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Geographical information systems as a tool in the control of heartworm infections in dogs and cats
Veterinary Parasitology 176 (2011) 286–290
Contents lists available at ScienceDirect
Veterinary Parasitology journal homepage: www.elsevier.com/locate/vetpar
Geographical information systems as a tool in the control of heartworm infections in dogs and cats L. Rinaldi a , C. Genchi b , V. Musella c , M. Genchi d , G. Cringoli a,∗ a b c d
Università degli Studi di Napoli Federico II, Napoli, Italy Università degli Studi di Milano, Milano, Italy Università degli Studi Magna Graecia di Catanzaro, Catanzaro, Italy Università degli Studi di Pavia, Pavia, Italy
a r t i c l e
i n f o
Keywords: Geospatial tools Geographical information systems Mapping Dirofilaria immitis Dirofilaria repens
a b s t r a c t Geospatial tools (e.g., geographical information systems, remote sensing, global positioning systems, and virtual globes) are very useful for the simultaneous visualization of health data with environmental data, which holds promise to understand environmental-health linkages and to generate new hypotheses to be tested in future research. Current epidemiological studies clearly show that the distribution patterns of vector-borne infections are changing; for example, in Europe, heartworm infection and subcutaneous dirofilariosis are spreading throughout areas that previously had little to no incidence of heartworm. In view of the changes of the distribution patterns of Dirofilaria immitis and Dirofilaria repens, geospatial tools are now more useful for mapping (including territorial sampling), monitoring, ecological analysis, risk assessment, forecasting (including the choose of the timing of treatment), early warning, and surveillance of both heartworm and subcutaneous dirofilariosis. All these issues have control of these infections as the ultimate goal. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Many factors have contributed to an increasing appreciation of the interdependency of human, animal and ecosystem health within the trans-disciplinary “One Medicine–One Health” approach to global health (Conrad et al., 2009). Health is an outcome of multiple determinants and the predisposing factors of human and pet health status are often interdependent and interrelated, creating a complex web of causation. At the beginning of the third millennium, within a changing climate and a changing
∗ Corresponding author at: Department of Pathology and Animal Health, Faculty of Veterinary Medicine, University of Naples “Federico II”, Via della Veterinaria, 1-80137 Naples, Italy. Tel.: +39 081 2536283; fax: +39 081 2536282. E-mail address: [email protected] (G. Cringoli). URL: http://www.parassitologia.unina.it/ (G. Cringoli). 0304-4017/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.vetpar.2011.01.010
environment, and considering the broad concept of geospatial health, geospatial tools can help answer questions about the complex web of causation of many health issues (Rinaldi et al., 2010), The past 20–25 years have seen an increasing reliance on the application of geospatial tools (i.e., geographical information systems (GIS), global positioning system (GPS), satellite-based remote sensing (RS), and virtual globes (VG), such as Google EarthTM ) to study the distribution of infectious and parasitic diseases and their vectors (Bergquist and Rinaldi, 2010). The present paper is aimed at showing the usefulness of GIS and other geospatial tools in the control of Dirofilaria immitis and Dirofilaria repens infections in dogs and cats. 2. Geospatial tools and Dirofilaria Global availability of geospatial health resource data and improved software analysis methodologies have enabled the development of digital ‘health maps’ and trans-
L. Rinaldi et al. / Veterinary Parasitology 176 (2011) 286–290
mission models for several parasitic infections including those caused by Dirofilaria spp. (Genchi et al., 2005, 2009; Vezzani and Carbajo, 2006; Medlock et al., 2007; Mortarino et al., 2008). Vector-borne infections, such as those caused by D. immitis and D. repens, can be considered “environmental” because a considerable fraction of their burden can be attributed to environmental factors and their distribution patterns are strongly associated with the spatially heterogeneous environment in which they are entrenched (Stensgaard et al., 2009). Generally speaking, a GIS is constructed with different data-layers, thus permitting the simultaneous visualization of Dirofilaria distribution with climatic and environmental data to assess ecological risk factors associated with infections. Current epidemiological studies clearly show that the distribution patterns of vector-borne infections are changing; for example, in Europe, heartworm infection and subcutaneous dirofilariosis are spreading throughout areas with few or no previous reports of heartworm, such as northern and eastern countries (Genchi et al., this issue). In view of these changes of the distribution patterns of D. immitis and D. repens, geospatial tools are more and more useful for mapping (including territorial sampling), monitoring, ecological analysis, risk assessment, forecasting (including the choose of the timing of treatment), early warning and surveillance of both heartworm and subcutaneous dirofilariosis. The ultimate goal of all these issues is the control of Dirofilaria and other vector-borne infections. Furthermore, knowledge of the prevailing climate and environmental characteristics of an area, which can be provided by GIS and RS, can help predicting disease seasonality by comparing this information with the known requirements of the species under study. Climate-based forecast systems have been developed using the concept of Growing Degree Days (GDD), a heuristic tool in phenology first used by horticulturists to predict the date that a flower will bloom or a crop reach maturity. GDD are calculated by taking the average of the daily maximum and minimum temperatures compared to a base temperature, Tbase using the equation GDD = (Tmax + Tmin )/2 − Tbase . When applied to parasites whose growth is also strongly influenced by the ambient temperature, the GDD concept can be useful in predicting risk and in deciding on disease intervention. GDD have been applied to several vector-borne infections, including Dirofilaria (Genchi et al., 2005, 2009, present issue; Vezzani and Carbajo, 2006; Medlock et al., 2007). Theoretical models for the prediction of Dirofilaria distribution based on the use of GIS have been validated with field data in many European countries (Genchi et al., 2005, 2009, present issue). However, the limitations of these models are based on the fact that they do not yet consider several potentially important factors, such as the influence of microclimate and the adaptations of the numerous mosquito vectors on larval development (McCall et al., 2008). It is also widely known that Dirofilaria transmission is dependent on multiple factors; therefore, risk assessment is a complex matter (Simón et al., 2009). As such, it is strongly advocated to build more efficient GIS-based epidemiological prediction models using parameters other than temperature to design new strategies for treatment and control of heartworm and subcutaneous dirofilariosis.
287
3. GIS-based sampling and mapping One of the most useful functions of GIS in epidemiology continues to be its utility in mapping. Representation of health (positivity or prevalence) data in the form of a map facilitates interpretation, synthesis, and recognition of frequency and clusters of phenomena (Rinaldi et al., 2006). The most recent papers pertaining to the epidemiology of Dirofilaria infections include maps (e.g., point maps, choropletic maps) drawn by the use of GIS or other geospatial tools (Cringoli et al., 2001; Mortarino et al., 2008; Otranto et al., 2009; Pantchev et al., 2009). GIS is also a tool for designing a study and territorial sampling through the following procedures: (i) selection of the study area; (ii) selection of the study population and calculation of the sample size, using as parameters the study population, the expected prevalence, the confidence level, and the standard error; (iii) selection of the sample in the study area; (iv) geo-referencing of the parasitological results pertinent to the study units, such as counties, municipalities, regions, or any other administrative unit; (vi) drawing maps by GIS (Rinaldi et al., 2006). 4. Methods The sampling procedures in the study area play obviously a key role in disease mapping. The methods of spatial sampling have been in practice for a while, but their applications have been restricted to sampling natural phenomena, such as plants, soil types and mineral deposition, and continuous phenomena, such as air pollution (Kumar, 2007). Epidemiological studies through the use of spatial sampling in parasitology as well as in veterinary medicine are relatively new (Rinaldi et al., 2006). Spatial sampling differs from non-spatial sampling methods because a sample is selected based on geographic locations and/or their associated characteristics. A number of spatial sampling methods have been developed and tested (Kumar, 2007). The main spatial sampling schemes are systematic grid sampling, random sampling, proportional allocation, and judgmental sampling based on “judgments and choice” of the researcher. Systematic grid sampling is set to a geometric pattern default, represented by a sampling grid placed on the territory under study. The basic parameters to define are origin and geometry of the grid (square, rectangular, triangular, hexagonal), the spacing of the grid (the distance between two centroids adjacent in the directions x and y), and the density of sampling. The samples usually are collected at regularly spaced intervals corresponding to the center of the mesh grid. This approach requires no knowledge ahead of the territorial distribution of population; it is easy to implement and provide samples”not biase”. Therefore, a systematic grid sampling could be a good sampling procedure for studying Dirofilaria infections in a large territory as the whole Europe (Fig. 1). The judgmental sampling is driven by prior knowledge (more or less detailed) of the characteristics of the phenomenon under study, with such knowledge originating from the study of areas with greater concentration of animals to be studied.
288
L. Rinaldi et al. / Veterinary Parasitology 176 (2011) 286–290
Fig. 1. Examples of grid sampling for studying Dirofilaria infections in Europe.
Another approach is the proportional allocation (i.e., the number of animals to be tested in each administrative unit (e.g., neighborhood, municipality) is proportional to the animal population in that administrative unit), or if the dog population is unknown, is proportional to the surface area of the administrative unit. This latter approach has been used in order to study canine filariosis in the Mt. Vesuvius area of southern Italy (Cringoli et al., 2001) and in the city of Naples (Rinaldi et al., 2007). In the study conducted in the city of Naples (40◦ 51 N and 14◦ 17 E), divided into 21 neighborhoods (Fig. 2), 358 dogs were surveyed. This sample size was calculated using the formula proposed by Thrusfield (1995) for a large (theoretically “infinite”) population using the following values: expected prevalence 16%, based on previous data of Cringoli et al. (2001), confidence interval (99%) and desired absolute precision (5%). In this study, we chose the neighborhood as geographic unit of reference, and in each neighborhood, the number of dogs sampled was proportional to its surface area. Based on the assumption that the dog population is homogeneously distributed throughout the study area, the GIS program
calculated a sample size for each neighborhood based on the proportion of its surface area to the total surface area covered by the study. Blood samples from asymptomatic dogs were collected during daytime hours in sodium citrate vacuum tubes and stored under refrigeration until analysis. Blood samples were analyzed on the day of arrival using the modified Knott technique (Knott, 1939), and morphometric identification of microfilariae were performed (McCall et al., 2008). 5. Results Microfilariae were detected from eight of the 358 dogs surveyed (prevalence = 2.2%). Of the eight dogs with microfilariae, five (1.4%) had only microfilariae of Dipetalonema (Acanthocheilonema) reconditum), and the other three dogs (0.8%) had only microfilariae of D. repens. There were no D. immitis microfilariae found in any of the dogs tested. A distribution map is shown in Fig. 3, with proportioned peaks, which uses the neighborhood as the geographic unit
L. Rinaldi et al. / Veterinary Parasitology 176 (2011) 286–290
289
Fig. 2. The city of Naples, showing 21 neighborhoods (administrative boundaries on digital aerial photographs).
Fig. 3. Distribution map with proportioned peaks. Dirofilaria repens in dogs from the city of Naples.
of reference and display the following information: (i) total study area divided into neighborhoods, (ii) neighborhoods with positive dogs (in red), and (iii) neighborhood prevalence (%) determined as follows: (number of positive dogs in the neighborhood)/(number of dogs examined in the total study area) × 100.
6. Conclusions The information derived from GIS-based descriptive maps, as shown in data obtained in this study, provides an operational tool for planning, monitoring, and managing control programs for Dirofilaria infections. Indeed, the
290
L. Rinaldi et al. / Veterinary Parasitology 176 (2011) 286–290
derivation of detailed epidemiological maps, at the relevant spatial resolution, is being increasingly recognized as vital to the effective design and implementation of successful for the control of parasites and their vectors (Sabesan et al., 2000). Due to their zoonotic role, D. immitis and D. repens infections are relevant examples of the interdependency of human, animal and ecosystem health within the transdisciplinary “One Medicine–One Health” approach to global health. One of the current priorities for global health research is information and communication technologies. Veterinary and public health planning can benefit from visual exploration and analysis of geospatial data. Static, animated, and interactive maps of health data can be very useful for decision-makers (Cinnamon et al., 2009) also as tools in the control of heartworm infections in dogs and cats. In view of the changes of the distribution patterns of D. immitis and D. repens, geospatial tools are more and more useful for mapping (including territorial sampling), ecological analysis, forecasting, early warning, and surveillance of both heartworm and subcutaneous dirofilariosis. In conclusion, the use of GIS is strongly advocate to be the milestone epidemiological tool of scientific societies, including the American Heartworm Society (AHS), the European Scientific Counsel Companion Animal Parasites (ESCCAP), and the new-borne European Dirofilaria Society (EDIS), whose ultimate mission is the advancement and standardization of quality procedures aimed at controlling heartworm and other infections in dogs and cats. Conflict of interest statement None of the authors of this paper has a financial or personal relationship with other people or organizations that could inappropriately influence or bias the content of the paper. The study was funded by the Regional Center for Monitoring Parasitic Infection, Campania Region, Italy. References Bergquist, R., Rinaldi, L., 2010. Health research based on geospatial tools: a timely approach in a changing environment. J. Helminthol. 84, 1–11. Cinnamon, J., Rinner, C., Cusimano, M.D., Marshall, S., Bakele, T., Hernandez, T., Glazier, R.H., Chipman, M.L., 2009. Evaluating web-based static,
animated and interactive maps for injury prevention. Geospat. Health 4, 3–16. Conrad, P.A., Mazet, J.A., Clifford, D., Scott, C., Wilkes, M., 2009. Evolution of a transdisciplinary “One Medicine-One Healt” approach to global health education at the University of California. Davis. Prev. Vet. Med. 92, 268–274. Cringoli, G., Rinaldi, L., Veneziano, V., Capelli, G., 2001. A prevalence survey and risk analysis of filariosis in dogs from the Mt. Vesuvius area of southern Italy. Vet. Parasitol. 102, 243–252. Genchi, C., Rinaldi, L., Cascone, C., Mortarino, M., Cringoli, G., 2005. Is heartworm really spreading in Europe? Vet. Parasitol. 133, 137–148. Genchi, C., Rinaldi, L., Mortarino, M., Genchi, M., Cringoli, G., 2009. Climate and Dirofilaria infection in Europe. Vet. Parasitol. 163, 286–292. Knott, J., 1939. A method for making microfilarial surveys on day blood. Trans. R. Soc. Trop. Med. Hyg. 33, 191. Kumar, N., 2007. Spatial sampling design for a demographic and health survey. Popul. Res. Policy Rev. 26, 581–599. McCall, J.W., Genchi, C., Kramer, L.H., Guerrero, J., Venco, L., 2008. Heartworm disease in animals and humans. Adv. Parasitol. 66, 193–285. Medlock, J.M., Barrass, I., Kerrod, E., Taylor, M.A., Leach, S., 2007. Analysis of climatic predictions for extrinsic incubation of Dirofilaria in the United Kingdom. Vector Borne Zoonotic Dis. 7, 4–14. Mortarino, M., Musella, V., Costa, V., Genchi, C., Cringoli, G., Rinaldi, L., 2008. GIS modeling for canine dirofilariosis risk assessment in central Italy. Geospat. Health 2, 253–261. Otranto, D., Capelli, G., Genchi, C., 2009. Changing distribution patterns of canine vector borne diseases in Italy: leishmaniosis vs. dirofilariosis. Parasit. Vectors 26, S2. Pantchev, N., Norden, N., Lorentzen, L., Rossi, M., Rossi, U., Brand, B., Dyachenko, V., 2009. Current surveys on the prevalence and distribution of Dirofilaria spp. in dogs in Germany. Parasitol. Res. 105, 63–74. Rinaldi, L., Musella, V., Biggeri, A., Cringoli, G., 2006. New insights into the application of geographical information systems and remote sensing in veterinary parasitology. Geospat. Health 1, 33–47. Rinaldi, L., Musella, V., Cringoli, G., 2010. Climatic changes and geospatial health. Parassitologia 52, 45–50. Rinaldi, L., Musella, V., Marzatico, G., Genchi, C., Cringoli, G., 2007. Geographical information systems in health application: experience on filariosis and other vector-borne diseases. In: Claerebout, E., Vercruysse, J. (Eds.), Proceedings of the WAAVP Congress, August 19–23. , p. 165. Sabesan, S., Palaniyandi, M., Das, P.K., Michael, E., 2000. Mapping of lymphatic filariasis in India. Ann. Trop. Med. Parasitol. 94, 591–606. Simón, F., Morchón, R., González-Miguel, J., Marcos-Atxutegi, C., SilesLucas, M., 2009. What is new about animal and human dirofilariosis? Trends Parasitol. 25, 404–409. Stensgaard, A.-S., Saarnak-Larsen, C., Utzinger, J., Vounatsou, P., Simoonga, C., Mushinge, G., Rahbek, C., Møhlenberg, F., Kristensen, T.K., 2009. Virtual globes and geospatial health: the potential uses of Google Earth in the management and control of vector-borne diseases. Geospat. Health 3, 127–141. Thrusfield, M., 1995. Veterinary Epidemiology. Blackwell Science, p. 183. Vezzani, D., Carbajo, A.E., 2006. Spatial and temporal transmission risk of Dirofilaria immitis in Argentina. Int. J. Parasitol. 36, 1463–1472.
Contents lists available at ScienceDirect
Veterinary Parasitology journal homepage: www.elsevier.com/locate/vetpar
Geographical information systems as a tool in the control of heartworm infections in dogs and cats L. Rinaldi a , C. Genchi b , V. Musella c , M. Genchi d , G. Cringoli a,∗ a b c d
Università degli Studi di Napoli Federico II, Napoli, Italy Università degli Studi di Milano, Milano, Italy Università degli Studi Magna Graecia di Catanzaro, Catanzaro, Italy Università degli Studi di Pavia, Pavia, Italy
a r t i c l e
i n f o
Keywords: Geospatial tools Geographical information systems Mapping Dirofilaria immitis Dirofilaria repens
a b s t r a c t Geospatial tools (e.g., geographical information systems, remote sensing, global positioning systems, and virtual globes) are very useful for the simultaneous visualization of health data with environmental data, which holds promise to understand environmental-health linkages and to generate new hypotheses to be tested in future research. Current epidemiological studies clearly show that the distribution patterns of vector-borne infections are changing; for example, in Europe, heartworm infection and subcutaneous dirofilariosis are spreading throughout areas that previously had little to no incidence of heartworm. In view of the changes of the distribution patterns of Dirofilaria immitis and Dirofilaria repens, geospatial tools are now more useful for mapping (including territorial sampling), monitoring, ecological analysis, risk assessment, forecasting (including the choose of the timing of treatment), early warning, and surveillance of both heartworm and subcutaneous dirofilariosis. All these issues have control of these infections as the ultimate goal. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Many factors have contributed to an increasing appreciation of the interdependency of human, animal and ecosystem health within the trans-disciplinary “One Medicine–One Health” approach to global health (Conrad et al., 2009). Health is an outcome of multiple determinants and the predisposing factors of human and pet health status are often interdependent and interrelated, creating a complex web of causation. At the beginning of the third millennium, within a changing climate and a changing
∗ Corresponding author at: Department of Pathology and Animal Health, Faculty of Veterinary Medicine, University of Naples “Federico II”, Via della Veterinaria, 1-80137 Naples, Italy. Tel.: +39 081 2536283; fax: +39 081 2536282. E-mail address: [email protected] (G. Cringoli). URL: http://www.parassitologia.unina.it/ (G. Cringoli). 0304-4017/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.vetpar.2011.01.010
environment, and considering the broad concept of geospatial health, geospatial tools can help answer questions about the complex web of causation of many health issues (Rinaldi et al., 2010), The past 20–25 years have seen an increasing reliance on the application of geospatial tools (i.e., geographical information systems (GIS), global positioning system (GPS), satellite-based remote sensing (RS), and virtual globes (VG), such as Google EarthTM ) to study the distribution of infectious and parasitic diseases and their vectors (Bergquist and Rinaldi, 2010). The present paper is aimed at showing the usefulness of GIS and other geospatial tools in the control of Dirofilaria immitis and Dirofilaria repens infections in dogs and cats. 2. Geospatial tools and Dirofilaria Global availability of geospatial health resource data and improved software analysis methodologies have enabled the development of digital ‘health maps’ and trans-
L. Rinaldi et al. / Veterinary Parasitology 176 (2011) 286–290
mission models for several parasitic infections including those caused by Dirofilaria spp. (Genchi et al., 2005, 2009; Vezzani and Carbajo, 2006; Medlock et al., 2007; Mortarino et al., 2008). Vector-borne infections, such as those caused by D. immitis and D. repens, can be considered “environmental” because a considerable fraction of their burden can be attributed to environmental factors and their distribution patterns are strongly associated with the spatially heterogeneous environment in which they are entrenched (Stensgaard et al., 2009). Generally speaking, a GIS is constructed with different data-layers, thus permitting the simultaneous visualization of Dirofilaria distribution with climatic and environmental data to assess ecological risk factors associated with infections. Current epidemiological studies clearly show that the distribution patterns of vector-borne infections are changing; for example, in Europe, heartworm infection and subcutaneous dirofilariosis are spreading throughout areas with few or no previous reports of heartworm, such as northern and eastern countries (Genchi et al., this issue). In view of these changes of the distribution patterns of D. immitis and D. repens, geospatial tools are more and more useful for mapping (including territorial sampling), monitoring, ecological analysis, risk assessment, forecasting (including the choose of the timing of treatment), early warning and surveillance of both heartworm and subcutaneous dirofilariosis. The ultimate goal of all these issues is the control of Dirofilaria and other vector-borne infections. Furthermore, knowledge of the prevailing climate and environmental characteristics of an area, which can be provided by GIS and RS, can help predicting disease seasonality by comparing this information with the known requirements of the species under study. Climate-based forecast systems have been developed using the concept of Growing Degree Days (GDD), a heuristic tool in phenology first used by horticulturists to predict the date that a flower will bloom or a crop reach maturity. GDD are calculated by taking the average of the daily maximum and minimum temperatures compared to a base temperature, Tbase using the equation GDD = (Tmax + Tmin )/2 − Tbase . When applied to parasites whose growth is also strongly influenced by the ambient temperature, the GDD concept can be useful in predicting risk and in deciding on disease intervention. GDD have been applied to several vector-borne infections, including Dirofilaria (Genchi et al., 2005, 2009, present issue; Vezzani and Carbajo, 2006; Medlock et al., 2007). Theoretical models for the prediction of Dirofilaria distribution based on the use of GIS have been validated with field data in many European countries (Genchi et al., 2005, 2009, present issue). However, the limitations of these models are based on the fact that they do not yet consider several potentially important factors, such as the influence of microclimate and the adaptations of the numerous mosquito vectors on larval development (McCall et al., 2008). It is also widely known that Dirofilaria transmission is dependent on multiple factors; therefore, risk assessment is a complex matter (Simón et al., 2009). As such, it is strongly advocated to build more efficient GIS-based epidemiological prediction models using parameters other than temperature to design new strategies for treatment and control of heartworm and subcutaneous dirofilariosis.
287
3. GIS-based sampling and mapping One of the most useful functions of GIS in epidemiology continues to be its utility in mapping. Representation of health (positivity or prevalence) data in the form of a map facilitates interpretation, synthesis, and recognition of frequency and clusters of phenomena (Rinaldi et al., 2006). The most recent papers pertaining to the epidemiology of Dirofilaria infections include maps (e.g., point maps, choropletic maps) drawn by the use of GIS or other geospatial tools (Cringoli et al., 2001; Mortarino et al., 2008; Otranto et al., 2009; Pantchev et al., 2009). GIS is also a tool for designing a study and territorial sampling through the following procedures: (i) selection of the study area; (ii) selection of the study population and calculation of the sample size, using as parameters the study population, the expected prevalence, the confidence level, and the standard error; (iii) selection of the sample in the study area; (iv) geo-referencing of the parasitological results pertinent to the study units, such as counties, municipalities, regions, or any other administrative unit; (vi) drawing maps by GIS (Rinaldi et al., 2006). 4. Methods The sampling procedures in the study area play obviously a key role in disease mapping. The methods of spatial sampling have been in practice for a while, but their applications have been restricted to sampling natural phenomena, such as plants, soil types and mineral deposition, and continuous phenomena, such as air pollution (Kumar, 2007). Epidemiological studies through the use of spatial sampling in parasitology as well as in veterinary medicine are relatively new (Rinaldi et al., 2006). Spatial sampling differs from non-spatial sampling methods because a sample is selected based on geographic locations and/or their associated characteristics. A number of spatial sampling methods have been developed and tested (Kumar, 2007). The main spatial sampling schemes are systematic grid sampling, random sampling, proportional allocation, and judgmental sampling based on “judgments and choice” of the researcher. Systematic grid sampling is set to a geometric pattern default, represented by a sampling grid placed on the territory under study. The basic parameters to define are origin and geometry of the grid (square, rectangular, triangular, hexagonal), the spacing of the grid (the distance between two centroids adjacent in the directions x and y), and the density of sampling. The samples usually are collected at regularly spaced intervals corresponding to the center of the mesh grid. This approach requires no knowledge ahead of the territorial distribution of population; it is easy to implement and provide samples”not biase”. Therefore, a systematic grid sampling could be a good sampling procedure for studying Dirofilaria infections in a large territory as the whole Europe (Fig. 1). The judgmental sampling is driven by prior knowledge (more or less detailed) of the characteristics of the phenomenon under study, with such knowledge originating from the study of areas with greater concentration of animals to be studied.
288
L. Rinaldi et al. / Veterinary Parasitology 176 (2011) 286–290
Fig. 1. Examples of grid sampling for studying Dirofilaria infections in Europe.
Another approach is the proportional allocation (i.e., the number of animals to be tested in each administrative unit (e.g., neighborhood, municipality) is proportional to the animal population in that administrative unit), or if the dog population is unknown, is proportional to the surface area of the administrative unit. This latter approach has been used in order to study canine filariosis in the Mt. Vesuvius area of southern Italy (Cringoli et al., 2001) and in the city of Naples (Rinaldi et al., 2007). In the study conducted in the city of Naples (40◦ 51 N and 14◦ 17 E), divided into 21 neighborhoods (Fig. 2), 358 dogs were surveyed. This sample size was calculated using the formula proposed by Thrusfield (1995) for a large (theoretically “infinite”) population using the following values: expected prevalence 16%, based on previous data of Cringoli et al. (2001), confidence interval (99%) and desired absolute precision (5%). In this study, we chose the neighborhood as geographic unit of reference, and in each neighborhood, the number of dogs sampled was proportional to its surface area. Based on the assumption that the dog population is homogeneously distributed throughout the study area, the GIS program
calculated a sample size for each neighborhood based on the proportion of its surface area to the total surface area covered by the study. Blood samples from asymptomatic dogs were collected during daytime hours in sodium citrate vacuum tubes and stored under refrigeration until analysis. Blood samples were analyzed on the day of arrival using the modified Knott technique (Knott, 1939), and morphometric identification of microfilariae were performed (McCall et al., 2008). 5. Results Microfilariae were detected from eight of the 358 dogs surveyed (prevalence = 2.2%). Of the eight dogs with microfilariae, five (1.4%) had only microfilariae of Dipetalonema (Acanthocheilonema) reconditum), and the other three dogs (0.8%) had only microfilariae of D. repens. There were no D. immitis microfilariae found in any of the dogs tested. A distribution map is shown in Fig. 3, with proportioned peaks, which uses the neighborhood as the geographic unit
L. Rinaldi et al. / Veterinary Parasitology 176 (2011) 286–290
289
Fig. 2. The city of Naples, showing 21 neighborhoods (administrative boundaries on digital aerial photographs).
Fig. 3. Distribution map with proportioned peaks. Dirofilaria repens in dogs from the city of Naples.
of reference and display the following information: (i) total study area divided into neighborhoods, (ii) neighborhoods with positive dogs (in red), and (iii) neighborhood prevalence (%) determined as follows: (number of positive dogs in the neighborhood)/(number of dogs examined in the total study area) × 100.
6. Conclusions The information derived from GIS-based descriptive maps, as shown in data obtained in this study, provides an operational tool for planning, monitoring, and managing control programs for Dirofilaria infections. Indeed, the
290
L. Rinaldi et al. / Veterinary Parasitology 176 (2011) 286–290
derivation of detailed epidemiological maps, at the relevant spatial resolution, is being increasingly recognized as vital to the effective design and implementation of successful for the control of parasites and their vectors (Sabesan et al., 2000). Due to their zoonotic role, D. immitis and D. repens infections are relevant examples of the interdependency of human, animal and ecosystem health within the transdisciplinary “One Medicine–One Health” approach to global health. One of the current priorities for global health research is information and communication technologies. Veterinary and public health planning can benefit from visual exploration and analysis of geospatial data. Static, animated, and interactive maps of health data can be very useful for decision-makers (Cinnamon et al., 2009) also as tools in the control of heartworm infections in dogs and cats. In view of the changes of the distribution patterns of D. immitis and D. repens, geospatial tools are more and more useful for mapping (including territorial sampling), ecological analysis, forecasting, early warning, and surveillance of both heartworm and subcutaneous dirofilariosis. In conclusion, the use of GIS is strongly advocate to be the milestone epidemiological tool of scientific societies, including the American Heartworm Society (AHS), the European Scientific Counsel Companion Animal Parasites (ESCCAP), and the new-borne European Dirofilaria Society (EDIS), whose ultimate mission is the advancement and standardization of quality procedures aimed at controlling heartworm and other infections in dogs and cats. Conflict of interest statement None of the authors of this paper has a financial or personal relationship with other people or organizations that could inappropriately influence or bias the content of the paper. The study was funded by the Regional Center for Monitoring Parasitic Infection, Campania Region, Italy. References Bergquist, R., Rinaldi, L., 2010. Health research based on geospatial tools: a timely approach in a changing environment. J. Helminthol. 84, 1–11. Cinnamon, J., Rinner, C., Cusimano, M.D., Marshall, S., Bakele, T., Hernandez, T., Glazier, R.H., Chipman, M.L., 2009. Evaluating web-based static,
animated and interactive maps for injury prevention. Geospat. Health 4, 3–16. Conrad, P.A., Mazet, J.A., Clifford, D., Scott, C., Wilkes, M., 2009. Evolution of a transdisciplinary “One Medicine-One Healt” approach to global health education at the University of California. Davis. Prev. Vet. Med. 92, 268–274. Cringoli, G., Rinaldi, L., Veneziano, V., Capelli, G., 2001. A prevalence survey and risk analysis of filariosis in dogs from the Mt. Vesuvius area of southern Italy. Vet. Parasitol. 102, 243–252. Genchi, C., Rinaldi, L., Cascone, C., Mortarino, M., Cringoli, G., 2005. Is heartworm really spreading in Europe? Vet. Parasitol. 133, 137–148. Genchi, C., Rinaldi, L., Mortarino, M., Genchi, M., Cringoli, G., 2009. Climate and Dirofilaria infection in Europe. Vet. Parasitol. 163, 286–292. Knott, J., 1939. A method for making microfilarial surveys on day blood. Trans. R. Soc. Trop. Med. Hyg. 33, 191. Kumar, N., 2007. Spatial sampling design for a demographic and health survey. Popul. Res. Policy Rev. 26, 581–599. McCall, J.W., Genchi, C., Kramer, L.H., Guerrero, J., Venco, L., 2008. Heartworm disease in animals and humans. Adv. Parasitol. 66, 193–285. Medlock, J.M., Barrass, I., Kerrod, E., Taylor, M.A., Leach, S., 2007. Analysis of climatic predictions for extrinsic incubation of Dirofilaria in the United Kingdom. Vector Borne Zoonotic Dis. 7, 4–14. Mortarino, M., Musella, V., Costa, V., Genchi, C., Cringoli, G., Rinaldi, L., 2008. GIS modeling for canine dirofilariosis risk assessment in central Italy. Geospat. Health 2, 253–261. Otranto, D., Capelli, G., Genchi, C., 2009. Changing distribution patterns of canine vector borne diseases in Italy: leishmaniosis vs. dirofilariosis. Parasit. Vectors 26, S2. Pantchev, N., Norden, N., Lorentzen, L., Rossi, M., Rossi, U., Brand, B., Dyachenko, V., 2009. Current surveys on the prevalence and distribution of Dirofilaria spp. in dogs in Germany. Parasitol. Res. 105, 63–74. Rinaldi, L., Musella, V., Biggeri, A., Cringoli, G., 2006. New insights into the application of geographical information systems and remote sensing in veterinary parasitology. Geospat. Health 1, 33–47. Rinaldi, L., Musella, V., Cringoli, G., 2010. Climatic changes and geospatial health. Parassitologia 52, 45–50. Rinaldi, L., Musella, V., Marzatico, G., Genchi, C., Cringoli, G., 2007. Geographical information systems in health application: experience on filariosis and other vector-borne diseases. In: Claerebout, E., Vercruysse, J. (Eds.), Proceedings of the WAAVP Congress, August 19–23. , p. 165. Sabesan, S., Palaniyandi, M., Das, P.K., Michael, E., 2000. Mapping of lymphatic filariasis in India. Ann. Trop. Med. Parasitol. 94, 591–606. Simón, F., Morchón, R., González-Miguel, J., Marcos-Atxutegi, C., SilesLucas, M., 2009. What is new about animal and human dirofilariosis? Trends Parasitol. 25, 404–409. Stensgaard, A.-S., Saarnak-Larsen, C., Utzinger, J., Vounatsou, P., Simoonga, C., Mushinge, G., Rahbek, C., Møhlenberg, F., Kristensen, T.K., 2009. Virtual globes and geospatial health: the potential uses of Google Earth in the management and control of vector-borne diseases. Geospat. Health 3, 127–141. Thrusfield, M., 1995. Veterinary Epidemiology. Blackwell Science, p. 183. Vezzani, D., Carbajo, A.E., 2006. Spatial and temporal transmission risk of Dirofilaria immitis in Argentina. Int. J. Parasitol. 36, 1463–1472.