Spatial analysis of positive and negative Q fever laboratory results for identifying high- and low-risk areas of infection in the Netherlands

Abstract

Background

The Netherlands faced a large Q fever epidemic from 2007 to 2010, in which thousands of people were tested for the presence of antibodies against Coxiella burnetii as part of individual patient diagnosis. So far, only data of notified cases were used for the identification of high-risk areas, which can lead to misclassification of risk. Therefore, we identified high- and low-risk areas based on laboratory test results to make control measures more efficient.

Methods

Data on diagnostic Q fever laboratory tests were obtained from two regional laboratories of medical microbiology in the high-incidence area in the south of the Netherlands. The proportion of patients testing positive was mapped per postal code area. Patients testing positive were compared to patients testing negative based on the distance between residential address and the nearest infected goat farm with adjustment for age and sex.

Results and conclusion

Of 11,035 patients tested, 4,011 (36.4%) had a positive laboratory test result for Q fever. Maps showing the spatial pattern of tests performed and proportion of positive tests allowed for the identification of high- and low-risk Q fever areas. The proportion of patients testing positive was higher in areas close to infected goat farms compared to areas further away. Patients living <1 km from an infected goat farm had a substantially higher risk of testing positive for antibodies to C. burnetii than those living >10 km away (OR 21.70, 95% CI 16.28–28.92). Laboratory test results have the potential to make control measures more efficient by identifying high-risk areas as well as low-risk areas.

• zoonosis
• Coxiella burnetii
• goats
• the Netherlands
• case–control
• risk factors
• epidemiology

Acknowledgements

The authors thank Piet Vellema (Animal Health Service) for providing data on infected dairy goat farms, Ben Bom (Department of Statistics, Mathematical Modelling and Data Logistics of the National Institute for Public Health and the Environment) for helping with distance calculations and creating maps and Jan van de Kassteele (Department of Statistics, Mathematical Modelling and Data Logistics of the National Institute for Public Health and the Environment) for his statistical support.

Conflict of interest and funding

The authors have not received any funding or benefits from industry or elsewhere to conduct this study.