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PubMedDate not supplied

[The division of the territory of Azerbaijan into districts by hydatid echinococcosis].

The epizootiological and epidemiological stratification of the territory of Azerbaijan on hydatid disease was made. Four zones were identified as low, moderately, highly and extremely endemic. These zones are differentiated from their climatic and social conditions for echinococcosis distribution, human incidence and prevalence, canine and ovine number and prevalence, and the time of survival of echinococcus eggs in the soil. The most intensive disease control measures are recommended in mountainous regions of the Small Caucasus.

Animals↗
PubMedDate not supplied

Dental fluorosis and fluoride mapping in Langtang town, Nigeria.

This study is aimed at measuring the fluoride concentrations of different water supplies in a Nigerian community known to have endemic fluorosis. This is with the view of mapping out a pattern and to investigate the relationship of this pattern with the distribution of dental fluorosis among residents of the community. A representative sample of 475 persons selected on the basis of criteria described in an earlier publication, constituted the study subjects. Clinical examination were carried out after obtaining sociodemographic information from the subjects. Analyses of fluoride concentrations in 136 water samples revealed, in general the highest levels in stream water range: 2.39-3.96 ppm, followed by wells (range: 1.26-2.82 ppm) and the least in pipe-borne water (range: 0.5-0.97 ppm). In plotting specific fluoride readings from the different identified sources on the geographical map of the study area, a distinctive pattern emerged. High fluoride readings were generally in the highland areas from which rivers and streams took origin. Approximately 50% of the town was supplied with water containing fluoride above the optimum. As reported in the earlier publication, the prevalence of dental fluorosis was found to be 26.1%. The age specific prevalence rate indicated the highest occurrence rate among those aged 10-19 years. Six of the participating children had involvement of deciduous teeth. Even though no correlation was established between dental fluorosis and sex on one hand and fluorosis and ethnicity on the other, there was a markedly significant association between fluorosis and source of drinking water (P < 0.05). Those who drank from streams appeared more likely to have fluorosis. It was concluded that though other sources of fluoride ingestion have been documented, it appeared that water may play a very significant role in the aetiology of fluorosis in this community.

Adult↗
PubMed2004

[Statistic-mapping modeling of rheumatic diseases prevalence among population of different regions of Russia].

AIM: To assess feasibility of the method of mapping modeling for spacial featuring of rheumatic diseases (RD) morbidity statistics for population of Russia. MATERIAL AND METHODS: Population morbidity statistics for 78 administrative units of the Russian Federation (1993-2000) were processed. Statistic-mapping modeling employed weighted mean interpolation i.e. mathematic prediction of the sign value depending on its magnitude in basic points. RESULTS: Mapped images of RD prevalence, prevalence/primary morbidity values and number of documented cases/number of rheumatologists demonstrate special distribution of the signs studied about the RF territory. The estimation of mean, minimal, maximal values and dispersion of each sign established dynamic trends in the markers for the period under study(increased area with RD prevalence above 95/1000 from 15.9 to 44.5%; increased mean ratio prevalence/morbidity from 2.8 to 3.2 and decreased ratio of patients/rheumatologists from 10882 to 9031). Regions with minimal and maximal values are shown. CONCLUSION: Statistic-mapping modeling demonstrates spacial distribution of the markers, reveals the relations between them.

Humans↗
PubMed2004

[Statistical models for spatial analysis in parasitology].

The simplest way to study the spatial pattern of a disease is the geographical representation of its cases (or some indicators of them) over a map. Maps based on raw data are generally "wrong" since they do not take into consideration for sampling errors. Indeed, the observed differences between areas (or points in the map) are not directly interpretable, as they derive from the composition of true, structural differences and of the noise deriving from the sampling process. This problem is well known in human epidemiology, and several solutions have been proposed to filter the signal from the noise. These statistical methods are usually referred to as Disease Mapping. In geographical analysis a first goal is to evaluate the statistical significance of the heterogeneity between areas (or points). If the test indicates rejection of the hypothesis of homogeneity the following task is to study the spatial pattern of the disease. The spatial variability of risk is usually decomposed into two terms: a spatially structured (clustering) and a non spatially structured (heterogeneity) one. The heterogeneity term reflects spatial variability due to intrinsic characteristics of the sampling units (e.g. igienic conditions of farms), while the clustering term models the association due to proximity between sampling units, that usually depends on ecological conditions that vary over the study area and that affect in similar way breedings that are close to each other. Hierarchical bayesian models are the main tool to make inference over the clustering and heterogeneity components. The results are based on the marginal posterior distributions of the parameters of the model, that are approximated by Monte Carlo Markov Chain methods. Different models can be defined depending on the terms that are considered, namely a model with only the clustering term, a model with only the heterogeneity term and a model where both are included. Model selection criteria based on a compromise between degree of complexity and goodness of fit are then needed to discriminate among them, because each specification has a different biological meaning. Our aim is to demonstrate that these techniques can be used to study the geographical distribution of a parasite infection. Our analyses are based on data collected in 142 farms of the province of Latina. In each breeding a fixed number of sheeps has been sampled (20) and checked for the presence of C. daubneyi. We have specified a Binomial model for the proportion of infected animals in each breeding. The heterogeneity component is modelled in a standard way, while we have used different prior specifications for the clustering term to show how they affect the results. When we use the usual specification also for clustering, the two models show a completely different spatial pattern of infection, probably because the intrinsic spatial structure of the clustering term tend to bias our inferences. The selection criterion indicates in this case the heterogeneity model as the "best" one. However, if we modify the prior so that a lower degree of spatial interaction is assumed, the clustering model is less complex and its goodness of fit better and it should be preferred.

Animal Husbandry↗
PubMed2005

Disease mapping and risk assessment in veterinary parasitology: some case studies.

Disease mapping and risk assessment are important tasks in the area of medical and veterinary epidemiology. The development of methods for mapping diseases has progressed considerably in recent years. Geographical Information Systems (GIS), Remote Sensing (RS), and Spatial Analysis represent new tools for the study of epidemiology, and their application to parasitology has become more and more advanced, in particular to study the spatial and temporal patterns of diseases. The present review highlights the usefulness of GIS and RS in veterinary parasitology in order to better know the epidemiology of parasite organisms, causing either snail/arthropod borne diseases or direct transmissible diseases, mostly in small areas with a strong impact by man. It demonstrates the potential of these technologies to serve as effective tools for: data capture, mapping and analysis for the development of descriptive parasitological maps; studying the environmental features that influence the distribution of parasites; predicting parasite occurrence/seasonality based on their environmental requirements and as decision support for disease intervention; and surveillance and monitoring of animal diseases.

Animals↗
PubMed2005

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.

Biomass↗
PubMed2005

Geographical Information Systems and on-line GIServices for health data sharing and management.

Integrating Geographical Information Systems (GIS) technology and public health experience may represent a solution for a better comprehension of spatial and temporal trends of phenomena. Useful applications can be built that support practitioners in their daily tasks, from risk assessment to prevention programmes. Also, making available data on the Internet through GIServices represents an important goal. Institutions and public health practitioners may benefit from the technological integration of GIS, the Web, handheld and mobile global positioning systems (GPS) devices. Expert users may be supported in deriving thematic maps which represent a spatial synthesis documentation starting from an analytic study expressed in terms of numbers and features. In this paper we show an example of an on-line data sharing and processing application, emphasizing ways GIS can provide added value to health research and management.

Cluster Analysis↗
PubMed2005

A classification model of grazing areas in southern Italy using Remote Sensing data.

Detection of vegetation typologies is of particular interest in epidemiological studies on animal diseases. This paper is focused on the automatic detection of grazing areas in southern Italy browsed by equines, oxen and sheep, mostly sedentary, with many in fenced areas for overnight stays. Results revealed Satellite Remote Sensing was an indispensable tool in area surveys for vegetation cover characterization. Our classification model shows an accuracy level of 90.21% and a precision of 92.69%. Results suggest similar application protocols can be used in areas with different vegetation cover to characterizing potential infection risk areas for geohelminths and other diseases.

Agriculture↗
PubMedDate not supplied

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Hobbies↗
PubMed2005

GIS and epidemiology.

Understanding the spatial patterns of infectious diseases can provide insight as to their causes and controls. Geographic information systems (GIS) and related technologies like remote sensing are increasingly used to analyze geographical distribution of diseases as well as relationships between pathogenic factors (causative agents, patients, vectors and hosts) and their geographic environments. Basic and analytical applications of GIS in epidemiology can help in visualizing and analyzing geographic distribution of diseases through time, thus revealing spatio-temporal trends, patterns, and relationships that would be more difficult or obscure to discover in tabular or other formats. GIS can provide a means to meet the demands of outbreak investigation and response, where understanding the spatial spread and dynamics of an outbreak is central to the design of prevention and control strategies.

Communicable Diseases↗