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Livestock-associated risk factors for pneumonia in an area of intensive animal farming in the Netherlands

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Previous research conducted in 2009 found a significant positive association between pneumonia in humans and living close to goat and poultry farms. However, as this result might have been affected by a large goat-related Q fever epidemic, the aim of the current study was to re-evaluate this association, now that the Q-fever epidemic had ended. In 2014/15, 2,494 adults (aged 20–72 years) living in a livestock-dense area in the Netherlands participated in a medical examination and completed a questionnaire on respiratory health, lifestyle and other items. We retrieved additional information for 2,426/2,494 (97%) participants from electronic medical records (EMR) from general practitioners. The outcome was self-reported, physician-diagnosed pneumonia or pneumonia recorded in the EMR in the previous three years. Livestock license data was used to determine exposure to livestock. We quantified associations between livestock exposures and pneumonia using odds ratios adjusted for participant characteristics and comorbidities (aOR). The three-year cumulative frequency of pneumonia was 186/2,426 (7.7%). Residents within 2,000m of a farm with at least 50 goats had an increased risk of pneumonia, which increased the closer they lived to the farm (2,000m aOR 1.9, 95% CI 1.4–2.6; 500m aOR 4.4, 95% CI 2.0–9.8). We found no significant associations between exposure to other farm animals and pneumonia. However, when conducting sensitivity analyses using pneumonia outcome based on EMR only, we found a weak but statistically significant association with presence of a poultry farm within 1,000m (aOR: 1.7, 95% CI 1.1–2.7). Living close to goat and poultry farms still constitute risk factors for pneumonia. Individuals with pneumonia were not more often seropositive for Coxiella burnetii, indicating that results are not explained by Q fever. We strongly recommend identification of pneumonia causes by the use of molecular diagnostics and investigating the role of non-infectious agents such as particulate matter or endotoxins.
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RESEARCH ARTICLE
Livestock-associated risk factors for
pneumonia in an area of intensive animal
farming in the Netherlands
Gudrun S. Freidl
1,2
*, Ineke T. Spruijt
1
, Floor Borle
´e
3,4
, Lidwien A. M. Smit
3
, Arianne B. van
Gageldonk-Lafeber
1
, Dick J. J. Heederik
3
, Joris Yzermans
4
, Christel E. van Dijk
4
,
Catharina B. M. Maassen
1
, Wim van der Hoek
1
1Centre for Infectious Diseases, Epidemiology and Surveillance, Centre for Infectious Disease Control,
National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands, 2European
Programme for Intervention Epidemiology Training (EPIET), European Centre for Disease Prevention and
Control (ECDC), Stockholm, Sweden, 3Institute for Risk Assessment Sciences (IRAS), Division of
Environmental Epidemiology, Utrecht University, Utrecht, the Netherlands, 4Netherlands Institute for Health
Services Research (NIVEL), Utrecht, Netherlands
*gudrun.freidl@rivm.nl
Abstract
Previous research conducted in 2009 found a significant positive association between pneu-
monia in humans and living close to goat and poultry farms. However, as this result might
have been affected by a large goat-related Q fever epidemic, the aim of the current study
was to re-evaluate this association, now that the Q-fever epidemic had ended. In 2014/15,
2,494 adults (aged 20–72 years) living in a livestock-dense area in the Netherlands partici-
pated in a medical examination and completed a questionnaire on respiratory health, life-
style and other items. We retrieved additional information for 2,426/2,494 (97%) participants
from electronic medical records (EMR) from general practitioners. The outcome was self-
reported, physician-diagnosed pneumonia or pneumonia recorded in the EMR in the previ-
ous three years. Livestock license data was used to determine exposure to livestock. We
quantified associations between livestock exposures and pneumonia using odds ratios
adjusted for participant characteristics and comorbidities (aOR). The three-year cumulative
frequency of pneumonia was 186/2,426 (7.7%). Residents within 2,000m of a farm with at
least 50 goats had an increased risk of pneumonia, which increased the closer they lived to
the farm (2,000m aOR 1.9, 95% CI 1.4–2.6; 500m aOR 4.4, 95% CI 2.0–9.8). We found no
significant associations between exposure to other farm animals and pneumonia. However,
when conducting sensitivity analyses using pneumonia outcome based on EMR only, we
found a weak but statistically significant association with presence of a poultry farm within
1,000m (aOR: 1.7, 95% CI 1.1–2.7). Living close to goat and poultry farms still constitute
risk factors for pneumonia. Individuals with pneumonia were not more often seropositive for
Coxiella burnetii, indicating that results are not explained by Q fever. We strongly recom-
mend identification of pneumonia causes by the use of molecular diagnostics and investigat-
ing the role of non-infectious agents such as particulate matter or endotoxins.
PLOS ONE | https://doi.org/10.1371/journal.pone.0174796 March 31, 2017 1 / 16
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OPEN ACCESS
Citation: Freidl GS, Spruijt IT, Borle
´e F, Smit LAM,
van Gageldonk-Lafeber AB, Heederik DJJ, et al.
(2017) Livestock-associated risk factors for
pneumonia in an area of intensive animal farming
in the Netherlands. PLoS ONE 12(3): e0174796.
https://doi.org/10.1371/journal.pone.0174796
Editor: Christophe Leroyer, Universite de Bretagne
Occidentale, FRANCE
Received: December 1, 2016
Accepted: March 15, 2017
Published: March 31, 2017
Copyright: ©2017 Freidl et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: In consultation with
the Medical Ethical Committee that approved the
study protocol, data from the VGO study are not
publicly available due to privacy protection of
participants. The study’s privacy regulations stated
that only researchers from NIVEL, IRAS, and RIVM
(consortium partners) have access to the study
database. Sharing an anonymized and de-identified
dataset is not possible as it would still contain
Electronical Medical Records and the personal data
of participants, which could potentially lead to the
identification of subjects. Researchers may reach a
Introduction
In the Netherlands, the number of intensive livestock farms doubled within the first decade of
the 21
st
century [1]. Although the total number of farms has decreased over the past decades,
the number of farm animals has increased [2]. This trend has raised concern about potential
negative health effects on residents living close to intensive livestock farms. The debate
between civil groups opposed to intensive livestock farming, the farming sector and policy
makers was further fueled by the recent Q fever epidemic that occurred between 2007 and
2009 in the southern part of the Netherlands [3]. Caused by the zoonotic bacterium Coxiella
burnetii, this epidemic resulted in more than 4000 notified human cases, mostly presenting as
pneumonia [4]. Aborting dairy goats and dairy sheep were found to be the main cause of infec-
tion in humans, who were infected through inhalation of contaminated dust or aerosols dis-
tributed via the airborne route. Human cases started to decrease in 2010 in the Netherlands,
coinciding with the introduction of veterinary interventions comprising of culling of pregnant
goats and sheep on Q fever positive farms and vaccination of dairy goats and dairy sheep [3,
5]. Since the start of this vaccination campaign in early 2009, farms with at least 50 sheep or
goats are obliged to vaccinate, which is strictly reinforced [6].
The health risks for residents living in the vicinity of livestock farms in the Netherlands
were first addressed within the “Intensive Animal Husbandry and Health” study [7]. Within
this project Smit et al. [8] studied the relationship between living in the vicinity of livestock-
farms and Q fever or pneumonia diagnoses in 70,142 adults. Outcomes were retrieved from
electronic medical records from general practitioners (GP) located in an area with Q fever
positive farms during 2009. This study found that a high number of goats within 5km of the
home address [quartile (Q) 4: 17,191–20,969 versus Q1: 0–2,250 goats] was indeed associated
with Q fever [Adjusted Odds ratio (aOR) 12.03, 95% confidence interval (CI) 8.79–16.46] and
pneumonia (aOR 1.86, 95% CI 1.28–2.21). Presence of poultry farms within 1km of the home
address was also identified as a risk factor for pneumonia among adults (OR 1.25, 95% CI
1.06–1.47), which was hypothesized to be potentially linked to exposure to pathogens or air
pollutants. Potential exposure to other pathogens, such as influenza viruses, and higher suscep-
tibility to community-acquired pneumonia due to exposure to high levels of fine dust and
endotoxins emitted by poultry farms were considered likely explanations of this finding [8,9].
Residual confounding by goat exposure could not be fully ruled out in explaining the high inci-
dence of pneumonia close to poultry farms.
Therefore, the aim of the current study was to re-investigate previously found associations
between pneumonia and goat/ poultry exposure (as well as other types of animals) in the same
study area in a period in which the Q-fever incidence among humans has dropped to pre-epi-
demic levels.
Methods
Study population and study design
As part of the “Livestock Farming and Neighbouring Residents’ Health” (VGO) project, a pop-
ulation-based cross-sectional study was conducted to investigate the relationship between
adverse health effects in humans living close to livestock farms. A detailed description of the
study design can be found in a project report [10].
In short, a questionnaire study was conducted among 14,882 adults living in the east of
North-Brabant and the north of Limburg, an area characterized by a high density of livestock
farms [11].
Livestock-associated risk factors for pneumonia in the Netherlands
PLOS ONE | https://doi.org/10.1371/journal.pone.0174796 March 31, 2017 2 / 16
privacy agreement to access the data by contacting
Prof. Dr. Dick Heederik (d.heederik@uu.nl) or Dr. L.
A.M. Smit (l.a.smit@uu.nl).
Funding: The Livestock Farming and Neighbouring
Residents’ Health (VGO) study was funded by the
Ministry of Health, Welfare and Sports and the
Ministry of Economic Affairs of The Netherlands,
and supported by a grant from the Lung
Foundation Netherlands (Grant number:
3.2.11.022). The funders had no role in study
design, data collection and analysis, decision to
publish, or preparation of the manuscript.
Competing interests: The authors have declared
that no competing interests exist.
In total, 9220 (62%) gave consent to be contacted for further studies, of which 7180 (72%)
met the following inclusion criteria and were invited to participate: (i) not working or living
on a farm, and (ii) living within a 10 km radius of one of the 12 temporary research centers. Of
these, 2494 (34.7%) participated in the medical examination which was conducted between
March 2014 and February 2015 and included, among others, filling in an extended question-
naire comprising questions on demographics, respiratory health and lifestyle and providing a
serum sample. We also investigated a possible association between having children in the
household and pneumonia, as was shown in previous research [12,13].
Electronic medical record (EMR) data were used, if (i) GPs registered according to certain
quality criteria [8,11] and (ii) if participants granted access to their EMR. EMR data were
available through the NIVEL Primary Care Database of the Netherlands Institute for Health
Sciences Research. Of the 2,494 participants, we excluded 68 individuals from the analysis. Of
these, 66 did not provide consent to access their EMR data, whereas for two other individuals
EMR data were unavailable. The final study population amounted to 2,426 individuals.
Ethical aspects
The VGO study protocol was approved by the Medical Ethical Committee of the University
Medical Centre Utrecht (protocol number 13/533). All 2,494 subjects signed informed con-
sent. Patients’ privacy was ensured by keeping medical information and address records sepa-
rated at all times by using a Trusted Third Party.
Data availability statement
In consultation with the Medical Ethical Committee that approved the study protocol, data
from the VGO study are not publicly available due to privacy protection of participants. The
study’s privacy regulations stated that only researchers from NIVEL, IRAS, and RIVM (con-
sortium partners) have access to the study database. Sharing an anonymized and de-identified
dataset is not possible as it would still contain Electronical Medical Records and the personal
data of participants, which could potentially lead to the identification of subjects. Researchers
may reach a privacy agreement to access the data by contacting Prof. Dr. Dick Heederik (d.
heederik@uu.nl) or Dr. L.A.M. Smit (l.a.smit@uu.nl).
Data collection
Questionnaire. Study participants provided information on personal characteristics and
lifestyle factors through the questionnaire, including age, gender, smoking habits, and educa-
tion level. Body mass index (BMI) was calculated based on weight and height measured during
the medical examination. In addition, information on respiratory diseases (i.e. pneumonia,
COPD and asthma) and related risk factors and determinants, e.g. growing up on a farm or in
the study area, keeping goats or poultry as a hobby, or reception of yearly influenza vaccination
was collected.
Pneumonia outcome. ‘Having had pneumonia in the three years preceding the medical
examination’ (i.e. between 2012 and 2014/15) was defined as the outcome variable. We defined
the outcome using two sources of information: (i) self-reported, physician-diagnosed pneumo-
nia over the past three years as reported in the questionnaire, or (ii) having had at least one
pneumonia episode recorded in the electronic medical record (EMR) during the three years
preceding the medical examination (Fig 1). The benefit of combining data sources in order
not to miss cases was described previously for COPD and asthma [14]. By asking for physi-
cian-diagnosed pneumonia in case of self-reporting we aimed to avoid misclassification bias.
Information on pneumonia episodes was extracted from EMR using International
Livestock-associated risk factors for pneumonia in the Netherlands
PLOS ONE | https://doi.org/10.1371/journal.pone.0174796 March 31, 2017 3 / 16
Pneumonia confirmed
by GP or specialist?
(based on questionnaire)
Pneumonia episode confirmed in EMR during last
three years(ICPC-code R81) (n=2301)
Study po pulati on
(n=2494)
Ye s
(n = 159)
Self-reported pneumonia over
the past three years
(based on questionnaire)
Pneumonia cases
N=186
Ye s
(n =127)
No
(n =29)
Missing
(n =3)
No
(n = 2246)
Missing
(n = 23)
Ye s
(n = 59)
No
(n = 2240)
Controls
N=2240
Missing
(n=2)
Excluded (n=66)
[no consent to access EMR]
Eligible study pop ula tion
(consent for EMR data)
(n=2428)
Fig 1. Construction of outcome variable ‘Having had pneumonia in the past three years’ based on information from questionnaires and
electronic medical records (EMR).
https://doi.org/10.1371/journal.pone.0174796.g001
Livestock-associated risk factors for pneumonia in the Netherlands
PLOS ONE | https://doi.org/10.1371/journal.pone.0174796 March 31, 2017 4 / 16
Classification of Primary Care (ICPC) code ‘R81’. To account for possible recall bias of partici-
pants, we also conducted sensitivity analysis using ‘Having had a pneumonia episode regis-
tered in the EMR in the three years preceding the medical examination’ as an outcome.
Risk factors extracted from electronic medical records. We identified co-morbidities
regarded as risk factor for pneumonia from the literature. Information on comorbidities of
participants was similarly extracted from EMRs using ICPC codes. Individual risk factors were
grouped into the following categories: chronic lung disease, chronic cardiovascular disease,
cerebrovascular disease, chronic liver disease, chronic nephropathy, malignancies, auto-
immune disease and neurological comorbidities (S2 Table).
Exposure to livestock farms. We obtained livestock data from provincial databases of
mandatory environmental licenses for keeping livestock for 2012 to determine exposure to
livestock [11]. Using a Geographic Information System (ArcGis), exposure to farms was quan-
tified for each study participant individually based on geocodes of home address and farms
(centroid of stable complex), as described previously [8]. The following variables were ana-
lyzed: (i) presence of livestock farms with a minimum number of animals (in increments of
500m) around the residence, (ii) distance between residence and poultry and goat farms
expressed in quartiles, (iii) number of farms expressed in tertiles and (iv) number of animals
expressed in tertiles (cattle, goats, horses, pigs, poultry, sheep) within 1000m of the home. As
the distribution of number of goats within 1000m of their home address was highly skewed
(S1 Fig), the construction of tertiles for this variable was not possible. Therefore, we decided to
set the minimum number of goats to 50 in line with the threshold applied during the compul-
sory vaccination campaign during the Q-fever epidemic [6]. Farms were considered ‘Q-fever
positive’ if they had experienced Q-fever-induced abortion waves or if they tested positive in
bulk tank milk monitoring (data from GD Animal Health and the Food and Consumer Prod-
uct Safety Authority).
Serology for Q fever. Serological analyses were performed using a commercial enzyme-
linked immunosorbent assay (Serion ELISA classic, Virion/Serion, Wu¨rzburg, Germany) to
test for IgG to C. burnetii phase II antigen. A titer of <20 IU/ml was considered negative,
between 20 to 30 IU/ml borderline, and >30 IU/ml was classified seropositive. More details on
the serological findings are described elsewhere [10].
Non-response analyses. To assess whether self-selection was present in this study, we
conducted non-response analyses on different subpopulations. We first compared characteris-
tics of individuals who were invited and responded (responders) or did not respond to the
short questionnaire (non-responders), as well as individuals who were invited for the medical
examination and participated (responders) or did not participate (non-responders).
Statistical analysis
We used univariate logistic regression analysis (Wald Chi Square test statistic) to explore asso-
ciations between risk factors related to lifestyle and livestock exposure and the occurrence of
pneumonia, respectively. To adjust p-values retrieved from the univariate analyses for multiple
testing, we used the Benjamini-Hochberg correction with a false discovery rate set to 10% [15].
We decided a priori to include age and gender in the multiple logistic regression models. Addi-
tional determinants with a p-value of less than 0.15 were also included in multivariable analy-
ses investigating associations between livestock exposure and pneumonia. To retrieve the most
accurate estimates for the odds ratio, multiple logistic regression models were used with three
incremental sets of risk factors included in addition to livestock exposure variables: model
A (age and gender), model B (age, gender, smoking, education level and BMI) and model C
(age, gender, smoking, education level, BMI, chronic lung diseases and other comorbidities).
Livestock-associated risk factors for pneumonia in the Netherlands
PLOS ONE | https://doi.org/10.1371/journal.pone.0174796 March 31, 2017 5 / 16
Univariate and multiple logistic regression analyses were conducted in SAS 9.4 (SAS Institute
Inc., Cary, NC, USA). For the multiple logistic regression models, co-morbidities described
above–with exception of chronic lung disease–were combined in one dichotomous variable
expressed as ‘Having had at least one registered episode of any listed comorbidity in the
three years preceding the medical examination’. As ORs retrieved from the three adjusted
models did not substantially differ in magnitude, this article focuses on results of the most par-
simonious model, model A. Results of model B and C are shown in S3 Table. To rule out that
associations between goat exposure and pneumonia were not due to Q fever, we analyzed asso-
ciations between seropositivity against C.burnetii and pneumonia by creating a dichotomous
variable in which seropositive and borderline results were grouped together.
To assess how many cases would be avoided if exposures were to be removed we used the
following formula to calculate the population attributable fraction (PAF):
PAF = P
e
[(adjusted OR -1)/ adjusted OR], where P
e
is the proportion of cases that is
exposed.
Results
Characteristics of study population
Among the 2,426 eligible study participants, we identified 186 pneumonia cases and 2,240 individ-
uals without a history of recent pneumonia (Fig 1). Among the twelve research centers where
medical examinations were conducted, location Heeswijk-Dinther had the highest percentage of
pneumonia cases (16.3%), whereas the percentage was lowest in location Horn (3.7%, Table 1).
Study participants were between 20 and 72 years old. Pneumonia cases were slightly older
than non-cases, with a median age of 61.8 [interquartile range (IQR) 54.9–66.9] versus 58.6
years (IQR 48.7–65.4), respectively (p-value<0.001). A comparison of other characteristics
between cases and non-cases is shown in Table 2. In addition to gender, age and smoking, a
low educational level, being under- and overweight, having chronic lung diseases and having
other comorbidities were identified as risk factors (Table 2). Compared to non-cases (30.9%),
pneumonia cases were more often vaccinated against influenza (46.2%) (Table 2). Influenza
vaccination was strongly correlated with increasing age (Chi squared test, p-value <0.001) and
was therefore excluded from multivariable analyses to avoid multicollinearity. Having grown
up on a farm or outside the study area, or keeping goats or poultry for a hobby over the past
five years did not constitute a risk factor (Table 2). Neither the presence, nor the number of
children in the household (age categories: <4, 4–17, >17 years) were associated with pneumo-
nia (data not shown). Adding ‘Living within 2000m of a Q-fever positive farm’ to the model
did not change the OR between presence of goat farm within 500 to 2000m meters and pneu-
monia substantially and was therefore omitted from the model [range: OR 4.4, 95% CI 2.0–9.9
(within 500m) to OR 2.0, 95% CI 1.4–2.8 (within 2000m)].
General findings regarding associations between livestock farm
exposure and pneumonia
The main study focus was on goat and poultry exposure, hence, results presented here are pri-
marily focused on these two species.
Overall, different goat farm exposures were consistently associated with pneumonia in the
univariate analysis (Table 3), whereas we found no significant associations for poultry or other
animals/ animal farms (Table 3). After adjusting for risk factors, the significant positive associ-
ations between goats/ goat farms and pneumonia remained (Table 3). Overall, adjusted odds
ratios (aOR) did not vary substantially between the three adjusted models (S3 Table).
Livestock-associated risk factors for pneumonia in the Netherlands
PLOS ONE | https://doi.org/10.1371/journal.pone.0174796 March 31, 2017 6 / 16
In multivariable logistic regression analyses, none of the other determinants adjusted for
remained an independent risk factor with exception of chronic lung diseases.
Association between seropositivity against Coxiella burnetii and pneumonia. For 2,358
of the 2,426 participants (97%), serological results for Coxiella burnetii were available. Of these,
146 were seropositive (6.2%). Among those seropositive, 13 (8.9%) also have had pneumonia
in the previous three years. However, no statistically significant association between having
had pneumonia and being seropositive for Coxiella burnetii (dichotomous variable) was found
(Table 4). Results did not change when using the three-category serostatus for C.burnetii (neg-
ative, borderline or positive; p-value>0.7).
Exposure to Q fever-positive farms. When investigating associations between goat farms
that tested positive for Q fever during the epidemic and pneumonia, we found that no Q fever-
positive farm was located within 500 or 1000m of the residence of a case. The number of pneumo-
nia cases that lived within 1500 or 2000m to Q fever-positive farms was low (Table 4). Neither liv-
ing within 1500m, nor 2000m of a Q fever positive farm was associated with pneumonia (Table 4).
Distance to farms with Q fever-positive bulk milk samples in 2010 was similar for cases and
non-cases (median distance 3777m, IQR: 2924–5611 versus 4023m, IQR 2860–6108; p-value
0.133). Median distance between residence and farms with Q fever-induced abortions was sig-
nificantly shorter for cases (median distance 7042m, IQR: 3233–12,550 versus 9760, IQR
3770–12,748; p-value 0.0013). However, distances to Q fever-positive farms were still substan-
tially larger compared to distances to farms with 50 goats, as described above.
Presence of farms within 500m increments to residence. The presence of at least one
goat farm (with a minimum of 50 goats) within 500m increments of the home address (rang-
ing from 500m to 2000m), was significantly positively associated with pneumonia, whereas
such associations were not, or not consistently, found for other types of livestock farms
(Table 3). Adjusted ORs for presence of goat farm increased in magnitude the closer to the res-
idence goat farms were located (Table 3); a trend analysis confirmed the observed dose-
response relationship (p-value <0.001). This was also observed when an adjustment was made
for the presence of other animal farms than goat farms.
Distance between farms and residence. Pneumonia cases lived significantly closer to
farms with 50 goats, compared to non-cases (median distance 2090m, IQR 1222–3291 versus
2501m, IQR 1663-3511m; p-value 0.0015). A shorter distance to goat farms with at least 50
Table 1. Three-year cumulative frequency of pneumonia cases (defined as having had at least one pneumonia episode during the three years pre-
ceding the medical examination) including 95% confidence intervals (CI), overall and per location of the research center where the medical exami-
nation took place.
Research center Total number of participants Number of pneumonia cases Percentage (95% CI)
Heeswijk-Dinther 369 60 16.3 (14.8–17.8)
Heusden 72 7 9.7 (8.5–10.9)
Deurne 129 11 8.5 (7.4–9.6)
Afferden 48 4 8.3 (7.2–9.4)
Boxtel 165 11 6.7 (5.7–7.7)
Asten 289 19 6.6 (5.6–7.6)
Stramproy 227 15 6.6 (5.6–7.6)
Bakel 305 18 5.9 (5.0–6.8)
Someren 169 10 5.9 (5.0–6.8)
Budel 186 10 5.4 (4.5–6.3)
St. Anthonis 386 18 4.7 (3.9–5.5)
Horn 81 3 3.7 (3.0–4.5)
Total 2,426 186 7.7 (6.6–8.8)
https://doi.org/10.1371/journal.pone.0174796.t001
Livestock-associated risk factors for pneumonia in the Netherlands
PLOS ONE | https://doi.org/10.1371/journal.pone.0174796 March 31, 2017 7 / 16
goats (Q4, ~100 to 1,600m) compared to a longer one (Q1, ~3,500 to 11,500m) was signifi-
cantly associated with pneumonia (Table 3).
Poultry farms with a minimum amount of 250 birds were located closer to cases than non-
cases (median distance 794m, IQR 560–1291 versus 936m, 647–1298; p-value 0.041). A shorter dis-
tance to poultry farms with at least 250 birds, was also positively associated with pneumonia com-
pared to a larger distance (Q4 vs. Q1), but associations were not statistically significant (Table 3).
Table 2. Characteristics of the study population (n = 2,426) and risk factors for pneumonia (186 pneumonia cases and 2,240 non-cases) based on
univariate logistic regression analyses. Significant associations are depicted in bold face.
Characteristic Pneumonia cases Non-cases Crude odds ratio p-value
a
n (%) n (%) (95% CI)
Age categories 49 34 (18.3) 573 (25.6) Ref 0.004
d
>49 & 59 36 (19.4) 588 (26.3) 1.0 (0.6–1.7)
>59 & 66 61 (32.8) 571 (25.5) 1.8 (1.2–2.8)
>66 55 (29.6) 508 (22.7) 1.8 (1.2–2.8)
Gender Male 74 (39.8) 1038 (46.3) Ref 0.086
e
Female 112 (60.2) 1202 (53.7) 1.3 (1.0–1.8)
Smoking habits Never smoked 65 (35.0) 959 (42.8) Ref 0.114
e
Ex-smoker 101 (54.3) 1076 (48.0) 1.4 (1.0–1.9)
Smoker 20 (10.6) 205 (9.2) 1.4 (0.9–2.4)
Education level High 41 (22.0 681 (30.4 Ref <0.001
d
Middle 76 (40.9 999 (44.6 1.3 (0.9–1.9
Low 69 (37.1 560 (25.0 2.1 (1.4–3.1
Body mass index (BMI) Normal 51 (27.4 725 (32.4 Ref 0.010
d
Obese 51 (27.4 446 (19.9 1.6 (1.1–2.4
Overweight 73 (39.3 1001 (44.7 1.0 (0.7–1.5
Underweight 11 (5.9 68 (3.0 2.3 (1.2–4.6
Kept goats as hobby over past 5 years (46 missing) No 173 (96.7 2141 (97.3 Ref 0.624
e
Yes 6 (3.6 60 (2.7) 1.2 (0.5–2.9)
Kept poultry as hobby over past 5 years (93 missing) No 149 (85.2 1848 (86.6 1.2 (0.7–1.7) 0.598
e
Yes 26 (14.9) 290 (13.4)
Childhood on farm (24 missing) No 112 (61.5 1472 (66.3 Ref 0.193
e
Yes 70 (38.5) 748 (33.7) 1.2 (0.9–1.7)
Grew up outside study area (26 missing) No 141 (77.5 1684 (75.9 Ref 0.638
e
Yes 41 (22.5) 534 (24.1) 0.9 (0.6–1.3)
Chronic lung diseases
b
No 138 (74.2 2051 (91.6 Ref <0.001
d
Yes 48 (25.8) 189 (8.4) 3.8 (2.6–5.4)
Comorbidities
c
No 136 (73.1 1854 (82.8 Ref 0.001
d
Yes 50 (26.9) 386 (17.2) 1.8 (1.3–2.5)
Reception of yearly influenza vaccination (19 missing) No 98 (53.9 1537 (69.1 Ref <0.001
Yes 84 (46.2) 688 (30.9) 1.9 (1.4–2.6)
a
Based on Wald chi square test statistic. Variables with a p-value lower than 0.15 were included in the multivariable models in addition to livestock exposure
variables
b
Variable consists of at least one episode of chronic bronchitis, COPD or asthma recorded in the electronic medical record during the three years preceding
the medical examination
c
Comorbidity ‘1’ means that participant had at least one (maximum 5) episodes of any hereafter mentioned comorbidity groups recorded in the electronic
medical record during the three years preceding the medical examination: chronic cardiovascular disease, cerebrovascular disease, chronic liver disease,
chronic nephropathy, malignancies, auto-immune diseases or neurological comorbidities.
d
Significant or
e
non-significant p-value when adjusting for multiple testing using the Benjamini-Hochberg correction with a false discovery rate of 10%
https://doi.org/10.1371/journal.pone.0174796.t002
Livestock-associated risk factors for pneumonia in the Netherlands
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Table 3. Exposure status and associations from univariate (crude OR) and multiple logistic regression analyses (adjusted OR) between pneumo-
nia and livestock exposure variables. The proportions of cases attributable to respective exposure is indicated by the population attributable fraction
(PAF).
Pneumonia cases (%) Non-cases (%) Crude OR Adjusted OR
a
PAF
9
n = 186 n = 2240 (95% CI) (95% CI) (%)
Number of farms (any type) within 1000m of residence
1
<6 36 (19.4) 596 (26.6) Ref Ref
6 and <11 88 (47.3) 842 (37.6) 1.7 (1.2–2.6) 1.8 (1.2–2.6) 20.4
11 (max 32) 62 (33.3) 802 (35.8) 1.3 (0.8–2.0) 1.35 (0.9–2.1)
Presence of any type of farm within a certain distance of residence
2
500m 125 (67.2) 1445 (64.5) 1.1 (0.8–1.6) 1.14 (0.8–1.6)
1000m 176 (94.6) 2151 (96.0) 0.7 (0.4–1.4) 0.73 (0.4–1.4)
Presence of farm with minimum amount of animals within 500m-intervals of residence
3
500m
4
Goat 11 (5.9) 31 (1.4) 4.5 (2.2–9.1) 4.4 (2.0–9.8) 4.6
Poultry 32 (17.2) 322 (14.4) 1.2 (0.8–1.8) 0.95 (0.6–1.5)
Pig 60 (32.3) 626 (27.9) 1.2 (1.0–1.7) 1.21 (0.8–1.8)
Cattle 100 (53.8) 1176 (52.5) 1.1 (0.8–1.4) 0.91 (0.7–1.3)
Horse 50 (26.9) 521 (23.3) 1.2 (0.9–1.7) 1.03 (0.7–1.5)
Sheep 12 (6.5) 169 (7.5) 0.9 (0.5–1.6) 0.91 (0.5–1.7)
1000m
5
Goat 35 (18.8) 229 (10.2) 2.0 (1.4–3.0) 2.0 (1.3–3.1) 9.5
Poultry 112 (60.2) 1226 (54.7) 1.3 (0.9–1.7) 1.10 (0.8–1.6)
Pig 152 (81.7) 1773 (79.2) 1.2 (0.8–1.7) 1.02 (0.7–1.6)
Cattle 174 (93.5) 2110 (94.2) 0.9 (0.5–1.7) 0.64 (0.3–1.3)
Horse 143 (76.9) 1599 (71.4) 1.3 (0.9–1.9) 1.28 (0.8–2.0)
Sheep 58 (31.2) 701 (31.3) 1.0 (0.7–1.4) 0.93 (0.7–1.3)
1500m
6
Goat 62 (33.3) 485 (21.7) 1.8 (1.3–2.5) 1.9 (1.4–2.7) 15.9
Poultry 156 (83.9) 1890 (84.4) 0.96 (0.6–1.5) 0.74 (0.5–1.2)
Pig 183 (98.4) 2172 (97.0) 1.91 (0.6–6.1) 1.65 (0.5–5.6)
Cattle 186 (100) 2233 (99.7) / /
Horse 172 (92.5) 2032 (90.7) 1.26 (0.7–2.2) 1.34 (0.7–2.6)
Sheep 102 (54.8) 1357 (60.6) 0.79 (0.6–1.1) 0.78 (0.6–1.1)
2000m
7
Goat 90 (48.4) 742 (33.1) 1.9 (1.4–2.6) 1.9 (1.4–2.6) 23.1
Poultry 175 (94.1) 2040 (91.1) 1.56 (0.8–2.9) 1.35 (0.7–2.7)
Pig 186 (100) 2228 (99.5) / /
Cattle 186 (100) 2240 (100) / /
Horse 180 (96.8) 2138 (95.4) 1.43 (0.6–3.3) 1.15 (0.5–2.9)
Sheep 145 (78) 1776 (79.3) 0.92 (0.6–1.3) 0.88 (0.6–1.3)
Distance (quartiles expressed in meters) between residence and closest farm with minimum number of animals
50 goats >3490 to 11477 39 (21.0) 568 (25.4) Ref Ref
>2478 to 3490 41 (22.0) 566 (25.3) 1.06 (0.7–1.7) 1.05 (0.7–1.7)
>1629 to 2478 41 (22.0) 565 (25.2) 1.06 (0.7–1.7) 1.02 (0.7–1.6)
99 to 1629 65 (34.9) 541 (24.2) 1.8 (1.2–2.7) 1.8 (1.2–2.7) 15.3
250 poultry >1296 to 4145 46 (24.7) 561 (25.0) Ref Ref
>923to 1296 33 (17.7) 573 (25.6) 0.70 (0.4–1.1) 0.69 (0.4–1.1)
>644 to 923 47(25.3) 557 (24.9) 1.03 (0.7–1.6) 1.03 (0.7–1.6)
39 to 644 60(32.2) 549 (24.5) 1.33 (0.9–2.0) 1.38 (0.9–2.1)
Number of animals within 1000m of the residence
8
Goats (no tertiles) 0 133 (71.5) 1857 (82.9) Ref Ref
>0 to 50 18 (9.7) 155 (6.9) 1.62 (0.97–2.72) 1.54 (0.88–2.69) 3.4
>50 35 (18.8) 228 (10.2) 2.14 (1.44–3.19) 1.67 (1.06–2.63) 7.5
(Continued)
Livestock-associated risk factors for pneumonia in the Netherlands
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Number of animals within 1000m of the residence. The presence of more than 50 goats
within 1000m of the residence was significantly associated with pneumonia (aOR 1.7, 95% CI
1.1–2.6), compared to having no goats close to the home (Table 3). Although number of poul-
try within 1000m of the residence was substantially higher, no association with pneumonia
was found (Table 3).
Population attributable fraction. The number of pneumonia cases exposed to goat farms
ranged from 11 (living within 500m) to 90 (living within 2000m of a goat farm; Table 3). The
population attributable fraction (PAF; based on model A), i.e. the fraction of cases that could
Table 3. (Continued)
Pneumonia cases (%) Non-cases (%) Crude OR Adjusted OR
a
PAF
9
n = 186 n = 2240 (95% CI) (95% CI) (%)
Poultry (tertiles) 0 55 (29.6) 794 (35.4) Ref Ref
>0 to 28250 64 (34.4) 689 (30.8) 1.34 (0.92–1.95) 1.12 (0.74–1.69)
>28250 67 (36.0) 757 (33.8) 1.28 (0.88–1.85) 1.09 (0.70–1.69)
a
Adjusted for age and gender
1
Any type of animal farm (main farming category, expressed in tertiles)
2
Main farm category (any type of animal farm) as registered in the livestock database
3
Minimum amount of animals: 50 goats, 250 poultry, 25 pigs, 5 cattle, 5 horses, 50 sheep
4
Also adjusted for presence of other farms within 500m with a minimum number of animals
5
Also adjusted for presence of other farms within 1000m with a minimum number of animals
6
Also adjusted for presence of other farms within 1500m with a minimum number of animals
7
Also adjusted for presence of other farms within 2000m with a minimum number of animals
8
Also adjusted for number of other animals within 1000m
9
Population attributable fraction based on model A
https://doi.org/10.1371/journal.pone.0174796.t003
Table 4. Characteristics of the study population for Q fever related exposures.
Characteristic Pneumonia cases Non-cases Crude odds ratio p-value
a
n (%) n (%) (95% CI)
Serostatus for Coxiella burnetii (Q-fever) (68
missing
Negative 165 (92.7) 2047
(93.9)
Ref >0.5
d
Positive & borderline
combined
13 (7.3) 133 (6.1) 1.2 (0.7–2.2)
Serostatus for Coxiella burnetii
b
Negative 165 (94.8) 2047
(96.1)
Ref >0.4
d
Only positive 9 (5.2) 84 (3.9) 1.3 (0.7–2.7)
Living within 1500m of Q-fever positive farm
c
No 177 (95.2) 2120
(94.6)
Ref >0.7
d
Yes 9 (4.8) 120 (5.4) 0.9 (0. 5–1.8)
Living within 2000m of Q-fever positive farm No 159 (85.5) 1996
(89.1)
Ref >0.1
d
Yes 27 (14.5) 244 (10.9) 1.4 (0.9–2.1)
a
Based on Wald chi square test statistic. Variables with a p-value lower than 0.15 were included in the multivariable models in addition to livestock exposure
variables
b
For the sensitivity analysis we excluded participants with borderline serological results.
c
No pneumonia cases lived within 500 or 1000m of a Q-fever positive goat farm (defined as testing positive for Coxiella burnetii in bulk milk or having had
abortion storms during the Q fever epidemic)
d
non-significant when adjusting for multiple testing using the Benjamini-Hochberg correction with a false discovery rate of 10%
https://doi.org/10.1371/journal.pone.0174796.t004
Livestock-associated risk factors for pneumonia in the Netherlands
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be avoided if the exposure to goat farms were to be removed, was low (4.6%) for those living
within 500m of a goat farm. For a 1000m to 2000m-perimeter, the PAF gradually increased
from 9.6% to 23.1%, respectively (Table 3).
Sensitivity analyses using EMR recorded pneumonia as outcome
variable
When repeating the analyses using pneumonia based on the EMR only as outcome, i.e. using
‘Having had a pneumonia episode registered in the EMR in the three years preceding the med-
ical examination’, the associations between goat farm exposure and pneumonia remained
robust and the magnitude was comparable to when the combined pneumonia outcome was
used (S4 Table). When using the EMR-based pneumonia variable, associations between pneu-
monia and presence of a poultry farm (with at least 250 birds) within 1000m of the residence
reached statistical significance (OR adjusted for age and sex: 1.7, 95% CI 1.1–2.7). For other
500m-intervals (500m, 1500m, 2000m), no statistically significant association with poultry
farm presence was found (S4 Table).
Non-response analyses
Participants who were older, female and lived closer to a livestock farm were more likely to fill
in the short questionnaire, as well as more likely to participate in the medical examination
(data not shown). Also, people with a goat farm present within 1000m of their home (irrespec-
tive of minimum number of goats) were more likely to participate in the medical examination.
However, in all subpopulations (total population, individuals who responded to the short
questionnaire, all individuals invited to the medical examination, individuals who took part in
the medical examination) [11], we found consistent associations between pneumonia (as regis-
tered in the EMR) and presence of a goat farm within 1000m of the home. ORs adjusted for
age and gender ranged from 2.1 (95% CI 1.6–2.8) for the total population to 2.4 (95% CI 1.3–
4.5) for those who participated in the medical examination.
Discussion
In this study in non-farming adults from the general population, we found that the presence of
goat farms near the home was strongly positively associated with pneumonia, with increasing
odds the closer the residence was located to the farm. Remarkably, the present study investi-
gated the occurrence of pneumonia between 2012 and 2015, whereas similar results were
found in a study conducted during a Q fever outbreak in 2009 [8]. A positive test for antibodies
against Coxiella burnetii was not associated with pneumonia. The magnitude of the association
between the presence of a poultry farm within 1000m and pneumonia was comparable to pre-
vious findings when using EMR-recorded pneumonia as an outcome. No associations between
other animal farms/ types and pneumonia was found.
The finding that goat farms still pose a risk factor for pneumonia in a period where the
Q-fever incidence among humans had dropped to pre-epidemic levels–i.e. 63 notifications
with onset of illness in 2012, 20 in 2013, and 26 in 2014 –was unexpected and we explored sev-
eral explanations for the association between pneumonia and proximity to goat farms. One
explanation is that people who lived closer to goat farms were more likely to participate in this
study compared to those living further away, which could possibly introduce selection bias.
However, as the non-response analyses resulted in comparable associations between presence
of goat farms and pneumonia in all three subpopulations, self-selection bias seems unlikely.
Bias in self-reported pneumonia is unlikely to be the explanation for the association as
they were also found with EMR-registered pneumonia. For data obtained through the
Livestock-associated risk factors for pneumonia in the Netherlands
PLOS ONE | https://doi.org/10.1371/journal.pone.0174796 March 31, 2017 11 / 16
questionnaire, we attempted to avoid misclassification bias–meaning that, for instance similar
clinical presentations, such as acute bronchitis/ bronchiolitis might falsely be classified as
pneumonia–by only including cases who reported pneumonia and who reported having had
their diagnosis confirmed by a physician. When comparing reports of self-reported, physician
diagnosed pneumonia with pneumonia records from the EMR, 121 (65%) participants had a
record of pneumonia in both sources. For 65 (35%) participants pneumonia was only recorded
in the questionnaire but not in the EMR. The data from the EMR represented data from a GP
primary care network, hence, we could not check whether the 35% that stated having had
pneumonia in the questionnaire were diagnosed by a specialist or in a hospital, which would
explain why no pneumonia was recorded in the EMR. In the Netherlands, about 80% of pneu-
monia cases are managed in primary care [16]. This would suggest that 15% of the pneumonia
cases in our study could be misclassified, which could overestimate odds ratios by introducing
differential misclassification bias, if cases who lived closer to goat farms falsely reported having
had pneumonia. Among the 65 cases who did not have an EMR entry for pneumonia in the
three years preceding the medical examination, 13 had an entry for acute bronchitis/bronchi-
olitis (20%), a condition that can present with similar symptoms as pneumonia [17].
A previous study conducted between April 2008 and March 2009 showed that cases with Q
fever pneumonia were more likely to live in a region with a high goat density or close to sheep
[18]. However, in the present study, we found no positive association between pneumonia and
being seropositive for Coxiella burnetii, or living within 500 or 1000m of a Q fever-positive
farm, respectively, which makes it unlikely that the associations presented here are primarily
attributable to this zoonosis. A limitation of our study was that no information was available
concerning causative pathogens or season in which pneumonia occurred, neither did we
conduct serological analyses for other pathogens. A previous study in a large hospital in the
province of North-Brabant, investigated which pathogens are most frequently detectable in
patients with community-acquired pneumonia (CAP) [19]. This prospective observational
study conducted between November 2007 and January 2010, coinciding with the Q fever epi-
demic, showed, that Streptococcus pneumoniae was most frequently identified bacterium
(22%), followed by Coxiella burnetii (14%), Mycoplasma pneumonia (6%) and Haemophilus
influenzae (6%). Although these results give interesting insights in the etiology of CAP, the
authors stressed that continuous microbiological surveillance in combination with clinical
symptoms is needed to be able to monitor seasonal variations and allow extrapolation to other
years [19]. More recently, another group studied CAP in four Dutch hospital cohorts covering
the periods 1998–2000 and 2004–2010, and found that atypical microorganisms, such as Legio-
nella species, Coxiella burnetii,Mycoplasma pneumoniae and Chlamydia species were predomi-
nantly detected during the non-respiratory season defined as week 20 to week 39 (40.4%), as
compared to the respiratory season (12.4%; week 40 to week 19) [20]. Awareness of the impor-
tance of seasonal patterns and including exposure to animals in medical history can help to
guide clinicians in targeted testing for atypical pathogens outside the respiratory season.
Whether atypical/ other zoonotic pathogens might also play a role in people living in a goat-
dense area presenting with CAP could be addressed in future studies.
Besides infectious causes, non-infectious causes, such as mold, inhalable dust and endotox-
ins, might also play a role in explaining the associations between animal farms and pneumonia.
Various experiments demonstrated that inhalation of fine dusts and endotoxins can increase
susceptibility for infection with common human pathogens [21,22] and that inhalation of
(urban) particulate matter can lead to pneumonia in humans, through increased adhesion of
S.pneumoniae to human airway epithelium [23]. Exposure to dust was also identified as a risk
factor for CAP among professionals exposed to different working conditions [24], however
measurements of such non-infectious exposures were not yet available in this study which
Livestock-associated risk factors for pneumonia in the Netherlands
PLOS ONE | https://doi.org/10.1371/journal.pone.0174796 March 31, 2017 12 / 16
constitutes a limitation. To analyze the relation between CAP and the composition of dust
emission from goat farms, air filter techniques, as described previously, could be utilized in
future research [25]. Dairy goats in the Netherlands are kept in deep litter stables with partly
open roofs or walls. The deep litter husbandry system is characterized by topping up soiled lit-
ter, such as straw or hay, with fresh litter such as straw or hay every few days. When the layer
of litter becomes too high, manure is removed and stored on a dunghill before being spread on
farmland as a fertilizer [26]. A small study investigating the contribution of different dust
sources to dust mass transmitted via air showed, that straw used for bedding contributed more
than 50% to fine and coarse dust emissions. However, compared to cow farms, goat farms
emit twelve times less total dust per kg metabolic weight [25], suggesting that dust emitted
from goat farms might not be the most evident explanation of our results.
Although dust and endotoxins might not contribute substantially to explaining the
observed association between goat farms and pneumonia, it might be relevant in explaining
part of the association between pneumonia and poultry farms (S3 and S4 Tables). A compre-
hensive study from Denmark studied emissions of inhalable dust and endotoxins between dif-
ferent farming types and seasons and found, that compared to other farming types, poultry
and pig farmers were exposed to the highest levels of fine dust and endotoxins [27]. That poul-
try farm emissions might also be relevant for the health of neighboring residents was shown in
a Dutch study, which found elevated endotoxin levels 250m downwind from poultry farms
[7].
Another interesting future research avenue is to investigate the indirect effects of non-infec-
tious agents on the risk of CAP by studying the composition of the human upper respiratory
tract microbiome in residents living close to goat farms. A recent study hypothesized that
exposure to farm emissions may result in changes of the composition of the upper airway
microbiome, which might lead to commensals, such as S.pneumoniae, to become pathogens.
An association was found between living close to poultry farms and CAP that possibly resulted
from alterations of the oropharyngeal microbiota composition. As this was the first study that
showed such an association, the researchers stressed that these findings need to be replicated
in larger studies [28].
The relatively small sample size constitutes a limitation of our study. In the study by Smit
et al. [8], the association between presence of a poultry farm within 1km of the home address
and pneumonia was relatively low (OR 1.25, 95% CI 1.06–1.47). It is conceivable that the cur-
rent study was underpowered to detect an association with poultry exposure when using the
combined pneumonia outcome, although results were close to statistical significance. When
using EMR-based pneumonia as outcome, a significant association with poultry exposure
within 1000m was found. Similarly, to above, zoonotic pathogens might play a role in those
presenting with CAP and living close to poultry. For instance, Chlamydia psittaci, a zoonotic
bacterium associated with poultry causes psittacosis in humans. In CAP-etiological studies,
this bacterium is often not considered or only incidentally isolated. However, a study from the
Netherlands identified 7/147 (4.8%) CAP patients with psittacosis [18,19,29], suggesting that
the role of C.psittaci is more important than often assumed.
The research center Heeswijk-Dinther had the highest prevalence of pneumonia (16.3%,
Table 1), which also coincided with the highest seroprevalence against Coxiella burnetii (10%;
average seroprevalence: 6%) [10]. We examined goat farm density per research center and
found that exposure to farms with 50 goats (defined as presence within 500m increments of
the residence) for individuals examined at Heeswijk-Dinther was highest compared to other
study centers. However, in the current study, no association between Q fever and pneumonia
was found (Table 4). As older age is as a risk factor for pneumonia, we examined whether a
higher percentage of older people lived in Heeswijk-Dinther compared to other study centers.
Livestock-associated risk factors for pneumonia in the Netherlands
PLOS ONE | https://doi.org/10.1371/journal.pone.0174796 March 31, 2017 13 / 16
However, although we found slight, nevertheless significant, differences between the centers
(Kruskall Wallis test, Chi
2
value 21.1, p-value = 0.03), the median score for age was generally
lower for Heeswijk-Dinther, compared to other study centers (data not shown), thereby dis-
missing this hypothesis. As there is currently no good explanation why the proportion of pneu-
monia cases was higher in Heeswijk-Dinther compared to other study centers, this question
could be addressed in future research.
In conclusion, in this study we showed that living in the vicinity of goat farms still consti-
tutes a risk factor for pneumonia. As it was surprising that the association between goat expo-
sure and pneumonia was still found in a period where Q fever in humans has only occurred
sporadically, however, Q fever itself seemed an unlikely explanation of the findings, future
research should be directed to studying the role of alternative infectious and non-infectious
causes to be able to assess possible implications for public health and provide evidence-based
recommendations. To shed light on the contribution of atypical pathogens other than Coxiella
burnetii [20]to the CAP burden near goat and poultry farms, molecular diagnostics for Legio-
nella species, Mycoplasma pneumoniae and Chlamydia species should be considered in CAP
patients from these regions. In addition, the role of the composition of the human upper air-
way microbiome [28] in people with CAP living close to goat farms should be investigated in
future research.
Supporting information
S1 Fig. Distribution of number of goats (x-axis) within 1000m of the residence. Due to the
highly skewed distribution, the construction of tertiles was not possible (minimum: 0, Q1: 0,
median: 0, Q3: 0, maximum: 5015). To analyse associations between pneumonia and number
of goats within 1000m, we therefore created a variable with three categories (0: 0 goats, 1: >0
and 50 goats, 2: >50 goats). The cut-off of 50 was chosen based on a threshold applied during
the compulsory vaccination campaign during the Q fever epidemic.
(TIF)
S1 Table. English translation of questions from the complete Dutch questionnaire used for
analyses described in this article. A full version of the Dutch questionnaire is appended.
(DOCX)
S2 Table. List of comorbidities regarded as a risk factor for pneumonia. A person was
counted as ‘Having had any comorbidity’, if at least one of the listed comorbidities was
recorded in the electronic medical record in the three years preceding the medical exam.
(DOCX)
S3 Table. Associations from univariate (crude OR) and multiple logistic regression analy-
ses between pneumonia and livestock exposure variables. For the multiple logistic regression
analysis, we ran three different models in which we added potential confounders consecutively
(Models A-C).
(DOCX)
S4 Table. Results from sensitivity analyses using ‘Having had a pneumonia episode regis-
tered in the EMR (ICPC code R81) in the three years preceding the medical exam’ (i.e.
between 2012 and 2014/15) as the outcome.
(DOCX)
S1 File. Full version of the original Dutch questionnaire.
(PDF)
Livestock-associated risk factors for pneumonia in the Netherlands
PLOS ONE | https://doi.org/10.1371/journal.pone.0174796 March 31, 2017 14 / 16
Acknowledgments
We would like to thank Christopher Williams and Mirjam Knol for critical review of the
manuscript.
Author Contributions
Conceptualization: DJJH WvdH JY LAMS CBMM.
Data curation: GSF ITS LAMS FB ABGL CEvD.
Formal analysis: GSF ITS FB.
Funding acquisition: WvdH DJJH JY CBMM.
Investigation: GSF ITS.
Methodology: GSF ITS FB LAMS.
Project administration: WvdH DJJH JY CBMM.
Writing – original draft: GSF.
Writing – review & editing: GSF ITS FB LAMS ABGL DJJH JY CEvD CBMM WvdH.
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Livestock-associated risk factors for pneumonia in the Netherlands
PLOS ONE | https://doi.org/10.1371/journal.pone.0174796 March 31, 2017 16 / 16
... Particularly effects on the health of residents living near (intensive) livestock farms have received considerable attention in recent years in the Netherlands after the emergence of antimicrobial resistant bacteria (MRSA and ESBLs) starting in 2005, the Q fever epidemic of 2007-2010, and SARS-CoV-2 outbreaks in mink farms in 2020 (Maassen et al., 2016;de Neeling et al., 2007;Oreshkova et al., 2020;Munnink et al., 2021;Overdevest et al., 2011). Several studies conducted after the Q-fever epidemic consistently showed increased risks for pneumonia in people living near goat farms and, to a lesser extent, poultry farms, although the exact causal mechanisms are still under investigation (Freidl et al., 2017;Kalkowska et al., 2018;Smit et al., 2012Smit et al., , 2017. These studies also showed that people living near livestock farms had poorer lung function and higher risk of COPD exacerbations, while, in contrast, a lower prevalence of asthma, allergies, and COPD was observed (Borlée et al., 2015;van Dijk et al., 2016). ...
... "low" (<median number of animals within a buffer) and "high" (≥median number of animals within a buffer) -see Table S2.1 for cut-off points (medians) of each buffer. The types of farms and buffer sizes were chosen based on results from the "Livestock Farming and Neighbouring Residents' Health" (VGO) project for which some evidence for associations to respiratory health was determined within the abovementioned buffer sizes around residences, namely slightly higher risk of pneumonia in people living near poultry and exacerbation of symptoms and/or more complications in COPD patients living near cattle, pigs, poultry and mink and reduced lung function in people living near livestock in general (Borlée et al., 2015;Freidl et al., 2017;Maassen et al., 2016). ...
... We applied a combination of increasingly adjusted models by adding potential confounders at individual and neighbourhood level and compared residents living within 500, 1000, 1500 and 2000 m from livestock farms ('exposed') to residents that did not have livestock farms within those distances from their residences ('unexposed' -referent group): All potential confounders were used as categorical variables (see Table 1 for classes). We also included goat and rabbit farms in all models in the same fashion as our selected types of farms (no animals, <median and > median number of animals) since living near rabbit and goat farms has been reported to be associated with adverse respiratory effects in the Netherlands (Freidl et al., 2017;Jonges et al., 2015;Klous et al., 2018;Maassen et al., 2016;Post et al., 2019;Smit et al., 2012). Nevertheless our exposure assessment for these types of farms presents important limitations that hamper the interpretation of their results. ...
Article
Background There is increasing evidence of associations between residential proximity to livestock farms and respiratory morbidity, but less is known about potential effects on respiratory mortality among residents. Objectives We aimed to assess potential associations between respiratory mortality and residential proximity to (intensive) livestock farming. Methods In DUELS, a national census-based cohort, we selected all inhabitants from rural and semi-urban areas of the Netherlands, aged ≥30 years and living at the same address for five years up to baseline (2004). We followed these ∼4 million individuals for respiratory mortality (respiratory system diseases, chronic lower respiratory diseases, pneumonia) from 2005 to 2012. We computed the average number of cattle, pigs, chicken, and mink present in 500 m, 1000 m, 1500 m and 2000 m of each individual’s residence in the period 1999–2003. Analyses were conducted using Cox proportional hazards regression, adjusting for potential confounders at individual and neighbourhood level. Results We found evidence that living up to 2000 m of pig farms was associated with respiratory mortality, namely from chronic lower respiratory diseases, with Hazard Ratios ranging from 1.06 (1.02, 1.10) in people living close to low numbers (<median number of animals) of pigs in 1000 m and 1.18 (1.13, 1.24) in those living near high numbers (≥median) of pigs in 2000 m. We also found indications of higher pneumonia mortality in people living near mink farms. Conclusion Our results are in line with previous findings of adverse respiratory effects in people living near livestock farms. Little is known about the physical, chemical, and biological exposures leading to respiratory morbidity and mortality warranting further explorations of air contaminants in the vicinity of livestock farms.
... Previous large epidemiological studies in the Netherlands showed an increased risk of community-acquired pneumonia (CAP) among adults living within a 1-2 km range of goat and poultry farms. [1][2][3][4][5] Based on data from electronic medical records, the association with general practitioner (GP) diagnosed CAP has been repeatedly present from 2009 to 2016 for goat farms. Although the association with poultry farms has also been found in the United States [6], in more recent studies in the Netherlands the associations with Open Access Pneumonia *Correspondence: inge.roof@rivm.nl 1 Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands Full list of author information is available at the end of the article Page 2 of 5 Roof et al. ...
... Pneumonia (2021) 13:13 poultry farms were not significant or positive in only a part of the analyses. [2,7] During 2014-2016, an estimated 6.0 to 7.8% of CAP cases in a study population in the south of the Netherlands were attributable to living in the vicinity of goat farms. [5] The association between CAP and goat farms led to a temporary stop on issuing building permits for new and existing goat farms in most Dutch provinces. ...
... The cut-off of two kilometer was selected based on the results of previous studies. [2,5] ...
Article
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Background In the Netherlands, an increased risk of community-acquired pneumonia (CAP) has been reported for adults living near goat and poultry farms. Previous results of respiratory microbiome studies in hospitalized CAP patients near poultry farms suggested a higher relative abundance of Streptococcus pneumoniae. This retrospective study, using routine laboratory data from hospitalized CAP patients, aims to explore possible aetiologic micro-organisms of CAP in relation to livestock exposure. Methods Patient characteristics and PCR and urinary antigen test results were retrieved retrospectively from electronic medical records of CAP patients admitted to the Jeroen Bosch Hospital or Gelre Hospital in the Netherlands during 2016–2017. Distances between the patients’ home address and the nearest poultry and goat farm were calculated. Differences in laboratory test results between CAP patients with and without goat or poultry farms within 2 km of their home address were analyzed using Fisher’s exact test. Results In total, 2230 CAP episodes with diagnostic results were included. In only 25% of the CAP episodes, a micro-organism was detected. A positive urinary antigen test for S. pneumoniae was found more often in patients living within two kilometers of goat (15.2% vs. 11.3%) and poultry farms (14.4% vs. 11.3%), however these differences were not statistically significant ( p = 0.1047 and p = 0.1376). Conclusion Our retrospective analysis did not show statistically significant differences in the identified micro-organisms in hospitalized CAP patients related to livestock farming. The study was hampered by limited statistical power and limited laboratory results. Therefore, the potential increased CAP risk around goat and poultry farms will be further explored in a prospective study among CAP patients in primary care.
... All cohort members underwent a medical examination in a field study that took place in 2014-2015. During the examination, blood samples were taken and participants were asked to fill in a baseline questionnaire (VGO questionnaire), including questions about demographics, health and lifestyle [19,22]. ...
... [25,26]. Farms with >50 goats were defined as goat farms, this cut-off was used because intervention steps were mandatory on farms with >50 goats during the outbreak [9,22]. Data concerning abortion-waves occurring on goat farms was provided by GD [27], data about C. burnetii positive bulk tank milk testing was available via the Dutch National Institute for Public Health and the Environment (RIVM), but originally collected by the Dutch food and consumer product safety authority [28]. ...
... Participants were considered C. burnetii antibody positive, if levels of IgG antibodies to C. burnetii phase II antigen were above 30 International Units/ml (I /ml) or between 20-30 I /ml ('borderline' positive). Levels below 20 I /ml were considered 'negative', according to the manufacturer's standards (Serion LIS classic, Virion/Serion, W r burg, Germany) [20,22]. ...
Article
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Background/aim From 2007 through 2010, the Netherlands experienced the largest recorded Q fever outbreak to date. People living closer to Coxiella burnetii infected goat farms were at increased risk for acute Q fever. Time spent outdoors near infected farms may have contributed to exposure to C. burnetii. The aim of this study was to retrospectively evaluate whether hours/week spent outdoors, in the vicinity of previously C. burnetii infected goat farms, was associated with presence of antibodies against C. burnetii in residents of a rural area in the Netherlands. Methods Between 2014-2015, we collected C. burnetii antibody serology and self-reported data about habitual hours/week spent outdoors near the home from 2494 adults. From a subgroup we collected 941 GPS tracks, enabling analyses of active mobility in the outbreak region. Participants were categorised as exposed if they spent time within specified distances (500m, 1000m, 2000m, or 4000m) of C. burnetii infected goat farms. We evaluated whether time spent near these farms was associated with positive C. burnetii serology using spline analyses and logistic regression. Results People that spent more hours/week outdoors near infected farms had a significantly increased risk for positive C. burnetii serology (time spent within 2000m of a C. burnetii abortion-wave positive farm, OR 3.6 (1.2-10.6)), compared to people spending less hours/week outdoors. Conclusions Outdoor exposure contributed to the risk of becoming C. burnetii serology positive. These associations were stronger if people spent more time near C. burnetii infected farms. Outdoor exposure should, if feasible, be included in outbreak investigations.
... Large epidemiological studies, mainly in the United States and western Europe, have shown associations between living in livestock-dense areas and adverse respiratory health effects, including increased respiratory symptoms and decreased lung function [7,8,[11][12][13]. In addition, an increased risk of community-acquired pneumonia (CAP) among adults living in close vicinity (1-2 km range) of poultry and goat farms has been observed [14][15][16][17][18][19][20]. The PM emitted from poultry farms has been proposed as a plausible explanation for the Antibiotics 2021, 10,1346 2 of 12 association between poultry farms and CAP. ...
... The PM emitted from poultry farms has been proposed as a plausible explanation for the Antibiotics 2021, 10,1346 2 of 12 association between poultry farms and CAP. However, not all prior studies in the Netherlands pertaining to poultry farms and CAP found consistent, significant results [14,21]. For goat farms, the association with CAP has been more consistent over a long period, from 2009 to 2016 [17,22,23]. ...
Article
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Prior regional studies found a high risk of pneumonia for people living close to poultry and goat farms. This epidemiological study in the Netherlands used nationwide antibiotic prescription data as a proxy for pneumonia incidence to investigate whether residents of areas with poultry and goat farms use relatively more antibiotics compared to areas without such farms. We used prescription data on antibiotics most commonly prescribed to treat pneumonia in adults and livestock farming data, both with nationwide coverage. Antibiotic use was expressed as defined daily doses per (4-digit Postal Code (PC4) area)-(age group)-(gender)-(month) combination for the year 2015. We assessed the associations between antibiotic use and farm exposure using negative binomial regression. The amoxicillin, doxycycline, and co-amoxiclav use was significantly higher (5–10% difference in use) in PC4 areas with poultry farms present compared to areas without, even after adjusting for age, gender, smoking, socio-economic status, and goat farm presence. The adjusted models showed no associations between antibiotic use and goat farm presence. The variables included in this study could only partly explain the observed regional differences in antibiotic use. This was an ecological study that precludes inference about causal relations. Further research using individual-level data is recommended.
... The current results are also consistent with analyses using individual estimates of exposure to different livestock types in relation to primary care-registered data, showing an increased prevalence of pneumonia with higher livestock density [9,14,16]. This was also the case for studies including self-reported outcome assessments [29,30], despite that prevalence estimates based on selfreported questionnaires can deviate from those extracted from medical records [31,32]. Moreover, although less research has focused on respiratory patients, the higher rates of respiratory symptoms and infections we found in COPD patients in the exposed areas seem to be in agreement with findings of earlier studies in the Netherlands [17,18]. ...
... Evidence suggests that prior infection with Coxiella burnetii, which is mostly found in goat farms and was the causative agent of the Q-fever epidemic in the Netherlands [13], could be a contributing factor to susceptibility to other infections [40][41][42]. However, the incidence of Q-fever in the last few years has dropped to preepidemic levels [21], and serological analyses in a subgroup of residents in the livestock dense areas showed no significant association between being seropositive for Coxiella burnetii and pneumonia history [29,30]. Although the Q-fever outbreak may have resulted in increased perceived risks of living close to goat farms, associations with pneumonia were not biased by residents who attributed their symptoms to farm proximity [43]. ...
Article
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There is continuing debate and public health concern regarding the previously confirmed association between high livestock density and human health. The primary aim of the current study is to assess the prevalence of respiratory and other health problems in a livestock dense area in the Netherlands, based on recent longitudinal health data and a large sample. Analyses are expanded with the investigation of different subgroups of patients with respiratory health problems and the inclusion of various chronic and acute health outcomes, as well as prescribed medication. Prevalence of health symptoms and chronic conditions was assessed for the period 2014–2016, based on electronic health records registered in 26 general practices located in areas with intensive livestock farming in the Netherlands (“livestock dense area”, n = 117,459 unique residents in total). These were compared with corresponding health data from general practices (n = 22) in different rural regions with a low density of livestock farms or other major environmental exposures (“control area”, n = 85,796 unique residents in total). Multilevel regression models showed a significantly higher prevalence of pneumonia in the total sample in the livestock dense area, which was also observed among susceptible subgroups of children, the elderly, and patients with chronic obstructive pulmonary disease (COPD). Lower respiratory tract infections, respiratory symptoms, vertigo, and depression were also more common in the livestock dense area compared to the control area. In general, there were no significant differences in chronic conditions such as asthma, COPD, or lung cancer. Prescription rates for broad-spectrum antibiotics were more common among patients with pneumonia in the livestock dense area. Acute respiratory infections and symptoms, but not chronic conditions, were considerably more common in areas with a high livestock density. Identification of causal pathogens on the basis of serological analyses could further elucidate the underlying mechanisms behind the observed health effects.
... Smit et al., (2017) and Poulsen et al., (2018) pointed out that ambient air pollution increases the risk of lower respiratory infections especially pneumonia as exposure to air pollutants such as particulate matter induces oxidative stress in pulmonary macrophages and epithelial cells by reducing the lungs defenses against bacterial pathogens and disrupting the composition of the oropharyngeal micro-biota thus increasing the susceptibility to respiratory infections; since a balanced micro-biota provides resistance to the acquisition and establishment of pathogens and prevents the overgrowth of pathobionts such as Streptococcus pneumoniae. Studies by Friedl et al., (2017) and the Netherlands National Institute of Public Health and the Environment (2018) affirms that living close to or working in poultry farms and/or goat farms has a significant positive association with pneumonia. According to Borghetti et al., (2002), it is possible for workers to be exposed to mites (storage mites: Lepidoglyphos destructor) and their allergens, aeroallergens, feeds and their contaminants; which in turn leads to allergic sensitization, wheezing, rhinitis, dermatitis and asthma among poultry farm workers. ...
... Studies conducted in South Africa and Mozambique showed that demographics, virulent bacteria, and malnutrition increase the risk of poor pneumonia treatment outcomes (McNally et al., 2007;Sigauque et al., 2009). Furthermore, the latest research conducted in the Netherlands proved that people who live close to poultry are prone to pneumonia (Freidl et al., 2017). Pneumonia is not only an independent variable but also a dependent variable of disease progression, as pneumonia has become a risk factor for liver abscess disease in Taiwan (Ho et al., 2017). ...
Article
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Abstract In developing countries, on average, one out of five children die from pneumonia. Death from pneumonia is most prevalent in Southeast Asia, particularly in Indonesia. Many factors can cause pneumonia. In Jakarta, the prevalence period of pneumonia in toddlers reached 19.6‰, which is higher than the national prevalence period of only 18.5‰. This study used a crosssectional research design to analyze the relationship between disease prevalence and risk factors simultaneously. Results showed that, out of 721 children under 5 years old investigated in this study, 31 toddlers suffered from pneumonia. Furthermore, bivariate analysis showed that two variables were related to the incidence of pneumonia, namely, low maternal education (odds ratio [OR] = 2.325, 95% confidence interval [CI] = 1.093–4.946) and living room ventilation 10% of the total space because the lack of air circulation induces the transmission of pneumonia. Keywords: maternal education; pneumonia; room ventilation; toddlers; urban.
... Once inhaled, fine particles can be trapped in the respiratory system [15] . Housing and climate conditions influenced the state of poultry by affecting the intake and utilization of food, which affected the PM concentrations in chicken coops [16,17] . ...
... Recent studies have shown an increased risk of pneumonia among residents living within up to 2000 m from goat farms (Freidl et al., 2017;Kalkowska et al., 2018;Post et al., 2019). In addition, a decreased prevalence of asthma and COPD has been reported among residents living within 500 m of livestock farms Smit et al., 2014). ...
Article
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Observed multiple adverse effects of livestock production have led to increasing calls for more sustainable livestock production. Quantitative analysis of adverse effects, which can guide public debate and policy development in this area, is limited and generally scattered across environmental, human health, and other science domains. The aim of this study was to bring together and, where possible, quantify and aggregate the effects of national-scale livestock production on 17 impact categories, ranging from impacts of particulate matter, emerging infectious diseases and odor annoyance to airborne nitrogen deposition on terrestrial nature areas and greenhouse gas emissions. Effects were estimated and scaled to total Dutch livestock production, with system boundaries including feed production, manure management and transport, but excluding slaughtering, retail and consumption. Effects were expressed using eight indicators that directly express Impact in the sense of the Drivers-Pressures-State-Impact-Response framework, while the remaining 14 express Pressures or States. Results show that livestock production may contribute both positively and negatively to human health with a human disease burden (expressed in disability-adjusted life years) of up to 4% for three different health effects: those related to particulate matter, zoonoses, and occupational accidents. The contribution to environmental impact ranges from 2% for consumptive water use in the Netherlands to 95% for phosphorus transfer to soils, and extends beyond Dutch borders. While some aggregation across impact categories was possible, notably for burden of disease estimates, further aggregation of disparate indicators would require normative value judgement. Despite difficulty of aggregation, the assessment shows that impacts receive a different contribution of different animal sectors. While some of our results are country-specific, the overall approach is generic and can be adapted and tuned according to specific contexts and information needs in other regions, to allow informed decision making across a broad range of impact categories.
... public health). Recent studies investigating the impact of livestock farms on the health of local residents (Freidl et al., 2017;IJzermans et al., 2018;Kalkowska et al., 2018;Poulsen et al., 2018;Smit et al., 2017; suggested an association between pneumonia and proximity of poultry farms (Kalkowska et al., 2018;Poulsen et al., 2018;Smit et al., 2017), but further research is required to establish such an association. The impact of particulate matter on health and working conditions of farmers and farm workers was mentioned as a social sustainability issue. ...
Article
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While global demand for eggs is increasing, concerns are being raised about the environmental, economic and social impact of egg production. Efforts to address these sustainability concerns can, however, result in trade-offs. To enhance a transparent debate about future options and limitations in the egg sector, insight is needed in environmental, economic and social sustainability challenges as well as in potential trade-offs involved in addressing these challenges. Based on interviews with 24 stakeholders and supported by scientific literature, this paper presents an overview of current sustainability challenges and trade-offs in the Dutch egg sector. Moreover, the paper provides an overview of innovations suggested by stakeholders that can help to address the identified sustainability challenges, and describes current limitations for the implementation of these innovations. Innovations identified were related to animal health and welfare (n = 13), housing systems (n = 7), economy (n = 8), environment (n = 9), and organisation (n = 6). Stakeholders considered innovations to reduce particulate matter emissions as one with priority. In addition, controlling poultry red mite, approaches to translate costs for environmental investments to consumers, closing manure-feed cycles and improved collaboration in the chain were considered as important steps to address current sustainability challenges. Our results reveal the complex interactions between sustainability challenges in the egg sector and give insight in the different perspectives and considerations stakeholders have. Steps towards sustainable egg production therefore require multi-stakeholder dialogue to find consensus and jointly identify so-called small wins, i.e. meaningful and feasible steps that can contribute to a more sustainable food system.
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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.
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Background: Of all hospitalised community-acquired pneumonias (CAPs) only a few are known to be caused by Chlamydia psittaci. Most likely the reported incidence, ranging from of 0% to 2.1%, is an underestimation of the real incidence, since detection of psittacosis is frequently not incorporated in the routine microbiological diagnostics in CAP or serological methods are used. Methods: C. psittaci real-time polymerase chain reaction (PCR) was routinely performed on the sputum of 147 patients hospitalised with CAP, who participated in a clinical trial conducted in two Dutch hospitals. In 119/147 patients the paired complement fixation test (CFT) was also performed for the presence of Chlamydia antibodies. Positive CFTs were investigated by micro- Immunofluorescence for psittacosis specificity. Case criteria for psittacosis were a positive PCR or a fourfold rise of antibody titre in CFT confirmed by micro- Immunofluorescence. Furthermore, we searched for parameters that could discriminate psittacosis from CAPs with other aetiology. Results: 7/147 (4.8%) patients were diagnosed with psittacosis: six with PCR and one patient with a negative PCR, but with CFT confirmed by micro- Immunofluorescence. Psittacosis patients had had a higher temperature (median 39.6 vs. 38.2 °C;) but lower white blood cell count (median 7.4 vs. 13.7 x 109/l) on admission compared with other CAP patients. Conclusion: In this study, C. psittaci as CAP-causing pathogen was much higher than previously reported. To detect psittacosis, PCR was performed on all CAP patients for whom a sputum sample was available. For clinical use, PCR is a fast method and sputum availability allows genotyping; additional serology can optimise epidemiological investigations.
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Background: Microorganisms causing community-acquired pneumonia (CAP) can be categorised into viral, typical and atypical (Legionella species, Coxiella burnetii, Mycoplasma pneumoniae, and Chlamydia species). Extensive microbiological testing to identify the causative microorganism is not standardly recommended, and empiric treatment does not always cover atypical pathogens. In order to optimize epidemiologic knowledge of CAP and to improve empiric antibiotic choice, we investigated whether atypical microorganisms are associated with a particular season or with the patient characteristics age, gender, or chronic obstructive pulmonary disease (COPD). Methods: A data-analysis was performed on databases from four prospective studies, which all included adult patients hospitalised with CAP in the Netherlands (N = 980). All studies performed extensive microbiological testing. Results: A main causative agent was identified in 565/980 (57.7 %) patients. Of these, 117 (20.7 %) were atypical microorganisms. This percentage was 40.4 % (57/141) during the non-respiratory season (week 20 to week 39, early May to early October), and 67.2 % (41/61) for patients under the age of 60 during this season. Factors that were associated with atypical causative agents were: CAP acquired in the non-respiratory season (odds ratio (OR) 4.3, 95 % CI 2.68-6.84), age <60 year (OR 2.9, 95 % CI 1.83-4.66), male gender (OR 1.7, 95 % CI 1.06-2.71) and absence of COPD (OR 0.2, 95 % CI 0.12-0.52). Conclusions: Atypical causative agents in CAP are associated with respectively non-respiratory season, age <60 years, male gender and absence of COPD. Therefore, to maximise its yield, extensive microbiological testing should be considered in patients <60 years old who are admitted with CAP from early May to early October. Trial registration: NCT00471640 , NCT00170196 (numbers of original studies).
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