Increased risk of pneumonia in residents living near poultry farms: does the upper respiratory tract microbiota play a role?
Smit et al. Pneumonia
Increased risk of pneumonia in residents living near poultry farms: does the upper respiratory tract microbiota play a role?
Lidwien A. M. Smit 0
Gert Jan Boender
Wouter A. A. de Steenhuijsen Piters
Thomas J. Hagenaars
Elisabeth G. W. Huijskens
John W. A. Rossen
Elisabeth A. M. Sanders
Dick Heederik 0
0 Institute for Risk Assessment Sciences (IRAS), Division Environmental Epidemiology, Utrecht University , PO Box 80178, 3508 TD Utrecht , The Netherlands
Background: Air pollution has been shown to increase the susceptibility to community-acquired pneumonia (CAP). Previously, we observed an increased incidence of CAP in adults living within 1 km from poultry farms, potentially related to particulate matter and endotoxin emissions. We aim to confirm the increased risk of CAP near poultry farms by refined spatial analyses, and we hypothesize that the oropharyngeal microbiota composition in CAP patients may be associated with residential proximity to poultry farms. Methods: A spatial kernel model was used to analyze the association between proximity to poultry farms and CAP diagnosis, obtained from electronic medical records of 92,548 GP patients. The oropharyngeal microbiota composition was determined in 126 hospitalized CAP patients using 16S-rRNA-based sequencing, and analyzed in relation to residential proximity to poultry farms. Results: Kernel analysis confirmed a significantly increased risk of CAP when living near poultry farms, suggesting an excess risk up to 1.15 km, followed by a sharp decline. Overall, the oropharyngeal microbiota composition differed borderline significantly between patients living <1 km and ≥1 km from poultry farms (PERMANOVA p = 0. 075). Results suggested a higher abundance of Streptococcus pneumoniae (mean relative abundance 34.9% vs. 22. 5%, p = 0.058) in patients living near poultry farms, which was verified by unsupervised clustering analysis, showing overrepresentation of a S. pneumoniae cluster near poultry farms (p = 0.049). Conclusion: Living near poultry farms is associated with an 11% increased risk of CAP, possibly resulting from changes in the upper respiratory tract microbiota composition in susceptible individuals. The abundance of S. pneumoniae near farms needs to be replicated in larger, independent studies.
Air pollution; Environment; Microbiome; Pneumonia
Community-acquired pneumonia (CAP) is among the
leading causes of morbidity and mortality in adults
worldwide, and the burden is markedly higher among
the oldest adults [1–3]. Environmental risk factors, such
as active and passive smoking, household air pollution
due to biomass fuel use, and outdoor air pollution, have
been shown to increase the susceptibility to lower
respiratory tract infections, including CAP [1, 4–7].
In a rural population in the south of The Netherlands,
we previously studied environmental risk factors of CAP
using electronic GP medical records of 70,142 adult
patients seen during 2009 . In this study, we observed
an increased risk of CAP in adults living at a distance of
1 km or less from a poultry farm . Although poultry
farms can be a source of zoonotic pathogens such as
avian influenza or Chlamydia psittaci, a study of
zoonotic infections in hospitalized CAP patients in a nearby
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
area revealed very few cases caused by avian pathogens
(e.g. C. psittaci: 1.7%) .
Recently, scientists have sounded the alarm over the
large contribution of agriculture to particulate matter
(PM) air pollution [10, 11]. Poultry farms in particular are
known to emit large quantities of air pollutants such as
PM and endotoxins [12–15]. Exposure levels in stables
have been shown to cause airway inflammation,
respiratory symptoms and airway obstruction in farmers [16, 17].
Despite ambient pollutant concentrations being
considerably lower than levels in stables, there is growing evidence
of respiratory health effects in susceptible individuals
living near livestock farms as well . Exposure to PM air
pollution may predispose these individuals to respiratory
infections through chronic airway inflammation and
subsequent host–immune responses [4, 7], which might be
amplified by exposure to environmental endotoxin .
With the advent of new molecular microbial
identification methods, it has been proposed that respiratory
infections might emerge from the disruption of the otherwise
balanced upper respiratory tract (URT) bacterial
ecosystem or ‘microbiome’ . Spread of overgrown pathogens
originating from the URT to the lower respiratory tract
could perturb the lung microbial ecosystem and lead to
respiratory tract infections, including pneumonia .
Indeed, recent studies have shown that CAP in adults was
associated with dysbiosis of the oropharyngeal microbiota,
characterized by increased abundance of pathogens such
as S. pneumoniae, and the absence of facultative anaerobic
commensals . We hypothesized that environmental
exposure from poultry farms may contribute to a shift in
the oropharyngeal microbiome, and thereby facilitate the
development of lower respiratory infections.
In the present study, refined spatial analyses are used to
confirm the increased risk of CAP near poultry farms.
Furthermore, the oropharyngeal microbiota composition in
CAP patients is analyzed in relation to residential proximity
to poultry farms.
The study was carried out in the province of
NoordBrabant, The Netherlands, a region with a high density
of intensive livestock farms. Two cross-sectional analyses
were performed: (i) the association between poultry
farms, home address and GP-registered pneumonia 
was re-analyzed using a spatial kernel model; and (ii) the
oropharyngeal microbiota composition of hospitalized
CAP patients was analyzed in relation to residential
proximity to poultry farms.
Medical Research Involving Human Subjects Act) and
with the Code of Conduct for Medical Research. All
experimental protocols were approved by the Medical
Ethical Committee of the University Medical Centre
Utrecht (NL45307.041.13, GP-registered pneumonia; as
part of the Farming and Neighbouring Residents’ Health
Study), and by the Medical Ethical Committee of the St.
Elisabeth Hospital, Tilburg (hospitalized CAP patients).
Ethical aspects of the study have been described earlier
[8, 23–25]. In short, medical information and address
records of GPs’ patients were kept separated at all times
by using a trusted third party. The need to obtain
informed consent from individual patients was waived.
Written informed consent was obtained from all
hospitalized CAP patients.
Spatial kernel analysis
Briefly, the data on GP-diagnosed pneumonia and spatial
location of home address of 92,548 GP patients (70,142
adults [18–70 year] and 22,406 children [0–17 years])
during the year 2009  was re-analyzed by estimating
the parameters of a spatial kernel model from this data.
In this model it is assumed that each poultry farm
independently generates a probability for individuals of
experiencing GP-diagnosed pneumonia within this year.
This probability is modelled through a function
dependent on the distance between farm and home
address (i.e. the spatial kernel). The parameterization of
the spatial kernel utilizes three parameters and offers
sufficient flexibility for identifying any distance
dependency from the data, from short-range to long-range
patterns and from very sharp to more gradual dependencies
on distance. In addition, a constant background
probability is assumed to allow for quantification of the part
of CAP incidence that is not associated with proximity
to poultry farms. The background probability and the
three parameters of the kernel are all estimated from the
data by maximum likelihood (ML). The method is
described previously [26, 27], and more details are
provided in Additional file 1.
Oropharyngeal microbiota composition
The microbiota composition of 126 patients hospitalized
with CAP in the same region was analyzed, in relation
to residential distance to poultry farms (Fig. 1).
Characteristics, bacterial and viral etiology, and in-/exclusion
criteria of the study population of hospitalized CAP
patients have been described earlier [22, 25].
Oropharyngeal samples were taken before hospitalization at the
emergency ward. Storage of samples, isolation of
bacterial DNA, and 16S-rRNA-based sequencing of the V5–V7
region was performed as previously described .
Patients who received antibiotics during the two weeks
before sampling, and patients with a positive qPCR result
Fig. 1 Geographic distribution of 126 adult CAP patients and poultry farms around Tilburg, The Netherlands
for C. burnetii were excluded from microbiota
composition analysis. The present analysis was performed
on 126 patients from whom microbiota data and
address records were available. The presence of animal
farms around the home address was assessed using a
geographic information system (ARCGIS 9.3.1., Esri,
Redlands, California, United States), as described
Statistical analyses of oropharyngeal microbiota data
were performed in R version 3.2.2. The study visualized
the association between the vicinity of poultry farms and
the overall bacterial community composition by
nonmetric multidimensional scaling (nMDS) of the
BrayCurtis dissimilarity metric and assessed its statistical
significance by permutation analysis of variance .
Differentially abundant bacterial community members
that might be related to changes in the overall
microbiota composition were detected by Mann-Whitney
Utests, comparing patients living <1 km versus ≥1 km
from poultry farms. The 1 km radius was chosen based
on the original findings . Sensitivity analyses were
performed using a 1.15 km radius, based on the spatial
kernel analysis results. The false discovery rate was
controlled for by using the Benjamini-Hochberg
technique . Additionally, unsupervised hierarchical
clustering was used to group patients based on similarity of
microbial composition, which was visualized in a
dendrogram . The optimal number of clusters was based
on clustering indices , and only clusters including >3
patients were considered for subsequent analyses.
Fisher’s exact tests were exploited to compare the proportion
of patients living <1 km from a poultry farm over all
patients in the cluster of interest versus that proportion in
the other clusters.
Spatial kernel analysis
The data was analyzed using spatial kernel models to
characterize the association between living in the vicinity
of poultry farms and CAP. In total, 993 patients (702/
70,142 adults and 221/22,406 children) were diagnosed
with CAP in 2009. Two alternative models were used: a
distance-independent model with one estimable
parameter (namely a constant per-individual probability of
GPreported pneumonia), and a model with a
distancedependent risk component (three additional estimable
parameters) added to a constant background risk.
Estimation of the distance-dependent risk component
(spatial kernel) yields the results displayed in Fig. 2. The
distance dependence of the fitted distance-dependent
risk component (full line) is sharp, displaying a stepwise
decline to zero at a distance of approximately 1.15 km.
The excess CAP risk is approximately equal to 0.001
(per person year) per poultry farm within 1.15 km,
which compares to a total background risk of 0.009 per
person year (0.001/0.009 = ~11% increase in risk).
Comparison with the distance-independent model shows that
the distance-dependent component significantly
contributes to the model fit (p = 0.0067, likelihood-ratio test,
see Additional file 1). The sharp bound at 1.15 km of the
distance-dependent risk model is largely maintained,
even in the model including the 95% lower confidence
bound value for the parameter determining the
sharpness of the distance dependence (dashed line). For this
lower confidence bound it was calculated (see Additional
file 1) that 81% of the total CAP incidence attributed to
proximity to individual poultry farms occurs within a
1.15 km distance from farms, and 95% within 1.35 km.
To investigate potential age effects, kernel estimation
Fig. 2 Spatial kernel estimated from morbidity data: probability for an
individual of experiencing GP-diagnosed pneumonia in 2009 as
attributed to an individual poultry farm, as a function of the distance from the
individual’s residential location to that of the poultry farm. Red, full line:
Maximum-Likelihood estimate (= fitted model). Grey, dotted line: Kernel
for the lower confidence bound for the sharpness of the distance
dependence and the corresponding profile-likelihood values for the other
was repeated for the partial datasets of 70,142 adults,
and 22,406 children. No significant differences were
observed between the parameter values estimated for these
partial datasets, implying that both subpopulations show
similar excess risks at close proximity from poultry
farms (see Additional file 1).
Oropharyngeal microbiota analysis
The oropharyngeal microbiota composition of 100 adult
hospitalized CAP patients living <1 km and 26 patients
living ≥1 km to one or more poultry farms was studied
to explore the hypothesis that the association between
CAP and living near poultry farms is mediated through
a direct effect on microbial community composition,
including pathogen colonization within the respiratory
tract (Fig. 1).
Table 1 shows characteristics of the 26 CAP patients
who lived within 1 km of one or more poultry farms,
and 100 patients who lived at ≥1 km. Sex, age, smoking,
chronic obstructive pulmonary disease (COPD),
immunocompromized status, and pneumonia severity did not
differ between the two groups (p > 0.25). All subjects
were included in the same winter season, between
November 2008 and February 2009. Bacterial and viral
etiology also did not differ between the two groups (see
Additional file 1: Table S3).
The association between living within a 1 km radius
from a poultry farm and the overall oropharyngeal
microbiota composition was borderline significant (Fig. 3,
PERMANOVA p = 0.075). This overall difference in
bacterial community composition was related to a higher
abundance of S. pneumoniae (mean relative abundance
34.9% versus 22.5%, Mann-Whitney U-test p = 0.058, q
= 0.541) and a lower abundance of Lactobacillus (1.4%
Table 1 Characteristics of 126 CAP patients, by the presence of
a poultry farm within 1 km of the home address
Age, mean ± SD, yrs
CAP at age ≥60 year, n (%)
Current smoking, n (%)
Month of admission, n (%)
Recent antibiotics usageb
aChi-square test, Fisher’s exact test, or t-test. bUse of antibiotics <2 weeks
COPD chronic obstructive pulmonary disease, NA not available, ICS
immunocompromized status, PSI pneumonia severity index
versus 3.8%, p = 0.049, q = 0.541) in patients living at
<1 km compared to ≥1 km from a poultry farm.
These findings were verified using an unsupervised
clustering approach, showing a total of seven microbiota
clusters in our study cohort, six of which were
characterized by either Rothia, S. pneumoniae, Gemellales,
Neisseria, Actinomyces or Lactobacillus predominance.
In addition, we identified one cluster without clear
predominance of any species (Fig. 4). We observed an
overrepresentation of individuals with the S. pneumoniae
profile near poultry farms (12/36 versus 14/90 for the
other clusters, Fisher’s exact p = 0.049). All remaining
individual clusters, including Rothia (9/60 in close
proximity versus 17/66 in the remaining clusters, p = 0.186) and
Gemellales (3/9 in close proximity versus 23/117 in the
remaining clusters, p = 0.390) showed no statistically
significant associations with residential proximity to poultry
farms. These findings suggest an association between
living in close vicinity to poultry farms and enrichment for
S. pneumoniae in particular.
We performed a sensitivity analysis, in which we
compared the microbiota profiles of 98 CAP patients living
<1.15 km and 28 patients living ≥1.15 km to one or
more poultry farms, based on the spatial kernel analysis
results. In patients who—compared to the primary
Fig. 3 Non-metric multidimensional scaling (nMDS) plot of the
oropharyngeal microbiota based on Bray-Curtis dissimilarity metric. Each
data point depicts the oropharyngeal bacterial communities of one
patient. Data points and population standard deviation of data points
(ellipses) are colored based on vicinity to poultry farm (dark gray, <1 km;
light gray, ≥1 km). The stress-value indicates that the multi-dimensional
structure of the data is well captured by the nMDS visualization.
The figure suggests an association between living close to a poultry farm
and the abundance of S. pneumoniae, which was verified by both
supervised and unsupervised learning methods
analysis—were now considered “exposed” (living
between 1.0 and 1.15 km from a poultry farm), the relative
abundance of S. pneumoniae was below the 10th
percentile, which attenuated the difference in the
microbiota composition between the two groups (overall
microbiota comparison: PERMANOVA p = 0.23, mean
relative abundance of S. pneumoniae: Mann-Whitney
Utest p = 0.14, over-representation of the S. pneumoniae
cluster near poultry farms: Fisher’s exact p = 0.095).
In this study, we found that living near poultry farms is
associated with an ~11% increase in risk of CAP for each
poultry farm within a distance of 1.15 km. Furthermore,
the results suggested an association between living close
to a poultry farm and the abundance of S. pneumoniae
in adult patients hospitalized with CAP. To the best of
our knowledge, this study is the first to show an
association between the residential environment and the
respiratory microbiota composition in adults. Because of
the lack of a healthy control group and the relatively
small sample size, results should be interpreted with
caution. The abundance of S. pneumoniae in relation to
living in the vicinity of poultry/livestock farms needs to
be reassessed in future studies.
Kernel analysis confirmed the excess risk of CAP in
the vicinity of poultry farms, as identified earlier using
regression analysis . In addition, it indicated that the
excess risk has a sharp bound at around 1.15 km. The
Fig. 4 Hierarchical clustering of patients based on oropharyngeal microbiome composition. Patients were hierarchically clustered based on their
oropharyngeal bacterial communities using the Bray-Curtis dissimilarity measure, which was visualized in the dendrogram. Adjacent to the
branches of the dendrogram information on age (yellow; elderly, red; adults) and proximity to poultry farms (dark gray, <1 km; light gray, ≥1 km)
is shown. In addition, the relative abundance of the 15 highest ranking operational taxonomic units (OTUs) is shown per patient in vertical stacked
bar plots. The colored horizontal bars represent the three major clusters we discerned based on clustering indices which were enriched for Rothia
(R), S. pneumoniae (SP) and Gemellales (G). Furthermore, four smaller clusters were observed, distinguished by predominance of Actinomyces (A),
Neisseria (N) and Lactobacillus (L). In the fourth cluster no predominance for any OTU was observed (mixed; M)
advantage of the kernel analysis compared to earlier
analyses is that instead of regressing CAP occurrence to
distance-to-the-nearest-farm, it models the
distancedependent per-farm contribution to the risk of CAP.
Thus, the kernel models can assess the incremental risk
per every additional poultry farm as well as local
accumulation of risk due to the presence of multiple farms in
the vicinity of an individual’s home. In the earlier
analyses, we considered an individual exposed when poultry
farms were present within a 1 km radius of the home
address. The distance dependence of the fitted kernel
model is remarkably sharp, suggesting that the risk is
strongly localized, possibly as a result of movement
patterns around the home. This suggestion is supported by
considering the kernel corresponding to the lower
confidence bound of the parameter determining the
sharpness of the distance dependence. Even for that kernel
parameter set, we calculated that as much as 81% (on
average) of the CAP incidence attributed to proximity to
individual poultry farms occurs within a 1.15 km
distance. This roughly corresponds to the earlier assumed
1 km radius around the home address. It should be
realized that all risk estimates make use of the distance
between a poultry farm and the home address as a proxy
of exposure. Activity patterns lead to exposure
misclassification and this may have modified the shape of the
kernel function to some extent.
We hypothesized that the association between
residential proximity to poultry farms and CAP might be
mediated through an effect of emissions from poultry
farms on microbiota composition in the URT. Our
finding that the abundance of S. pneumoniae was
increased in CAP patients living close to a poultry farm
underlines our hypothesis that exposure to
farmrelated air pollutants may result in alterations of the
oropharyngeal microbiota composition. An
imbalanced URT microbiome may lead to decreased
colonization resistance and reduced containment of
commensals that may become pathogens
(pathobionts) such as S. pneumoniae, consequently
increasing the risk of respiratory tract infections .
We speculate that PM and endotoxin emissions from
intensive agricultural operations are central to our
findings, putatively by modulating innate immune responses
resulting in an imbalanced respiratory microbiome,
which is supported by animal and in vitro experiments
[31, 32]. Alveolar macrophages remove inhaled PM by
phagocytosis and release pro-inflammatory mediators,
including reactive oxygen species. Their function is
altered by particulate loading, which was shown to result
in reduced phagocytosis of S. pneumoniae .
Furthermore, PM increases adhesion of S. pneumoniae to
human airway epithelial cells, which is mediated by
oxidative stress and upregulation of platelet-activating
factor receptors (PAFR) [34, 35]. In aged rats, a single
exposure to ambient PM reduced the ability to handle
ongoing pneumococcal infections . It was recently
shown that the composition of the lung microbiota
shifted towards endogenous opportunistic pathogens in
a lipopolysaccharide (LPS) induced lung inflammation
mouse model . It has earlier been proposed that a
perturbed respiratory microbiome may be involved in
the association between cigarette smoking and
respiratory tract infections. Both culture-based and
cultureindependent studies have shown that smoking can
simultaneously deplete members of the normal commensal
airway flora and enrich for potential pathogens .
Morris et al.  showed significant differences in the
microbiome of the oral cavity of smokers compared with
nonsmokers. We postulate similar mechanisms are
responsible for the current observations. Smoking status
(and other potential confounders such as COPD) were
not associated with residential proximity to a poultry
farm, thus it is unlikely that confounding has influenced
In this study, bacterial community changes were
observed on top of the previously described dysbiotic
oropharyngeal microbiota composition in CAP patients
compared to healthy adults, which was characterized by
an increase of S. pneumoniae, Rothia and Lactobacillus
. Most strikingly, we showed an increased abundance
of the most important human pathobiont causing
pneumonia (i.e. S. pneumoniae) in oropharyngeal samples of
patients living near poultry farms, which was a
consistent finding across supervised and unsupervised learning
methods. Our results are of great relevance for studies
in individuals exposed to other sources of PM such as
traffic and solid biomass fuel combustion, which are
linked to acute respiratory infections as well [4, 5, 7],
and may also be associated with an altered respiratory
The observation of enhanced growth of a human
pathobiont suggests an indirect effect of exposure to
poultry farm emissions on pathogenesis of CAP. This is
in contrast to a Finnish study , which observed that
environmental biodiversity was associated with a higher
generic diversity of skin gammaproteobacteria,
presumably resulting from direct contact with environmental
microbiota. Although there is some evidence that
transmission of farm-related bacteria may take place through
air—for example, in pig and veal calf farmers and their
family members, nasal colonization by
livestockassociated methicillin-resistant Staphylococcus aureus
(LA-MRSA) occurs frequently —we did not find
evidence of zoonotic infections explaining the increased
risk of CAP around poultry farms .
Around 40% of the CAP patients also had COPD. It
has been demonstrated that COPD patients living near
livestock farms are more likely to use inhaled
corticosteroids and report respiratory symptoms than patients
living further away from farms, suggesting an increased
risk of exacerbations [18, 24]. Dickson et al. 
proposed that exacerbations of COPD are occasions of
respiratory dysbiosis: disorder and dysregulation of the
microbial ecosystem of the respiratory tract, coupled
with a dysregulated host immune response. A similar
mechanism as proposed in the present study, hinging on
farm-related air pollutants and dysbiosis of the
respiratory tract, may play a role in individuals with COPD
living near livestock farms. We intend to study a rural
population of both COPD patients and control subjects
in a larger-scale future study. Including control subjects
will help to interpret results, because the microbiota
changes within patient groups may be a cause or a
consequence of disease, or merely coinciding with disease
Indeed, a limitation of our preliminary microbiota study
is that we did not include healthy controls from the same
study area. However, hospitalized patients living near a
poultry farm and those living further away from poultry
farms did not differ with regard to potential confounders
such as age, smoking, COPD, or pneumonia severity.
Residual confounding due to demographic differences
between the two groups (e.g. household size), contact with
children or other risk factors for pneumonia or
pneumococcal colonization cannot be entirely dismissed. Because
of the relatively small sample size, the results are sensitive
to the effect of outliers. In addition, there may be false
positive results due to multiple comparisons. This should
therefore be regarded as a hypothesis-generating study,
and results need to be confirmed in larger, preferably
Kernel analysis demonstrated an excess risk of
GPdiagnosed pneumonia in areas of approximately 1.15 km
around poultry farms. Oropharyngeal microbiota of
patients with CAP living within 1 km of a poultry farm
showed an increased abundance of S. pneumoniae.
Exposure to air pollutants such as PM and endotoxin may
contribute to dysbiosis of URT microbiota in susceptible
individuals, which may consequently result in respiratory
infections. These preliminary observations need to be
replicated in larger, independent studies.
Additional file 1: Table S1. Kernel parameter and background
probability estimates obtained using ML estimation, with 95% confidence
bounds based on the likelihood-ratio test. Table S2.: Comparison
between the distance-dependent en distance-independent model. Table
S3. Viral and bacterial etiology of CAP, by the presence of a poultry farm
within 1 km of the home address. (DOCX 122 kb)
This research has received funding from the Dutch Ministry of Economic
Affairs (project code BO-20-009-030), the (former) Dutch Ministry of
Economic Affairs, Agriculture and Innovation and the Dutch Ministry of Health,
Welfare and Sport, the Wilhelmina Children’s Hospital Fund, ZonMW (grant
91209010) and NWO-VENI (grant 91610121).
Availability of data and materials
The datasets generated during and/or analyzed during the current study are
not publicly available due to privacy reasons.
LAMS, GJB, TJH, DB and DH conceived and designed the study. LAMS, GJB,
WAAdSP, TJH, DB and DH wrote the manuscript. LAMS provided the farm
exposure data. GJB and TJH were responsible for the spatial kernel analysis.
DB provided the microbiota data. WAAdSP and DB analyzed the microbiota
data. EGWH provided the hospitalized patients’ data. JY provided the general
practioners’ data. All authors contributed to interpretation of the results, and
critically reviewed the manuscript. All authors read and approved the final
Ethics approval and consent to participate
All experimental protocols were approved by the Medical Ethical Committee
of the University Medical Centre Utrecht (NL45307.041.13, GP-registered
pneumonia; as part of the Farming and Neighbouring Residents’ Health
Study), and by the Medical Ethical Committee of the St. Elisabeth Hospital,
Tilburg (hospitalized CAP patients). The need to obtain informed consent
from individual GP patients was waived. Written informed consent was
obtained from all hospitalized CAP patients.
1. Torres A , Peetermans WE , Viegi G , Blasi F. Risk factors for communityacquired pneumonia in adults in Europe: a literature review . Thorax . 2013 ; 68 : 1057 - 65 .
2. GBD 2013 Mortality and Causes of Death Collaborators . Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013 . Lancet. 2015 ; 385 : 117 - 71 .
3. Jain S , Self WH , Wunderink RG , Fakhran S , Balk R , Bramley AM , Reed C , Grijalva CG , Anderson EJ , Courtney DM , Chappell JD , Qi C , Hart EM , Carroll F , Trabue C , Donnelly HK , Williams DJ , Zhu Y , Arnold SR , Ampofo K , Waterer GW , Levine M , Lindstrom S , Winchell JM , Katz JM , Erdman D , Schneider E , Hicks LA , McCullers JA , Pavia AT , Edwards KM , Finelli L , CDC EPIC Study Team. Community-Acquired Pneumonia Requiring Hospitalization among U. S. Adults . N Engl J Med . 2015 ; 373 : 415 - 27 .
4. Neupane B , Jerrett M , Burnett RT , Marrie T , Arain A , Loeb M. Long-term exposure to ambient air pollution and risk of hospitalization with community-acquired pneumonia in older adults . Am J Respir Crit Care Med . 2010 ; 181 : 47 - 53 .
5. Gordon SB , Bruce NG , Grigg J , Hibberd PL , Kurmi OP , Lam KB , Mortimer K , Asante KP , Balakrishnan K , Balmes J , Bar-Zeev N , Bates MN , Breysse PN , Buist S , Chen Z , Havens D , Jack D , Jindal S , Kan H , Mehta S , Moschovis P , Naeher L , Patel A , Perez-Padilla R , Pope D , Rylance J , Semple S , Martin II WJ . Respiratory risks from household air pollution in low and middle income countries . Lancet Respir Med . 2014 ; 2 : 823 - 60 .
6. Almirall J , Bolibar I , Serra-Prat M , Roig J , Hospital I , Carandell E , Agusti M , Ayuso P , Estela A , Torres A. Community-Acquired Pneumonia in Catalan Countries (PACAP) Study Group . New evidence of risk factors for community-acquired pneumonia: a population-based study . Eur Respir J . 2008 ; 31 : 1274 - 84 .
7. MacIntyre EA , Gehring U , Molter A , Fuertes E , Klumper C , Kramer U , Quass U , Hoffmann B , Gascon M , Brunekreef B , Koppelman GH , Beelen R , Hoek G , Birk M , de Jongste JC , Smit HA , Cyrys J , Gruzieva O , Korek M , Bergstrom A , Agius RM , de Vocht F , Simpson A , Porta D , Forastiere F , Badaloni C , Cesaroni G , Esplugues A , Fernandez-Somoano A , Lerxundi A , Sunyer J , Cirach M , Nieuwenhuijsen MJ , Pershagen G , Heinrich J. Air pollution and respiratory infections during early childhood: an analysis of 10 European birth cohorts within the ESCAPE Project . Environ Health Perspect . 2014 ; 122 : 107 - 13 .
8. Smit LA , van der Sman-de Beer F , Opstal-van Winden AW , Hooiveld M , Beekhuizen J , Wouters IM , Yzermans J , Heederik D. Q Fever and pneumonia in an area with a high livestock density: a large population-based study . PLoS One . 2012 ; 7 : e38843 .
9. Huijskens EGW , Smit LAM , Rossen JWA , Heederik D , Koopmans M. Evaluation of patients with community-acquired pneumonia caused by zoonotic pathogens in an area with a high density of animal farms . Zoonoses Public Health . 2016 ; 63 : 160 - 6 .
10. Brunekreef B , Harrison RM , Kunzli N , Querol X , Sutton MA , Heederik DJ , Sigsgaard T. Reducing the health effect of particles from agriculture . Lancet Respir Med . 2015 ; 3 : 831 - 2 .
11. Lelieveld J , Evans JS , Fnais M , Giannadaki D , Pozzer A. The contribution of outdoor air pollution sources to premature mortality on a global scale . Nature . 2015 ; 525 : 367 - 71 .
12. Cambra-Lopez M , Aarnink AJ , Zhao Y , Calvet S , Torres AG. Airborne particulate matter from livestock production systems: a review of an air pollution problem . Environ Pollut . 2010 ; 158 : 1 - 17 .
13. Jonges M , van Leuken J , Wouters I , Koch G , Meijer A , Koopmans M. WindMediated Spread of Low-Pathogenic Avian Influenza Virus into the Environment during Outbreaks at Commercial Poultry Farms . PLoS One . 2015 ; 10 :e0125401.
14. Seedorf J , Hartung J , Schröder M , Linkert KH , Phillips VR , Holden MR , Sneath RW , Short JL , White RP , Pedersen S , Takai H , Johnsen JO , Metz JHM , Groot Koerkamp PWG , Uenk GH , Wathes CM . Concentrations and Emissions of Airborne Endotoxins and Microorganisms in Livestock Buildings in Northern Europe . J Agric Eng Res . 1998 ; 70 : 97 - 109 .
15. Schulze A , van Strien R , Ehrenstein V , Schierl R , Kuchenhoff H , Radon K. Ambient endotoxin level in an area with intensive livestock production . Ann Agric Environ Med . 2006 ; 13 : 87 - 91 .
16. Schenker MB , Christiani D , Cormier Y , Dimich-Ward H , Doekes G , Dosman J , Douwes J , Dowling K , Enarson D , Green F , Heederik D , Husman K , Kennedy S , Kullman G , Lacasse Y , Lawson B , Malmberg P , May J , McCurdy S , Merchant J , Myers J , Nieuwenhuijsen M , Olenchock S , Saiki C , Schwartz D , Seiber J , Thorne P , Wagner G , White N , Xu XP , Chan-Yeung M. Respiratory health hazards in agriculture . Am J Respir Crit Care Med . 1998 ; 158 : S1 - S76 .
17. Smit LA , Heederik D , Doekes G , Blom C , van Zweden I , Wouters IM . Exposure-response analysis of allergy and respiratory symptoms in endotoxin-exposed adults . Eur Respir J . 2008 ; 31 : 1241 - 8 .
18. Borlée F , Yzermans CJ , van Dijk CE , Heederik D , Smit LAM . Increased respiratory symptoms in COPD patients living in the vicinity of livestock farms . Eur Respir J . 2015 ; 46 : 1605 - 14 .
19. Sandstrom T , Bjermer L , Rylander R. Lipopolysaccharide ( LPS) inhalation in healthy subjects increases neutrophils, lymphocytes and fibronectin levels in bronchoalveolar lavage fluid . Eur Respir J . 1992 ; 5 : 992 - 6 .
20. de Steenhuijsen Piters WA , Sanders EA , Bogaert D. The role of the local microbial ecosystem in respiratory health and disease . Philos Trans R Soc Lond B Biol Sci . 2015 ; 370 (1675). doi:10.1098/rstb.2014.0294.
21. Dickson RP , Erb-Downward JR , Huffnagle GB . Towards an ecology of the lung: new conceptual models of pulmonary microbiology and pneumonia pathogenesis . Lancet Respir Med . 2014 ; 2 : 238 - 46 .
22. de Steenhuijsen Piters WA , Huijskens EG , Wyllie AL , Biesbroek G , van den Bergh MR , Veenhoven RH , Wang X , Trzcinski K , Bonten MJ , Rossen JW , Sanders EA , Bogaert D. Dysbiosis of upper respiratory tract microbiota in elderly pneumonia patients . ISME J . 2016 ; 10 : 97 - 108 .
23. Smit LAM , Hooiveld M , van der Sman-de Beer F , Opstal-van Winden AWJ , Beekhuizen J , Wouters IM , Yzermans CJ , Heederik D. Air pollution from livestock farms, and asthma, allergic rhinitis and COPD among neighbouring residents . Occup Environ Med . 2014 ; 71 : 134 - 40 .
24. van Dijk CE , Garcia-Aymerich J , Carsin AE , Smit LAM , Borlée F , Heederik DJ , Donker GA , Yzermans CJ , Zock JP . Risk of exacerbations in COPD and asthma patients living in the neighbourhood of livestock farms: observational study using longitudinal data . Int J Hyg Environ Health . 2016 ; 219 : 278 - 87 .
25. Huijskens EG , van Erkel AJ , Palmen FM , Buiting AG , Kluytmans JA , Rossen JW . Viral and bacterial aetiology of community-acquired pneumonia in adults . Influenza Other Respir Viruses . 2013 ; 7 : 567 - 73 .
26. Lindstrom T , Hakansson N , Wennergren U. The shape of the spatial kernel and its implications for biological invasions in patchy environments . Proc Biol Sci . 2011 ; 278 : 1564 - 71 .
27. Boender GJ , Hagenaars TJ , Bouma A , Nodelijk G , Elbers AR , de Jong MC , van Boven M. Risk maps for the spread of highly pathogenic avian influenza in poultry . PLoS Comput Biol . 2007 ; 3 : e71 .
28. Oksanen J , Blanchet FG , Friendly M , Kindt R , Legendre P , McGlinn D , Minchin PR , O'Hara RB , Simpson GL , Solymos P , Stevens MHH , Szoecs E , Wagner H. vegan: Community Ecology Package . R package version 2.4-1 . https://CRAN.R-project.org/package=vegan. 2016 .
29. Benjamini Y , Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing . J R Statist Soc B. 1995 ; 57 : 289 - 300 .
30. Letunic I , Bork P. Interactive Tree Of Life v2: online annotation and display of phylogenetic trees made easy . Nucleic Acids Res . 2011 ; 39 : W475 - 8 .
31. Lee A , Kinney P , Chillrud S , Jack D. A Systematic Review of Innate Immunomodulatory Effects of Household Air Pollution Secondary to the Burning of Biomass Fuels . Ann Glob Health . 2015 ; 81 : 368 - 74 .
32. Poroyko V , Meng F , Meliton A , Afonyushkin T , Ulanov A , Semenyuk E , Latif O , Tesic V , Birukova AA , Birukov KG . Alterations of lung microbiota in a mouse model of LPS-induced lung injury . Am J Physiol Lung Cell Mol Physiol . 2015 ; 309 : L76 - 83 .
33. Rylance J , Fullerton DG , Scriven J , Aljurayyan AN , Mzinza D , Barrett S , Wright AK , Wootton DG , Glennie SJ , Baple K , Knott A , Mortimer K , Russell DG , Heyderman RS , Gordon SB . Household air pollution causes dose-dependent inflammation and altered phagocytosis in human macrophages . Am J Respir Cell Mol Biol . 2015 ; 52 : 584 - 93 .
34. Mushtaq N , Ezzati M , Hall L , Dickson I , Kirwan M , Png KM , Mudway IS , Grigg J. Adhesion of Streptococcus pneumoniae to human airway epithelial cells exposed to urban particulate matter . J Allergy Clin Immunol . 2011 ; 127 : 1236 - 42 .
35. Grigg J , Walters H , Sohal SS , Wood-Baker R , Reid DW , Xu CB , Edvinsson L , Morissette MC , Stampfli MR , Kirwan M , Koh L , Suri R , Mushtaq N. Cigarette smoke and platelet-activating factor receptor dependent adhesion of Streptococcus pneumoniae to lower airway cells . Thorax . 2012 ; 67 : 908 - 13 .
36. Zelikoff JT , Chen LC , Cohen MD , Fang K , Gordon T , Li Y , Nadziejko C , Schlesinger RB . Effects of inhaled ambient particulate matter on pulmonary antimicrobial immune defense . Inhal Toxicol . 2003 ; 15 : 131 - 50 .
37. Charlson ES , Chen J , Custers-Allen R , Bittinger K , Li H , Sinha R , Hwang J , Bushman FD , Collman RG . Disordered microbial communities in the upper respiratory tract of cigarette smokers . PLoS One . 2010 ; 5 : e15216 .
38. Morris A , Beck JM , Schloss PD , Campbell TB , Crothers K , Curtis JL , Flores SC , Fontenot AP , Ghedin E , Huang L , Jablonski K , Kleerup E , Lynch SV , Sodergren E , Twigg H , Young VB , Bassis CM , Venkataraman A , Schmidt TM , Weinstock GM . Lung HIV Microbiome Project. Comparison of the respiratory microbiome in healthy nonsmokers and smokers . Am J Respir Crit Care Med . 2013 ; 187 : 1067 - 75 .
39. Hanski I , von Hertzen L , Fyhrquist N , Koskinen K , Torppa K , Laatikainen T , Karisola P , Auvinen P , Paulin L , Makela MJ , Vartiainen E , Kosunen TU , Alenius H , Haahtela T. Environmental biodiversity, human microbiota, and allergy are interrelated . Proc Natl Acad Sci U S A . 2012 ; 109 : 8334 - 9 .
40. Bos ME , Verstappen KM , van Cleef BA , Dohmen W , Dorado-Garcia A , Graveland H , Duim B , Wagenaar JA , Kluytmans JA , Heederik DJ . Transmission through air as a possible route of exposure for MRSA . J Expo Sci Environ Epidemiol . 2016 ; 26 : 263 - 9 .
41. Dickson RP , Martinez FJ , Huffnagle GB . The role of the microbiome in exacerbations of chronic lung diseases . Lancet . 2014 ; 384 : 691 - 702 .