An approximation for the distribution of the scan statistic

Recognizing spatial and temporal clustering patterns of dengue outbreaks in Taiwan

BACKGROUND

Dengue fever is the most common arboviral infection in humans, with viral transmissions occurring in more than 100 countries in tropical regions. A global strategy for dengue prevention and control was established more than 10 years ago. However, the factors that drive the transmission of the dengue virus and subsequent viral infection continue unabated. The largest dengue outbreaks in Taiwan since World War II occurred in two recent successive years: 2014 and 2015.

METHODS

We performed a systematic analysis to detect and recognize spatial and temporal clustering patterns of dengue incidence in geographical areas of Taiwan, using the map-based pattern recognition procedure and scan test. Our aim was to recognize geographical heterogeneity patterns of varying dengue incidence intensity and detect hierarchical incidence intensity clusters.

RESULTS

Using the map-based pattern recognition procedure, we identified and delineated two separate hierarchical dengue incidence intensity clusters that comprise multiple mutually adjacent geographical units with high dengue incidence rates. We also found that that dengue incidence tends to peak simultaneously and homogeneously among the neighboring geographic units with high rates in the same cluster.

CONCLUSION

Beyond significance testing, this study is particularly desired by and useful for health authorities who require optimal characteristics of disease incidence patterns on maps and over time. Among the integrated components for effective prevention and control of dengue and dengue hemorrhagic fever are active surveillance and community-based integrated mosquito control, for which this study provides valuable inferences. Effective dengue prevention and control programs in Taiwan are critical, and have the added benefit of controlling the potential emergence of Zika.

Assessing current temporal and space-time anomalies of disease incidence

Approaches used to early and accurately characterize epidemiologic patterns of disease incidence in a temporal and spatial series are becoming increasingly important. Cluster tests are generally designed for retrospective detection of epidemiologic anomalies in a temporal or space-time series. Timely identification of anomalies of disease or poisoning incidence during ongoing surveillance or an outbreak requires the use of sensitive statistical methods that recognize an incidence pattern at the time of occurrence. This report describes 2 novel analytical methods that focus on detecting anomalies of incidence at the time of occurrence in a temporal and space-time series. The first method describes the paucity of incidence at the time of occurrence in an ongoing surveillance and is designed to evaluate whether a decline in incidence occurs on the single current day or during the most recent few days. The second method provides an overall assessment of current clustering or paucity of incidence in a space-time series, allowing for several space regions. We illustrate the application of these methods using a subsample of a temporal series of data on the largest dengue outbreak in Taiwan in 2015 since World War II and demonstrate that they are useful to efficiently monitor incoming data for current clustering and paucity of incidence in a temporal and space-time series. In light of the recent global emergence and resurgence of Zika, dengue, and chikungunya infection, these approaching for detecting current anomalies of incidence in the ongoing surveillance of disease are particularly desired and needed.

Poisson approximations for the distribution and moments of ordered m-spacings

This article investigates the accuracy of approximations for the distribution of ordered m-spacings for i.i.d. uniform observations in the interval (0, 1). Several Poisson approximations and a compound Poisson approximation are studied. The result of a simulation study is included to assess the accuracy of these approximations. A numerical procedure for evaluating the moments of the ordered m-spacings is developed and evaluated for the most accurate approximation.

[Temporal analysis of mortality due to intimate partner violence in Spain]

OBJECTIVES

To analyze the temporal distribution of mortality due to violence by intimate partners (VIP) and to identify possible temporal clusters in women deaths by VIP in Spain.

METHODS

We performed a descriptive epidemiological study based on the VIP deaths included in the database of the Federation of Divorced and Separated Women (1998-2003). The epidemic index (EI) was calculated as the ratio between the actual number of VIP deaths in a given month from January to July 2003 and the median number in the same month in the five preceding years. A Poisson model was used to analyze the distribution by years (1998-2002), seasons, months, and days. Simple regression analysis was performed with three-monthly means. A temporal cluster analysis was also carried out.

RESULTS

In 2003, the EI of VIP mortality was high in January (EI = 1.6), March (EI = 1.2), May (EI = 1.5), June (EI = 2), and July (EI = 2.5). Compared with 1998 and Sundays, respectively, mortality due to VIP was significantly increased in 2001 (relative risk, RR = 1.52; 95% confidence interval [CI], 1.05-2.20) and on Mondays (RR = 1.77; 95%CI, 1.13-2.76). The regression analyses confirmed an increase between the first three-month period of 1998 and the last three-month period of 2001. There were no differences between seasons and months. No temporal clusters of deaths were detected.

CONCLUSIONS

VIP is currently an increasing epidemic in Spain with no clear temporal pattern. Political and legal efforts to reduce this problem do not seem to be successful.

A negative feedback loop of transcription factors specifies alternative dendritic cell chromatin States

During hematopoiesis, cells originating from the same stem cell reservoir differentiate into distinct cell types. The mechanisms enabling common progenitors to differentiate into alternative cell fates are not fully understood. Here, we identify cell-fate-determining transcription factors (TFs) governing dendritic cell (DC) development by annotating the enhancer landscapes of the DC lineage. Combining these analyses with detailed overexpression, knockdown, and ChIP-Seq studies, we show that Irf8 functions as a plasmacytoid DC epigenetic and fate-determining TF, regulating massive, cell-specific chromatin changes in thousands of pDC enhancers. Importantly, Irf8 forms a negative feedback loop with Cebpb, a monocyte-derived DC epigenetic fate-determining TF. We show that using this circuit logic, a pulse of TF expression can stably define epigenetic and transcriptional states, regardless of the microenvironment. More broadly, our study proposes a general paradigm that allows closely related cells with a similar set of signal-dependent factors to generate differential and persistent enhancer landscapes.

Improving Detection of Driver Genes: Power-Law Null Model of Copy Number Variation in Cancer

In this paper, we study Copy Number Variation (CNV) data. The underlying process generating CNV segments is generally assumed to be memory-less, giving rise to an exponential distribution of segment lengths. In this paper, we provide evidence from cancer patient data, which suggests that this generative model is too simplistic, and that segment lengths follow a power-law distribution instead. We conjecture a simple preferential attachment generative model that provides the basis for the observed power-law distribution. We then show how an existing statistical method for detecting cancer driver genes can be improved by incorporating the power-law distribution in the null model.

Identification of chromosomal translocation hotspots via scan statistics

MOTIVATION

The detection of genomic regions unusually rich in a given pattern is an important undertaking in the analysis of next-generation sequencing data. Recent studies of chromosomal translocations in activated B lymphocytes have identified regions that are frequently translocated to c-myc oncogene. A quantitative method for the identification of translocation hotspots was crucial to this study. Here we improve this analysis by using a simple probabilistic model and the framework provided by scan statistics to define the number and location of translocation breakpoint hotspots. A key feature of our method is that it provides a global chromosome-wide nominal control level to clustering, as opposed to previous methods based on local criteria. While being motivated by a specific application, the detection of unusual clusters is a widespread problem in bioinformatics. We expect our method to be useful in the analysis of data from other experimental approaches such as of ChIP-seq and 4C-seq.

RESULTS

The analysis of translocations from B lymphocytes with the method described here reveals the presence of longer hotspots when compared with those defined previously. Further, we show that the hotspot size changes substantially in the absence of DNA repair protein 53BP1. When 53BP1 deficiency is combined with overexpression of activation-induced cytidine deaminase, the hotspot length increases even further. These changes are not detected by previous methods that use local significance criteria for clustering. Our method is also able to identify several exclusive translocation hotspots located in genes of known tumor supressors.

AVAILABILITY AND IMPLEMENTATION

The detection of translocation hotspots is done with hot_scan, a program implemented in R and Perl. Source code and documentation are freely available for download at https://github.com/itojal/hot_scan.

Whole-genome detection of disease-associated deletions or excess homozygosity in a case-control study of rheumatoid arthritis

Unlike genome-wide association studies, few comprehensive studies of copy number variation's contribution to complex human disease susceptibility have been performed. Copy number variations are abundant in humans and represent one of the least well-studied classes of genetic variants; in addition, known rheumatoid arthritis susceptibility loci explain only a portion of familial clustering. Therefore, we performed a genome-wide study of association between deletion or excess homozygosity and rheumatoid arthritis using high-density 550 K SNP genotype data from a genome-wide association study. We used a genome-wide statistical method that we recently developed to test each contiguous SNP locus between 868 cases and 1194 controls to detect excess homozygosity or deletion variants that influence susceptibility. Our method is designed to detect statistically significant evidence of deletions or homozygosity at individual SNPs for SNP-by-SNP analyses and to combine the information among neighboring SNPs for cluster analyses. In addition to successfully detecting the known deletion variants on major histocompatibility complex, we identified 4.3 and 28 kb clusters on chromosomes 10p and 13q, respectively, which were significant at a Bonferroni-type-corrected 0.05 nominal significant level. Independently, we performed analyses using PennCNV, an algorithm for identifying and cataloging copy numbers for individuals based on a hidden Markov model, and identified cases and controls that had chromosomal segments with copy number <2. Using Fisher's exact test for comparing the numbers of cases and controls with copy number <2 per SNP, we identified 26 significant SNPs (protective; more controls than cases) aggregating on chromosome 14 with P-values <10(-8).

Scan-statistic approach identifies clusters of rare disease variants in LRP2, a gene linked and associated with autism spectrum disorders, in three datasets

Cluster-detection approaches, commonly used in epidemiology and astronomy, can be applied in the context of genetic sequence data for the identification of genetic regions significantly enriched with rare disease-risk variants (DRVs). Unlike existing association tests for sequence data, the goal of cluster-detection methods is to localize significant disease mutation clusters within a gene or region of interest. Here, we focus on a chromosome 2q replicated linkage region that is associated with autism spectrum disorder (ASD) and that has been sequenced in three independent datasets. We found that variants in one gene, LRP2, residing on 2q are associated with ASD in two datasets (the combined variable-threshold-test p value is 1.2 × 10(-5)). Using a cluster-detection method, we show that in the discovery and replication datasets, variants associated with ASD cluster preponderantly in 25 kb windows (adjusted p values are p(1) = 0.003 and p(2) = 0.002), and the two windows are highly overlapping. Furthermore, for the third dataset, a 25 kb region similar to those in the other two datasets shows significant evidence of enrichment of rare DRVs. The region implicated by all three studies is involved in ligand binding, suggesting that subtle alterations in either LRP2 expression or LRP2 primary sequence modulate the uptake of LRP2 ligands. BMP4 is a ligand of particular interest given its role in forebrain development, and modest changes in BMP4 binding, which binds to LRP2 near the mutation cluster, might subtly affect development and could lead to autism-associated phenotypes.

Detection of disease-associated deletions in case-control studies using SNP genotypes with application to rheumatoid arthritis

Genomic deletions have long been known to play a causative role in microdeletion syndromes. Recent whole-genome genetic studies have shown that deletions can increase the risk for several psychiatric disorders, suggesting that genomic deletions play an important role in the genetic basis of complex traits. However, the association between genomic deletions and common, complex diseases has not yet been systematically investigated in gene mapping studies. Likelihood-based statistical methods for identifying disease-associated deletions have recently been developed for familial studies of parent-offspring trios. The purpose of this study is to develop statistical approaches for detecting genomic deletions associated with complex disease in case-control studies. Our methods are designed to be used with dense single nucleotide polymorphism (SNP) genotypes to detect deletions in large-scale or whole-genome genetic studies. As more and more SNP genotype data for genome-wide association studies become available, development of sophisticated statistical approaches will be needed that use these data. Our proposed statistical methods are designed to be used in SNP-by-SNP analyses and in cluster analyses based on combined evidence from multiple SNPs. We found that these methods are useful for detecting disease-associated deletions and are robust in the presence of linkage disequilibrium using simulated SNP data sets. Furthermore, we applied the proposed statistical methods to SNP genotype data of chromosome 6p for 868 rheumatoid arthritis patients and 1,197 controls from the North American Rheumatoid Arthritis Consortium. We detected disease-associated deletions within the region of human leukocyte antigen in which genomic deletions were previously discovered in rheumatoid arthritis patients.

Epicardial mapping of ventricular fibrillation over the posterior descending artery and left posterior papillary muscle of the swine heart

BACKGROUND

Recent studies suggest that during ventricular fibrillation (VF) epicardial vessels may be a site of conduction block and the posterior papillary muscle (PPM) in the left ventricle (LV) may be the location of a "mother rotor." The goal of this study was to obtain evidence to support or refute these possibilities.

METHODS

Epicardial activation over the posterior LV and right ventricle (RV) was mapped during the first 20 s of electrically induced VF in six open-chest pigs with a 504 electrode plaque covering a 20 cm(2) area centered over the posterior descending artery (PDA).

RESULTS

The locations of epicardial breakthrough as well as reentry clustered in time and space during VF. Spatially, reentry occurred significantly more frequently over the LV than the RV in all 48 episodes, and breakthrough clustered near the PPM (p < 0.001). Significant temporal clustering occurred in 79% of breakthrough episodes and 100% of reentry episodes. These temporal clusters occurred at different times so that there was significantly less breakthrough when reentry was present (p < 0.0001). Conduction block occurred significantly more frequently near the PDA than elsewhere.

CONCLUSIONS

The PDA is a site of epicardial block which may contribute to VF maintenance. Epicardial breakthrough clusters near the PPM. Reentry also clusters in space but at a separate site. The fact that breakthrough and reentry cluster at different locations and at different times supports the possibility of a drifting filament at the PPM so that at times reentry is present on the surface but at other times the reentrant wavefront breaks through to the epicardium.

A scan statistic for identifying chromosomal patterns of SNP association

We have developed a single nucleotide polymorphism (SNP) association scan statistic that takes into account the complex distribution of the human genome variation in the identification of chromosomal regions with significant SNP associations. This scan statistic has wide applicability for genetic analysis, whether to identify important chromosomal regions associated with common diseases based on whole-genome SNP association studies or to identify disease susceptibility genes based on dense SNP positional candidate studies. To illustrate this method, we analyzed patterns of SNP associations on chromosome 19 in a large cohort study. Among 2,944 SNPs, we found seven regions that contained clusters of significantly associated SNPs. The average width of these regions was 35 kb with a range of 10-72 kb. We compared the scan statistic results to Fisher's product method using a sliding window approach, and detected 22 regions with significant clusters of SNP associations. The average width of these regions was 131 kb with a range of 10.1-615 kb. Given that the distances between SNPs are not taken into consideration in the sliding window approach, it is likely that a large fraction of these regions represents false positives. However, all seven regions detected by the scan statistic were also detected by the sliding window approach. The linkage disequilibrium (LD) patterns within the seven regions were highly variable indicating that the clusters of SNP associations were not due to LD alone. The scan statistic developed here can be used to make gene-based or region-based SNP inferences about disease association.

Mapping tumor-suppressor genes with multipoint statistics from copy-number-variation data

Array-based comparative genomic hybridization (arrayCGH) is a microarray-based comparative genomic hybridization technique that has been used to compare tumor genomes with normal genomes, thus providing rapid genomic assays of tumor genomes in terms of copy-number variations of those chromosomal segments that have been gained or lost. When properly interpreted, these assays are likely to shed important light on genes and mechanisms involved in the initiation and progression of cancer. Specifically, chromosomal segments, deleted in one or both copies of the diploid genomes of a group of patients with cancer, point to locations of tumor-suppressor genes (TSGs) implicated in the cancer. In this study, we focused on automatic methods for reliable detection of such genes and their locations, and we devised an efficient statistical algorithm to map TSGs, using a novel multipoint statistical score function. The proposed algorithm estimates the location of TSGs by analyzing segmental deletions (hemi- or homozygous) in the genomes of patients with cancer and the spatial relation of the deleted segments to any specific genomic interval. The algorithm assigns, to an interval of consecutive probes, a multipoint score that parsimoniously captures the underlying biology. It also computes a P value for every putative TSG by using concepts from the theory of scan statistics. Furthermore, it can identify smaller sets of predictive probes that can be used as biomarkers for diagnosis and therapeutics. We validated our method using different simulated artificial data sets and one real data set, and we report encouraging results. We discuss how, with suitable modifications to the underlying statistical model, this algorithm can be applied generally to a wider class of problems (e.g., detection of oncogenes).

Temporal surveillance using scan statistics

We describe two classes of statistics for testing an arbitrary model of disease incidence over time against an alternative model involving a spike (pulse) superimposed on this background. The statistics are each based on taking the maximum of some function comparing observed and expected numbers of events in a window of width w. One approach applies p-values for scan statistics calculated for a constant background rate to this more general problem. For a fixed window, w, the approach gives a simple formula to determine p-values for retrospective analysis, or to sound an alarm for either continuous or grouped prospective data. The latter application involves a new approximation for the distribution of the maximum number of cases in w consecutive intervals. The second approach based on generalized likelihood ratio tests (GLRTs), sounds an alarm for a higher than anticipated rate of events in a scanning window of fixed length, or for window sizes that lie in a region. GLRTs are constructed for continuous observations, for grouped data, or for a sequence of trials. As for GLRTs used in retrospective evaluations, simulation is required to implement the prospective procedure. For grouped surveillance data, we compare by simulation, operating characteristics of the P-scan with fixed windows (both correctly specified and not), the fixed-window GLRT, the variable-window GLRT, and a variant of the CUSUM. The simulations demonstrate a very high correlation between the P-scan and corresponding fixed-window GLRT.

Automated DNA sequence-based early warning system for the detection of methicillin-resistant Staphylococcus aureus outbreaks

BACKGROUND

The detection of methicillin-resistant Staphylococcus aureus (MRSA) usually requires the implementation of often rigorous infection-control measures. Prompt identification of an MRSA epidemic is crucial for the control of an outbreak. In this study we evaluated various early warning algorithms for the detection of an MRSA cluster.

METHODS AND FINDINGS

Between 1998 and 2003, 557 non-replicate MRSA strains were collected from staff and patients admitted to a German tertiary-care university hospital. The repeat region of the S. aureus protein A (spa) gene in each of these strains was sequenced. Using epidemiological and typing information for the period 1998-2002 as reference data, clusters in 2003 were determined by temporal-scan test statistics. Various early warning algorithms (frequency, clonal, and infection control professionals [ICP] alerts) were tested in a prospective analysis for the year 2003. In addition, a newly implemented automated clonal alert system of the Ridom StaphType software was evaluated. A total of 549 of 557 MRSA were typeable using spa sequencing. When analyzed using scan test statistics, 42 out of 175 MRSA in 2003 formed 13 significant clusters (p < 0.05). These clusters were used as the "gold standard" to evaluate the various algorithms. Clonal alerts (spa typing and epidemiological data) were 100% sensitive and 95.2% specific. Frequency (epidemiological data only) and ICP alerts were 100% and 62.1% sensitive and 47.2% and 97.3% specific, respectively. The difference in specificity between clonal and ICP alerts was not significant. Both methods exhibited a positive predictive value above 80%.

CONCLUSIONS

Rapid MRSA outbreak detection, based on epidemiological and spa typing data, is a suitable alternative for classical approaches and can assist in the identification of potential sources of infection.

Genomic anatomy of the specific reciprocal translocation t(15;17) in acute promyelocytic leukemia

The genomic breakpoints in the t(15;17)(q22;q21), associated with acute promyelocytic leukemia (APL), are known to occur within three different PML breakpoint cluster regions (bcr) on chromosome 15 and within RARA intron 2 on chromosome 17; however, the precise mechanism by which this translocation arises is unclear. To clarify this mechanism, we (i). assembled the sequence of RARA intron 2, (ii). amplified and sequenced the genomic PML-RARA junction sequences from 37 APL patients, and (iii). amplified and sequenced the reverse RARA-PML genomic fusion in 29 of these cases. Three significant breakpoint microclusters within RARA intron 2 were identified, suggesting that sequence-associated or structural factors play a role in the formation of the t(15;17). There was no evidence that the location of a breakpoint in PML had any relationship to the location of the corresponding breakpoint in RARA. Although some sequence motifs previously implicated in illegitimate recombinations were found in the microcluster regions, these associations were not significant. Comparison of forward and reverse genomic junctions revealed microhomologies, deletions, and/or duplications of either gene in all but one case, in which a complex rearrangement with inversion of the PML-derived sequence was found. These findings are consistent with the hypothesis that the t(15;17) occurs by nonhomologous recombination of DNA after processing of the double-strand breaks by a dysfunctional DNA damage-repair mechanism.

Cluster and regional influences on suicide in a Southwestern American Indian tribe

Suicide is the second leading cause of death among American Indian youth. Elevated rates of suicide in Indian communities have been attributed both to outbreaks and to regional trends. We assessed the contribution of these two factors for a single tribe, and attempted to define a profile of individuals at risk. Data came from the tribe's registry of suicide attempts and completions for 1990-1993 and analysis of death certificates for the period 1985-1996. Using combined tribal and death certificate data, the average annual (age-adjusted) rate of completed suicide among tribal members was 44.7/100,000 for 1990-1993. Within the 45 suicide deaths and serious attempts in this time period, we identified one grouping of seven cases taking place in a 40-day period. All seven involved hanging and youth (13-28 years old). Using death certificate data alone, the average annual rate of suicide death for non-natives in the surrounding county in the period 1985-1996 was 22.7/100,000. Age-adjusted to the county population, the tribal rate for the same period was not significantly different (24.6/100,000). Tribal and county suicide patterns differed by age distribution and method but not by gender. We concluded that both regional trends and clustering contribute to suicide in this community. Further prevention efforts may need to focus on both unique tribal characteristics and shared factors among non-native neighbors.

Methods to investigate spatial and temporal clustering in veterinary epidemiology

Due to their contagious or point-source nature, ill-health and diseases often cluster in time and/or space. Overlooking this characteristic can lead to a delay in the control or eradication of the health problem. In addition to potentially expediting control efforts, cluster identification techniques enable the researcher or health-care official to identify and adjust for confounding factors and to generate new hypotheses regarding disease transmission. This paper examines a variety of temporal and spatial clustering techniques and focuses on those which have been reported recently in the veterinary literature.

A method for detecting current temporal clusters of toxic events through data monitoring by poison control centers

BACKGROUND

Poison control centers have become a widely recognized source of data for chemical and other environmental exposures. Concurrently, increased emphasis on early identification of temporal clustering of toxic exposures has stimulated development of sensitive statistical methods to detect clusters at the time of occurrence.

METHOD

This paper discusses the scan test which has been applied to retrospective data to detect carbon monoxide poisoning clusters. We propose a more sensitive method, based on the binomial distribution to detect current clusters of only 1 or more days duration during the ongoing data collection and monitoring process.

RESULTS

Applied to daily carbon monoxide poisoning incidence data on which the scan test has been applied, the new test for current clustering evidently has much more power. For retrospective identification of previous clusters lasting more than 1 day, the scan test is recommended. However, for previous daily clusters, a third method is recommended.

CONCLUSION

Certain toxic events such as carbon monoxide poisoning occasionally occur in daily clusters, or in clusters lasting a few days. Timely detection of clusters requires application of early intervention strategies fostered by sensitive statistical methods of detection, as presented here.

Association of Ixodes pacificus (Acari: ixodidae) with the spatial and temporal distribution of equine granulocytic ehrlichiosis in California

This study was conducted to determine if the biology of certain ticks associated with horses regulates the spatial and temporal distribution of equine granulocytic ehrlichiosis (EGE) in California north of Monterey County. We compared the spatial and temporal distribution of EGE cases with the seasons of activity and life histories of ticks that infest horses. Spatially, cases collected from equine veterinarians clustered around each other in a manner different from the way in which control cities of practice were distributed, with foci limited to the Sierra Nevada and coastal foothills. Cases also clustered seasonally: most were diagnosed between November and April. The spatial and temporal pattern of EGE cases closely parallels the well-characterized life history and distribution of Ixodes pacificus Cooley & Kohls, but not other ticks commonly associated with horses. Building on previous studies, there is compelling evidence that this tick has the vectorial capacity to transmit Ehrlichia equi to horses. Based on the life history and distribution of I. pacificus in relation to EGE cases, we reason that this tick is the only biologically plausible vector of E. equi in California, and provide evidence for a tightly linked association between I. pacificus and the epidemiology of EGE.

Clinical implications of cytogenetic abnormalities in melanoma

Unlike leukemia, in which specific reciprocal translocations are frequently observed, melanomas involve complex recurring chromosome anomalies. Analysis of the constituted genome of melanoma patients should identify cancer susceptibility genes and at-risk individuals in families with a history of melanoma. The first of these genes to be cloned is the cell cycle regulatory protein inhibitor--the p16 gene-- and a second gene locus for melanoma predisposition has been linked to the chromosome 1p36 band region. Detection of the most common somatic genetic alterations in melanoma enhances our understanding of molecular mechanisms of melanoma development and may lead to genetic markers in melanoma. Some alterations may be used to identify interesting subpopulations. Others may be of prognostic value when they are considered in tandem with clinical data.

Investigating perinatal calf mortality in beef herds

This article summarizes the information presented in this issue to identify the reasons for a perinatal calf mortality (PCM) problem through a risk-factor-evaluation approach. The benefits of necropsies in PCM investigations are discussed, and the gross lesions of the major causes of PCM are described. The use of 2 x 2 tables to calculate odds ratios and the Chi-square method to test the validity of the odds ratios are presented as methods to determine suspected risk factors. The article concludes with a discussion of the management plan used to solve a PCM problem.

Poisson approximations for the distribution and moments of ordered m-spacings

This article investigates the accuracy of approximations for the distribution of ordered m-spacings for i.i.d. uniform observations in the interval (0, 1). Several Poisson approximations and a compound Poisson approximation are studied. The result of a simulation study is included to assess the accuracy of these approximations. A numerical procedure for evaluating the moments of the ordered m-spacings is developed and evaluated for the most accurate approximation.

Approximations for the tail probabilities and moments of the scan statistic

The scan statistic is used to test the hypothesis that the observed events occur at random (uniformly distributed) in time or space versus the hypothesis that they cluster within a moving window of size w. To implement the testing procedure based on the scan statistic its tail probabilities have to be effectively evaluated. In this article a survey of results on the approximations of the distribution of the scan statistic and its moments is presented. Numerical results comparing these approximations are also given. Numerous references with applications in epidemiological studies using the scan statistics are mentioned. Related scan statistics that have been used in many other interesting applications are listed in the reference section. The article concludes with the presentation of unsolved problems related to the scan statistic.

Power of the scan statistic for detection of clustering

The scan statistic is the maximum number of events in an interval of fixed length w as the subinterval moves over the entire time frame. Previous research derived the null distribution of the scan statistic under the conditional model which assumed that the total number of events was fixed, and under the unconditional model which let the total number of events be a random variable. This paper derives approximations for the power of the scan test for a pulse alternative. Under this alternative, the relative risk of disease on a subinterval (tau, tau + w), tau unknown, is theta-fold as high as it is for other subintervals of length w. Two sets of approximations are given for each model. The first approximation gives highly accurate results, but requires use of a personal computer. The second procedure can be performed on a hand-held calculator and appears very accurate for the cases examined.

Effect of relative risk and cluster configuration on the power of the one-dimensional scan statistic

The scan test for clustering in time is based on the maximum number of events in an interval (window) of width w as the window moves across the entire time frame. Power estimates of the scan statistic are simulated for a variety of epidemiologically motivated situations. Two cluster configurations are used: a rectangular pulse, and a triangular pulse designed to emulate environmental contamination. For a rectangular pulse, the relative risk R of disease in the cluster region is R-fold as high as it is for the background region. The power is strongly influenced by the sample size, the relative risk, and the width or duration of the cluster region, whereas the effect of the cluster configuration is small. Using a 5 per cent significance level, a relative risk of 4, a standardized cluster duration of 0.10, a relative window width of 1.5, and a (non-random) sample size of 50, the simulated power is approximately 80 per cent, indicating that the minimum sample size in the cluster region for adequate power is in the 12-32 range for values of the parameters used in this study.

Disease clusters, exact distributions of maxima, and P-values

This paper presents combinatorial (exact) methods that are useful in the analysis of disease cluster data obtained from small environments, such as buildings and neighbourhoods. Maxwell-Boltzmann and Fermi-Dirac occupancy models are compared in terms of appropriateness of representation of disease incidence patterns (space and/or time) in these environments. The methods are illustrated by a statistical analysis of the incidence pattern of bone fractures in a setting wherein fracture clustering was alleged to be occurring. One of the methodological results derived in this paper is the exact distribution of the maximum cell frequency in occupancy models.

Chance and statistical significance in protein and DNA sequence analysis

Statistical approaches help in the determination of significant configurations in protein and nucleic acid sequence data. Three recent statistical methods are discussed: (i) score-based sequence analysis that provides a means for characterizing anomalies in local sequence text and for evaluating sequence comparisons; (ii) quantile distributions of amino acid usage that reveal general compositional biases in proteins and evolutionary relations; and (iii) r-scan statistics that can be applied to the analysis of spacings of sequence markers.

Clustering in sparse data and an analysis of rhabdomyosarcoma incidence

Time series of epidemiologic events often contain periods of atypically low or high frequency. Correspondingly, for quite rate diseases there occur instances of long vacuous durations interrupted noticeably by periods of some disease activity. A recent community-based observation of the incidence of rhabdomyosarcoma (RMS), and an investigation of it, yielded sparse data of this general description. We introduce a combinatorial test for patchy time series and apply it to the RMS data. We comment on the prevalent practice of post hoc data analysis of alleged clusters, and on scale effects.

Use of the scan statistic to detect temporal clustering of poisonings

The purpose of this study is to explore the use of the scan test to detect temporal clustering of poison control center data. We applied the scan test to a computerized data set consisting of all cases of carbon monoxide poisoning reported to our Poison Control Center in 1988. Using a time window of three days, the scan test identified three statistically significant temporal clusters. A review of case and climatologic data revealed that two of the clusters were due to family outbreaks and one was due to a sudden cold spell. We conclude that the scan test is a potentially useful surveillance tool which can be easily applied to poison control center data.

A bibliography and comments on the use of statistical models in epidemiology in the 1980s

This paper reviews developments in statistical modelling in epidemiology in the 1980's, with emphasis on cohort and case-control studies. The central roles of the logistic and proportional hazard models are highlighted, and it is shown how these models lead to a deeper understanding of classical designs and methods of analysis as well as to efficient new designs and analytical procedures. The important area of model misspecification is discussed, including the problems of omitted latent structure, mis-modelling of available measurements, missing data and errors in measurements. Various designs motivated by the logistic model are illustrated numerically, and designs based on the proportional hazards model are discussed, as are papers on sample size determination. There are brief introductions to the literature on other topics, including attributable risk, disease clustering, family studies and genetics, analysis of disease incidence data, infectious disease, longitudinal data, screening and miscellaneous related topics in statistics. An extensive bibliography is indexed according to the outline of the paper.

The identification of clustering in parasuicide

A straightforward test for detecting clustering in patterns of parasuicide in individual patients is described and applied to data from the Edinburgh Regional Poisoning Treatment Centre.