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PubMed2002

Spatial analysis of rabies cases in foxes in Hungary between 1990 and 2001: a preliminary report.

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.

Animal Diseases↗

PubMed2002

Spatial analysis of human granulocytic ehrlichiosis near Lyme, Connecticut.

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.

Cluster Analysis↗

PubMed1986

The uses of spatial analysis in medical geography: a review.

This paper is a review of how geographers and others have used spatial analysis to study disease and health care delivery patterns. Point, line, area and surface patterns, as well as map comparisons and relative spaces are discussed. Problems encountered in applying spatial analytic techniques in medical geography are pointed out. The paper is intended to stimulate discussion about where medical geography can and should go in this area of study. Point pattern techniques include standard distance, standard deviational ellipses, gradient analysis and space and space-time clustering. Line methods include random walks, vectors and graph theory or network analysis. Under areas, location quotients, standardized mortality ratios, Poisson probabilities, space and space-time clustering, autocorrelation measures and hierarchical clustering are discussed. Surface techniques mentioned are isolines and trend surfaces. For map comparisons, Lorenz curves, coefficients of areal correspondence and correlation coefficients have been used. Case-control matching, acquaintance networks, multidimensional scaling and cluster analysis are examples of methods that are based on relative or non-metric spaces. The review gives rise to the discussion of several general points: problems encountered in spatial analysis, theory building and verification, the appropriate role of technique and computer use. Some suggestions are made for further use of spatial analytic techniques in medical geography: Monte Carlo simulation of point patterns, network analysis to study referral systems and health care for pastoralists, geographic information systems to assess environmental risk, difference mapping for disease and risk factor map comparisons and multidimensional scaling to measure social distance.

Cross-Sectional Studies↗

PubMed2002

Spatial analysis of disease.

In this chapter, we have reviewed the history of the spatial analysis of disease and the statistical methods used for the exploratory analysis, testing and modeling of spatial patterns. In the next chapter, the principles described here will be illustrated.

Data Interpretation, Statistical↗

PubMed2000

[Geographic information systems spatial analysis on transmission of schistosomiasis in China].

OBJECTIVE: To understand the epidemiologic status and geographical distribution of schistosomiasis in China. METHODS: Relevant detabases were set up after collection of data from two National Sampling Surveys on Schistosomiasis, in 1989 and 1995. Spatial analysis was undertaken after the database linked to the GIS software which was supported by Arc View 3.0a. Correlation analysis was performed to understand the relationship between rates of human infection and cattle infection. RESULTS: The epidemic areas of schistosomiasis with high risk are mainly distributed in the marshland along the Yangtze River and can be identified as five spatial distribution regions based on the results of spatial analysis. Both epidemic areas and positive rates of stool examination in cattle and water buffalo are much wider and higher than that in humans. The positive correlation was seen between infection rate in human and in cattle from data of both sampling surveys. CONCLUSION: The relevant strategy for schistosomiasis control in different spatial regions should be performed accordingly and the measures of control for cattle and water buffalo should be strengthened in the endemic areas.

Adolescent↗

PubMed2006

Joint spatial analysis of gastrointestinal infectious diseases.

A major obstacle in the spatial analysis of infectious disease surveillance data is the problem of under-reporting. This article investigates the possibility of inferring reporting rates through joint statistical modelling of several infectious diseases with different aetiologies. Once variation in under-reporting can be estimated, geographic risk patterns for infections associated with specific food vehicles may be discerned. We adopt the shared component model, proposed by Knorr-Held and Best for two chronic diseases and further extended by (Held L, Natario I, Fenton S, Rue H, Becker N. Towards joint disease mapping. Statistical Methods in Medical Research 2005b; 14: 61-82) for more than two chronic diseases to the infectious disease setting. Our goal is to estimate a shared component, common to all diseases, which may be interpreted as representing the spatial variation in reporting rates. Additional components are introduced to describe the real spatial variation of the different diseases. Of course, this interpretation is only allowed under specific assumptions, in particular, the geographical variation in under-reporting should be similar for the diseases considered. In addition, it is vital that the data do not contain large local outbreaks, so adjustment based on a time series method recently proposed by (Held L, Höhle M, Hofmann M. A statistical framework for the analysis of multivariate infectious disease surveillance data. Statistical Modelling 2005a; 5: 187-99) is made at a preliminary stage. We will illustrate our approach through the analysis of gastrointestinal diseases notification data obtained from the German infectious disease surveillance system, administered by the Robert Koch Institute in Berlin.

Adolescent↗

PubMed2002

Data quality and the spatial analysis of disease rates: congenital malformations in New York State.

Spatial analyses of disease rates are increasing as the hardware and software used in disease surveillance and cluster investigations become more accessible and easier to use. The results of these analyses should be interpreted with caution since inconsistencies in health outcome reporting and population estimates may lead to erroneous conclusions. In this report we provide an example, using data on congenital malformations in New York State, to show how under-reporting of malformations by some New York City hospitals can lead to apparent clusters of malformations in other areas of the state where reporting is more complete. We illustrate how spatial analysis techniques can be used to locate under-reporting problems and determine the extent to which the problem exists.

Congenital Abnormalities↗

PubMed2006

Spatial analysis of hemorrhagic fever with renal syndrome in China.

BACKGROUND: Hemorrhagic fever with renal syndrome (HFRS) is endemic in many provinces with high incidence in mainland China, although integrated intervention measures including rodent control, environment management and vaccination have been implemented for over ten years. In this study, we conducted a geographic information system (GIS)-based spatial analysis on distribution of HFRS cases for the whole country with an objective to inform priority areas for public health planning and resource allocation. METHODS: Annualized average incidence at a county level was calculated using HFRS cases reported during 1994-1998 in mainland China. GIS-based spatial analyses were conducted to detect spatial autocorrelation and clusters of HFRS incidence at the county level throughout the country. RESULTS: Spatial distribution of HFRS cases in mainland China from 1994 to 1998 was mapped at county level in the aspects of crude incidence, excess hazard and spatial smoothed incidence. The spatial distribution of HFRS cases was nonrandom and clustered with a Moran's I = 0.5044 (p = 0.001). Spatial cluster analyses suggested that 26 and 39 areas were at increased risks of HFRS (p < 0.01) with maximum spatial cluster sizes of < or = 20% and < or = 10% of the total population, respectively. CONCLUSION: The application of GIS, together with spatial statistical techniques, provide a means to quantify explicit HFRS risks and to further identify environmental factors responsible for the increasing disease risks. We demonstrate a new perspective of integrating such spatial analysis tools into the epidemiologic study and risk assessment of HFRS.

China↗

PubMed2005

Scales of environmental justice: combining GIS and spatial analysis for air toxics in West Oakland, California.

This paper examines the spatial point pattern of industrial toxic substances and the associated environmental justice implications in the San Francisco Bay Area, California, USA. Using a spatial analysis method called Ripley's K we assess environmental justice across multiple spatial scales, and we verify and quantify the West Oakland neighborhood as an environmental justice site as designated by the US Environmental Protection Agency. Further, we integrate the ISCST3 air dispersion model with Geographic Information Systems (GIS) to identify the number of people potentially affected by a particular facility, and engage the problem of non-point sources of diesel emissions with an analysis of the street network.

Air Pollution↗

PubMedDate not supplied

[Use of spatial analysis tools in the epidemiological surveillance of American visceral leishmaniasis, Araçatuba, São Paulo, Brazil, 1998-1999].

The control of American visceral leishmaniasis (AVL) is based on combating the vector and eliminating the domestic reservoir of the focus area - defined as 200 meters around human or canine cases. This paper discusses the use of spatial analysis techniques in the epidemiological surveillance of AVL in Araçatuba, São Paulo State, in order to propose a model for territorial epidemiological surveillance, reformulating current control strategies. The results showed that AVL transmission was not homogeneous; human cases were more frequent in areas with higher canine prevalence rates. Vector dispersion appeared to be restricted to a few houses, although it was not possible to model the vector density. In order to study the vector distribution and correlated covariates, a field study based on house sampling is being conducted. The results will aid the development of new spatial analysis tools and possibly redefine protocols and routines for the control of this endemic disease in urban areas.

Adolescent↗

PubMed1995

Spatial analysis of Honolulu motor vehicle crashes: I. Spatial patterns.

This study describes spatial patterns in Honolulu motor vehicle accidents for 1990. A method for geo-coding accident locations is utilized with approximately 98% of the crash locations being identified. Spatial software tools are developed for describing the degree of spatial concentration. The spatial patterns of different types of accidents and accidents for every hour of the day, weekdays and weekends separately, are analyzed. Accidents spatially fluctuate dynamically, as a response to changing traffic patterns and volume. Generally, most accidents are closer to employment centers than to residential areas. In the suburban and rural areas, however, accidents are more likely to involve fatalities or serious injuries and be related to night-time driving and alcohol. It is shown that these conditions spatially correlate with single-vehicle crashes and crashes with opposite direction vehicles. The spatial patterns point to the limits of "blackspot" analysis.

Accidents, Traffic↗

PubMed2002

[Temporal-spatial analysis of evoked potentials].

Evoked potentials are widely used in clinical neurophysiology. The conventional analysis methods of evoked potentials are based on the waves in time domain. Analysis based on time-spatial domain will provide more information than simple time domain analysis. The existing temporal-spatial analysis methods, such as microstate, frequency domain analysis and event-related coherence, are introduced in this paper.

Evoked Potentials↗

PubMed2002

A spatial analysis of obesogenic environments for children.

In this study, we use spatial analysis techniques to explore environmental and social predictors of obesity in children. We constructed a merged database, incorporating clinical data from an electronic medical record system, the Regenstrief Medical Record System (RMRS) and societal & environmental data from a geographical information system, the Social Assets and Vulnerabilities Indicators (SAVI) Project. We used the RMRS to identify cohorts of children that were normal weight, overweight, or obese. The RMRS records were geocoded and merged into the SAVI database. Using the merged databases, we analyzed the relationships between markers of socioeconomic status and obesity outcomes in children. Our preliminary analyses show that markers of low socioeconomic status at the census tract level correlate with both overweight and obese outcomes in our study population. Utilization of geographic information systems (GIS) for the study of health epidemiology is discussed.

Adolescent↗

PubMedDate not supplied

Spatial analysis of motor unit potentials of the rat medial gastrocnemius.

The spatial analysis of the potentials of single motor units of the rat medial gastrocnemius muscle evoked by stimulation of the fibres of split ventral roots was carried out with a bipolar electrode moving in the direction perpendicular to the longitudinal axis of the muscle fibres. During this movement of the electrode a variability was observed in the time of the biphasic potential from its maximum to minimum, and in the peak-to-peak amplitude of these potentials. The potentials recorded outside the territory of the motor unit had a lower amplitude in relation to the potentials from the territory of the unit. This made localization of the motor unit on the cross-section of the muscle possible. Differences in the duration of the potential from maximal to minimal amplitude (maximum-minimum amplitude time--M-MAT) of each investigated motor unit from successive recording sites reflected the number of fibres contributing to the action potential and the distance of the recording surface of the electrode from the zone of the motor end-plates of this motor unit. The greatest diameter of the territory of the observed motor units reached 2.5 mm.

Action Potentials↗

PubMed2002

Spatial analysis of disease--applications.

The application of spatial statistical analysis to health data has reached adolescence. The theory and the software are both still maturing. We are drawing upon the experiences of the geostatisticians in modeling surfaces and the econometricians in modeling time series. "New and improved" computer algorithms are constantly being provided to implement the evolving theory or to improve the processing in terms of stability, reliability, and efficiency. We will come of age when we have the theory, the software, and the process to reliably produce "generalized spatio-temporal" models suitable for health data. In the meantime, biostatisticians need to acknowledge when their data is not independently distributed and to consider the spatial correlation in their analysis. This chapter provided examples using four available methods. The methods were spatial filtering, identifying clusters using the spatial scan statistic, hierarchical modeling, and conditional autoregression modeling.

Cluster Analysis↗

PubMed2003

[Spatial analysis on the distribution of hemorrhagic fever with renal syndrome by geographic information system in Haidian district, Beijing].

OBJECTIVE: To analyze the spatial distribution of hemorrhagic fever with renal syndrome (HFRS) in Haidian district, Beijing and to explore the geographical characteristics of HFRS in highly endemic areas. METHODS: Administration boundary layer was established under the background of 1:100,000 map in the ArcInfo 8.1 software. The HFRS cases from 1997 to 2002 were positioned on the map. Highly endemic areas were identified by spatial cluster analysis using SaTScan 3.0 software. Distribution of HFRS cases was shown in different colors and contours by spatial analysis of geographic information system (GIS). RESULTS: Spatial Cluster Analysis of confirmed cases of HFRS identified in 1997 - 2002 in Haidian district showed that HFRS patients were not randomly distributed. The highly endemic areas were founded in Sujiatuo township, Yongfeng township, Shangzhuang township, Wenquan town and Bei'anhe township (relative risk = 4.43, P = 0.001). A thematic map of HFRS in haidian district was set up. CONCLUSION: HFRS infections were not randomly distributed, since the distribution was related to geographic-environmental factors.

China↗

PubMed2004

Spatial analysis of childhood leukemia in a case/control study.

A simple and direct analysis of the spatial distribution of childhood leukemia was performed using geographic data from a large case/control study. The data consist of cases of childhood leukemia and their corresponding birth cohort controls located in seven San Francisco Bay Area counties. Both parametric and randomization analyses show no evidence of a non-random spatial pattern of childhood leukemia among six of these counties. The data from San Francisco County, however, produce a moderately small significance probability (0.08) arising from a distance analysis and a significant p-value (0.01) arising from a frequency analysis of concordant case pairs. Although these p-values accurately reflect the probability of the observed spatial pattern occurring by chance alone, these results are based on only four cases of leukemia.

Adolescent↗

PubMed2005

Spatial analysis of respiratory disease on an urbanized geothermal field.

Chronic exposure to hydrogen sulfide (H(2)S) in the parts per billion-parts per million range occurs in the population of Rotorua, a city built upon an actively degassing geothermal field in the Taupo Volcanic Zone, New Zealand. H(2)S is acutely toxic at high concentrations but little is understood of the health effects of chronic, low-level exposure. In Rotorua, H(2)S emissions and ambient concentrations are heterogeneous and approximately 30% of the greater urban area's population live upon or <4 km downwind of the geothermal field. Spatial analysis of disease incidence clustering using a spatial scan statistic is a powerful tool with which to investigate the spatial relationship which may exist between H(2)S and respiratory disease. This paper reports findings from a spatial cluster analysis of 11 years of hospital discharge data at the census area unit resolution. Results indicate that the relative risk (RR) of incidence of noninfectious respiratory diseases may be substantially higher among residents living in the geothermal area than have been reported previously. RR >5 for chronic obstructive pulmonary disease and its associated conditions are found in clusters which are spatially coincident with the geothermal field. Future work which investigates neurological and circulatory disease groups at the same or better spatial resolution may provide further insight into the chronic health effects of H(2)S exposure than these preliminary findings indicate.

Adolescent↗