Spatial Patterns Of Disease Research Articles

Residential Mobility, Cancer and Local Indicators of Geocoding Error

ISEE-0108 Background and Objective: Although sources of error in geographic locations (e.g. geocoding error) used for describing and modeling spatial disease patterns are widely acknowledged, research on how such error impacts the results has been limited. In this paper we explore techniques for quantifying the perturbability of spatial weights to different specifications of geocoding error. Methods: New measures for assessing impacts of geocoding error on spatial weights called leverage and perturbability are used to construct the LIGE (Local Indicators of Geocoding Error) scatterplot. We evaluate the approach in simulation studies, and then demonstrate it using a case-control study of bladder cancer in south-eastern Michigan. Results: The LIGE scatterplot may be used to evaluate sensitivity of alternative spatial weight specifications to geocoding error both globally (when all locations are considered simultaneously) and locally (to identify those locations that would benefit most from increased geocoding accuracy). Conclusion: Three results are significant. First, the shape of the geocoding error functions we explored (e.g. circular, elliptical, greek cross) is not important; what matters is the mean geocoding error and its variance. Second, our methodology allows researchers to evaluate the sensitivity of spatial statistics to geocoding error for specific geographies. This has substantial practical implications since it makes possible routine sensitivity analysis of spatial statistics to geocoding error. Third, those locations with high perturbability (most sensitive to geocoding error) and high leverage (that contribute the most to the spatial weight specification being considered) will benefit the most from increased geocoding accuracy, and are rapidly identified using the new visualization tool we call the LIGE scatterplot.

Relapse of Invasive Lobular Carcinoma

The diagnosis of Invasive Lobular Carcinoma (ILC) in the relapse setting is as problematic and challenging as in the primary setting due to the enigmatic nature of this malignant process. As discussed in previous chapters for early ILC, this special neoplastic process is often difficult to identify by clinical and imaging studies, which applies to the metastatic situation, too. These difficulties relate to a variety of factors, including the diffusely infiltrative nature of the malignant process, the spatial and site specific pattern of metastatic disease which is associated with ILC. It is of the utmost importance that the past history of Invasive Lobular Carcinoma of breast is available to the entire clinical team involved in the management of the individual patient, even if this history is many (often 20 or 30) years prior to the current clinical episode. Invasive Breast Carcinoma of any histological type may be associated with relapse which may be loco regional, involving the breast, chest wall or regional lymph nodes, or distant relapse at any anatomical site. Due to the possibility of relapse and the increased risk of a second primary breast carcinoma, it is recommended that all individuals should be followed up. Follow up should include clinical and radiological options for assessment of the patient as a whole as well as both the treated and contra lateral breast. The aim of the follow up is that the early detection of recurrence either local or distant will be more effective and be associated with a survival benefit however no robust evidence exists to support this belief. Through regular surveillance the clinician aims to identify recurrence or be informed of new symptoms affecting the patient at the earliest opportunity. It is known that Invasive Lobular Carcinoma has an early advantage in disease free survival when compared to Invasive Ductal Carcinoma, but when compared at 6 & 10 years the invasive ductal carcinoma group has a late advantage. Invasive Lobular Carcinoma is associated with an increased incidence of bone metastases but a decrease in regional and lung relapses [1]. Thus, due to the nature of this process, the diagnosis of cancer relapse can be difficult.

The choice of the number of bins for the [formula omitted] statistic

Methods to monitor spatial patterns of disease in populations are of interest in public health practice. The M statistic uses interpoint distances between cases to detect abnormalities in the spatial patterns of diseases. This statistic compares the observed distribution of interpoint distances with that which is expected when no unusual spatial patterns exist. We show the relationship of M to Pearson’s Chi Square statistic, X n 2 . Both statistics require the discretization of continuous data into bins and then are formed by creating a quadratic form, scaled by an appropriate variance covariance matrix. We seek to choose the number and type of these bins for the M statistic so as to maximize the power to detect spatial anomalies. By showing the relationship between M to X n 2 , we argue for the extension of the theory that has been developed for the selection of the number and type of bins for X n 2 to M . We further show that spatial data provides a unique insight into the problem through examples with simulated data and spatial data from a health care provider. In the spatial setting, these indicate that the optimal number of bins depends on the size of the cluster. For large clusters, a smaller number of bins appears to be preferable, however for small clusters having many bins increases the power. Further, results indicate that the number of bins does not appear to vary with m , the number of spatial locations. We discuss the implications of this result for further work.

Breadth and depth in research on health disparities: commentary on the work of Nancy Krieger

Geocoding and spatial analysis of data describing populations and health events are important methods in health social science now carried out using GIS technology. This commentary considers Nancy Krieger’s work on health disparities in light of the various ways individuals and organizations use geocoded population and health data: analyzing spatial patterns of health and disease including health disparities, aggregating data spatially, assessing health status of individuals based on characteristics of aggregates, modeling neighborhood contextual factors affecting health, designing observation and intervention studies, and delivering health interventions and services. The extent to which her work addresses each of these purposes is considered. The strengths and limitations of the research including choice of spatial analytic units and techniques as reported in the published work are discussed. Krieger’s work, with her colleagues, has used standard methods of spatial analysis to raise the profile of GIS and spatial analysis in the public health community.

Demographic, seasonal, and spatial differences in acute myocardial infarction admissions to hospital in Melbourne Australia

BackgroundSeasonal patterns in cardiac disease in the northern hemisphere are well described in the literature. More recently age and gender differences in cardiac mortality and to a lesser extent morbidity have been presented. To date spatial differences between the seasonal patterns of cardiac disease has not been presented. Literature relating to seasonal patterns in cardiac disease in the southern hemisphere and in Australia in particular is scarce. The aim of this paper is to describe the seasonal, age, gender, and spatial patterns of cardiac disease in Melbourne Australia by using acute myocardial infarction admissions to hospital as a marker of cardiac disease.ResultsThere were 33,165 Acute Myocardial Infarction (AMI) admissions over 2186 consecutive days. There is a seasonal pattern in AMI admissions with increased rates during the colder months. The peak month is July. The admissions rate is greater for males than for females, although this difference decreases with advancing age. The maximal AMI season for males extends from April to November. The difference between months of peak and minimum admissions was 33.7%. Increased female AMI admissions occur from May to November, with a variation between peak and minimum of 23.1%. Maps of seasonal AMI admissions demonstrate spatial differences. Analysis using Global and Local Moran's I showed increased spatial clustering during the warmer months. The Bivariate Moran's I statistic indicated a weaker relationship between AMI and age during the warmer months.ConclusionThere are two distinct seasons with increased admissions during the colder part of the year. Males present a stronger seasonal pattern than females. There are spatial differences in AMI admissions throughout the year that cannot be explained by the age structure of the population. The seasonal difference in AMI admissions warrants further investigation. This includes detailing the prevalence of cardiac disease in the community and examining issues of social and environmental justice.

Landscape genetics and the spatial distribution of chronic wasting disease

Predicting the spread of wildlife disease is critical for identifying populations at risk, targeting surveillance and designing proactive management programmes. We used a landscape genetics approach to identify landscape features that influenced gene flow and the distribution of chronic wasting disease (CWD) in Wisconsin white-tailed deer. CWD prevalence was negatively correlated with genetic differentiation of study area deer from deer in the area of disease origin (core-area). Genetic differentiation was greatest, and CWD prevalence lowest, in areas separated from the core-area by the Wisconsin River, indicating that this river reduced deer gene flow and probably disease spread. Features of the landscape that influence host dispersal and spatial patterns of disease can be identified based on host spatial genetic structure. Landscape genetics may be used to predict high-risk populations based on their genetic connection to infected populations and to target disease surveillance, control and preventative activities.

Temporal changes in the spatial pattern of disease rates incorporating known risk factors

Examining the geographical pattern of temporal changes in infant mortality rates illustrates the methodological problems of documenting and understanding temporal changes in any spatial pattern of disease. Early research on geographical differences in infant mortality rates showed strong ecological correlations with socio-economic factors such as poverty rates. More recent research established relationships between individual-level socio-economic values and probabilities of death. With geographic information available at the level of individuals, it is possible to estimate the probabilities of death on a person-by-person basis from knowledge of the relationships between individual factors and socio-economic measures. These estimated probabilities provide an expected geographic pattern of deaths. The difference between the observed spatial pattern and the expected pattern is the remaining spatial variation adjusted for this knowledge. For the study area, individual factors and some socio-economic measures were available for each year of the study period. Using data from the Iowa Birth Defects Registry and the Iowa Department of Public Health (USA), I tested the stability and continuity of these cross-sectional relationships and investigated whether any temporal lags in these variables relate to the unexplained spatial variations in infant mortality rates that remain. I accounted for the ‘Change of Support Problem’ [Gotway C. A. & Young L. J. (2002). Combining incompatible spatial data. Journal of the American Statistical Association, 97458, 632–648] inherent in frame-based geographical analysis. The analysis involved a generalized linear model (GLM) to estimate individual risks and a Monte Carlo simulation model to generate the non-linear probability density functions for disease rates whose densities are theoretically intractable. Results show the temporal changes in the observed spatial pattern and the expected spatial pattern differ by geographic location. In conclusion such differences are the result of a combination of unexplained place-based risk and unmeasured individual risks.

EXPLORATORY SPATIAL ANALYSIS OF SOCIAL AND ENVIRONMENTAL FACTORS ASSOCIATED WITH THE INCIDENCE OF ROSS RIVER VIRUS IN BRISBANE, AUSTRALIA

We used geographic information systems and a spatial analysis approach to explore the pattern of Ross River virus (RRV) incidence in Brisbane, Australia. Climate, vegetation and socioeconomic data in 2001 were obtained from the Australian Bureau of Meteorology, the Brisbane City Council and the Australian Bureau of Statistics, respectively. Information on the RRV cases was obtained from the Queensland Department of Health. Spatial and multiple negative binomial regression models were used to identify the socioeconomic and environmental determinants of RRV transmission. The results show that RRV activity was primarily concentrated in the northeastern, northwestern, and southeastern regions in Brisbane. Multiple negative binomial regression models showed that the spatial pattern of RRV disease in Brisbane seemed to be determined by a combination of local ecologic, socioeconomic, and environmental factors.

Tuberculosis in intra‐urban settings: a Bayesian approach

To model the effect of socio-economic deprivation and a few transmission-related indicators of the tuberculosis (TB) incidence at small area level, to discuss the potential of each indicator in targeting places for developing preventive action. Ecological spatial study of TB incidence in Olinda, a city in the north-east of Brazil, during the period 1996-2000. Three socio-economic indicators (mean number of inhabitants per household; percentage of heads of household with <1 year's formal education; percentage of heads of households with monthly income lower than the minimum wage) and two transmission-related indicators (number of cases of retreatment; number of households with more than one case during the period under study), all calculated per census tract, were used. We adopted four different full hierarchical Bayesian models to estimate the relative risk of the occurrence of TB via Markov chain Monte Carlo. The best specified model includes all the selected covariates and the spatially structured random effect. The gain in goodness-of-fit statistic when the spatial structure was included confirms the clustered spatial pattern of disease and poverty. In this model, the covariates within the non-zero credibility interval were the number of persons per house, the number of cases of retreatment and the number of households with more than one case (all with relative risk > or = 1.8) in each census tract. The possibility to estimate in the same framework both the contribution of covariates at ecological level and the spatial pattern should be encouraged in epidemiology, and may help with establishing Epidemiological Surveillance Systems on a territorial basis, that allows rational planning of interventions and improvement of the Control Programme effectiveness.

Epidemiology of sharka disease in France

Plum pox virus was first detected in France in the 1960s. Both PPV‐D and PPV‐M strains are present but epidemics related to the PPV‐M strain detected in the late 1980s are the most problematic. The two PPV strains have unequal distributions in peach and apricot orchards and different prevalences. More than 20 different aphid species have been identified as vectors of PPV but most of them do not colonize Prunus species. Thus, aphids involved in the spread of PPV in orchards are essentially visiting aphids. The main sources of inoculum for the vectors are leaves and fruits of infected stone‐fruit trees. Spontaneous, wild and ornamental Prunus species such as Prunus dulcis, P. spinosa or P. pissardii are susceptible to PPV isolates found in France but their role as a reservoir in sharka epidemics is probably negligible. The disease spreads rapidly in orchards but the rate of progression may vary according to the identity of the PPV strain and the Prunus species. Analysis of spatial patterns of disease has shown that secondary spread by aphids frequently occurs over short distances in the orchards (aggregated patterns) but also that dissemination at longer distances (of several hundred metres) is a common event.

On the use of ZIP codes and ZIP code tabulation areas (ZCTAs) for the spatial analysis of epidemiological data

BackgroundWhile the use of spatially referenced data for the analysis of epidemiological data is growing, issues associated with selecting the appropriate geographic unit of analysis are also emerging. A particularly problematic unit is the ZIP code. Lacking standardization and highly dynamic in structure, the use of ZIP codes and ZIP code tabulation areas (ZCTA) for the spatial analysis of disease present a unique challenge to researchers. Problems associated with these units for detecting spatial patterns of disease are explored.ResultsA brief review of ZIP codes and their spatial representation is conducted. Though frequently represented as polygons to facilitate analysis, ZIP codes are actually defined at a narrower spatial resolution reflecting the street addresses they serve. This research shows that their generalization as continuous regions is an imposed structure that can have serious implications in the interpretation of research results. ZIP codes areas and Census defined ZCTAs, two commonly used polygonal representations of ZIP code address ranges, are examined in an effort to identify the spatial statistical sensitivities that emerge given differences in how these representations are defined. Here, comparative analysis focuses on the detection of patterns of prostate cancer in New York State. Of particular interest for studies utilizing local, spatial statistical tests, is that differences in the topological structures of ZIP code areas and ZCTAs give rise to different spatial patterns of disease. These differences are related to the different methodologies used in the generalization of ZIP code information. Given the difficulty associated with generating ZIP code boundaries, both ZIP code areas and ZCTAs contain numerous representational errors which can have a significant impact on spatial analysis. While the use of ZIP code polygons for spatial analysis is relatively straightforward, ZCTA representations contain additional topological features (e.g. lakes and rivers) and contain fragmented polygons that can hinder spatial analysis.ConclusionCaution must be exercised when using spatially referenced data, particularly that which is attributed to ZIP codes and ZCTAs, for epidemiological analysis. Researchers should be cognizant of representational errors associated with both geographies and their resulting spatial mismatch, especially when comparing the results obtained using different topological representations. While ZCTAs can be problematic, topological corrections are easily implemented in a geographic information system to remedy erroneous aggregation effects.

Cluster detection methods applied to the Upper Cape Cod cancer data

BackgroundA variety of statistical methods have been suggested to assess the degree and/or the location of spatial clustering of disease cases. However, there is relatively little in the literature devoted to comparison and critique of different methods. Most of the available comparative studies rely on simulated data rather than real data sets.MethodsWe have chosen three methods currently used for examining spatial disease patterns: the M-statistic of Bonetti and Pagano; the Generalized Additive Model (GAM) method as applied by Webster; and Kulldorff's spatial scan statistic. We apply these statistics to analyze breast cancer data from the Upper Cape Cancer Incidence Study using three different latency assumptions.ResultsThe three different latency assumptions produced three different spatial patterns of cases and controls. For 20 year latency, all three methods generally concur. However, for 15 year latency and no latency assumptions, the methods produce different results when testing for global clustering.ConclusionThe comparative analyses of real data sets by different statistical methods provides insight into directions for further research. We suggest a research program designed around examining real data sets to guide focused investigation of relevant features using simulated data, for the purpose of understanding how to interpret statistical methods applied to epidemiological data with a spatial component.

Spatiotemporal Description of Epidemics Caused by Phoma ligulicola in Tasmanian Pyrethrum Fields

ABSTRACT Spatial and temporal patterns of foliar disease caused by Phoma ligulicola were quantified in naturally occurring epidemics in Tasmanian pyrethrum fields. Disease assessments (defoliation incidence, defoliation severity, incidence of stems with ray blight, and incidence of flowers with ray blight) were performed four times each year in 2002 and 2003. Spatial analyses based on distribution fitting, runs analysis, and spatial analysis by distance indices (SADIE) demonstrated aggregation in fields approaching their first harvest for all assessment times between September and December. In second-year harvest fields, however, the incidence of stems with ray blight was random for the first and last samplings, but aggregated between these times. Spatiotemporal analyses were conducted between the same disease intensity measures at subsequent assessment times with the association function of SADIE. In first-year harvest fields, the presence of steep spatial gradients was suggested, most likely from dispersal of conidia from foci within the field. The importance of exogenous inoculum sources, such as wind-dispersed ascospores, was suggested by the absence of significant association between defoliation intensity (incidence and severity) and incidence of stems with ray blight in second-year harvest fields. The logistic model provided the best temporal fit to the increase in defoliation severity in each of six first-year harvest fields in 2003. The logistic model also provided the best fit for the incidence of stems with ray blight and the incidence of flowers with ray blight in four of six and three of six fields, respectively, whereas the Gompertz model provided the best fit in the remaining fields. Fungicides applied prior to mid-October (early spring) significantly reduced the area under disease progress curve (P < 0.001) for defoliation severity, the incidence of stems with ray blight, and the incidence of flowers with ray blight for epidemics at all field locations. This study provides information concerning the epidemiology of foliar disease and ray blight epidemics in pyrethrum and offers insight on how to best manage these diseases.

Spatial analysis and mapping of sexually transmitted diseases to optimise intervention and prevention strategies

Objective: We analysed and mapped the distribution of four reportable sexually transmitted diseases, chlamydial infection/non-gonococcal urethritis (chlamydial infection), gonorrhoea, primary and secondary syphilis (syphilis), and HIV infection, for Wake County,...

Impact of Climate Variability on Infectious Disease in West Africa

The importance of infectious disease as a determinant (as well as an outcome) of poverty has recently become a prominent argument for international and national investment in the control of infectious disease, as can be seen in the recently articulated United Nations (UN) Millennium Development Goals (MDGs). Climate variability and land use change have an enormous impact on health in West Africa, and may yet undermine the potential for achieving the MDGs, in certain economic-ecological zones. However, their underlying role in determining the burden of disease in the region on a yearly or decadal basis has never been systematically studied. In order to improve our understanding of the future impacts of climate change, it may be more effective to start by investigating the impact of inter-annual climate variability, and short-term shifts in climate (e.g., decadal), on disease transmission dynamics. This information may inform both current and future policy decisions with regard to prediction, prevention, and management of adverse climate-related health outcomes. This article reviews current knowledge of changes in the epidemiology of infectious diseases associated with climate variability in West Africa over the last 40 years. Selected examples are considered from bacterial (meningococcal meningitis), protozoan (malaria), and filarial (onchocerciasis and lymphatic filariasis) infections where spatial and temporal disease patterns have been directly influenced by seasonal, inter-annual, or decadal changes in climate.

Evidence that climate change has caused 'emergence' of tick-borne diseases in Europe?

Even though tick-borne disease systems are highly susceptible to climatic influences, climate change to date is not necessarily the cause of the marked increased incidence of a variety of tick-borne diseases in many parts of Europe over the past two decades. To test for causality, rather than coincidence, we need to examine whether the right sorts of climate change have occurred at the right time and in the right places to account for the observed heterogeneous temporal and spatial patterns of tick-borne disease 'emergence'. Tick-borne encephalitis (TBE) incidence, for example, showed a 3-fold step increase from 1983 to 1986 in Sweden, doubled in 1993 in the Czech Republic, increased even more dramatically in the same year in Lithuania and Poland, but declined markedly in 1997 in Hungary, Croatia and Slovenia. Within each country, TBE incidence has changed to different degrees in different regions. Because other tick-borne diseases, notably Lyme borreliosis, has commonly 'emerged' in parallel with TBE, we should first examine climate variables predicted to have a general effect on tick abundance, which has indeed increased in the past decade. These include temperature and moisture stress, which have seasonally differential impacts. Monthly mean records for 1960-2000 from the UK Climate Research Unit's interpolated global climate surface reveal that mean spring, spring-autumn and winter temperatures have all increased gradually over the past 40 years, but apparently most sharply in the late 1980s, when moisture stress also increased. These climate data do not reveal any obvious differences between sites where TBE did or did not 'emerge', and in Sweden increases in TBE pre-dated the onset of warmer springs and winters. If recorded climate changes cannot yet satisfactorily explain the temporal and spatial patterns of tick-borne disease change in Europe, the impact of biotic factors, such as increases in deer abundance and changing habitat structure, and of socio-political changes following the end of communist rule, demand more detailed quantitative analyses.

On ‘Analytical models for the patchy spread of plant disease’

Epidemiologists are interested in using models that incorporate the effects of clustering in the spatial pattern of disease on epidemic dynamics. Bolker (1999, Bull. Math. Biol. 61, 849–874) has developed an approach to study such models based on a moment closure assumption. We show that the assumption works above a threshold initial level of disease that depends on the spatial dispersal of the pathogen. We test an alternative assumption and show that it does not have this limitation. We examine the relation between lattice and continuous-medium implementations of the approach.

Effects of dispersal mechanisms on spatio-temporal development of epidemics

The nature of pathogen transport mechanisms strongly determines the spatial pattern of disease and, through this, the dynamics and persistence of epidemics in plant populations. Up to recently, the range of possible mechanisms or interactions assumed by epidemic models has been limited: either independent of the location of individuals ( mean-field models) or restricted to local contacts (between nearest neighbours or decaying exponentially with distance). Real dispersal processes are likely to lie between these two extremes, and many are well described by long-tailed contact kernels such as power laws. We investigate the effect of different spatial dispersal mechanisms on the spatio-temporal spread of disease epidemics by simulating a stochastic Susceptible-infective model motivated by previous data analyses. Both long-term stationary behaviour (in the presence of a control or recovery process) and transient behaviour (which varies widely within and between epidemics) are examined. We demonstrate the relationship between epidemic size and disease pattern (characterized by spatial autocorrelation), and its dependence on dispersal and infectivity parameters. Special attention is given to boundary effects, which can decrease disease levels significantly relative to standard, periodic geometries in cases of long-distance dispersal. We propose and test a definition of transient duration which captures the dependence of transients on dispersal mechanisms. We outline an analytical approach that represents the behaviour of the spatially-explicit model, and use it to prove that the epidemic size is predicted exactly by the mean-field model (in the limit of an infinite system) when dispersal is sufficiently long ranged (i.e. when the power-law exponent a⩽2).

A hierarchical model for spatially clustered disease rates.

Maps of regional disease rates are potentially useful tools in examining spatial patterns of disease and for identifying clusters. Bayes and empirical Bayes approaches to this problem have proven useful in smoothing crude maps of disease rates. In recent years, models including both spatially correlated random effects and spatially unstructured random effects have been very popular. The spatially correlated random effects have been proposed in an attempt to capture a general clustering in the data. As an alternative, we propose replacing the spatially structured random effect with fixed clustering effects associated with particular areas. A reversible jump Markov chain Monte Carlo (RJMCMC) algorithm for posterior inference is described. We illustrate the model using the well-known New York leukaemia data.

Local clustering in breast, lung and colorectal cancer in Long Island, New York

BackgroundAnalyses of spatial disease patterns usually employ a univariate approach that uses one technique to identify disease clusters. Because different methods are sensitive to different aspects of spatial pattern, an approach employing a battery of techniques is expected to describe geographic variation in human health more fully. This two-part study employs a multi-method approach to elucidate geographic variation in cancer incidence in Long Island, New York, and to evaluate spatial association with air-borne toxics. This first paper uses the local Moran statistic to identify cancer hotspots and spatial outliers. We evaluated the geographic distributions of breast cancer in females and colorectal and lung cancer in males and females in Nassau, Queens, and Suffolk counties, New York, USA. We calculated standardized morbidity ratios (SMR values) from New York State Department of Health (NYSDOH) data.ResultsWe identified significant local clusters of high and low SMR and significant spatial outliers for each cancer-gender combination. We then compared our results with the study conducted by NYSDOH using Kulldorff's spatial scan statistic. We identified patterns on a smaller spatial scale with different cluster shapes than the NYSDOH analysis did, a consequence of different statistical methods and analysis scale.ConclusionThis is a methodological and comparative study to evaluate whether there is substantial benefit added by using a variety of techniques for geographic pattern detection at different spatial scales. We located significant spatial pattern in cancer morbidity in Nassau, Queens, and Suffolk counties. These results broadly agree with the results of other studies that used different techniques, but differ in specifics. The differences in our results and that of the NYSDOH underscore the need for an exploratory, integrative, and multi-scalar approach to assessing geographic patterns of disease, as different methods identify different patterns. We recommend that future studies of geographic patterns use a concordance of evidence from a multiscalar integrative geographic approach to assure that 1) different aspects of spatial pattern are fully identified and 2) the results from the suite of analyses are logically consistent.