Guía para la elaboración de un croquis / Guide for drawing a sketch

Con esta guía se busca poder consolidar el ordenamiento territorial según los territorios y sectores, recabar información importante de la fuente directa, misma que será un insumo trascendental para definir el tipo de servicios que se necesitan brindar en cada ubicación, al contar con información importante del terreno, la distribución de las viviendas y de la población que son determinantes para la programación de las acciones intra y extramuros, así como para la promoción de acciones sanitarias. Este recurso es importante para implementar y definir la cartera de servicios de salud así como la estrategia de las Redes Integradas de Servicios de Salud dentro del marco de la Atención Primaria en Salud, con la cual se espera poder fortalecer la atención integral e integrada a la población en general. El Croquis es un recurso indispensable para la planificación, programación, organización y monitoreo de los servicios que entregan los equipos de salud.

Topografia do cone medular da irara (Eira barbara) e sua relevância em anestesias epidurais / Topography of the conus medullari in tayra (Eira barbara) and their relevance in epidural anesthesia

Encontrada principalmente na América do Sul, a irara é um carnívoro pertencente à família Mustelidae, a qual pouco se tem informações sobre sua morfologia de forma geral. Diante disso, objetivou-se conhecer melhor parte do sistema nervoso desta espécie, mais precisamente a topografia do cone medular, a fim de subsidiar intervenções anestésicas peridurais nesta, uma vez que a clínica de animais selvagens vem crescendo a cada dia. Foram estudados três exemplares machos, adultos, provenientes da área de Mina Bauxita, Paragominas, doados ao Laboratório de Pesquisa Morfológica Animal (LaPMA), Universidade Federal Rural da Amazônia (UFRA), Belém, os quais foram radiografados e dissecados ao nível lombossacral, seguido de exposição do cone medular. Este, por sua vez, situou-se entre L4-L6 possuindo comprimento médio de 4,31cm, o que nos levou a sugerir a região sacrococcígea como ponde ideal para prática de anestesias epidurais nesta espécie.

Mainly found in South America, tayra is a carnivore belonging to the Mustelidae family, of which is little information regarding its morphology in general. The study aimed to characterize the topography of the medullar cone in order to subsidize epidural anesthetic interventions, since the clinics of wild animals is growing each day. We studied three adult male tayras from the Bauxite Mine area of Paragominas, donated to the Research Laboratory of Animal Morphology, Federal Rural University of Amazonia, Belém. They were x-rayed and dissected at the lumbosacral level to expose the medullar cone, which was found between L4-L6 with an average length of 4.31cm. This led us to suggest the sacrococcygeal region as ideal site for practice of epidural anesthesia in this species.

New tools for epidemiology: a space odyssey.

Geographical Information Systems (GIS) facilitate access to epidemiological data through visualization and may be consulted for the development of mathematical models and analysis by spatial statistics. Variables such as land-cover, land-use, elevations, surface temperatures, rainfall etc. emanating from earth-observing satellites, complement GIS as this information allows the analysis of disease distribution based on environmental characteristics. The strength of this approach issues from the specific environmental requirements of those causative infectious agents, which depend on intermediate hosts for their transmission. The distribution of these diseases is restricted, both by the environmental requirements of their intermediate hosts/vectors and by the ambient temperature inside these hosts, which effectively govern the speed of maturation of the parasite. This paper discusses the current capabilities with regard to satellite data collection in terms of resolution (spatial, temporal and spectral) of the sensor instruments on board drawing attention to the utility of computer-based models of the Earth for epidemiological research. Virtual globes, available from Google and other commercial firms, are superior to conventional maps as they do not only show geographical and man-made features, but also allow instant import of data-sets of specific interest, e.g. environmental parameters, demographic information etc., from the Internet.

New tools for epidemiology: a space odyssey

Geographical Information Systems (GIS) facilitate access to epidemiological data through visualization and may be consulted for the development of mathematical models and analysis by spatial statistics. Variables such as land-cover, land-use, elevations, surface temperatures, rainfall etc. emanating from earth-observing satellites, complement GIS as this information allows the analysis of disease distribution based on environmental characteristics. The strength of this approach issues from the specific environmental requirements of those causative infectious agents, which depend on intermediate hosts for their transmission. The distribution of these diseases is restricted, both by the environmental requirements of their intermediate hosts/vectors and by the ambient temperature inside these hosts, which effectively govern the speed of maturation of the parasite. This paper discusses the current capabilities with regard to satellite data collection in terms of resolution (spatial, temporal and spectral) of the sensor instruments on board drawing attention to the utility of computer-based models of the Earth for epidemiological research. Virtual globes, available from Google and other commercial firms, are superior to conventional maps as they do not only show geographical and man-made features, but also allow instant import of data-sets of specific interest, e.g. environmental parameters, demographic information etc., from the Internet.

Emerging viral zoonoses: frameworks for spatial and spatiotemporal risk assessment and resource planning.

Spatial epidemiological tools are increasingly being applied to emerging viral zoonoses (EVZ), partly because of improving analytical methods and technologies for data capture and management, and partly because the demand is growing for more objective ways of allocating limited resources in the face of the emerging threat posed by these diseases. This review documents applications of geographical information systems (GIS), remote sensing (RS) and spatially-explicit statistical and mathematical models to epidemiological studies of EVZ. Landscape epidemiology uses statistical associations between environmental variables and diseases to study and predict their spatial distributions. Phylogeography augments epidemiological knowledge by studying the evolution of viral genetics through space and time. Cluster detection and early warning systems assist surveillance and can permit timely interventions. Advanced statistical models can accommodate spatial dependence present in epidemiological datasets and can permit assessment of uncertainties in disease data and predictions. Mathematical models are particularly useful for testing and comparing alternative control strategies, whereas spatial decision-support systems integrate a variety of spatial epidemiological tools to facilitate widespread dissemination and interpretation of disease data. Improved spatial data collection systems and greater practical application of spatial epidemiological tools should be applied in real-world scenarios.

Satellite imaging and vector-borne diseases: the approach of the French National Space Agency (CNES).

Tele-epidemiology consists in studying human and animal epidemic, the spread of which is closely tied to environmental factors, using data from earth-orbiting satellites. By combining various data originated from satellites such as SPOT (vegetation indexes), Meteosat (winds and cloud masses) and other Earth observation data from Topex/Poseidon and Envisat (wave height, ocean temperature and colour) with hydrology data (number and distribution of lakes, water levels in rivers and reservoirs) and clinical data from humans and animals (clinical cases and serum use), predictive mathematical models can be constructed. A number of such approaches have been tested in the last three years. In Senegal, for example, Rift Valley fever epidemics are being monitored using a predictive model based on the rate at which water holes dry out after the rainy season, which affects the number of mosquito eggs which carry the virus.

Spatial and temporal modelling for parasite transmission studies and risk assessment.

Spatial and temporal modelling of parasite transmission and risk assessment require relevant spatial information at appropriate spatial and temporal scales. There is now a large literature that demonstrates the utility of satellite remote sensing and spatial modelling within geographical information systems (GIS) and firmly establishes these technologies as the key tools for spatial epidemiology. This review outlines the strength of satellite remotely sensed data for spatial mapping of landscape characteristics in relation to disease reservoirs, host distributions and human disease. It is suggested that current satellite technology can fulfill the spatial mapping needs of disease transmission and risk modelling, but that temporal resolution, which is a function of the satellite data acquisition characteristics, may be a limitating factor for applications requiring information about landscape or ecosystem dynamics. The potential of the Modis sensor for spatial epidemiology is illustrated with reference to mapping spatial and temporal vegetation dynamics and small mammal parasite hosts on the Tibetan plateau. Future research directions and priorities for landscape epidemiology are considered.

Remote and field level quantification of vegetation covariates for malaria mapping in three rice agro-village complexes in Central Kenya.

BACKGROUND: We examined algorithms for malaria mapping using the impact of reflectance calibration uncertainties on the accuracies of three vegetation indices (VI)'s derived from QuickBird data in three rice agro-village complexes Mwea, Kenya. We also generated inferential statistics from field sampled vegetation covariates for identifying riceland Anopheles arabiensis during the crop season. All aquatic habitats in the study sites were stratified based on levels of rice stages; flooded, land preparation, post-transplanting, tillering, flowering/maturation and post-harvest/fallow. A set of uncertainty propagation equations were designed to model the propagation of calibration uncertainties using the red channel (band 3: 0.63 to 0.69 microm) and the near infra-red (NIR) channel (band 4: 0.76 to 0.90 microm) to generate the Normalized Difference Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index (SAVI). The Atmospheric Resistant Vegetation Index (ARVI) was also evaluated incorporating the QuickBird blue band (Band 1: 0.45 to 0.52 microm) to normalize atmospheric effects. In order to determine local clustering of riceland habitats Gi*(d) statistics were generated from the ground-based and remotely-sensed ecological databases. Additionally, all riceland habitats were visually examined using the spectral reflectance of vegetation land cover for identification of highly productive riceland Anopheles oviposition sites. RESULTS: The resultant VI uncertainties did not vary from surface reflectance or atmospheric conditions. Logistic regression analyses of all field sampled covariates revealed emergent vegetation was negatively associated with mosquito larvae at the three study sites. In addition, floating vegetation (-ve) was significantly associated with immature mosquitoes in Rurumi and Kiuria (-ve); while, turbidity was also important in Kiuria. All spatial models exhibit positive autocorrelation; similar numbers of log-counts tend to cluster in geographic space. The spectral reflectance from riceland habitats, examined using the remote and field stratification, revealed post-transplanting and tillering rice stages were most frequently associated with high larval abundance and distribution. CONCLUSION: NDVI, SAVI and ARVI generated from QuickBird data and field sampled vegetation covariates modeled cannot identify highly productive riceland An. arabiensis aquatic habitats. However, combining spectral reflectance of riceland habitats from QuickBird and field sampled data can develop and implement an Integrated Vector Management (IVM) program based on larval productivity.

Biology-based mapping of vector-borne parasites by Geographic Information Systems and Remote Sensing.

Applications of growing degree day-water budget analysis and satellite climatology to vector-borne parasites are reviewed to demonstrate the value of using the unique thermal-hydrological preferences and limits of tolerance of individual parasite-vector systems to define the environmental niche of disease agents in the landscape by modern geospatial analysis methods.

Sizing up human health through remote sensing: uses and misuses.

Following the launch of new satellites, remote sensing (RS) has been increasingly implicated in human health research for thirty years, providing a growing availability of images with higher resolution and spectral ranges. However, the scope of applications, beyond theoretical large potentialities, appears limited both by their technical nature and the models developed. An exhaustive review of RS applications in human health highlights the real implication thus far regarding the diversity and range of health issues, remotely sensed data, processes and interpretations. The place of RS is far under its expected potential, revealing fundamental barriers in its implementation for health applications. The selection of images is done by practical considerations as trivial as price and availability, which are often not relevant to addressing health questions requiring suitable resolutions and spatio-temporal range. The relationships of environmental variables from RS, geospatial data from other sources for health investigations are poorly addressed and usually simplified. A discussion covering the potential of RS for human health is developed here to assist health scientists deal with spatial and temporal dynamics of health, by finding the most relevant data and analysis procedures.

Advances in satellite remote sensing of pheno-climatic features for epidemiological applications.

Geographical Information Systems (GIS) and Remote Sensing (RS) technologies are being used increasingly to study the spatial and temporal patterns of diseases. They can be used to complement conventional ecological monitoring and modelling techniques, and provide a means to portray complex relationships in the ecology of diseases with strong environmental determinants. In particular, satellite technology has been extraordinarily improved during recent years, providing new parameters useful to understand the epidemiology of parasites, such as vegetation indices, land surface temperatures, soil moisture and rainfall indices. In the present review, Normalized Difference Vegetation Index (NDVI) is primarily considered, since it is the index characterizing vegetation that is most used in epidemiological studies. Multi-temporal study of RS data allows collection of bio-climatic information about risk area distribution, along with predictive studies and anticipatory models of diseases, at different geographic scales ranging from global to local. The main physical and technological basis of a mathematical model, effective at different scales, for identification of landscape pheno-climatic features is described in the current paper.

Application of Geographical Information Systems and Remote Sensing technologies for assessing and monitoring malaria risk.

Despite over 30 years of scientific research, algorithm development and multitudes of publications relating Remote Sensing (RS) information with the spatial and temporal distribution of malaria, it is only in recent years that operational products have been adopted by malaria control decision-makers. The time is ripe for the wealth of research knowledge and products from developed countries be made available to the decision-makers in malarious regions of the globe where this information is urgently needed. This paper reviews the capability of RS to provide useful information for operational malaria early warning systems. It also reviews the requirements for monitoring the major components influencing emergence of malaria and provides examples of applications that have been made. Discussion of the issues that have impeded implementation on a global scale and how those barriers are disappearing with recent economic, technological and political developments are explored; and help pave the way for implementation of an integrated Malaria Early Warning System framework using RS technologies.

A study of the environmental determinants of malaria and schistosomiasis in the Philippines using Remote Sensing and Geographic Information Systems.

Malaria and schistosomiasis are two water-related parasitic diseases affecting millions of people worldwide particularly tropical and subtropical countries. In the Philippines, malaria is found in 72 out of 78 provinces while schistosomiasis is endemic in 24 provinces. The Anopheles mosquito and the Oncomelania snail involved in the transmission of these diseases depend on certain environmental determinants that support mosquito and snail populations. This study, done for the first time in the Philippines, successfully showed how Remote Sensing (RS) and Geographical Information Systems (GIS) can be effectively used in showing how these environmental factors affect the spatial distribution of these two diseases. The study sites, i.e. the municipalities of Asuncion and Kapalong, are known endemic sites for both malaria and schistosomiasis. Georeferenced data enabled visualization of prevalence data in relation to physical maps thus facilitating assessment of disease situation in the two municipalities. RS and GIS data proved that other factors aside from climate influence the epidemiology of the diseases in the two sites. Topography and slope as main physical factors influence the vegetation cover, land use and soil type prevailing in particular areas. In addition, water sources especially irrigation networks differed in various places in the study sites in turn affecting the magnitude and distribution of malaria and schistosomiasis. Significant correlations found between the diseases and the environmental variables formed the basis for development of models to predict the disease prevalence in the two municipalities. Proximity to snail breeding sites and irrigation networks and the highly agricultural nature of the barangays were identified as the most common factors that define the high prevalence areas for schistosomiasis confirming the fact that conditions that support the snail populations will in turn favor the presence of the disease. For malaria, the predictive models included temperature, humidity, soil type, predominance of reproduction brush, presence of cultivated areas, distance from deep wells and distance from conventional water source which are in turn influenced by the factor of elevation.

Modeling the biocoenose of parasitic diseases using remote sensing and geographic information systems.

Application of growing degree day-water budget analysis concepts to snail-borne diseases are reviewed to demonstrate use of the unique thermal-hydrological preferences and limits of tolerance of individual parasite-vector systems to define the environmental niche of disease agents in the landscape by modern geospatial analysis tools.

[Pheno-climatic profiles of vegetation based on multitemporal analysis of satellite data]. / Profili feno-climatici della vegetazione mediante analisi multitemporale di dati telerilevati.

Satellite Remote Sensing offers numerous advantages: study of large areas in a short time, study of areas with not easy accessibility, synoptic observation of territory, multitemporal observations of the same area, monitoring land modifications and change detection studies. The effectiveness of using satellite images for studying and mapping vegetation and land use has been stressed since the early 1980s. The photosynthetically active vegetation presents a very characteristic spectral response. In fact, leaves absorb red radiation (RED) in order to do photosynthetic process and reflect almost completely near infrared (NIR) wavelengths. The most diffused index for quantifying photosynthetically active biomass is the NDVI (Normalized Difference Vegetation Index): NDVI = (NIR-RED)/(NIR+RED). The NDVI is calculated, for each pixel of the images analysed, through an appropriate software. Low values of NDVI correspond to scarcely vegetated areas, while high values indicate densely vegetated ones. In order to distinguish among vegetation typologies we need some images of the same territory, well distributed during the year, showing seasonal variations of vegetation photosynthetic activity. Then it will be e.g. very easy distinguish between evergreen species (with NDVI almost steady during the year) and deciduous ones. Several types of sensors aboard some satellites allow different investigations to be done. AVHRR sensor on NOAA and TM sensor on Landsat are among the best known sensors available. They have different characteristics as for spectral resolution (number of spectral bands), spatial resolution (size of each elementary cell) and temporal resolution (the period of the satellite passes on the same territory). Vegetation phenology (including biomass and photosynthetic activity) heavily depends on climatic factors. The most important are: solar radiance, with an annual cycle and maximum at summer solstice; air temperature, (depending on solar radiance) with an annual cycle and maximum more than one month later; water availability, which is strongly dependent on rainfalls; in the Mediterranean area they can have an annual cycle (maximum during winter) or a six-monthly one (maxima near the equinoxes). Having a set of multitemporal satellite data (e.g. 12 monthly NOAA-AVHRR images) we can use a mathematical model able to discriminate annual and six-monthly cycles. Through Fourier analysis, the mathematical model calculate, for each pixel of the image, the parameters of the annual NDVI profile and create a synthetic image (pheno-climatic map), in which the values of the three RGB components (Red, Green, Blue ) are proportional to the integral of the NDVI profile for the following three periods: B=Nov-Feb G=Mar-Jun R=Jul-Oct. A similarly analysis is possible with Landsat satellite data, which have a higher spatial resolution, given that some shrewdness are taken. In fact, it is necessary to select satellite images according to the presence of cloud cover, which is--over the Italian peninsula--quite common during the March-April and October-November intervals. The purpose of carrying out pheno-climatic maps can be accomplished using 6 Landsat-TM images well-distributed during a year, every two months, even if the images have been taken during different years.

[Geographical Information Systems and remote sensing technologies in parasitological epidemiology]. / I GIS (Geographical Information Systems) e la tecnologia satellitare nella epidemiologia delle parassitosi.

Parasites have natural habitats in the same way as a species: they are found in focal areas where the spatial distribution of the parasite, host, vector and required environmental conditions coincide. The spatial distribution of parasites is, therefore, a function of the interaction between abiotic and biotic environmental factors. The boundaries of distributions are not strictly fixed in space and time and may fluctuate with climate and other components of the environment or anthropical factors. Geographic Information Systems (GIS) and remote sensing (RS) technologies are being used increasingly to study the spatial and temporal patterns of disease. GIS can be used to complement conventional ecological monitoring and modelling techniques, and provide means to portray complex relationships in the ecology of disease. In addition, the use of GIS and RS to identify environmental features allows determination of risk factors and delimitation of areas at risk, permitting more rational allocation of resources for cost-effective control. Since 1996, GIS have been used in our territorial cross-sectional and longitudinal parasitological surveys in order to experiment new applications to plan sampling protocols and to display quickly, clearly, and analytically the spatial and/or temporal distribution of parasitological data. The use of GIS allowed us to draw the following types of descriptive parasitological maps: distribution maps, distribution maps with proportioned peaks, choroplethic maps with proportioned peaks, point distribution maps and point distribution maps with proportioned peaks. In a recent study, GIS and RS technologies have been used also to identify environmental features that influence the distribution of paramphistomosis in sheep from the southern Italian Apennines and to develop a preliminary risk assessment model. A GIS was constructed using RS and landscape feature data together with paramphistome positive survey records from 197 georeferenced ovine farms with animals pasturing in an area of the southern Italian Apennines. The GIS for the study area was constructed utilizing the following environmental variables: Normalized Difference Vegetation Index (NDVI), land cover, elevation, slope, aspect, and total length of rivers. In addition, data regarding the presence of watercourses smaller than rivers, namely, streams, springs and brooks were recorded in the field. All these variables were then calculated for "buffer zones" consisting of the areas included in a circle of 3 Km diameter centred on 197 farms. The environmental data obtained were analyzed by univariate and multivariate statistical analyses using the paramphistome farm coprological status (positive/negative) as the dependent variable. A multivariate stepwise discriminant analysis model was developed that included moors and heathland, sclerophyllus and coniferous forest vegetation, autumn-winter NDVI and presence of streams, springs and brooks on pasture. The variables entered in the model are consistent with the environmental requirements of paramphistomes and their snail intermediate host. In particular, the land cover types entered in the model in this area are indicators of marginal uncultivable and sloping zones where typically there is the presence of water (permanently or temporarily). In addition, since NDVI can be used as an indicator of regional thermal-moisture regime, the distribution of farms positive for paramphistomosis corresponding to relatively high values of winter NDVI indicated the presence of adequate moisture and temperatures favourable to the rumen fluke and the snails. In conclusion, GIS and RS are useful to define the habitats of parasites, especially for those with strong environmental determinants, and to produce forecasting maps requested for the planning and the monitoring of control strategies on small and large scale.

Geographic information system as a tool to study malaria receptivity in Nadiad Taluka, Kheda district, Gujarat, India.

Nadiad taluka, Kheda district, Gujarat State, India, comprising of 100 villages with unstable malaria and periodic epidemics, was selected for the study. Using topo sheets and satellite imageries thematic maps on water table, water quality, hydro-geomorphology, soil type, relief, irrigation channels, were prepared, overlaid and integrated sequentially using Arclnfo software. The composite map resulted in 13 stratification classes. Stratification classes 1-12 fell in non-irrigated tracts and exhibited 95% matching of areas of high receptivity as revealed by geographical information systems (GIS) and annual malaria parasite incidence (API). Stratification class 13, an irrigated area, showed poor matching but the ground verification established low receptivity of the area. Thus the study resulted in complete reconciliation of cause and effect relationship as established as per GIS in explaining malaria epidemiology. In general, the study revealed that high malaria in villages of Nadiad is mainly due to high water table, soil type, irrigation and water quality. Based on local malaria transmission determinants, a revised malaria control strategy has been suggested.