Relationship between Coxiella burnetii (Q fever) antibody serology and time spent outdoors.

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.

Prediction of human active mobility in rural areas: development and validity tests of three different approaches.

BACKGROUND/AIM: Active mobility may play a relevant role in the assessment of environmental exposures (e.g. traffic-related air pollution, livestock emissions), but data about actual mobility patterns are work intensive to collect, especially in large study populations, therefore estimation methods for active mobility may be relevant for exposure assessment in different types of studies. We previously collected mobility patterns in a group of 941 participants in a rural setting in the Netherlands, using week-long GPS tracking. We had information regarding personal characteristics, self-reported data regarding weekly mobility patterns and spatial characteristics. The goal of this study was to develop versatile estimates of active mobility, test their accuracy using GPS measurements and explore the implications for exposure assessment studies. METHODS: We estimated hours/week spent on active mobility based on personal characteristics (e.g. age, sex, pre-existing conditions), self-reported data (e.g. hours spent commuting per bike) or spatial predictors such as home and work address. Estimated hours/week spent on active mobility were compared with GPS measured hours/week, using linear regression and kappa statistics. RESULTS: Estimated and measured hours/week spent on active mobility had low correspondence, even the best predicting estimation method based on self-reported data, resulted in a R2 of 0.09 and Cohen's kappa of 0.07. A visual check indicated that, although predicted routes to work appeared to match GPS measured tracks, only a small proportion of active mobility was captured in this way, thus resulting in a low validity of overall predicted active mobility. CONCLUSIONS: We were unable to develop a method that could accurately estimate active mobility, the best performing method was based on detailed self-reported information but still resulted in low correspondence. For future studies aiming to evaluate the contribution of home-work traffic to exposure, applying spatial predictors may be appropriate. Measurements still represent the best possible tool to evaluate mobility patterns.

Pneumonia risk of people living close to goat and poultry farms - Taking GPS derived mobility patterns into account.

We previously observed an increased incidence of pneumonia in persons living near goat and poultry farms, using animal presence around the home to define exposure. However, it is unclear to what extent individual mobility and time spent outdoors close to home contributes to this increased risk. Therefore, the aim of the current study was to investigate the role of mobility patterns and time spent outdoors in the vicinity of goat or poultry farms in relation to pneumonia risk. In a rural Dutch cohort, 941 members logged their mobility using GPS trackers for 7 days. Pneumonia was diagnosed in 83 subjects (participants reported that pneumonia had been diagnosed by a medical doctor, or recorded in EMR from general practitioners, 2011-2014). We used logistic regression to evaluate pneumonia-risk by presence of goat farms within 500 and 1000 m around the home and around GPS-tracks (only non-motorised mobility), also we evaluated whether more time spent outdoors increased pneumonia-risks. We observed a clearly increased risk of pneumonia among people living in close proximity to goat farms, ORs increased with closer distances of homes to farms (500 m: 6.2 (95% CI 2.2-16.5) 1000 m: 2.5 (1.4-4.3)) The risk increased for individuals who spent more time outdoors close to home, but only if homes were close to goat farms (within 500 m and often outdoors: 12.7 (3.6-45.4) less often: 2.0 (0.3-9.2), no goat farms and often outdoors: 1.0 (0.6-1.6)). For poultry we found no increased risks. Pneumonia-risks increased when people lived near goat farms, especially when they spent more time outdoors, mobility does not seem to add to these risks.

Mobility assessment of a rural population in the Netherlands using GPS measurements.

BACKGROUND: The home address is a common spatial proxy for exposure assessment in epidemiological studies but mobility may introduce exposure misclassification. Mobility can be assessed using self-reports or objectively measured using GPS logging but self-reports may not assess the same information as measured mobility. We aimed to assess mobility patterns of a rural population in the Netherlands using GPS measurements and self-reports and to compare GPS measured to self-reported data, and to evaluate correlates of differences in mobility patterns. METHOD: In total 870 participants filled in a questionnaire regarding their transport modes and carried a GPS-logger for 7 consecutive days. Transport modes were assigned to GPS-tracks based on speed patterns. Correlates of measured mobility data were evaluated using multiple linear regression. We calculated walking, biking and motorised transport durations based on GPS and self-reported data and compared outcomes. We used Cohen's kappa analyses to compare categorised self-reported and GPS measured data for time spent outdoors. RESULTS: Self-reported time spent walking and biking was strongly overestimated when compared to GPS measurements. Participants estimated their time spent in motorised transport accurately. Several variables were associated with differences in mobility patterns, we found for instance that obese people (BMI > 30 kg/m2) spent less time in non-motorised transport (GMR 0.69-0.74) and people with COPD tended to travel longer distances from home in motorised transport (GMR 1.42-1.51). CONCLUSIONS: If time spent walking outdoors and biking is relevant for the exposure to environmental factors, then relying on the home address as a proxy for exposure location may introduce misclassification. In addition, this misclassification is potentially differential, and specific groups of people will show stronger misclassification of exposure than others. Performing GPS measurements and identifying explanatory factors of mobility patterns may assist in regression calibration of self-reports in other studies.

Human-livestock contacts and their relationship to transmission of zoonotic pathogens, a systematic review of literature.

BACKGROUND: Micro-organisms transmitted from vertebrate animals - including livestock - to humans account for an estimated 60% of human pathogens. Micro-organisms can be transmitted through inhalation, ingestion, via conjunctiva or physical contact. Close contact with animals is crucial for transmission. The role of intensity and type of contact patterns between livestock and humans for disease transmission is poorly understood. In this systematic review we aimed to summarise current knowledge regarding patterns of human-livestock contacts and their role in micro-organism transmission. METHODS: We included peer-reviewed publications published between 1996 and 2014 in our systematic review if they reported on human-livestock contacts, human cases of livestock-related zoonotic diseases or serological epidemiology of zoonotic diseases in human samples. We extracted any information pertaining the type and intensity of human-livestock contacts and associated zoonoses. RESULTS: 1522 papers were identified, 75 were included: 7 reported on incidental zoonoses after brief animal-human contacts (e.g. farm visits), 10 on environmental exposures and 15 on zoonoses in developing countries where backyard livestock keeping is still customary. 43 studies reported zoonotic risks in different occupations. Occupations at risk included veterinarians, culling personnel, slaughterhouse workers and farmers. For culling personnel, more hours exposed to livestock resulted in more frequent occurrence of transmission. Slaughterhouse workers in contact with live animals were more often positive for zoonotic micro-organisms compared to co-workers only exposed to carcasses. Overall, little information was available about the actual mode of micro-organism transmission. CONCLUSIONS: Little is known about the intensity and type of contact patterns between livestock and humans that result in micro-organism transmission. Studies performed in occupational settings provide some, but limited evidence of exposure response-like relationships for livestock-human contact and micro-organism transmission. Better understanding of contact patterns driving micro-organism transmission from animals to humans is needed to provide options for prevention and thus deserves more attention.