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Abstract Background The spatial and space-time scan statistics are commonly applied for the detection of geographical disease clusters. Monte Carlo hypothesis testing is typically used to test whether the geographical clusters are statistically significant as there is no known way to calculate the null distribution analytically. In Monte Carlo hypothesis testing, simulated random data are generated multiple times under the null hypothesis, and the p-value is r/(R + 1), where R is the number of simulated random replicates of the data and r is the rank of the test statistic from the real data compared to the same test statistics calculated from each of the random data sets. A drawback to this powerful technique is that each additional digit of p-value precision requires ten times as many replicated datasets, and the additional processing can lead to excessive run times. Results We propose a new method for obtaining more precise p-values with a given number of replicates. The collection of test statistics from the random replicates is used to estimate the true distribution of the test statistic under the null hypothesis by fitting a continuous distribution to these observations. The choice of distribution is critical, and for the spatial and space-time scan statistics, the extreme value Gumbel distribution performs very well while the gamma, normal and lognormal distributions perform poorly. From the fitted Gumbel distribution, we show that it is possible to estimate the analytical p-value with great precision even when the test statistic is far out in the tail beyond any of the test statistics observed in the simulated replicates. In addition, Gumbel-based rejection probabilities have smaller variability than Monte Carlo-based rejection probabilities, suggesting that the proposed approach may result in greater power than the true Monte Carlo hypothesis test for a given number of replicates. Conclusions For large data sets, it is often advantageous to replace computer intensive Monte Carlo hypothesis testing with this new method of fitting a Gumbel distribution to random data sets generated under the null, in order to reduce computation time and obtain much more precise p-values and slightly higher statistical power.

Abstract Temporal, spatial and space-time scan statistics are commonly used to detect and evaluate the statistical significance of temporal and/or geographical disease clusters, without any prior assumptions on the location, time period or size of those clusters. Scan statistics are mostly used for count data, such as disease incidence or mortality. Sometimes there is an interest in looking for clusters with respect to a continuous variable, such as lead levels in children or low birth weight. For such continuous data, we present a scan statistic where the likelihood is calculated using the the normal probability model. It may also be used for other distributions, while still maintaining the correct alpha level. In an application of the new method, we look for geographical clusters of low birth weight in New York City.

Abstract Background Spatial global clustering tests can be used to evaluate the geographical distribution of health outcomes. The power of several of these tests has been evaluated and compared using simulated data, but their performance using real unadjusted data and data adjusted for individual- and area-level covariates has not been reported previously. We evaluated data on prostate cancer histologic tumor grade and stage of disease at diagnosis for incident cases of prostate cancer reported to the Maryland Cancer Registry during 1992–1997. We analyzed unadjusted data as well as expected counts from models that were adjusted for individual-level covariates (race, age and year of diagnosis) and area-level covariates (census block group median household income and a county-level socioeconomic index). We chose 3 spatial clustering tests that are commonly used to evaluate the geographic distribution of disease: Cuzick-Edwards' k-NN (k-Nearest Neighbors) test, Moran's I and Tango's MEET (Maximized Excess Events Test). Results For both grade and stage at diagnosis, we found that Cuzick-Edwards' k-NN and Moran's I were very sensitive to the percent of population parameter selected. For stage at diagnosis, all three tests showed that the models with individual- and area-level adjustments reduced clustering the most, but did not reduce it entirely. Conclusion Based on this specific example, results suggest that these tests provide useful tools for evaluating spatial clustering of disease characteristics, both before and after consideration of covariates.

Abstract Background Early detection of disease outbreaks enables public health officials to implement disease control and prevention measures at the earliest possible time. A time periodic geographical disease surveillance system based on a cylindrical space-time scan statistic has been used extensively for disease surveillance along with the SaTScan software. In the purely spatial setting, many different methods have been proposed to detect spatial disease clusters. In particular, some spatial scan statistics are aimed at detecting irregularly shaped clusters which may not be detected by the circular spatial scan statistic. Results Based on the flexible purely spatial scan statistic, we propose a flexibly shaped space-time scan statistic for early detection of disease outbreaks. The performance of the proposed space-time scan statistic is compared with that of the cylindrical scan statistic using benchmark data. In order to compare their performances, we have developed a space-time power distribution by extending the purely spatial bivariate power distribution. Daily syndromic surveillance data in Massachusetts, USA, are used to illustrate the proposed test statistic. Conclusion The flexible space-time scan statistic is well suited for detecting and monitoring disease outbreaks in irregularly shaped areas.

Abstract Background With the objective of identifying spatial and temporal patterns of enzootic raccoon variant rabies, a spatial scan statistic was utilized to search for significant terrestrial rabies clusters by year in New York State in 1997–2003. Cluster analyses were unadjusted for other factors, adjusted for covariates, and adjusted for covariates and large scale geographic variation (LSGV). Adjustments were intended to identify the unusual aggregations of cases given the expected distribution based on the observed locations. Results Statistically significant clusters were identified particularly in the Albany, Finger Lakes, and South Hudson areas. The clusters were generally persistent in the Albany area, but demonstrated cyclical changes in rabies activity every few years in the other areas. Cluster adjustments allowed the discussion of possible causes for the high risk raccoon rabies areas identified. Conclusion This study analyzed raccoon variant rabies spatial and temporal patterns in New York that have not been previously described at a focal (census tract) level. Comparisons across the type of spatial analysis performed with various degrees of adjustment allow consideration of the potential influence of geographical factors for raccoon rabies and possible reasons for the highest risk areas (statistically significant clusters).

Abstract Background Tango's maximized excess events test (MEET) has been shown to have very good statistical power in detecting global disease clustering. A nice feature of this test is that it considers a range of spatial scale parameters, adjusting for the multiple testing. This means that it has good power to detect a wide range of clustering processes. The test depends on the functional form of a weight function, and it is unknown how sensitive the test is to the choice of this weight function and what function provides optimal power for different clustering processes. In this study, we evaluate the performance of the test for a wide range of weight functions. Results The power varies greatly with different choice of weight. Tango's original choice for the weight function works very well. There are also other weight functions that provide good power. Conclusion We recommend the use of Tango's MEET to test global disease clustering, either with the original weight or one of the alternate weights that have good power.

Abstract Background Findings are compared on geographic variation of incident and late-stage cancers across Connecticut using different areal units for analysis. Results Few differences in results were found for analyses across areal units. Global clustering of incident prostate and breast cancer cases was apparent regardless of the level of geography used. The test for local clustering found approximately the same locales, populations at risk and estimated effects. However, some discrepancies were uncovered. Conclusion In the absence of conditions calling for surveillance of small area cancer clusters ('hot spots'), the rationale for accepting the burdens of preparing data at levels of geography finer than the census tract may not be compelling.

Abstract Background Spatial variation in patterns of disease outcomes is often explored with techniques such as cluster detection analysis. In other types of investigations, geographically varying individual or community level characteristics are often used as independent predictors in statistical models which also attempt to explain variation in disease outcomes. However, there is a lack of research which combines geographically referenced exploratory analysis with multilevel models. We used a spatial scan statistic approach, in combination with predicted block group-level disease patterns from multilevel models, to examine geographic variation in prostate cancer grade and stage at diagnosis. Results We examined data from 20928 Maryland men with incident prostate cancer reported to the Maryland Cancer Registry during 1992–1997. Initial cluster detection analyses, prior to adjustment, indicated that there were four statistically significant clusters of high and low rates of each outcome (later stage at diagnosis and higher histologic grade of tumor) for prostate cancer cases in Maryland during 1992–1997. After adjustment for individual case attributes, including age, race, year of diagnosis, patterns of clusters changed for both outcomes. Additional adjustment for Census block group and county-level socioeconomic measures changed the cluster patterns further. Conclusions These findings provide evidence that, in locations where adjustment changed patterns of clusters, the adjustment factors may be contributing causes of the original clusters. In addition, clusters identified after adjusting for individual and area-level predictors indicate area of unexplained variation, and merit further small-area investigations.

Abstract Background The aims of this study were to determine whether observed geographic variations in breast cancer incidence are random or statistically significant, whether statistically significant excesses are temporary or time-persistent, and whether they can be explained by covariates such as socioeconomic status (SES) or urban/rural status? Results A purely spatial analysis found fourteen geographic areas that deviated significantly from randomness: ten with higher incidence rates than expected, four lower than expected. After covariate adjustment, three of the ten high areas remained statistically significant and one new high area emerged. The space-time analysis identified eleven geographic areas as statistically significant, seven high and four low. After covariate adjustment, four of the seven high areas remained statistically significant and a fifth high area also identified in the purely spatial analysis emerged. Conclusions These analyses identify geographic areas with invasive breast cancer incidence higher or lower than expected, the times of their excess, and whether or not their status is affected when the model is adjusted for risk factors. These surveillance findings can be a sound starting point for the epidemiologist and has the potential of monitoring time trends for cancer control activities.

Abstract Background Many different test statistics have been proposed to test for spatial clustering. Some of these statistics have been widely used in various applications. In this paper, we use an existing collection of 1,220,000 simulated benchmark data, generated under 51 different clustering models, to compare the statistical power of several disease clustering tests. These tests are Besag-Newell's R, Cuzick-Edwards' k-Nearest Neighbors (k-NN), the spatial scan statistic, Tango's Maximized Excess Events Test (MEET), Swartz' entropy test, Whittemore's test, Moran's I and a modification of Moran's I. Results Except for Moran's I and Whittemore's test, all other tests have good power for detecting some kind of clustering. The spatial scan statistic is good at detecting localized clusters. Tango's MEET is good at detecting global clustering. With appropriate choice of parameter, Besag-Newell's R and Cuzick-Edwards' k-NN also perform well. Conclusion The power varies greatly for different test statistics and alternative clustering models. Consideration of the power is important before we decide which test statistic to use.

Spatiotemporal analysis of high-resolution coronavirus disease (COVID-19) data can help health officials monitor disease spread and target interventions (1,2). Publicly available data have been used to detect COVID-19 spatiotemporal clusters [...]

The Bureau of Communicable Disease (BCD) at the New York City Department of Health and Mental Hygiene (DOHMH) monitors and investigates >70 reportable diseases among the city's 8.49 million residents. [...]

Background Kawasaki disease is an acute vasculitis that primarily affects children younger than 5 years of age. Its etiology is unknown. The United States Vaccine Safety Datalink conducted postlicensure safety surveillance for 13-valent pneumococcal conjugate vaccine (PCV13), comparing the risk of Kawasaki disease within 28 days of PCV13 vaccination with the historical risk after 7-valent PCV (PCV7) vaccination and using chart-validation. A relative risk (RR) of 2.38 (95% CI 0.92-6.38) was found. Concurrently, the Food and Drug Administration (FDA) conducted a postlicensure safety review that identified cases of Kawasaki disease through adverse event reporting. The FDA decided to initiate a larger study of Kawasaki disease risk following PCV13 vaccination in the claims-based Sentinel/Postlicensure Rapid Immunization Safety Monitoring (PRISM) surveillance system. The objective of this study was to determine the existence and magnitude of any increased risk of Kawasaki disease in the 28 days following PCV13 vaccination. Methods and findings The study population included mostly commercially insured children from birth to Conclusions With more than 6 million doses of PCV13 administered, no evidence was found of an association between PCV13 vaccination and Kawasaki disease onset in the 4 weeks after vaccination nor of an elevated risk extending or concentrated somewhat beyond 4 weeks. These null results were consistent across alternative designs, age-adjustment methods, control intervals, and categories of Kawasaki disease case included., Author(s): Meghan A. Baker 1,2, Bethany Baer 3, Martin Kulldorff 4, Lauren Zichittella 1, Rebecca Reindel 3, Sandra DeLuccia 1, Hana Lipowicz 1, Katherine Freitas 1, Robert Jin 1, W. [...]

Tree-based scan statistics have been successfully used to study the safety of several vaccines without prespecifying health outcomes of concern. In this study, the binomial tree-based scan statistic was applied sequentially to detect adverse events in days 1–28 compared with days 29–56 after recombinant herpes zoster (RZV) vaccination, with 5 looks at the data and formal adjustment for the repeated analyses over time. IBM MarketScan data on commercially insured persons ≥50 years of age receiving RZV during January 1, 2018, to May 5, 2020, were used. With 999,876 doses of RZV included, statistically significant signals were detected only for unspecified adverse effects/complications following immunization, with attributable risks as low as 2 excess cases per 100,000 vaccinations. Ninety percent of cases in the signals occurred in the week after vaccination and, based on previous studies, likely represent nonserious events like fever, fatigue, and headache. Strengths of our study include its untargeted nature, self-controlled design, and formal adjustment for repeated testing. Although the method requires prespecification of the risk window of interest and may miss some true signals detectable using the tree-temporal variant of the method, it allows for early detection of potential safety problems through early initiation of ongoing monitoring. [ABSTRACT FROM AUTHOR]