A Predictive Model Has Identified Tick-Borne Encephalitis High-Risk Areas in Regions Where No Cases Were Reported Previously, Poland, 1999–2012

Pawel Stefanoff, Barbara Rubikowska, Jakub Bratkowski, Zbigniew Ustrnul, Sophie O. Vanwambeke and Magdalena Rosinska
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Pawel Stefanoff: Department of Epidemiology of Infectious Diseases and Surveillance, National Institute of Public Health—National Institute of Hygiene, 00-791 Warsaw, Poland
Barbara Rubikowska: Department of Population Health Monitoring and Analysis, National Institute of Public Health—National Institute of Hygiene, 00-791 Warsaw, Poland
Jakub Bratkowski: Institute of Environmental Protection—National Research Institute (IOS—PIB), 00-548 Warsaw, Poland
Zbigniew Ustrnul: Department of Climatology, Jagiellonian University, 30-387 Krakow, Poland
Sophie O. Vanwambeke: Georges Lemaître Centre for Earth and Climate Research, Earth & Life Institute, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium
Magdalena Rosinska: Department of Epidemiology of Infectious Diseases and Surveillance, National Institute of Public Health—National Institute of Hygiene, 00-791 Warsaw, Poland

IJERPH, 2018, vol. 15, issue 4, 1-17

Abstract: During 1999–2012, 77% of the cases of tick-borne encephalitis (TBE) were recorded in two out of 16 Polish provinces. However, historical data, mostly from national serosurveys, suggest that the disease could be undetected in many areas. The aim of this study was to identify which routinely-measured meteorological, environmental, and socio-economic factors are associated to TBE human risk across Poland, with a particular focus on areas reporting few cases, but where serosurveys suggest higher incidence. We fitted a zero-inflated Poisson model using data on TBE incidence recorded in 108 NUTS-5 administrative units in high-risk areas over the period 1999–2012. Subsequently we applied the best fitting model to all Polish municipalities. Keeping the remaining variables constant, the predicted rate increased with the increase of air temperature over the previous 10–20 days, precipitation over the previous 20–30 days, in forestation, forest edge density, forest road density, and unemployment. The predicted rate decreased with increasing distance from forests. The map of predicted rates was consistent with the established risk areas. It predicted, however, high rates in provinces considered TBE-free. We recommend raising awareness among physicians working in the predicted high-risk areas and considering routine use of household animal surveys for risk mapping.

Keywords: tick-borne encephalitis; ecologic study; epidemiologic determinants; land use predictors; zero-inflated Poisson model (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2018
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