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Surveillance research is of great importance for effective and efficient epidemiological monitoring of case counts and disease prevalence. Taking specific motivation from ongoing efforts to identify recurrent cases based on the Georgia Cancer Registry, we extend recently proposed "anchor stream" sampling design and estimation methodology. Our approach offers a more efficient and defensible alternative to traditional capture-recapture (CRC) methods by leveraging a relatively small random sample of participants whose recurrence status is obtained through a principled application of medical records abstraction. This sample is combined with one or more existing signaling data streams, which may yield data based on arbitrarily non-representative subsets of the full registry population. The key extension developed here accounts for the common problem of false positive or negative diagnostic signals from the existing data stream(s). In particular, we show that the design only requires documentation of positive signals in these non-anchor surveillance streams, and permits valid estimation of the true case count based on an estimable positive predictive value (PPV) parameter. We borrow ideas from the multiple imputation paradigm to provide accompanying standard errors, and develop an adapted Bayesian credible interval approach that yields favorable frequentist coverage properties. We demonstrate the benefits of the proposed methods through simulation studies, and provide a data example targeting estimation of the breast cancer recurrence case count among Metro Atlanta area patients from the Georgia Cancer Registry-based Cancer Recurrence Information and Surveillance Program (CRISP) database.

Heterogeneous population densities complicate comparisons of statistical power between hypothesis tests evaluating spatial clusters or clustering of disease. Specifically, the location of a cluster within a heterogeneously distributed population at risk impacts power properties, complicating comparisons of tests, and allowing one to map spatial variations in statistical power for different tests. Such maps provide insight into the overall power of a particular test, and also indicate areas within the study area where tests are more or less likely to detect the same local increase in relative risk. While such maps are largely driven by local sample size, we also find differences due to features of the statistics themselves. We illustrate these concepts using two tests: Tango's index of clustering and the spatial scan statistic. Furthermore, assessments of the accuracy of the 'most likely cluster' involve not only statistical power, but also spatial accuracy in identifying the location of a true underlying cluster. We illustrate these concepts via induction of artificial clusters within the observed incidence of severe cardiac birth defects in Santa Clara County, CA in 1981.

Statistical tests to detect clustering of a rare disease investigate whether an observed spatial pattern of cases appears to be due to chance alone. Heterogeneous population density and the geographic structure of the data under consideration complicate the ability to make comparisons of different tests. Further, interpretation of test results depends on the nature of the test used and what feature of the data it is designed to detect. With these issues in mind, we compare three recent tests for assessing general clustering among cases where the population is distributed heterogeneously across the study area, namely those of Besag and Newell, Turnbull et al. and Tango. We compare these methods using 1981 incidence data for severe cardiac birth defects from Santa Clara County, California. Copyright © 2000 John Wiley & Sons, Ltd.

Breakthroughs in imaging of skin tissue reveal new details on the distribution of nerve fibers in the epidermis. Preliminary neurologic studies indicate qualitative differences in the spatial patterns of nerve fibers based on pathophysiologic conditions in the subjects. Of particular interest is the evolution of spatial patterns observed in the progression of diabetic neuropathy. It appears that the spatial distribution of nerve fibers becomes more 'clustered' as neuropathy advances, suggesting the possibility of diagnostic prediction based on patterns observed in skin biopsies. We consider two approaches to establish statistical inference relating to this observation. First, we view the set of locations where the nerves enter the epidermis from the dermis as a realization of a spatial point process. Secondly, we treat the set of fibers as a realization of a planar fiber process. In both cases, we use estimated second-order properties of the observed data patterns to describe the degree and scale of clustering observed in the microscope images of blister biopsies. We illustrate the methods using confocal microscopy blister images taken from the thigh of one normal (disease-free) individual and two images each taken from the thighs of subjects with mild, moderate, and severe diabetes and report measurable differences in the spatial patterns of nerve entry points/fibers associated with disease status.

Surveillance of a large geographic region for 'clusters' of adverse health events, particularly cancers, often involves searching for raised incidence in the vicinity of prespecified putative sources of hazard. For reasons of practicality or of confidentiality, incidence and population data are usually only available aggregated over subregions or 'cells'. The performance of statistical procedures designed to detect the presence of clusters can be highly sensitive to the level of aggregation, that is to the choice of partition of the region into the cells. We investigate this sensitivity in the cases of three recently proposed procedures, namely those of Besag and Newell, Stone, and Waller et al. For illustration, we use leukaemia incidence data for 1978-82 in a region of upstate New York, with inactive hazardous waste sites containing trichloroethylene acting as suspected sources.

The Centers for Disease Control and Prevention defined epilepsy as an emerging public health issue in a recent report and emphasized the importance of epilepsy studies in minorities and people of low socioeconomic status. Previous research has suggested that the incidence rate for epilepsy is positively associated with various measures of social and economic disadvantage. In response, we utilize hierarchical Bayesian models to analyze health disparities in epilepsy and seizure risks among multiple ethnicities in the city of Philadelphia, Pennsylvania. The goals of the analysis are to highlight any overall significant disparities in epilepsy risks between the populations of Caucasians, African Americans, and Hispanics in the study area during the years 2002--2004 and to visualize the spatial pattern of epilepsy risks by ethnicity to indicate where certain ethnic populations were most adversely affected by epilepsy within the study area. Results of the Bayesian model indicate that Hispanics have the highest epilepsy risk overall, followed by African Americans, and then Caucasians. There are significant increases in relative risk for both African Americans and Hispanics when compared with Caucasians, as indicated by the posterior mean estimates of 2.09 with a 95 per cent credible interval of (1.67, 2.62) for African Americans and 2.97 with a 95 per cent credible interval of (2.37, 3.71) for Hispanics. Results also demonstrate that using a Bayesian analysis in combination with geographic information system (GIS) technology can reveal spatial patterns in patient data and highlight areas of disparity in epilepsy risk among subgroups of the population.

Cardiac imaging with single photon emission computed tomography (SPECT) is a common approach for quantifying myocardial perfusion. Fusing serial SPECT studies allows detection and quantification of changes in myocardial perfusion such as those resulting from disease progression or successful treatment therapy for patients with coronary artery disease. The abundance of data for each subject along with the inherent intra-subject correlation due to spatial proximity of multiple perfusion measurements present special analytical challenges. We utilize a standard physiological model of the left ventricle (LV) to construct a general statistical model for cardiac perfusion that incorporates spatial correlation. We illustrate the use of mixed effects models and linear models with correlated errors to estimate myocardial perfusion counts and to compare these counts across serial studies. We address different types of spatial correlation among perfusion measurements in the LV, and we consider various parametric structures for these correlations. We apply the model to data from serial SPECT studies conducted while subjects were in both restful and stressful states approximately two days and one year following a myocardial infarction.

We review some recent statistical methods for examining geographic patterns of disease incidence for the presence of clusters. General methods search for clusters throughout the study area and then assess the statistical significance of any clusters detected. Focused methods check for elevated incidence rates close to prespecified locations of putative sources of hazard. We apply the methods to leukaemia incidence data for children aged 0–15 years in Sweden (1980–1990), particularly in reference to locations of nuclear power facilities. Unlike some other studies, notably in the United Kingdom, we do not find any significant clusters.