Signs and symptoms do not predict, but may help rule out acute Q fever in favour of other respiratory tract infections, and reduce antibiotics overuse in primary care

BackgroundFrom early 2009, the Dutch region of South Limburg experienced a massive outbreak of Q fever, overlapping with the influenza A(H1N1)pdm09 pandemic during the second half of the year and affecting approximately 2.9% of a 300,000 population. Acute Q fever shares clinical features with other respiratory conditions. Most symptomatic acute infections are characterized by mild symptoms, or an isolated febrile syndrome. Pneumonia was present in a majority of hospitalized patients during the Dutch 2007-2010 Q fever epidemic. Early empiric doxycycline, guided by signs and symptoms and patient history, should not be delayed awaiting laboratory confirmation, as it may shorten disease and prevent progression to focalized persistent Q fever. We assessed signs' and symptoms' association with acute Q fever to guide early empiric treatment in primary care patients.MethodsIn response to the outbreak, regional primary care physicians and hospital-based medical specialists tested a total of 1218 subjects for Q fever. Testing activity was bimodal, a first "wave" lasting from March to December 2009, followed by a second "wave" which lasted into 2010 and coincided with peak pandemic influenza activity. We approached all 253 notified acute Q fever cases and a random sample of 457 Q fever negative individuals for signs and symptoms of disease. Using data from 140/229(61.1%) Q fever positive and 194/391(49.6%) Q fever negative respondents from wave 1, we built symptom-based models predictive of Q-fever outcome, validated against subsets of data from wave 1 and wave 2.ResultsOur models had poor to moderate AUC scores (0.68 to 0.72%), with low positive (4.6-8.3%), but high negative predictive values (91.7-99.5%). Male sex, fever, and pneumonia were strong positive predictors, while cough was a strong negative predictor of acute Q fever in these models.ConclusionWhereas signs and symptoms of disease do not appear to predict acute Q fever, they may help rule it out in favour of other respiratory conditions, prompting a delayed or non-prescribing approach instead of early empiric doxycycline in primary care patients with non-severe presentations. Signs and symptoms thus may help reduce the overuse of antibiotics in primary care during and following outbreaks of Q fever

A Model for the Early Identification of Sources of Airborne Pathogens in an Outdoor Environment

<div><p>Background</p><p>Source identification in areas with outbreaks of airborne pathogens is often time-consuming and expensive. We developed a model to identify the most likely location of sources of airborne pathogens.</p><p>Methods</p><p>As a case study, we retrospectively analyzed three Q fever outbreaks in the Netherlands in 2009, each with suspected exposure from a single large dairy goat farm. Model input consisted only of case residential addresses, day of first clinical symptoms, and human population density data. We defined a spatial grid and fitted an exponentially declining function to the incidence-distance data of each grid point. For any grid point with a fit significant at the 95% confidence level, we calculated a measure of risk. For validation, we used results from abortion notifications, voluntary (2008) and mandatory (2009) bulk tank milk sampling at large (i.e. >50 goats and/or sheep) dairy farms, and non-systematic vaginal swab sampling at large and small dairy and non-dairy goat/sheep farms. In addition, we performed a two-source simulation study.</p><p>Results</p><p>Hotspots – areas most likely to contain the actual source – were identified at early outbreak stages, based on the earliest 2–10% of the case notifications. Distances between the hotspots and suspected goat farms varied from 300–1500 m. In regional likelihood rankings including all large dairy farms, the suspected goat farms consistently ranked first. The two-source simulation study showed that detection of sources is most clear if the distance between the sources is either relatively small or relatively large.</p><p>Conclusions</p><p>Our model identifies the most likely location of sources in an airborne pathogen outbreak area, even at early stages. It can help to reduce the number of potential sources to be investigated by microbial testing and to allow rapid implementation of interventions to limit the number of human infections and to reduce the risk of source-to-source transmission.</p></div

Conviction and punishment:Free press and competitive election as deterrents to corruption

Background. In early 2009, a dairy-goat annex care farm in South Limburg, the Netherlands, reported 220 Coxiella burnetii-related abortions in 450 pregnant goats. These preceded human cases and occurred in a region that was Q-fever free before 2009, providing a unique quasi-experimental setting for investigating regional transmission patterns associated with a Q-fever point source. Methods. Index-farm residents/employees, visitors, and their household contacts were traced and screened for C. burnetii. Distribution of community cases was analysed using a geographic information system. True incidence, including undetected infections, was estimated regionwide by seroprevalence in a pre-versus postoutbreak sample, and near-farm by immunoglobulin M seroprevalence in a municipal population sample. Environmental bacterial load was repeatedly measured in surface and aerosol samples. Results. Serological attack rate was 92% (24/26) in index-farm residents/employees, 56% (28/50) in visitors, and 50% (7/14) in household contacts, and the clinical attack rate (ie, the proportion of persons seropositive for acute infection who also had clinical illness) was &gt;= 80%. Notified symptomatic community cases (n = 253) were scattered downwind from the index farm, following a significant exposure-response gradient. Observed incidence ranged from 6.3% (0-1 km) to 0.1% (4-5 km), and remained high beyond. True incidence of infections was estimated at 2.9% regionwide, extrapolating to 8941 infections; estimated near-farm incidence was 12%. Coxiella burnetii load was high on-farm (2009), and lower off-farm (2009-2010). Conclusions. Linking a single dairy-goat farm to a human Q-fever cluster, we show widespread transmission, massive numbers of undetected infections, and high attack rates on-and off-farm, even beyond a 5-km high-risk zone. Our investigation may serve as an essential case study for risk assessment in public health and related fields such as bioterrorism response and preparedness

Overview of the data selection in outbreak area C. The PC4-polygons are indicated by the green lines.

<p>The center of the case cluster is indicated by the green star. This star is also the center of the spatial grid with a resolution of 250×250 m (black squares). Around each of the grid points (example indicated by the large red square) the distance <i>r</i> to all PC6's (small blue dots) within <i>Z</i> = 5000 m is determined, as well as the number of cases <i>k</i> and inhabitants <i>n</i> in these PC6's.</p

Cumulative number of cases per week (solid lines) and the temporal nMR-fraction (“tf-nMR”) (i.e. the spatial cumulative nMR-values per week as fraction of spatial cumulative nMR-values using all cases of 2009) (dashed lines) for the areas A (green), B (red), and C (blue).

<p>Cumulative number of cases per week (solid lines) and the temporal nMR-fraction (“tf-nMR”) (i.e. the spatial cumulative nMR-values per week as fraction of spatial cumulative nMR-values using all cases of 2009) (dashed lines) for the areas A (green), B (red), and C (blue).</p

Two source simulations with the distance from the two sources to their closest hotspot as function of the distance between the two sources.

<p>The one source is indicated in red; the other in blue. The distance to all grid cells with nMR>0.9 is listed by small open circles; the grid cell with the maximum nMR-value in the hotspot is indicated by a large closed circle. If a local maximum with nMR-values<0.9 appeared, then triangle symbols are used, taking into account all grid cells with nMR-values of 0.10 lower than the local maximum. If a source could not be attributed to a single hotspot, then the distance to both hotspots was indicated (e.g., at x = 3.2 km). Results with just one hotspot are indicated by the grey rectangles at the x-axis.</p

Increased transmissibility of SARS-CoV-2 alpha variant (B.1.1.7) in children:three large primary school outbreaks revealed by whole genome sequencing in the Netherlands

Background: Variant of concern (VOC) SARS-CoV-2 alpha variant (B.1.1.7) was the dominant strain in the Netherlands between March 2021–June 2021. We describe three primary school outbreaks due to the alpha variant using whole genome sequencing with evidence of large-scale transmission among children, teachers and their household contacts. Method: All outbreaks described were investigated by the South Limburg Public Health Service, the Netherlands. A case was defined as an individual with a real-time polymerase chain reaction test or antigen test positive for SARS-CoV-2. Whole genome sequencing was performed on random samples from at least one child and one teacher of each affected class. Results: Peak attack rates in classes were 53%, 33% and 39%, respectively. Specific genotypes were identified for each school across a majority of affected classes. Attack rates were high among staff members, likely to promote staff-to-children transmission. Cases in some classes were limited to children, indicating child-to-child transmission. At 39%, the secondary attack rate (SAR) in household contacts of infected children was remarkably high, similar to SAR in household contacts of staff members (42%). SAR of household contacts of asymptomatic children was only 9%. Conclusion: Our findings suggest increased transmissibility of the alpha variant in children compared to preceding non-VOC variants, consistent with a substantial rise in the incidence of cases observed in primary schools and children aged 5–12 since the alpha variant became dominant in March 2021. Lack of mandatory masking, insufficient ventilation and lack of physical distancing also probably contributed to the school outbreaks. The rise of the delta variant (B.1.617.2) since July 2021 which is estimated to be 55% more transmissible than the alpha variant, provides additional urgency to adequate infection prevention in school settings