Bruno Barbosa, Melissa Silva, César Capinha, Ricardo A C Garcia, Jorge Rocha
The first case of COVID-19 in continental Portugal was documented on the 2nd of March 2020 and about seven months later more than 75 thousand infections had been reported. Although several factors correlate significantly with the spatial incidence of COVID-19 worldwide, the drivers of spatial incidence of this virus remain poorly known and need further exploration. In this study, we analyse the spatiotemporal patterns of COVID-19 incidence in the at the municipality level and test for significant relationships between these patterns and environmental, socioeconomic, demographic and human mobility factors to identify the mains drivers of COVID-19 incidence across time and space. We used a generalized liner mixed model, which accounts for zero inflated cases and spatial autocorrelation to identify significant relationships between the spatiotemporal incidence and the considered set of driving factors. Some of these relationships were particularly consistent across time, including the 'percentage of employment in services'; 'average time of commuting using individual transportation'; 'percentage of employment in the agricultural sector'; and 'average family size'. Comparing the preventive measures in Portugal (e.g., restrictions on mobility and crowd around) with the model results clearly show that COVID-19 incidence fluctuates as those measures are imposed or relieved. This shows that our model can be a useful tool to help decision-makers in defining prevention and/or mitigation policies.
{"title":"Spatial correlates of COVID-19 first wave across continental Portugal.","authors":"Bruno Barbosa, Melissa Silva, César Capinha, Ricardo A C Garcia, Jorge Rocha","doi":"10.4081/gh.2022.1073","DOIUrl":"https://doi.org/10.4081/gh.2022.1073","url":null,"abstract":"<p><p>The first case of COVID-19 in continental Portugal was documented on the 2nd of March 2020 and about seven months later more than 75 thousand infections had been reported. Although several factors correlate significantly with the spatial incidence of COVID-19 worldwide, the drivers of spatial incidence of this virus remain poorly known and need further exploration. In this study, we analyse the spatiotemporal patterns of COVID-19 incidence in the at the municipality level and test for significant relationships between these patterns and environmental, socioeconomic, demographic and human mobility factors to identify the mains drivers of COVID-19 incidence across time and space. We used a generalized liner mixed model, which accounts for zero inflated cases and spatial autocorrelation to identify significant relationships between the spatiotemporal incidence and the considered set of driving factors. Some of these relationships were particularly consistent across time, including the 'percentage of employment in services'; 'average time of commuting using individual transportation'; 'percentage of employment in the agricultural sector'; and 'average family size'. Comparing the preventive measures in Portugal (e.g., restrictions on mobility and crowd around) with the model results clearly show that COVID-19 incidence fluctuates as those measures are imposed or relieved. This shows that our model can be a useful tool to help decision-makers in defining prevention and/or mitigation policies.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40253522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Heitor Victor Veiga Da Costa, Cristine Vieira do Bonfim, Wilson Fusco, Morvan de Melo Moreira, Fernando Maciano de Paula Neto
This study aimed at analysing the potential effects of the COVID-19 pandemic on the time series and spatial patterns of live births in the state of Pernambuco, Brazil, from 2010 to 2021. This is an ecological study that applied intervention analysis in time series, with the goal to identify how projected data behaved in relation to observed data in the months December 2020 to November 2021, i.e. months representing conceptions from March2020 to April 2021. For the state of Pernambuco, a discrepancy up to +5.7% was found between the observed and projected data, while the respective difference for the São Francisco mesoregion showed the opposite trend with maximum discrepancy of -9%. The results did not indicate a clear change in the number of live births but supported the expected continuation of the downward trend of the previous years. Considering the importance of the number of live births in the context of demography, economy and public health, monitoring must be maintained to analyse the possible future impact of the COVID-19 pandemic on live birth projections.
{"title":"Impact of the COVID-19 pandemic on the number of births in Pernambuco Brazil.","authors":"Heitor Victor Veiga Da Costa, Cristine Vieira do Bonfim, Wilson Fusco, Morvan de Melo Moreira, Fernando Maciano de Paula Neto","doi":"10.4081/gh.2022.1069","DOIUrl":"https://doi.org/10.4081/gh.2022.1069","url":null,"abstract":"<p><p>This study aimed at analysing the potential effects of the COVID-19 pandemic on the time series and spatial patterns of live births in the state of Pernambuco, Brazil, from 2010 to 2021. This is an ecological study that applied intervention analysis in time series, with the goal to identify how projected data behaved in relation to observed data in the months December 2020 to November 2021, i.e. months representing conceptions from March2020 to April 2021. For the state of Pernambuco, a discrepancy up to +5.7% was found between the observed and projected data, while the respective difference for the São Francisco mesoregion showed the opposite trend with maximum discrepancy of -9%. The results did not indicate a clear change in the number of live births but supported the expected continuation of the downward trend of the previous years. Considering the importance of the number of live births in the context of demography, economy and public health, monitoring must be maintained to analyse the possible future impact of the COVID-19 pandemic on live birth projections.</p>","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40253523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. M. Rodgers, E. Fonseca, P. Nieto, J. Malone, J. Luvall, J. McCarroll, R. Avery, M. Bavia, R. Guimarães, Xue Wen, M. M. N. Silva, D. D. M. T. Carneiro, L. Cardim
Visceral leishmaniasis (VL) is a neglected tropical disease transmitted by Lutzomyia longipalpis, a sand fly widely distributed in Brazil. Despite efforts to strengthen national control programs reduction in incidence and geographical distribution of VL in Brazil has not yet been successful; VL is in fact expanding its range in newly urbanized areas. Ecological niche models (ENM) for use in surveillance and response systems may enable more effective operational VL control by mapping risk areas and elucidation of eco-epidemiologic risk factors. ENMs for VL and Lu. longipalpis were generated using monthly WorldClim 2.0 data (30-year climate normal, 1-km spatial resolution) and monthly soil moisture active passive (SMAP) satellite L4 soil moisture data. SMAP L4 Global 3-hourly 9-km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V004 were obtained for the first image of day 1 and day 15 (0:00-3:00 hour) of each month. ENM were developed using MaxEnt software to generate risk maps based on an algorithm for maximum entropy. The jack-knife procedure was used to identify the contribution of each variable to model performance. The three most meaningful components were used to generate ENM distribution maps by ArcGIS 10.6. Similar patterns of VL and vector distribution were observed using SMAP as compared to WorldClim 2.0 models based on temperature and precipitation data or water budget. Results indicate that direct Earth-observing satellite measurement of soil moisture by SMAP can be used in lieu of models calculated from classical temperature and precipitation climate station data to assess VL risk.
{"title":"Use of soil moisture active passive satellite data and WorldClim 2.0 data to predict the potential distribution of visceral leishmaniasis and its vector Lutzomyia longipalpis in Sao Paulo and Bahia states, Brazil.","authors":"M. M. Rodgers, E. Fonseca, P. Nieto, J. Malone, J. Luvall, J. McCarroll, R. Avery, M. Bavia, R. Guimarães, Xue Wen, M. M. N. Silva, D. D. M. T. Carneiro, L. Cardim","doi":"10.4081/gh.2022.1095","DOIUrl":"https://doi.org/10.4081/gh.2022.1095","url":null,"abstract":"Visceral leishmaniasis (VL) is a neglected tropical disease transmitted by Lutzomyia longipalpis, a sand fly widely distributed in Brazil. Despite efforts to strengthen national control programs reduction in incidence and geographical distribution of VL in Brazil has not yet been successful; VL is in fact expanding its range in newly urbanized areas. Ecological niche models (ENM) for use in surveillance and response systems may enable more effective operational VL control by mapping risk areas and elucidation of eco-epidemiologic risk factors. ENMs for VL and Lu. longipalpis were generated using monthly WorldClim 2.0 data (30-year climate normal, 1-km spatial resolution) and monthly soil moisture active passive (SMAP) satellite L4 soil moisture data. SMAP L4 Global 3-hourly 9-km EASE-Grid Surface and Root Zone Soil Moisture Geophysical Data V004 were obtained for the first image of day 1 and day 15 (0:00-3:00 hour) of each month. ENM were developed using MaxEnt software to generate risk maps based on an algorithm for maximum entropy. The jack-knife procedure was used to identify the contribution of each variable to model performance. The three most meaningful components were used to generate ENM distribution maps by ArcGIS 10.6. Similar patterns of VL and vector distribution were observed using SMAP as compared to WorldClim 2.0 models based on temperature and precipitation data or water budget. Results indicate that direct Earth-observing satellite measurement of soil moisture by SMAP can be used in lieu of models calculated from classical temperature and precipitation climate station data to assess VL risk.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41254278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Zare, A. Semati, A. Mirahmadizadeh, Abdulrasool Hemmati, M. Ebrahimi
This study aimed at detecting space-time clusters of COVID-19 cases in Fars Province, Iran and at investigating their potential association with meteorological factors, such as temperature, precipitation and wind velocity. Time-series data including 53,554 infected people recorded in 26 cities from 18 February to 30 September 2020 together with 5876 meteorological records were subjected to the analysis. Applying a significance level of P<0.05, the analysis of space-time distribution of COVID-19 resulted in nine significant outbreaks within the study period. The most likely cluster occurred from 27 March to 13 July 2020 and contained 11% of the total cases with eight additional, secondary clusters. We found that the COVID-19 incidence rate was affected by high temperature (OR=1.64; 95% CI: 1.44-1.87), while precipitation and wind velocity had less effect (OR=0.84; 95% CI: 0.75-0.89 and OR=0.27; 95% CI: 0.14-0.51), respectively.
{"title":"Spatial epidemiology and meteorological risk factors of COVID-19 in Fars Province, Iran.","authors":"M. Zare, A. Semati, A. Mirahmadizadeh, Abdulrasool Hemmati, M. Ebrahimi","doi":"10.4081/gh.2022.1065","DOIUrl":"https://doi.org/10.4081/gh.2022.1065","url":null,"abstract":"This study aimed at detecting space-time clusters of COVID-19 cases in Fars Province, Iran and at investigating their potential association with meteorological factors, such as temperature, precipitation and wind velocity. Time-series data including 53,554 infected people recorded in 26 cities from 18 February to 30 September 2020 together with 5876 meteorological records were subjected to the analysis. Applying a significance level of P<0.05, the analysis of space-time distribution of COVID-19 resulted in nine significant outbreaks within the study period. The most likely cluster occurred from 27 March to 13 July 2020 and contained 11% of the total cases with eight additional, secondary clusters. We found that the COVID-19 incidence rate was affected by high temperature (OR=1.64; 95% CI: 1.44-1.87), while precipitation and wind velocity had less effect (OR=0.84; 95% CI: 0.75-0.89 and OR=0.27; 95% CI: 0.14-0.51), respectively.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48152656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"There is more to satellite imagery than meets the eye.","authors":"R. Bergquist, J. Malone","doi":"10.4081/gh.2022.1106","DOIUrl":"https://doi.org/10.4081/gh.2022.1106","url":null,"abstract":"Not available.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45501983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cristián Cáceres, Marcelo Leiva-Bianchi, Yony Ormazábal, Carlos Mena, Juan Carlos Cantillana
Progressive changes in local environmental scenarios, accelerated by global climate change, can negatively affect the mental health of people who inhabit these areas. The magnitude of these effects may vary depending on the socioeconomic conditions of people and the characteristics of the environment, so certain territories can be more vulnerable than others. In this context, the present study aimed to geographically analyse the levels of psychosocial impact and the types of disruptive responses related to the new territorial scenarios caused by climate change in the coastal drylands of the Maule region, Chile. For this purpose, 223 people from two communes (Curepto and Pencahue) were psychosocially evaluated for post-traumatic stress disorder (PTSD) together with a survey of the prevailing sociodemographic and socioeconomic conditions in relation to the environmental variables of the territory. All information was georeferenced, stored within an ArcGIS Desktop geographic information system (GIS) and then investigated by application of contingency tables, ANOVA and local clustering analysis using SSP statistical software. The results indicated a high level of PTSD in the population, with significant differences related to age and education as well as employment conditions and income. The spatial results showed high PTSD values in the communal capital of Curepto in the central agricultural valley near the estuary of the local river, while the existence of coldspots was observed in the central valley of the Pencahue commune. It was concluded that proximity to population centres and surface water sources played the greatest role for the development of PTSD.
{"title":"Post-traumatic stress in people from the interior drylands of the Maule region, Chile in the context of climate change.","authors":"Cristián Cáceres, Marcelo Leiva-Bianchi, Yony Ormazábal, Carlos Mena, Juan Carlos Cantillana","doi":"10.4081/gh.2022.1045","DOIUrl":"https://doi.org/10.4081/gh.2022.1045","url":null,"abstract":"Progressive changes in local environmental scenarios, accelerated by global climate change, can negatively affect the mental health of people who inhabit these areas. The magnitude of these effects may vary depending on the socioeconomic conditions of people and the characteristics of the environment, so certain territories can be more vulnerable than others. In this context, the present study aimed to geographically analyse the levels of psychosocial impact and the types of disruptive responses related to the new territorial scenarios caused by climate change in the coastal drylands of the Maule region, Chile. For this purpose, 223 people from two communes (Curepto and Pencahue) were psychosocially evaluated for post-traumatic stress disorder (PTSD) together with a survey of the prevailing sociodemographic and socioeconomic conditions in relation to the environmental variables of the territory. All information was georeferenced, stored within an ArcGIS Desktop geographic information system (GIS) and then investigated by application of contingency tables, ANOVA and local clustering analysis using SSP statistical software. The results indicated a high level of PTSD in the population, with significant differences related to age and education as well as employment conditions and income. The spatial results showed high PTSD values in the communal capital of Curepto in the central agricultural valley near the estuary of the local river, while the existence of coldspots was observed in the central valley of the Pencahue commune. It was concluded that proximity to population centres and surface water sources played the greatest role for the development of PTSD.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44897794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Rejeki, Sri Nurlaela, Devi Octaviana, Bangun Wijayanto, Solikhah Solikhah
Malaria remains one of the essential public health problems in Indonesia. The year 2015 was originally set as the elimination target in Java Island, but there are still several regencies on Java reporting malaria cases. Spatial technology helps determine local variations in malaria transmission, control risk areas and assess the outcome of interventions. Information on distribution patterns of malaria at the sub-district level, presented as spatial, temporal, and spatiotemporal data, is vital in planning control interventions. Information on malaria transmission at the sub-district level in three regencies in Java (Banyumas, Kebumen, and Purbalingga) was collected from the Agency for Regional Development (Bappeda), the Population and Civil Registration Agency (Disdukcapil) and Statistics Indonesia (BPS). Global spatial autocorrelation and space-time clustering was investigated together with purely spatial and purely temporal analyses using geographical information systems (GIS) by ArcGis 10.2 and SaTScan 8.0 to detect areas at high risk of malaria. Our results show that malaria was spatially clustered in the study area in central Java, in particular in the Banyumas and Purbalingga regencies. The temporal analysis revealed that malaria clusters predominantly appeared in the period January-April. The results of the spatiotemporal analysis showed that there was one most likely malaria cluster and three secondary clusters in southern central Java. The most likely cluster was located in Purbalingga Regency covering one sub-district and remaining from the beginning of 2016 to the end of 2018. The approach used can assist the setting of resource priorities to control and eliminate malaria.
{"title":"Clusters of malaria cases at sub-district level in endemic area in Java Island, Indonesia.","authors":"D. Rejeki, Sri Nurlaela, Devi Octaviana, Bangun Wijayanto, Solikhah Solikhah","doi":"10.4081/gh.2022.1048","DOIUrl":"https://doi.org/10.4081/gh.2022.1048","url":null,"abstract":"Malaria remains one of the essential public health problems in Indonesia. The year 2015 was originally set as the elimination target in Java Island, but there are still several regencies on Java reporting malaria cases. Spatial technology helps determine local variations in malaria transmission, control risk areas and assess the outcome of interventions. Information on distribution patterns of malaria at the sub-district level, presented as spatial, temporal, and spatiotemporal data, is vital in planning control interventions. Information on malaria transmission at the sub-district level in three regencies in Java (Banyumas, Kebumen, and Purbalingga) was collected from the Agency for Regional Development (Bappeda), the Population and Civil Registration Agency (Disdukcapil) and Statistics Indonesia (BPS). Global spatial autocorrelation and space-time clustering was investigated together with purely spatial and purely temporal analyses using geographical information systems (GIS) by ArcGis 10.2 and SaTScan 8.0 to detect areas at high risk of malaria. Our results show that malaria was spatially clustered in the study area in central Java, in particular in the Banyumas and Purbalingga regencies. The temporal analysis revealed that malaria clusters predominantly appeared in the period January-April. The results of the spatiotemporal analysis showed that there was one most likely malaria cluster and three secondary clusters in southern central Java. The most likely cluster was located in Purbalingga Regency covering one sub-district and remaining from the beginning of 2016 to the end of 2018. The approach used can assist the setting of resource priorities to control and eliminate malaria.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45191086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
T. Eryando, Tiopan Sipahutar, Meiwita Paulina Budhiharsana, K. Siregar, Muhammad Nur Aidi, Minarto Minarto, D. Utari, Martya Rahmaniati, H. Hendarwan
While the national prevalence of stunting in Indonesia has decreased, the level remains high in many districts/cities and there is significant variation. This ecological study employed aggregated data from the Basic Health Research Report and the District/City Poverty Data from 2018. We investigated the determinants of stunting prevalence at the district/city level, including autocorrelation applying the spatial autoregressive (SAR) model. The analyses revealed stunting prevalence above the national average in 282 districts/cities (54.9%), i.e. ≥30% in 297 districts/cities (57.8%) and ≥40% in 91 districts/cities (17.7%). Autocorrelation was found between Sumatra, Java, Sulawesi as well as Bali, East Nusa Tenggara and West Nusa Tenggara (Bali NTT NTB). The SAR modelling revealed the following variables with significant impact on the stunting prevalence in various parts of the country: closet defecation, hand washing, at least four antenatal care visits during pregnancy, poverty, immunisation and supplementary food for children under 5 years.
{"title":"Spatial analysis of stunting determinants in 514 Indonesian districts/cities: Implications for intervention and setting of priority.","authors":"T. Eryando, Tiopan Sipahutar, Meiwita Paulina Budhiharsana, K. Siregar, Muhammad Nur Aidi, Minarto Minarto, D. Utari, Martya Rahmaniati, H. Hendarwan","doi":"10.4081/gh.2022.1055","DOIUrl":"https://doi.org/10.4081/gh.2022.1055","url":null,"abstract":"While the national prevalence of stunting in Indonesia has decreased, the level remains high in many districts/cities and there is significant variation. This ecological study employed aggregated data from the Basic Health Research Report and the District/City Poverty Data from 2018. We investigated the determinants of stunting prevalence at the district/city level, including autocorrelation applying the spatial autoregressive (SAR) model. The analyses revealed stunting prevalence above the national average in 282 districts/cities (54.9%), i.e. ≥30% in 297 districts/cities (57.8%) and ≥40% in 91 districts/cities (17.7%). Autocorrelation was found between Sumatra, Java, Sulawesi as well as Bali, East Nusa Tenggara and West Nusa Tenggara (Bali NTT NTB). The SAR modelling revealed the following variables with significant impact on the stunting prevalence in various parts of the country: closet defecation, hand washing, at least four antenatal care visits during pregnancy, poverty, immunisation and supplementary food for children under 5 years.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2022-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45605820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I. Lebert, Séverine Bord, C. Saint-Andrieux, Eva Cassar, P. Gasqui, F. Beugnet, K. Chalvet-Monfray, S. Vanwambeke, G. Vourc'h, M. René-Martellet
The tick Ixodes ricinus is widely distributed across Europe and is responsible for the transmission of several pathogens to humans and animals. In this study, we used a knowledge-based method to map variations in habitat suitability for I. ricinus ticks throughout continental France and Corsica. The multi-criteria decision analysis (MCDA) integrated four major biotic and abiotic factors known to influence tick populations: climate, land cover, altitude and the density of wild ungulates. For each factor, habitat suitability index (HSI) values were attributed to different locations based on knowledge regarding its impact on tick populations. For the MCDA, two methods of factor combination were tested, additive and multiplicative, both which were evaluated at the spatial scales of departments and local municipalities. The resulting habitat suitability maps (resolution=100x100 m) revealed that conditions are suitable for I. ricinus over most of France and Corsica. Particularly suitable habitats were located in central, north-eastern and south-western France, while less-suitable habitats were found in the Mediterranean and mountainous regions. To validate the approach, the HSI scores were compared to field data of I. ricinus nymph abundance. Regardless of scale, the correlation between abundance indicator and HSI score was stronger for the additive than for the multiplicative approach. Overall, this study demonstrates the value of MCDA for estimating habitat suitability maps for I. ricinus abundance, which could be especially useful in highlighting areas of the tick's distribution where preventive measures should be prioritised.
{"title":"Habitat suitability map of Ixodes ricinus tick in France using multi-criteria analysis.","authors":"I. Lebert, Séverine Bord, C. Saint-Andrieux, Eva Cassar, P. Gasqui, F. Beugnet, K. Chalvet-Monfray, S. Vanwambeke, G. Vourc'h, M. René-Martellet","doi":"10.4081/gh.2022.1058","DOIUrl":"https://doi.org/10.4081/gh.2022.1058","url":null,"abstract":"The tick Ixodes ricinus is widely distributed across Europe and is responsible for the transmission of several pathogens to humans and animals. In this study, we used a knowledge-based method to map variations in habitat suitability for I. ricinus ticks throughout continental France and Corsica. The multi-criteria decision analysis (MCDA) integrated four major biotic and abiotic factors known to influence tick populations: climate, land cover, altitude and the density of wild ungulates. For each factor, habitat suitability index (HSI) values were attributed to different locations based on knowledge regarding its impact on tick populations. For the MCDA, two methods of factor combination were tested, additive and multiplicative, both which were evaluated at the spatial scales of departments and local municipalities. The resulting habitat suitability maps (resolution=100x100 m) revealed that conditions are suitable for I. ricinus over most of France and Corsica. Particularly suitable habitats were located in central, north-eastern and south-western France, while less-suitable habitats were found in the Mediterranean and mountainous regions. To validate the approach, the HSI scores were compared to field data of I. ricinus nymph abundance. Regardless of scale, the correlation between abundance indicator and HSI score was stronger for the additive than for the multiplicative approach. Overall, this study demonstrates the value of MCDA for estimating habitat suitability maps for I. ricinus abundance, which could be especially useful in highlighting areas of the tick's distribution where preventive measures should be prioritised.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2022-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46663833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The study, carried out at the Local Healthcare Authority in Reggio Emilia, Italy, focused on required travel of its employees with reference to length of travel route, degree of coverage through local public transport, degree of overlapping travel (useful to assess the feasibility of car sharing initiatives) and plans for shift work. The goal was to identify main obstacles when attempting to improve the reliability and scalability of this type of analysis, so that it can be used for regular updates. It was found that, on the one hand, it is necessary to promptly identify changes in the staff structure due to turnover that particularly affects health practitioners, such as doctors and fixed-term employees, while, on the other it is mandatory to comply with the provisions of Italian Law according to which, mobility managers must draw up annual commuting plans with an analysis of the routes travelled. The results of this work are likely to have useful implications for the improvement of the logistics system and, in particular, for the resilience of healthcare organizations.
{"title":"The Role of geography in the reorganization of the Italian National Health Service: Implementation of geographic information in the logistics and resilience of organizational structures.","authors":"Carla Dieci, G. Rinaldi","doi":"10.4081/gh.2022.1041","DOIUrl":"https://doi.org/10.4081/gh.2022.1041","url":null,"abstract":"The study, carried out at the Local Healthcare Authority in Reggio Emilia, Italy, focused on required travel of its employees with reference to length of travel route, degree of coverage through local public transport, degree of overlapping travel (useful to assess the feasibility of car sharing initiatives) and plans for shift work. The goal was to identify main obstacles when attempting to improve the reliability and scalability of this type of analysis, so that it can be used for regular updates. It was found that, on the one hand, it is necessary to promptly identify changes in the staff structure due to turnover that particularly affects health practitioners, such as doctors and fixed-term employees, while, on the other it is mandatory to comply with the provisions of Italian Law according to which, mobility managers must draw up annual commuting plans with an analysis of the routes travelled. The results of this work are likely to have useful implications for the improvement of the logistics system and, in particular, for the resilience of healthcare organizations.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2022-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45863374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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