Cargando…

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

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Stefanoff, Pawel, Rubikowska, Barbara, Bratkowski, Jakub, Ustrnul, Zbigniew, Vanwambeke, Sophie O., Rosinska, Magdalena
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5923719/
https://www.ncbi.nlm.nih.gov/pubmed/29617333
http://dx.doi.org/10.3390/ijerph15040677
_version_ 1783318408378449920
author Stefanoff, Pawel
Rubikowska, Barbara
Bratkowski, Jakub
Ustrnul, Zbigniew
Vanwambeke, Sophie O.
Rosinska, Magdalena
author_facet Stefanoff, Pawel
Rubikowska, Barbara
Bratkowski, Jakub
Ustrnul, Zbigniew
Vanwambeke, Sophie O.
Rosinska, Magdalena
author_sort Stefanoff, Pawel
collection PubMed
description 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.
format Online
Article
Text
id pubmed-5923719
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-59237192018-05-03 A Predictive Model Has Identified Tick-Borne Encephalitis High-Risk Areas in Regions Where No Cases Were Reported Previously, Poland, 1999–2012 Stefanoff, Pawel Rubikowska, Barbara Bratkowski, Jakub Ustrnul, Zbigniew Vanwambeke, Sophie O. Rosinska, Magdalena Int J Environ Res Public Health Article 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. MDPI 2018-04-04 2018-04 /pmc/articles/PMC5923719/ /pubmed/29617333 http://dx.doi.org/10.3390/ijerph15040677 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Stefanoff, Pawel
Rubikowska, Barbara
Bratkowski, Jakub
Ustrnul, Zbigniew
Vanwambeke, Sophie O.
Rosinska, Magdalena
A Predictive Model Has Identified Tick-Borne Encephalitis High-Risk Areas in Regions Where No Cases Were Reported Previously, Poland, 1999–2012
title A Predictive Model Has Identified Tick-Borne Encephalitis High-Risk Areas in Regions Where No Cases Were Reported Previously, Poland, 1999–2012
title_full A Predictive Model Has Identified Tick-Borne Encephalitis High-Risk Areas in Regions Where No Cases Were Reported Previously, Poland, 1999–2012
title_fullStr A Predictive Model Has Identified Tick-Borne Encephalitis High-Risk Areas in Regions Where No Cases Were Reported Previously, Poland, 1999–2012
title_full_unstemmed A Predictive Model Has Identified Tick-Borne Encephalitis High-Risk Areas in Regions Where No Cases Were Reported Previously, Poland, 1999–2012
title_short A Predictive Model Has Identified Tick-Borne Encephalitis High-Risk Areas in Regions Where No Cases Were Reported Previously, Poland, 1999–2012
title_sort predictive model has identified tick-borne encephalitis high-risk areas in regions where no cases were reported previously, poland, 1999–2012
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5923719/
https://www.ncbi.nlm.nih.gov/pubmed/29617333
http://dx.doi.org/10.3390/ijerph15040677
work_keys_str_mv AT stefanoffpawel apredictivemodelhasidentifiedtickborneencephalitishighriskareasinregionswherenocaseswerereportedpreviouslypoland19992012
AT rubikowskabarbara apredictivemodelhasidentifiedtickborneencephalitishighriskareasinregionswherenocaseswerereportedpreviouslypoland19992012
AT bratkowskijakub apredictivemodelhasidentifiedtickborneencephalitishighriskareasinregionswherenocaseswerereportedpreviouslypoland19992012
AT ustrnulzbigniew apredictivemodelhasidentifiedtickborneencephalitishighriskareasinregionswherenocaseswerereportedpreviouslypoland19992012
AT vanwambekesophieo apredictivemodelhasidentifiedtickborneencephalitishighriskareasinregionswherenocaseswerereportedpreviouslypoland19992012
AT rosinskamagdalena apredictivemodelhasidentifiedtickborneencephalitishighriskareasinregionswherenocaseswerereportedpreviouslypoland19992012
AT stefanoffpawel predictivemodelhasidentifiedtickborneencephalitishighriskareasinregionswherenocaseswerereportedpreviouslypoland19992012
AT rubikowskabarbara predictivemodelhasidentifiedtickborneencephalitishighriskareasinregionswherenocaseswerereportedpreviouslypoland19992012
AT bratkowskijakub predictivemodelhasidentifiedtickborneencephalitishighriskareasinregionswherenocaseswerereportedpreviouslypoland19992012
AT ustrnulzbigniew predictivemodelhasidentifiedtickborneencephalitishighriskareasinregionswherenocaseswerereportedpreviouslypoland19992012
AT vanwambekesophieo predictivemodelhasidentifiedtickborneencephalitishighriskareasinregionswherenocaseswerereportedpreviouslypoland19992012
AT rosinskamagdalena predictivemodelhasidentifiedtickborneencephalitishighriskareasinregionswherenocaseswerereportedpreviouslypoland19992012