Space-Time Clustering Characteristics of Tuberculosis in China, 2005-2011

Zhao, Fei; Cheng, Shiming; He, Guangxue; Huang, Fei; Zhang, Hui; Xu, Biao; Murimwa, Tonderayi C.; Cheng, Jun; Hu, Dongmei; Wang, Lixia

doi:10.1371/journal.pone.0083605

Cited by 66 publications

(81 citation statements)

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“…Kulldorff's retrospective scan statistics take multiple testing problems into account, which is known as the most powerful method for evaluating geographical and temporal distribution by using routinely collected data [18]. This method has been used worldwide to detect the clusters of diseases [2,4,5,30,31]. As is known, in the temporal and spatial model, selection of a suitable time window and spatial window was very important for model identification.…”

Section: Discussionmentioning

confidence: 99%

“…According to a World Health Organization report, the TB burden of China is the second largest in the world [1]. In recent years, although the Chinese Government has paid an increasing amount of attention to the control of TB, prevention measures are still insufficient, especially in areas with inadequate medical resources, such as Qinghai, a province where most of the population suffers a high risk of TB even now [2].…”

Section: Introductionmentioning

confidence: 99%

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Using the Kulldorff’s scan statistical analysis to detect spatio-temporal clusters of tuberculosis in Qinghai Province, China, 2009–2016

Rao

Shi

2017

BMC Infect Dis

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BackgroundAlthough the incidence of tuberculosis (TB) in most parts of China are well under control now, in less developed areas such as Qinghai, TB still remains a major public health problem. This study aims to reveal the spatio-temporal patterns of TB in the Qinghai province, which could be helpful in the planning and implementing key preventative measures.MethodsWe extracted data of reported TB cases in the Qinghai province from the China Information System for Disease Control and Prevention (CISDCP) during January 2009 to December 2016. The Kulldorff’s retrospective space-time scan statistics, calculated by using the discrete Poisson probability model, was used to identify the temporal, spatial, and spatio-temporal clusters of TB at the county level in Qinghai.ResultsA total of 48,274 TB cases were reported from 2009 to 2016 in Qinghai. Results of the Kulldorff’s scan revealed that the TB cases in Qinghai were significantly clustered in spatial, temporal, and spatio-temporal distribution. The most likely spatio-temporal cluster (LLR = 2547.64, RR = 4.21, P < 0.001) was mainly concentrated in the southwest of Qinghai, covering seven counties and clustered in the time frame from September 2014 to December 2016.ConclusionThis study identified eight significant space-time clusters of TB in Qinghai from 2009 to 2016, which could be helpful in prioritizing resource assignment in high-risk areas for TB control and elimination in the future.Electronic supplementary materialThe online version of this article (doi:10.1186/s12879-017-2643-y) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using the Kulldorff’s scan statistical analysis to detect spatio-temporal clusters of tuberculosis in Qinghai Province, China, 2009–2016

Rao

Shi

2017

BMC Infect Dis

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“…The geocoding process was applied, as described in previous studies [13–15]. Demographic information and the administrative code of Zhaotong were obtained from the Annual Statistical Report and integrated into the study database.…”

Section: Methodsmentioning

confidence: 99%

Spatial-temporal analysis of pulmonary tuberculosis in the northeast of the Yunnan province, People’s Republic of China

Huang

Abe

et al. 2017

Infect Dis Poverty

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BackgroundThe number of pulmonary tuberculosis (PTB) cases in China ranks third in the world. A continuous increase in cases has recently been recorded in Zhaotong prefecture-level city, which is located in the northeastern part of Yunnan province. This study explored the space-time dynamics of PTB cases in Zhaotong to provide useful information that will help guide policymakers to formulate effective regional prevention and control strategies.MethodsThe data on PTB cases were extracted from the nationwide tuberculosis online registration system. Time series and spatial cluster analyses were applied to detect PTB temporal trends and spatial patterns at the town level between 2011 and 2015 in Zhaotong. Three indicators of PTB treatment registration history were used: initial treatment registration rate, re-treatment registration rate, and total PTB registration rate.ResultsSeasonal trends were detected with an apparent symptom onset peak during the winter season and a registration peak during the spring season. A most likely cluster and six secondary clusters were identified for the total PTB registration rate, one most likely cluster and five secondary clusters for the initial treatment registration rate, and one most likely cluster for the re-treatment registration rate. The most likely cluster of the three indicators had a similar spatial distribution and size in Zhenxiong County, which is characterised by a poor socio-economic level and the largest population in Yunnan.ConclusionThis study identified temporal and spatial distribution of PTB in a high PTB burden area using existing health data. The results of the study provide useful information on the prevailing epidemiological situation of PTB in Zhaotong and could be used to develop strategies for more effective PTB control at the town level. The cluster that overlapped the three PTB indicators falls within the geographic areas where PTB control efforts should be prioritised.

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“…, Zhao et al. ), and therefore, one often explores the spatial autocorrelation (SA) of the data before clustering. However, measurements of SA are affected by how one defines neighbors and the spatial weights between neighbors.…”

Section: Methodsmentioning

confidence: 99%

“…Clustering of spatial features relies on locating neighbors with similar habitat values (AssunC ßão et al 2006, Zhao et al 2013, and therefore, one often explores the spatial autocorrelation (SA) of the data before clustering. However, measurements of SA are affected by how one defines neighbors and the spatial weights between neighbors.…”

Section: Spatial Clustering Of Lek Locationsmentioning

confidence: 99%

Designing multi‐scale hierarchical monitoring frameworks for wildlife to support management: a sage‐grouse case study

et al. 2019

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Population monitoring is integral to the conservation and management of wildlife; yet, analyses of population demographic data rarely consider processes occurring across spatial scales, potentially limiting the effectiveness of adaptive management. Therefore, we developed a method to identify hierarchical levels of organization (i.e., populations) to define multiple spatial scales, specifically intended to help guide appropriate conservation and management actions. This approach can support mobile species with high site fidelity where surveys occur on birthing/breeding grounds or migratory stopovers. Our approach used a graph‐based clustering algorithm (Spatial K'luster Analysis by Tree Edge Removal) that explicitly included habitat selection information at multiple scales and further refined with constraint‐based rules. We applied these concepts to greater sage‐grouse leks (breeding grounds), a species of conservation concern, in two different ecological contexts (Nevada and Wyoming, USA). The constraint‐based rules accounted for inter‐lek movement distances based on literature and field studies in Nevada from 2012 to 2016, included methods to support a spatially balanced monitoring design, and identified barriers to movements among leks based on resistance surfaces. We evaluated the performance of our hierarchical clusters in Nevada using independent data from radio‐marked sage‐grouse, and we found the finest‐scaled cluster level captured ~90% of sage‐grouse movements and mid‐level scales captured ~97–99% of movements. We expected comparable performance for Wyoming, where we lacked radio‐marked sage‐grouse for an evaluation, because genetic studies estimate similar dispersal distances to our ~15 km inter‐lek movement distance in Nevada. For sage‐grouse and other mobile species with high site fidelity, our approach to defining these frameworks could prove valuable for conservation and management applications, such as improving estimation of scale‐dependent population trends and guiding the prescription of management actions at spatial scales that align with identified threats. Specific to sage‐grouse, our analysis sets the stage for designing a monitoring framework that relies on comparison of short‐ and long‐term population trends across our defined spatial scales and identifies and disentangles factors driving local (e.g., habitat quality) and regional (e.g., climate) population changes, thereby supporting scale‐dependent management and research needs for adaptive management practices.

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Space-Time Clustering Characteristics of Tuberculosis in China, 2005-2011

Cited by 66 publications

References 20 publications

Using the Kulldorff’s scan statistical analysis to detect spatio-temporal clusters of tuberculosis in Qinghai Province, China, 2009–2016

Using the Kulldorff’s scan statistical analysis to detect spatio-temporal clusters of tuberculosis in Qinghai Province, China, 2009–2016

Spatial-temporal analysis of pulmonary tuberculosis in the northeast of the Yunnan province, People’s Republic of China

Designing multi‐scale hierarchical monitoring frameworks for wildlife to support management: a sage‐grouse case study

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