SEARCH · PubMed Health
Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Explore the source record for details and available documents.
To investigate the relationship between four sources of environmental pollution (shipyard, iron foundry, incinerator, and city center) and lung cancer risk, we conducted a case-control study of decreased men in Trieste, Italy. We identified 755 cases of lung cancer and 755 controls through the local autopsy registry. Information on smoking habits, occupational history, and place of residence were obtained from the subject's next of kin. The case-control design was used to properly account for subject-specific confounders, which represent a major problem in geographical analysis. Spatial models were used to evaluate the effect of sources of pollution on lung cancer after adjustment for age, smoking habits, likelihood of exposure to occupational carcinogens, and levels of air particulate. The models are based on distance from the sources and enable estimation of the risk gradient and directional effects separately for each source. The risk of lung cancer was highly related to the city center (p = 0.0243), with an excess relative risk at zero distance of 2.2 and a smooth decrease moving away from the source (-0.015), and related to the incinerator (p = 0.0098), with an excess relative risk of 6.7 in the source and a very steep decrease (-0.176). These results are consistent with findings of previous analyses and provide further evidence that air pollution is a moderate risk factor of lung cancer.
A spatial analysis was carried out in order to analyse the reason why the risk of Bovine Spongiform Encephalopathy (BSE) was spatially heterogeneous in France, during the period following the feed ban of Meat and Bone Meal to cattle. The hypothesis of cross-contamination between cattle feedstuff and monogastric feedstuff, which was strongly suggested from previous investigations, was assessed, with the assumption that the higher the pig or poultry density is in a given area, the higher the risk of cross-contamination and cattle infection might be. The data concerned the 467 BSE cases born in France after the ban of meat and bone meal (July 1990) and detected between July 1st, 2001 and December 31, 2003, when the surveillance system was optimal and not spatially biased. The disease mapping models were elaborated with the Bayesian graphical modelling methods and based on a Poisson distribution with spatial smoothing (hierarchical approach) and covariates. The parameters were estimated by a Markov Chain Monte Carlo simulation method. The main result was that the poultry density did not significantly influence the risk of BSE whereas the pig density was significantly associated with an increase in the risk of 2.4% per 10 000 pigs. The areas with a significant pig effect were located in regions with a high pig density as well as a high ratio of pigs to cattle. Despite the absence of a global effect of poultry density on the BSE risk, some areas had a significant poultry effect and the risk was better explained in some others when considering both pig and poultry densities. These findings were in agreement with the hypothesis of cross-contamination, which could take place at the feedstuff factory, during the shipment of food or on the farm. Further studies are needed to more precisely explore how the cross-contamination happened.
The present study was aimed at assessment of a degree of regional differences in hospitalized morbidity in Poland for some of diseases considered to be related to environmental contamination and its relationship with general environmental characteristic of the regions. The performed analysis has shown that however in recent years there have been substantial differences in the discharge rates between provinces of Poland, these differences are not well explained by differences in general environmental factors. The possible reasons of these results have been discussed.
The analysis of spatial relationships among the distribution of environmental stressors and observed or predicted adverse effects may be a useful method of prioritizing hazards in regional ecological risk assessment (ERA). Geographic information systems were used to compare the spatial distribution of toxicant concentrations in sediments of Chesapeake Bay with the distribution of areas in the basin where ecological impacts have historically been observed. Toxicants were then prioritized based upon the strength of their spatial association with the high impact areas. This method of hazard identification/prioritization was validated against the Chesapeake Bay Program's lists of toxics of concern and toxics of potential concern (TOC and TOPC, respectively). Of the 18 toxicants on the TOC/TOPC lists that were considered in the current study, 15 (83%) were identified as priority contaminants in the current study, 11 (73%) of which were either of primary or secondary concern. The use of spatial analysis tools in ERA may lead to more rapid and rigorous methods for prioritizing environmental risks.
Spatial autocorrelation methods have commonly been applied to individual-based spatial genetic studies, although their properties and the relations among the statistics have not been carefully examined. This paper first introduces a reformulation of widely used spatial statistics using point processes. When Moran's I statistics are applied to allele frequencies within an individual, the frequencies are no longer continuous variables but have only three discrete values and specific interpretations of Moran's I statistics and the number of alleles in common (NAC) can be expressed as the weighted sum of join-count statistics. The distributions of minor genotypes are amplified in Moran's I depending on the allele frequency in the population, while NAC uses a constant weighting system. Under the point process framework, spatial analysis can be conducted on the common theoretical base, from individual locations to genetic distributions of different levels, (for example, genotype and allele). The methodology is demonstrated by application to field data for molecular ecological studies of Fagus crenata population dynamics.
Remote sensing, geographical information systems (GIS) and spatial analysis provide important tools that are as yet under-exploited in the fight against disease. As the use of such tools becomes more accepted and prevalent in epidemiological studies, so our understanding of the mechanisms of disease systems has the potential to increase. This paper introduces a range of techniques used in remote sensing, GIS and spatial analysis that are relevant to epidemiology. Possible future directions for the application of remote sensing, GIS and spatial analysis are also suggested.
While many lessons have been learned from the spatial analysis of cancer, there are several caveats that apply to many, if not all such analyses. As "flies in the ointment", these can substantially detract from a spatial analysis, and if not accounted for, can lead to weakened and erroneous conclusions. This paper discusses several assumptions and limitations of spatial analysis, identifies problems of scientific inference, and concludes with potential solutions and future directions.
Spatial analyses of disease patterns have been used by investigators as one tool with which to address problems of disease causation. However, while the results of spatial studies may be suggestive, they have been definitive only rarely. This is because there are numerous epistemological, logical and methodological problems which are encountered in spatial analysis. It is important to address these problems as one step in answering the basic issues concerning the role of spatial analysis in the detection of disease causation.
Do spatial operations on mental images and those on visually presented material share the same neural substrate? We used the high spatial resolution of functional magnetic resonance imaging to determine whether areas in the parietal lobe that have been implicated in the spatial transformation of visual percepts are also activated during the generation and spatial analysis of imagined objects. Using a behaviourally controlled mental imagery paradigm, which did not involve any visual stimulation, we found robust activation in posterior parietal cortex in both hemispheres. We could thus identify the subset of spatial analysis-related activity that is involved in spatial operations on mental images in the absence of external visual input. This result clarifies the nature of top-down processes in the dorsal stream of the human cerebral cortex and provides evidence for a specific convergence of the pathways of imagery and visual perception within the parietal lobes.
The spatial and temporal distribution of LaCrosse encephalitis cases in Illinois was analyzed using a geographic information system (GIS) and spatial statistics. Case data were obtained from the Illinois Department of Public Health and mapped on the county, town, and address level. Human cases were concentrated in and around the city of Peoria in central Illinois. Local spatial statistics were used to identify hot spots where cases appear to be concentrated in the Peoria region. Several small towns surrounding the city of Peoria appeared as foci where cases were most common. Second-order spatial analysis of the case distribution was conducted on the address level. Cases were clustered within a range of 3.0 km in the city of Peoria. Since most cases appear to be associated with residential (peridomestic) exposure, and since several cases have been reported from neighboring addresses, transmission may be concentrated around specific sites (hardwood ravines, tire piles). The GIS and spatial analysis may be useful in identifying and targeting for intervention potential sites of enzootic transmission.
This study examined the spatial distribution of childhood community-acquired pneumonia detected through prospective surveillance in Goiânia, Brazil. Three spatial analysis techniques were applied to detect intra-urban geographic aggregation of pneumonia cases: Kernel method, nearest neighbor hierarchical technique, and spatial scan statistic. A total of 724 pneumonia cases confirmed by chest radiography were identified from May 2000 to August 2001. All cases were geocoded on a digital map. The annual pneumonia risk rate was estimated at 566 cases/100,000 children. Analysis using traditional descriptive epidemiology showed a mosaic distribution of pneumonia rates, while GIS methodologies showed a non-random pattern with hot spots of pneumonia. Cluster analysis by spatial scan statistic identified two high-risk areas for pneumonia occurrence, including one most likely cluster (RR = 2.1; p < 0.01) and one secondary cluster (RR = 1.3; p = 0.01). The data used for the study are in line with recent WHO-led efforts to improve and standardize pediatric pneumonia surveillance in developing countries and show how GIS and spatial analysis can be applied to discriminate target areas of pneumonia for public heath intervention.
Surveillance measures for human cases of Lyme disease in Wisconsin were compared and associated with tick distribution and vegetation coverage. During 1991-1994, 1,759 confirmed human cases of Lyme disease reported to the Wisconsin Division of Health were assigned a county of residence, but only 329 (19%) could be assigned with certainty a county of exposure. Distributions of cases by county of exposure and residence were often consistent from year to year. Tick distribution in 46 of 72 Wisconsin counties was mapped based on collections by researchers, statewide surveys of infested deer, and submissions from the public. Satellite data were used to calculate a normalized difference vegetation index (NDVI) for each county. A geographic information system (GIS) was used to map distributions of human Lyme disease cases, ticks, and degree of vegetation cover. Human case distribution by county of exposure was significantly correlated with tick distribution; both were positively correlated with high NDVI values in spring and fall, when wooded vegetation could be distinguished from agricultural crops in the satellite image. Statistical analysis of spatial patterns using a measure of spatial autocorrelation indicated that counties with most human cases and ticks were clustered in parts of western Wisconsin. A map delineating the counties with highest risk for Lyme disease transmission was generated based on numbers of exposed human cases and tick concentrations.
Carbon and nitrogen stable isotopes were used to determine the ultimate autotrophic sources supporting production of three commercially important fish species over unvegetated mudflats in a subtropical estuary. Mean isotope values over the whole estuary for fish and autotroph sources were modeled to indicate feasible combinations of sources. Variability in isotope values among nine locations (separated by 3-10 km) was then used as a further test of the likelihood that sources were involved in fish nutrition. A positive spatial correlation between isotope values of a fish species and an autotroph indicates a substantial contribution from the autotroph. Spatial correlations were tested with a newly developed randomization procedure using differences between fish and autotroph values at each location, based on carbon and nitrogen isotopes combined in two-dimensional space. Both whole estuary modeling and spatial analysis showed that seagrass, epiphytic algae and particulate organic matter in the water column, including phytoplankton, are likely contributors to bream (Acanthopagrus australis) nutrition. However, spatial analysis also showed that mangroves were involved (up to 33% contribution), despite a very low contribution from whole estuary modeling. Spatial analysis on sand whiting (Sillago ciliata) demonstrated the importance of two sources, mangroves (up to 25%) and microalgae on the mudflats, considered unimportant based on whole estuary modeling. No spatial correlations were found between winter whiting (Sillago maculata) and autotrophs, either because fish moved among locations or relied on different autotrophs at different locations. Spatial correlations between consumer and source isotope values provide a useful analytical tool for identifying the role of autotrophs in foodwebs, and demonstrated here that both in situ production of microalgae and organic matter from adjacent habitats were important to fish over mudflats.
This study aims to help expand knowledge on Aedes aegypti in Rio de Janeiro, based on spatial analysis of the mosquito's breeding sites in the neighborhood of Ilha do Governador, Rio de Janeiro, from June 1992 to July 1994. Use of spatial analysis techniques is proposed for vector surveillance and control. Information obtained from the number of dwellings per block that were used for the analysis. A smoothing method, a Gaussian Kernel, was used in the spatial distribution analysis. Breaks in National Health Foundation intervention activities were observed between cycles 4 and 5 and 5 and 6, respectively, followed by increases in vector density and vector-positive areas. Permanently positive areas, like slums and a military compound, display the persistence of favorable environmental conditions for oviposition and larval growth, indicating flaws in the vector control program. Although Kernel is an exploratory analytical method involving subjective interpretation, it provides easy and fast visualization of sites subject to different degrees of risk, unaffected by existing political and administrative territorial apportionment.
One of the indicators of health system effectiveness with regard to maternal health is the maternal mortality ratio. Measuring this ratio in developing countries is, however, not an easy task since reliable information on mortality is rarely available. An alternative to the maternal mortality ratio measurement, as an indicator of effectiveness, is the assessment of the coverage of obstetrical intervention needs. The authors chose to restrict the notion of "needs" to the obstetrical interventions carried out in order to save a mother's life. Using data from a survey by the Ministry of Health of the Moroccan Kingdom on all the obstetrical interventions carried out in 1989, obstetrical intervention rates for "absolute maternal indications" are analysed according to the mother's origin, by province and urban/rural environment. The spatial analysis of these rates showed large variations in each of the environments (0 to 2.14 % of the expected births in urban areas and 0 to 1.25 % in rural areas) and a significative difference between the rural and urban distributions (median 0.80 % in urban areas versus 0.30 % in rural areas). Applying a reference rate of 1 %, deficits between the expected numbers of needed obstetrical interventions and the observed numbers were calculated for every province in both urban and rural areas. In the whole of Morocco, intervention rates are markedly below what is expected. The spatial analysis of the deficits helps to identify the provinces where the problem is the most prominent in terms of numbers of women whose intervention needs have to be covered. The authors discuss the validity of the reference rate and suggest several strategies to solve the problem. They conclude that the deficits map is a useful tool to decide on priorities for planning and monitoring of strategies to be implemented. The spatial analysis of obstetrical intervention deficits seems to be an instrument both cheaper and more relevant than a maternal mortality estimates survey.
In this paper we present the methodology and some preliminary results of the spatial analysis of rabies positive fox cases diagnosed in Hungary between 1990 and 2001. A database has been built based on the data provided by the Animal Health and Food Control Department of the Ministry of Agriculture, of all registered positive cases, specifying the date, location and affected species. We have developed a Geographical Information System for the spatial analysis. The aim of the study is to analyse the spatial patterns of subsequent rabies cases. Beyond the well-documented seasonality of the cases we want to find out whether they show regular spatial patterns and if yes what is their nature. Another important question is whether clustering of data can be observed and how stable or reoccurring these clusters are at a given location or area. The results might be important in a later stage of the eradication campaign when a strategy for the maintenance of large, rabies free areas should be developed.
Geographic information systems combined with methods of spatial analysis provide powerful new tools for understanding the epidemiology of diseases and for improving disease prevention and control. In this study, the spatial distribution of a newly recognized tick-borne disease, human granulocytic ehrlichiosis (HGE), was investigated for nonrandom patterns and clusters in an area known to be endemic for tick-borne diseases. Analysis of confirmed cases of HGE identified in 1997-2000 in a 12-town area around Lyme, Connecticut, showed that HGE infections are not distributed randomly. Smoothed HGE incidence was higher around the mouth of the Connecticut River and lower to the north and west. Cluster analysis identified one area of increased HGE risk (relative risk=1.8, p=0.001). This study demonstrates the utility of geographic information systems and spatial analysis to clarify the epidemiology of HGE.