Background
Q fever is a zoonosis caused by the bacterium
Coxiella burnetii and is present worldwide with the exception of New Zealand. Clinical disease in humans can range from an acute influenza-like illness to a chronic infection manifesting mainly as endocarditis or vascular infection [
1], though 60 % of infections are asymptomatic. There is also evidence of a “post Q-fever fatigue syndrome” manifesting as chronic fatigue following acute infection [
2]. The animal reservoir for Q fever is large as a wide range of mammals, birds, reptiles and fish can become infected with
C. burnetii, but cattle and small ruminants (sheep and goats) are the most affected [
1]. Clinical presentation of Q fever in domestic ruminants is most commonly associated with late abortions, stillbirth and delivery of weak offspring [
3‐
5]. The most frequent route of infection for humans is from inhalation of aerosols contaminated by infected ruminants at slaughter or parturition [
1]. Windborne spread of such aerosols can cause infections several kilometres from their origin [
1,
6‐
9]. Furthermore, environmental contamination related to parturition or slaughter can last potentially from months to years, making inhalation of contaminated dust a risk [
1].
Between 2007 and 2010, the Netherlands experienced the largest outbreak of Q fever ever reported, with over 4,000 notified cases [
10,
11] and mainly associated with dairy goat farms [
10‐
13]. Since that time, a number of methodologies have been developed and applied to investigate the epidemic. These methods incorporate traditional epidemiological approaches alongside Geographic Information Systems (GIS) [
12,
13], mathematical modelling [
14], and atmospheric dispersion models [
15]; and use data from a range of sectors and sources, including animal, human, environmental and meteorological. In this sense, the Q-fever epidemic has offered a unique opportunity to develop interdisciplinary, “One-Health” methodologies to investigate airborne and zoonotic disease.
In the spring/summer of 2008, a Q fever outbreak occurred in an urban area (approximately 88,000 inhabitants) in the south of the Netherlands with 96 cases notified to the local Municipal Health Service (MHS), Brabant-Zuidoost. Through use of a Geographic Information System, this outbreak was subsequently linked to a commercial dairy goat farm (Farm A) that had experienced a Q fever-related abortion wave in the weeks preceding the human outbreak [
12]. Persons living within 2 km of the farm had over a thirty times higher risk for Q fever than those living more than 5 km away, with risk declining with increasing distance from the farm. This spatial relationship, together with the fact that no other farms in the locality had reported Q-fever problems, supported the hypothesis that this single goat farm was the source of the human outbreak.
In 2009, an even larger human outbreak occurred in the town, with 347 cases notified to the same MHS between April and July. However, in this year no farm in the locality reported any clinical Q fever or related animal health problems. The reasons for the upsurge in human cases were unclear. Hypotheses included: increased reporting of Q fever owing to greater awareness of the disease amongst medical professionals and the community; the existence of a new source or sources of infection; or Farm A being again the source due to sub-clinical shedding or environmental contamination. Our study aimed to integrate the interdisciplinary methodologies and inter-sectoral data developed and gathered since the start of the national epidemic to identify the most likely origin of the 2009 outbreak in the area.
Discussion
This study aimed to identify the most likely origin of an outbreak of Q fever in an urban area in 2009 that occurred in the absence of any clinical Q-fever notifications from surrounding farms that year. Our results suggest that the most likely source of this outbreak was the same dairy goat farm, Farm A, as had been implicated in a smaller outbreak in the area the previous year. We gathered data from a wide range of sources and sectors (animal, human, environmental and meteorological) and applied a range of interdisciplinary methods incorporating both traditional and innovative techniques in order to reach this conclusion. Integrated approaches such as these are increasingly recognised as key to One Health research. The mechanisms underlying zoonotic disease emergence and transmission are invariably complex and multi-factorial, such that understanding them requires interdisciplinary work that applies a holistic set of methodologies and data from the human health, veterinary, and environmental sectors [
24,
25]. For the purposes of this study, we also developed a new method that enabled the integration of spatial data generated through an atmospheric dispersion model with temporal data concerning time of probable infection. This method could prove valuable to future Q fever outbreak investigations as it can serve to disentangle acute aerosol transmission from a point source from diffuse environmental transmission.
While comparison of the area’s 2008 outbreak data to its 2009 outbreak data suggests that there was a higher awareness of Q fever among the public and medical community in the area in 2009 compared to 2008 (demonstrated by the significantly shorter period between date of onset of disease and date of laboratory confirmation in 2009), the similar demographics of cases in terms of time, place and person and the lack of a reduction in hospitalisation rate do not support the hypothesis that the increase in cases of Q fever seen in 2009 was caused solely by changes in reporting behaviour. Instead, our results suggest that this was a true outbreak rather than a reporting artefact, with Farm A as the probable source. The timing of the 2009 outbreak (in the spring around the kidding season) suggests that the outbreak was due to goats sub-clinically shedding C. burnetii during this period, rather than due to environmental contamination of Farm A and its surrounds during 2008.
This is supported by the results of spatio-temporal analyses using an atmospheric distribution model. By means of this method, we calculated the proportion of cases that were mainly exposed during their probable period of infection (PPI), namely 60 – 65 % of the cases. Thus, these cases could be explained by airborne transmission of C. burnetii from Farm A. The other cases were mainly exposed prior to their PPI; explanations for this could be that (1) they were absent from home during high-exposure hours/days, or (2) they did not happen to inhale an infected particle during the PPI by chance, despite high concentrations around their area of residence at that time, or (3) they were infected by a different source (including re-aerosolised bacteria from contaminated environments).
It has been suggested that
C. burnetii may still be excreted via the placenta during normal parturition [
3,
4,
26,
27] even in vaccinated animals, albeit to a lesser extent [
28]. A recent meta-analysis of studies investigating the effect of vaccination on shedding suggested that while vaccination significantly reduced the risk of shedding form uterine secretions in previously exposed goats, shedding through all other routes (milk, vaginal secretions and faeces) was not significantly reduced. [
29] Furthermore, farms that have experienced Q fever-related abortions in the past have been shown to have higher proportions of
C. burnetii-positive animal and environmental samples, and higher levels of
C. burnetii DNA within those positive samples compared to both farms negative for Q fever and farms positive on bulk milk sampling but with no history of abortion. Unfortunately detailed veterinary/husbandry data were unavailable regarding the start, end and peak kidding periods or other on-farm events in 2009 (e.g. removal of litter from the pen) that might have contributed to the rise in human cases. For the purposes of the model, we assumed that
C. burnetti emission started in week 9, two weeks before the first notification. Although the proportion of explained cases would have been larger had we assumed an emission start in week 12 or 13 (Fig.
6), cases with weeks of onset between weeks 11 and 14 would subsequently have been missed by the model.
The model also demonstrated a strong relationship between distance from cases’ residence to Farm A and incidence of Q fever stratified by group. Cases belonging to group 1 (i.e. mainly exposed during their PPI) were over-represented at distances of up to 4 to 5 kilometres from Farm A, while cases in group 2 were living at relatively further distances of 5 to 8 kilometres (there were too few cases at 9 km and 10 km to draw conclusions about these distances). Also, cases in group 1 were generally exposed to higher doses during their PPI (Fig.
5) compared to cases in group 2. Note that we did not look at the difference in absolute exposure between cases, but instead at the exposure per case. After all, the probability of infection is always 100 % at the end of a case’s PPI, regardless of the actual dose. Nevertheless, other sources could have increased the actual dose and thus the probability of infection. Furthermore, while choice of emission profile (i.e.
conYear and
lNormEpi) and threshold wind velocity had only a minor effect on the proportion of explained cases (Fig.
5 and Additional file
2: Figure S1), exposure at short distances up to 4-5 km was an important indicator for disease. These results complement the findings from concentric rings attack rate analyses in both this study and previous studies [
12,
13] which have indicated that persons living within 5 km of an infected farm are at particular risk of contracting Q fever.
Although seventeen farms were included for investigation at the outset, we focussed our farm visit and spatio-temporal analyses to include Farm A only. It could be argued that, despite the weight of evidence pointing to Farm A, similar investigations should have been performed at all farms for the purposes of fair comparison. One argument for focussing on Farm A was its size; with 791 animals it was by far the largest farm of the seventeen: indeed, fifteen farms had under 40 animals, twelve of which had under ten. The only other large farm, Farm H (175 sheep), was not further investigated because the concentric rings analysis and smooth incidence mapping did not point towards it; however, five out of 20 environmental and aerosol samples taken at and around the farm in 2009/2010 tested positive for
C. burnetii [
21]
. Furthermore, although it may be considered unlikely that the small farms would be the source of such a large human outbreak, it is theoretically possible. Unfortunately, because these farms were so small, they did not fall under statutory regulations for bulk tank milk sampling; neither had they been included in previous investigations, so we had no data to rule out Q fever in these farm animals. Ideally, all seventeen farms would have been individually visited and animals sampled as part of our investigations; however, our resources did not permit this and we focussed on the most strongly suspected farm only. With regards the spatio-temporal modelling: the concentric rings analyses suggested most strongly that farms A, B and E, and possibly G, warranted further investigation. However, as these farms were so close to Farm A, any results yielded from atmospheric dispersion model centred on these farms would have been almost identical to that for Farm A and therefore would have yielded almost identical information.
Caution is called for in the interpretation of the spatio-temporal analyses. The method requires that cases are assigned into one of two distinct groups: those who were exposed to a higher modelled concentration of
C. burnetii in the 8–33 days before date of onset (the period of probable infection, PPI) compared to prior to it (group 1), and those who had a higher modelled concentration prior to their PPI compared to during the it (group 2). However, this was not always a straightforward distinction (Fig.
5) and some cases may have been mis-assigned. It is difficult to extrapolate from modelled concentration at a particular postcode area to the actual level of exposure of a person in reality: for example, a case may have been absent from their residence during their PPI; there may have been a relatively low number of contaminated particles in their particular street; or they may have been exposed to other sources in addition to Farm A.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
GL designed and implemented the study methods, except for those involving the application of the atmospheric distribution model and spatio-temporal analysis, which were designed and implemented by JvL. AS supervised the design, implementation and interpretation of the methods conducted by JvL. PV supervised and facilitated all parts of the study that related to animal data, and co-conducted the on-farm visit together with GL. BS contributed to the design of the methods. RTS provided data and background on the 2008 and 2009 outbreaks in the town. WvdH conceived of the project and provided overall supervision throughout. GL drafted the manuscript, which was reviewed and approved by all authors.
Joint first author: Georgia Ladbury and Jeroen van Leuken.