Dataset Information

Space Time Clustering and the Permutation Moments of Quadratic Form.

ABSTRACT: The Mantel and Knox space-time clustering statistics are popular tools to establish transmissibility of a disease and detect outbreaks. The most commonly used null distributional approximations may provide poor fits, and researchers often resort to direct sampling from the permutation distribution. However, the exact first four moments for these statistics are available, and Pearson distributional approximations are often effective. Thus, our first goal is to clarify the literature and to make these tools more widely available. In addition, by rewriting terms in the statistics we obtain the exact first four permutation moments for the most commonly used quadratic form statistics, which need not be positive definite. The extension of this work to quadratic forms greatly expands the utility of density approximations for these problems, including for high-dimensional applications, where the statistics must be extreme in order to exceed stringent testing thresholds. We demonstrate the methods using examples from the investigation of disease transmission in cattle, the association of a gene expression pathway with breast cancer survival, regional genetic association with cystic fibrosis lung disease, and hypothesis testing for smoothed local linear regression.

SUBMITTER: Zhou YH

PROVIDER: S-EPMC4157666 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Space Time Clustering and the Permutation Moments of Quadratic Form.

Zhou Yi-Hui YH Mayhew Gregory G Sun Zhibin Z Xu Xiaolin X Zou Fei F Wright Fred A FA

Stat 20130101 1

The Mantel and Knox space-time clustering statistics are popular tools to establish transmissibility of a disease and detect outbreaks. The most commonly used null distributional approximations may provide poor fits, and researchers often resort to direct sampling from the permutation distribution. However, the exact first four moments for these statistics are available, and Pearson distributional approximations are often effective. Thus, our first goal is to clarify the literature and to make t ...[more]

PMID: 25210205

Similar Datasets

Project description:BackgroundA spatial and temporal study of the distribution of facility-based deliveries can identify areas of low and high facility usage and help devise more targeted interventions to improve delivery outcomes. Developing countries like Bangladesh face considerable challenges in reducing the maternal mortality ratio to the targets set by the Sustainable Development Goals. Recent studies have already identified that the progress of reducing maternal mortality has stalled. Giving birth in a health facility is one way to reduce maternal mortality.MethodsFacility delivery data from a demographic surveillance site was analyzed at both village and Bari (comprising several households with same paternal origins) level to understand spatial and temporal heterogeneity. Global spatial autocorrelation was detected using Moran's I index while local spatial clusters were detected using the local Getis G i * statistics. In addition, space-time scanning using a discrete Poisson approach facilitated the identification of space-time clusters. The likelihood of delivering at a facility when located inside a cluster was calculated using log-likelihood ratios.ResultsThe three cluster detection approaches detected significant spatial and temporal heterogeneity in the distribution of facility deliveries in the study area. The hot and cold spots indicated contiguous and relocation type diffusion and increased in number over the years. Space-time scanning revealed that when a parturient woman is located in a Bari inside the cluster, the likelihood of delivering at a health facility increases by twenty-seven times.ConclusionsSpatiotemporal studies to understand delivery patterns are quite rare. However, in resource constraint countries like Bangladesh, detecting hot and cold spot areas can aid in the detection of diffusion centers, which can be targeted to expand regions with high facility deliveries. Places and periods with reduced health facility usages can be identified using various cluster detection techniques, to assess the barriers and facilitators in promoting health facility deliveries.

Project description:BACKGROUND:Pulmonary tuberculosis (PTB,both smear positive and smear negative) is an airborne infectious disease of major public health concern in China and other parts of the world where PTB endemicity is reported. This study aims at identifying PTB spatio-temporal clusters and associated risk factors in Zhaotong prefecture-level city, located in southwest China, where the PTB notification rate was higher than the average rate in the entire country. METHODS:Space-time scan statistics were carried out using PTB registered data in the nationwide TB online registration system from 2011 to 2015, to identify spatial clusters. PTB patients diagnosed between October 2015 and February 2016 were selected and a structured questionnaire was administered to collect a set of variables that includes socio-economic status, behavioural characteristics, local environmental and biological characteristics. Based on the discovery of detailed town-level spatio-temporal PTB clusters, we divided selected subjects into two groups including the cases that resides within and outside identified clusters. Then, logistic regression analysis was applied comparing the results of variables between the two groups. RESULTS:A total of 1508 subjects consented and participated in the survey. Clusters for PTB cases were identified in 38 towns distributed over south-western Zhaotong. Logistic regression analysis showed that history of chronic bronchitis (OR = 3.683, 95% CI: 2.180-6.223), living in an urban area (OR = 5.876, 95% CI: 2.381-14.502) and using coal as the main fuel (OR = 9.356, 95% CI: 5.620-15.576) were independently associated with clustering. While, not smoking (OR = 0.340, 95% CI: 0.137-0.843) is the protection factor of spatial clustering. CONCLUSIONS:We found PTB specially clustered in south-western Zhaotong. The strong associated factors influencing the PTB spatial cluster including: the history of chronic bronchitis, living in the urban area, smoking and the use of coal as the main fuel for cooking and heating. Therefore, efforts should be made to curtail these associated factors.

Dataset Information

Space Time Clustering and the Permutation Moments of Quadratic Form.

Publications

Space Time Clustering and the Permutation Moments of Quadratic Form.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets