Power evaluation of disease clustering tests

Song, Changhong; Kulldorff, Martin

doi:10.1186/1476-072x-2-9

Cited by 139 publications

(43 citation statements)

References 29 publications

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“…However, in our analysis, these factors showed neither an important effect size coefficient nor a statistically significant association with seropositivity in dogs. The study village is considered a rural town, but the distribution of households does not follow the typical scattered pattern of other rural areas (Song and Kulldorff, 2003). The houses in the study village have an area of 300m 2 on average and are in close proximity to each other, grouped into blocks that are arranged similarly to urban blocks.…”

Section: Discussionmentioning

confidence: 99%

The potential of canine sentinels for reemerging Trypanosoma cruzi transmission

Castillo-Neyra

Chu

Quıspe-Machaca

et al. 2015

Preventive Veterinary Medicine

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Background Chagas disease, a vector-borne disease transmitted by triatomine bugs and caused by the parasite Trypanosoma cruzi, affects millions of people in the Americas. In Arequipa, Peru, indoor residual insecticide spraying campaigns are routinely conducted to eliminate Triatoma infestans, the only vector in this area. Following insecticide spraying, there is risk of vector return and reinitiation of parasite transmission. Dogs are important reservoirs of T. cruzi and may play a role in reinitiating transmission in previously sprayed areas. Dogs may also serve as indicators of reemerging transmission. Methods We conducted a cross-sectional serological screening to detect T. cruzi antibodies in dogs, in conjunction with an entomological vector collection survey at the household level, in a disease endemic area that had been treated with insecticide 13 years prior. Spatial clustering of infected animals and vectors was assessed using Ripley’s K statistic, and the odds of being seropositive for dogs proximate to infected colonies was estimated with multivariate logistic regression. Results There were 106 triatomine-infested houses (41.1%), and 45 houses infested with T. cruzi-infected triatomine insects (17.4%). Canine seroprevalence in the area was 12.3% (n=154); all seropositive dogs were 9 months old or older. We observed clustering of vectors carrying the parasite, but no clustering of seropositive dogs. The age- and sex-adjusted odds ratio between seropositivity to T. cruzi and proximity to an infected triatomine (≤50m) was 5.67 (95% CI: 1.12 – 28.74; p=0.036). Conclusions Targeted control of reemerging transmission can be achieved by improved understanding of T. cruzi in canine populations. Our results suggest that dogs may be useful sentinels to detect re-initiation of transmission following insecticide treatment. Integration of canine T. cruzi blood sampling into existing interventions for zoonotic disease control (e.g. rabies vaccination programs) can be an effective method of increasing surveillance and improving understanding of disease distribution.

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Section: Discussionmentioning

confidence: 99%

The potential of canine sentinels for reemerging Trypanosoma cruzi transmission

Castillo-Neyra

Chu

Quıspe-Machaca

et al. 2015

Preventive Veterinary Medicine

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“…Although R scale is a global measure for the spread or dispersion of disease incidence for point data based on nearest neighbor distance (Eck and Weisburd 1995; Krebs 1989; Taylor 1977), Moran’s coefficient measures local spatial autocorrelation for area data (Getis and Ord 1992; Sawada 2001). A comparison of the power evaluation of disease clustering tests has been described by Song and Kulldorff (2003).…”

Section: Methodsmentioning

confidence: 99%

Understanding the Spatial Clustering of Severe Acute Respiratory Syndrome (SARS) in Hong Kong

Lai

Wong

Hedley

et al. 2004

Environ Health Perspect

101

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We applied cartographic and geostatistical methods in analyzing the patterns of disease spread during the 2003 severe acute respiratory syndrome (SARS) outbreak in Hong Kong using geographic information system (GIS) technology. We analyzed an integrated database that contained clinical and personal details on all 1,755 patients confirmed to have SARS from 15 February to 22 June 2003. Elementary mapping of disease occurrences in space and time simultaneously revealed the geographic extent of spread throughout the territory. Statistical surfaces created by the kernel method confirmed that SARS cases were highly clustered and identified distinct disease “hot spots.” Contextual analysis of mean and standard deviation of different density classes indicated that the period from day 1 (18 February) through day 16 (6 March) was the prodrome of the epidemic, whereas days 86 (15 May) to 106 (4 June) marked the declining phase of the outbreak. Origin-and-destination plots showed the directional bias and radius of spread of superspreading events. Integration of GIS technology into routine field epidemiologic surveillance can offer a real-time quantitative method for identifying and tracking the geospatial spread of infectious diseases, as our experience with SARS has demonstrated.

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“…In recent power evaluations, the scan statistic performed well with a single circular cluster [2,14,15] but underperformed with multiple and non-circular clusters [16]. When applied to case-control data, aside from stratified analyses, the scan statistic cannot be adjusted for covariates [16].…”

Section: Introductionmentioning

confidence: 99%

A power comparison of generalized additive models and the spatial scan statistic in a case-control setting

et al. 2010

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BackgroundA common, important problem in spatial epidemiology is measuring and identifying variation in disease risk across a study region. In application of statistical methods, the problem has two parts. First, spatial variation in risk must be detected across the study region and, second, areas of increased or decreased risk must be correctly identified. The location of such areas may give clues to environmental sources of exposure and disease etiology. One statistical method applicable in spatial epidemiologic settings is a generalized additive model (GAM) which can be applied with a bivariate LOESS smoother to account for geographic location as a possible predictor of disease status. A natural hypothesis when applying this method is whether residential location of subjects is associated with the outcome, i.e. is the smoothing term necessary? Permutation tests are a reasonable hypothesis testing method and provide adequate power under a simple alternative hypothesis. These tests have yet to be compared to other spatial statistics.ResultsThis research uses simulated point data generated under three alternative hypotheses to evaluate the properties of the permutation methods and compare them to the popular spatial scan statistic in a case-control setting. Case 1 was a single circular cluster centered in a circular study region. The spatial scan statistic had the highest power though the GAM method estimates did not fall far behind. Case 2 was a single point source located at the center of a circular cluster and Case 3 was a line source at the center of the horizontal axis of a square study region. Each had linearly decreasing logodds with distance from the point. The GAM methods outperformed the scan statistic in Cases 2 and 3. Comparing sensitivity, measured as the proportion of the exposure source correctly identified as high or low risk, the GAM methods outperformed the scan statistic in all three Cases.ConclusionsThe GAM permutation testing methods provide a regression-based alternative to the spatial scan statistic. Across all hypotheses examined in this research, the GAM methods had competing or greater power estimates and sensitivities exceeding that of the spatial scan statistic.

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Power evaluation of disease clustering tests

Cited by 139 publications

References 29 publications

The potential of canine sentinels for reemerging Trypanosoma cruzi transmission

The potential of canine sentinels for reemerging Trypanosoma cruzi transmission

Understanding the Spatial Clustering of Severe Acute Respiratory Syndrome (SARS) in Hong Kong

A power comparison of generalized additive models and the spatial scan statistic in a case-control setting

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