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G M Jacquez

Publications and source records attributed to G M Jacquez.

13 recordsLinked to original sources
PubMed1996

The analysis of disease clusters, Part I: State of the art.

Public health professionals often are asked to investigate apparent clusters of human health events, or "disease clusters." A cluster is an excess of cases in space (a geographic cluster), in time (a temporal cluster), or in both space and time. This is part I of an introductory-level review of the analysis of disease clusters for physicians and health professionals concerned with infection surveillance in hospitals. It reviews the status of the field with the hope of expanding the use of cluster analysis methods for the routine surveillance of infectious disease in the hospital environment.

Algorithms↗
PubMed1996

The analysis of disease clusters, Part II: Introduction to techniques.

Public health professionals often are asked to investigate apparent clusters of human health events or "disease clusters." A cluster is an excess of cases in space (a geographic cluster), in time (a temporal cluster), or in both space and time. This is the second part of an introductory-level review of the analysis of disease clusters for physicians and health professionals concerned with infection surveillance in hospitals. It reviews the status of the field with the hope of expanding the use of cluster analysis methods for the routine surveillance of infectious diseases in the hospital environment.

Cluster Analysis↗
PubMed1995

Disease models implicit in statistical tests of disease clustering.

State and local health departments investigate an increasing number of cluster allegations, for which the selection of appropriate statistical methods is an important problem. Many of the methods for the spatial analysis of health data assume, either implicitly or explicitly, some model of disease occurrence, and comparisons of methods can be difficult when their underlying disease models differ. We review some of the issues involved in the statistical analysis of spatial disease patterns and describe several methods recently proposed to detect areas of increased disease rates. The disease models upon which the methods are based are explicitly described, and they provide a useful basis for comparing alternative clustering methods.

Cluster Analysis↗
PubMed1994

Cuzick and Edwards' test when exact locations are unknown.

Cuzick and Edwards (JR Stat Soc [B] 1990;52:73-104) have proposed a case-control test to detect spatial clustering. The test statistic is the sum, over all cases, of the number of each case's k nearest neighbors that also are cases. Their approach is attractive in that it accounts for geographic variation in population density and because it allows one to account for confounders, both known and unknown, through the judicious selection of controls. However, the test assumes case locations are known exactly, when, in practice, case locations are usually approximated by the centers of areas such as census tracts and zip code zones. In such situations, "ties" arise when cases and controls are assigned to the same area, and the loss of information precludes calculation of the test statistic. The author's approach enumerates the ways in which the ties may be resolved to obtain upper and lower bounds on the exact, unobserved, test statistic. The null hypothesis of no clustering is rejected when the upper and lower bounds are significant, and it is accepted when they are not significant. Judgment is withheld when the upper bound is significant but the lower bound is not significant. This approach allows Cuzick and Edwards' test to be used with inexact locations typical of most cluster investigations.

Analysis of Variance↗
PubMed1993

Genetic relationships of European populations reflect their ethnohistorical affinities.

From 420 records of ethnic locations and movements since 2000 B.C., we computed vectors describing the proportions which peoples of the various European language families contributed to the gene pools within 85 land-based 5 x 5-degree quadrats in Europe. Using these language family vectors, we computed ethnohistorical affinities as arc distances between all pairs of the 85 quadrats. These affinities are significantly correlated with genetic distances based on 26 genetic systems, even when geographic distances, a common causative factor, are held constant. Thus, the ethnohistorical distances explain a significant amount of the genetic variation observed in modern populations. Randomizations of the records by chronology result in loss of significance for the observed partial correlation between genetics and ethnohistory, when geography is held constant. However, a randomization of records by location only results in reduced significance. Thus, while the historical sequence of the movements does not seem to matter in Europe, their geographic locations do. We discuss the implications of these findings.

Alleles↗
PubMed1993

Synthetic cancer variables and the construction and testing of synthetic risk maps.

Cancer cluster investigations are usually univariate in nature; they focus on a particular cancer, such as leukaemia, and attempt to determine whether excess risk is associated with a suspected cancer-causing agent. Although several causes of death (such as leukaemia, lymphoma, Hodgkin's) may be considered, the approach is univariate because the causes of death are analysed sequentially and independently of one another. This approach is consistent with a one-cause one-effect model. Rarely, however, is the action of a carcinogen manifested at only one body site, and correlations among causes of death are the norm rather than the exception. A multiple effects model is therefore appropriate, and the multivariate nature of cancer mortality data should be exploited when exploring geographic pattern in cancer risks. This paper describes such an approach. We construct maps based on a principal components analysis of cancer mortality rates from different geographic areas. The resulting principal components are called synthetic cancer variables (SCVs), and maps of the SCV scores are synthetic risk maps (SRMs). These maps quantify geographic variation in cancer risk at several body sites simultaneously, and may be analysed for (1) spatial structure and (2) geographic association with potential risk factors. As an example, we use synthetic risk maps to determine whether high-risk counties in Illinois cluster near nuclear facilities. Much work remains to be done, but synthetic cancer risk maps appear to be a useful tool for quantifying geographic pattern and multivariate structure in cancer mortality.

Air Pollutants, Radioactive↗
PubMed1990

Diversity of some gene frequencies in European and Asian populations. V. Steep multilocus clines.

Regions of abrupt genetic change, which result from either rapid spatial change of selective pressures or limited admixture, were investigated in Europe and Asia on the basis of eight red cell markers typed in 960 samples. Two methods were employed, one based on genetic distances and one on evaluation of the first derivative of the surfaces representing allele-frequency variation. Genetic divergence tends to be maximal between populations that are separated by physical factors (mountain ranges and seas) but also separated by cultural barriers (different language affiliation). This suggests that mating isolation, rather than adaptive response to environmental change, accounts for spatially abrupt genetic change at the loci studied and that cultural differences associated with language contribute to isolating populations. Although selection may have determined two wide allele-frequency gradients, the genetic structure of European and Asian populations seems primarily to reflect isolation by distance when investigated on a small scale and migration patterns (or absence of migration) when investigated on a larger scale.

Alleles↗
PubMed1989

Spatial autocorrelation analysis of migration and selection.

We test various assumptions necessary for the interpretation of spatial autocorrelation analysis of gene frequency surfaces, using simulations of Wright's isolation-by-distance model with migration or selection superimposed. Increasing neighborhood size enhances spatial autocorrelation, which is reduced again for the largest neighborhood sizes. Spatial correlograms are independent of the mean gene frequency of the surface. Migration affects surfaces and correlograms when immigrant gene frequency differentials are substantial. Multiple directions of migration are reflected in the correlograms. Selection gradients yield clinal correlograms; other selection patterns are less clearly reflected in their correlograms. Sequential migration from different directions and at different gene frequencies can be disaggregated into component migration vectors by means of principal components analysis. This encourages analysis by such methods of gene frequency surfaces in nature. The empirical results of these findings lend support to the inference structure developed earlier for spatial autocorrelation analysis.

Computer Simulation↗
PubMedDate not supplied

Disease cluster statistics for imprecise space-time locations.

Health professionals are investigating an increasing number of possible disease clusters, and statistical tests play an important role in cluster description and analysis. Existing cluster statistics assume precise data, when in reality health events are often imprecise (for example, place of residence is known only to the census district or zip code) and uncertain (for example, 'I first became ill sometime in 1985'). This incompatibility--precise methods used to analyse imprecise data--is largely ignored, resulting in test statistics of unknown accuracy. Most cluster statistics can be written as the cross-product of two matrices where one matrix reflects nearest-neighbour, distance or adjacency relationships and the second matrix is health related (for example, case-control identities). This paper explores a general approach to clustering, which incorporates uncertainty regarding space-time locations into these nearest neighbour, distance or adjacency relationships. Because the approach is general it can be used with almost all existing cluster tests, and, because it accounts for imprecise location data, it is suited to the 'real-world' nature of disease cluster investigations.

Bias↗
PubMedDate not supplied

A k nearest neighbour test for space-time interaction.

This paper describes a k nearest neighbour statistic sensitive to the pattern of cases expected of space-time clusters of health events. The Knox and Mantel tests are frequently used for space-time clustering but have two disadvantages. First, the selection of critical space-time distances for the Knox test and of a data transformation for the Mantel test is subjective. Second, the Mantel statistic is the sum of the products of space and time distances, is linear in form, and is not sensitive to non-linear associations between small space and time distances expected of contagious processes. The k nearest neighbour statistic is the number of case pairs that are k nearest neighbours in both space and time, and is evaluated under the null hypothesis of independent space and time nearest neighbour relationships. The test was applied to simulated and real data and compared to the Knox and Mantel tests using statistical power comparisons. The k nearest neighbour test proved sensitive to the space-time interaction pattern expected of disease clusters, does not require parameters (such as critical distances) to be estimated from the data, and may be used to test hypotheses about the spatial and temporal scale of the cluster process. The method addresses significant weaknesses in existing space-time cluster tests and should prove useful in the quantification and evaluation of clusters of human health events. Additional research is needed to further document the power of the test under different cluster processes.

Censuses↗
PubMedDate not supplied

The map comparison problem: tests for the overlap of geographic boundaries.

The quality of environmental studies is often compromised by the use of statistics, such as correlation and regression for example, which presuppose a statistical model, linear or otherwise, between two variables. When investigating hypotheses about relationships among geographically distributed variables, an alternative approach is to measure the amount of boundary overlap. Boundaries are geographic zones of rapid change in the intensity of a variable, and are often of scientific interest in their own right. Examples of boundaries include ecotones, genetic hybrid zones, pollution plumes, and the front of the wave of advance of an epidemic. Boundary overlap describes zones where boundaries from two or more variables coincide, and are useful for evaluating epidemiologic hypotheses relating health to environmental exposures. This paper proposes four statistics of boundary overlap, and explores their performance using simulation models and real data describing ozone concentrations and hospital admissions for respiratory conditions. The statistics are found sensitive to different aspects of boundary overlap, and provide an additional diagnostic tool in the analysis of geographically distributed variables. Overlap statistics are expected to come into increasing use as the installed base of geographic information systems increases.

Air Pollution↗