asthma. The basic adjusted analyses revealed a positive association between NH3, NH4+, and the sum NHx exposure and asthma diagnosis but showed no association between PM2.5 and asthma. In a second step, the positive associations of NH3 and NH4+ exposure with asthma were slightly attenuated, when adjusting for SES, whereas the associations disappeared when adjusting for region in a third step. Finally, in a fourth step, we adjusted additionally for any other pollutant in a two-pollutant model, where the positive association remained for NH4+ when adjusting for PM2.5, which was made possible by the virtue of the study size. These findings indicate that early life exposure to NH4+ may be a risk factor for onset asthma, but we cannot rule out confounding by region or other factors covarying with NH3, NH4+, and PM2.5. If regional confounding exists, this means that the regions either differ in diagnostic practises, population characteristics, concentrations of other pollutants, or in some other unknown factor(s) related to the region that may be the underlying cause of asthma and hereby introduced ecological fallacy. Additionally, it can be argued that stratification for and adjustment for region could have caused an over-adjustment due to the evident regional distribution of pollutants causing a lack of exposure contrast within regions. Furthermore, although PM2.5 followed the regional sections, associations of PM2.5 with asthma were generally inconsistent and negative when adjusting for region, not supporting earlier findings showing a positive association for PM2.5 with childhood asthma.2,3,17 The high correlation between NH4+ and PM2.5 (r = 0.91) and uneven distribution of the pollutants may thus be the reason for the change in association between NH4+ and asthma when adjusting for region. The adjustment for PM2.5 can be viewed as an adjustment for traffic and heating of houses by wood burning in the cities, unmasking the effect of NH4+ to be observed in more urban areas, as can be seen in Figure e2; http://links.lww.com/EE/A13.
Comparison with other studies
To our knowledge, this large-scale study is the first to explore ambient NH3 and NH4+ exposure and the risk of developing pediatric asthma from a nation-wide perspective. Among the studies performed with children, results showed no association with self-reported asthma symptoms and medication use,8,9 whereas an inverse association was found between NH3 concentrations with forced expiratory volume in 1 second.8 Moreover, some studies have suggested an increased risk of respiratory hospitalizations linked with high NH4+ concentrations in adults in Atlanta (USA)5 and New York (USA).6 Similarly, one study found an association of NH4+ exposure with increased emergency department visits due to upper respiratory infections, but not due to pneumonia or bronchiolitis, in a large population of 1- to 4-year-olds in Atlanta (USA).7 Generalizations of these results are problematic as the studies explored the respiratory health effects related to NH3 and NH4+ exposures either in small restricted areas, or during short time-periods, or in small-scale study populations, which increases the risk of bias affecting the results.
The positive associations between NH3, NH4+, and asthma may be causal relations or, alternatively, it may be that NH3 and NH4+ serve as markers for other farming exposures associated with asthma—for example, organic dust exposure that has previously been linked to respiratory symptoms and asthma,18 also in combination with NH.3,19 Other studies on respiratory morbidity relied on proxies as “living on or near a farm” due to the known large agricultural contribution of local and regional emissions of NH3 and NH4+. Some studies have indicated detrimental health effects in adults in terms of declined lung function,20 increased respiratory symptoms,18 and doctor-diagnosed asthma.21 Beneficial effects of farm contact have also been reported in terms of a lower prevalence of sensitization and atopic asthma.22 Studies have shown that children exposed to farming environments suffer more from asthma and respiratory symptoms23,24 and less of allergy1 than their counterparts. However, most farming studies use “general farming exposures” as the exposure variable, and therefore these findings cannot be specifically related to either NH3 or NH4+ exposures.
Although our findings indicated that only NH4+ is a risk factor for onset asthma early in life, emissions of NH3 are essential as NH3 is a precursor to NH4+ that is formed in a reaction between NH3 and acid aerosols. The spatial pattern of NH3 concentrations showed a south-west, north-east gradient and NH4+ showed a south-west north-east gradient. A validation study on the DEHM and DAMOS models concluded that these gradients are caused partly by atmospheric transport of primarily PM2.5 from northern Europe and by local and regional ammonia emissions south of Denmark,15 thus emphasizing transboundary movement of pollutants. In a previous study on European NH3 emissions, the NH3 emissions varied considerably with the month of the year, in which Danish emissions were low to moderate in February but moderate to high in April due to manure application. However, we did not observe an increase in asthma diagnoses in the 3-month period following April (data not shown).
Strengths and limitations
A major strength of our study is the large sample size that was possible owing to the register-based design. This design secures high completeness of data, prospective data collection and eliminates selection bias. Therefore, the study is representative of preschool children of parents born in Denmark, regardless of their place of residence, SES, sex, and age, but precludes children and parents that are emigrants to avoid handling the complexicity of different cultures and ethnicities that are known to influence on asthma. Additionally, as the exposure time window was the same for cases and their individually matched controls, the study design and the adjustment for sex, date of birth, age, and calendar year preclude confounding by time-invariant characteristics of participants. In our study, some covariates were found to be risk factors of onset asthma, including children’s mothers having a short education compared to a longer education and fathers having a low income compared to a higher income. A shortcoming of our study, however, is the lack of information on other potential confounders that might have played a role in our study, especially smoking and dampness in the children’s home, two factors known to greatly influence the incidence of childhood asthma.25 Information on smoking is of particular importance because tobacco smoke, on the one hand, is a strong predictor of asthma, and on the other hand, NH3 is an endogenous part of tobacco smoke chemistry.26,27 Furthermore, it would have been informative to know whether the children lived on or near a farm and spent time in enclosed livestock facilities to compare the risk of highly exposed children with low-exposed children.
Another strength of this study was the nationwide estimation of exposure that provides information on modeled NH3 and NH4+ exposure during the past year for all participants, comprising both long-term exposures with a high grid resolution and providing satisfying exposure contrasts. Although the validation of the DAMOS model has shown satisfying agreement between modeled and measured concentrations of NH3,28 a limitation of the model appears to be related to the performance of the wet deposition of NH3, which can potentially be improved by better estimation of precipitation.14 A clear advantage of this study is that it contained enough spatial variation in pollutants to enable us to explore the change in effects when adjusting for any other pollutant. Still, the complexity of pollutant mixtures like PM2.5 makes it difficult to identify causal components and study interactions in those mixtures. Also the high correlations between pollutants often makes it difficult to disentangle their individual effects on health. This may also explain why previous studies on PM2.5 constituents and health effects have been inconsistent in their results and PM2.5 mass persists as the best predictor of adverse health outcomes.29
We used modeled pollutant concentrations linked to each child’s residential address as proxy for each child’s exposure. Besides being at home, most Danish children attend daycare centers that most often are located in the same area as their residential address. Therefore, we assumed that the children remain in the same area of their home address the majority of their time.
An alternative to exposure modeling by residential address would be the measurement of exposures at residential addresses or personal air monitoring. However, although these exposure assessments are favored, it is impractical in a large study population over time. Validation of the DAMOS model has shown satisfying agreement between modeled and measured concentrations of NH3 (correlation coefficients for five monitoring stations, range 0.51–0.78) based on the daily mean values averaged over measurement stations as time series and annual mean values, as well as the daily maximum values as scatter plots.28
In epidemiological studies, various definitions of pediatric asthma are used, and therefore outcome misclassification is often a concern, especially in children with less distinctive symptoms. It is a strength of our study that we used information on doctor-diagnosed asthma derived from the Danish National Patient Register, which limits misclassification since diagnosis is based on validated criteria. However, the register most likely only reflects cases of severe asthma and leaves out children with mild to moderate asthma (or easy treatable asthma) diagnosed from general practitioners. Most children in our study were diagnosed with asthma before 3 years of age. These children may be defined as early wheezers as in early ages, it can be unclear whether respiratory symptoms are part of asthma development or attributable to respiratory virus infections. As a result, effect estimates may therefore bias associations towards the null. Our stratified analyses in different age groups revealed, however, similar results, although the findings were most consistent in the 2- to 3 year-olds. It should be noted though that asthma is a heterogeneous disease, and the time of diagnosis and progress may reflect different asthma phenotypes as recently suggested.30 Unfortunately, we had no information on atopy, lung function, inflammatory markers, and disease progress and prognosis, and therefore, we cannot distinguish further between phenotypes.
Our results indicate that high levels of NH4+ may be associated with an increased risk of developing asthma in preschool children causing an important public health problem affecting a significant number of children. Therefore, there is a continuous need to pay attention to air pollution policies and highly exposed populations of children to protect these children from developing asthma that may affect them and society throughout their lives. Further exploration in areas with a high agricultural activity is needed to confirm our result.
Conflict of interest statement
The authors declare that they have no conflicts of interest with regard to the content of this report.
Source of funding
Supported by dNmark: Danish Nitrogen Mitigation Assessment: Research and Know-how for a sustainable, low-nitrogen food production by the Danish Council for Strategic Research, the Danish Council for Strategic Research, the Centre for Integrated Register-Based Research at Aarhus University (CIRRAU) Denmark, and NordForsk under the Nordic Programme on Health and Welfare (project 75007).
Data availability Health and Welfare, NordicWelfAir: Data are not available due to a strict data-sharing agreement. Data on individual characteristics were requested through Statistics Denmark from the Danish Civil Registration System and data on asthma from the Danish National Patient Register. Environmental data can be requested for payment from the Department of Environmental Sciences, Aarhus University. The overall link of information on residential coordinates with exposure assessment, personal characteristics, and diagnosis history was performed within Statistics Denmark that requires all users to have permission to access data and carry out analyses.
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Asthma; Ammonia; Ammonium; 2.5 components">PM2.5 components; Air pollution; Register-based epidemiological study
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