Spatial cluster analysis of COVID-19 in Malaysia (Mar-Sep, 2020)

Ullah, Sami; Nor, Nurul Hidayah Mohd; Daud, Hanita; Zainuddin, Nooraini; Gandapur, Muhammad Salim Javed; Ali, Iftikhar; Khalil, Alamgir

doi:10.4081/gh.2021.961

Cited by 12 publications

(10 citation statements)

References 14 publications

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“…A retrospective spatial scan study in Southeast Asia during January 13, 2020 and March 16, 2020 further confirmed that Malaysia and Singapore were the most likely cluster between March 4-March 16, 2020 (RR = 72.07, LLR = 1910.08, p < 0.001) (44). The most likely cluster for the period 2 (January 25 to October 8, 2020) consisted of Negeri Sembilan, Putrajaya, Kuala Lumpur, and Selangor (10 districts) in this study correlates with a state-level spatial scan analysis study in Malaysia (45).…”

Section: Discussionsupporting

confidence: 88%

Assessing the Spatiotemporal Spread Pattern of the COVID-19 Pandemic in Malaysia

Cheong

Ghazali

Ibrahim

et al. 2022

Front. Public Health

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IntroductionThe unprecedented COVID-19 pandemic has greatly affected human health and socioeconomic backgrounds. This study examined the spatiotemporal spread pattern of the COVID-19 pandemic in Malaysia from the index case to 291,774 cases in 13 months, emphasizing on the spatial autocorrelation of the high-risk cluster events and the spatial scan clustering pattern of transmission.MethodologyWe obtained the confirmed cases and deaths of COVID-19 in Malaysia from the official GitHub repository of Malaysia's Ministry of Health from January 25, 2020 to February 24, 2021, 1 day before the national vaccination program was initiated. All analyses were based on the daily cumulated cases, which are derived from the sum of retrospective 7 days and the current day for smoothing purposes. We examined the daily global, local spatial autocorrelation and scan statistics of COVID-19 cases at district level using Moran's I and SaTScan™.ResultsAt the initial stage of the outbreak, Moran's I index > 0.5 (p < 0.05) was observed. Local Moran's I depicted the high-high cluster risk expanded from west to east of Malaysia. The cases surged exponentially after September 2020, with the high-high cluster in Sabah, from Kinabatangan on September 1 (cumulative cases = 9,354; Moran's I = 0.34; p < 0.05), to 11 districts on October 19 (cumulative cases = 21,363, Moran's I = 0.52, p < 0.05). The most likely cluster identified from space-time scanning was centered in Jasin, Melaka (RR = 11.93; p < 0.001) which encompassed 36 districts with a radius of 178.8 km, from November 24, 2020 to February 24, 2021, followed by the Sabah cluster.Discussion and ConclusionBoth analyses complemented each other in depicting underlying spatiotemporal clustering risk, giving detailed space-time spread information at district level. This daily analysis could be valuable insight into real-time reporting of transmission intensity, and alert for the public to avoid visiting the high-risk areas during the pandemic. The spatiotemporal transmission risk pattern could be used to monitor the spread of the pandemic.

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Section: Discussionsupporting

confidence: 88%

Assessing the Spatiotemporal Spread Pattern of the COVID-19 Pandemic in Malaysia

Cheong

Ghazali

Ibrahim

et al. 2022

Front. Public Health

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“…Also, this study is based on large population-based data from an NCI SEER program cancer registry that meets the high-quality standards. Furthermore, SaTScan spatial analysis used in identifying geographic clusters is a free globally used cluster-detecting epidemiologic tool for identifying spatial clusters of infectious and chronic diseases, including COVID-19, malaria, cancers, and others …”

Section: Discussionmentioning

confidence: 99%

“…24,[45][46][47][48][49][50][51][52][53][54] Figure 2. Census Tract-Level Characteristics of Patients With Late-Stage Cervical Cancer in Texas, 2014-2018 by Cluster Classification Bar chart showing the characteristics of census tracts containing late-stage cervical cancer cases stratified by cluster classification (hot spots, cold spots, rest of Texas).…”

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confidence: 99%

Geographic Variation in Late-Stage Cervical Cancer Diagnosis

Sokale,

Thrift,

Montealegre

et al. 2023

JAMA Netw Open

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ImportanceThere are stark disparities in cervical cancer burden in the United States, notably by race and ethnicity and geography. Late-stage diagnosis is an indicator of inadequate access to and utilization of screening.ObjectiveTo identify geospatial clusters of late-stage cervical cancer at time of diagnosis in Texas.Design, Setting, and ParticipantsThis population-based cross-sectional study used incident cervical cancer data from the Texas Cancer Registry from 2014 to 2018 of female patients aged 18 years or older. Late-stage cervical cancer cases were geocoded at the census tract level (n = 5265) using their residential coordinates (latitude and longitude) at the time of diagnosis. Statistical analysis was performed from April to September 2023.ExposuresCensus tract of residence at diagnosis.Main Outcome and MeasuresLate-stage cervical cancer diagnosis (ie, cases classified by the National Cancer Institute Surveillance, Epidemiology and End Results summary stages 2 to 4 [regional spread] or 7 [distant metastasis]). A Poisson probability-based model of the SaTScan purely spatial scan statistics was applied at the census tract–level to identify geographic clusters of higher (hot spots) or lower (cold spots) proportions than expected of late-stage cervical cancer diagnosis and adjusted for age.ResultsAmong a total of 6484 female patients with incident cervical cancer cases (mean [SD] age, 48.7 [14.7] years), 2300 (35.5%) were Hispanic, 798 (12.3%) were non-Hispanic Black, 3090 (47.6%) were non-Hispanic White, and 296 (4.6%) were other race or ethnicity. Of the 6484 patients, 2892 with late-stage diagnosis (mean [SD] age, 51.8 [14.4] years were analyzed. Among patients with late-stage diagnosis, 1069 (37.0%) were Hispanic, 417 (14.4%) were non-Hispanic Black, 1307 (45.2%) were non-Hispanic White, and 99 (3.4%) were other race or ethnicity. SaTScan spatial analysis identified 7 statistically significant clusters of late-stage cervical cancer diagnosis in Texas, of which 4 were hot spots and 3 were cold spots. Hot spots included 1128 census tracts, predominantly in the South Texas Plains, Gulf Coast, and Prairies and Lakes (North Texas) regions. Of the 2892 patients with late-stage cervical cancer, 880 (30.4%) were observed within hot spots. Census tract–level comparison of characteristics of clusters suggested that hot spots differed significantly from cold spots and the rest of Texas by proportions of racial and ethnic groups, non–US born persons, and socioeconomic status.Conclusions and RelevanceIn this cross-sectional study examining geospatial clusters of late-stage cervical cancer diagnosis, place-based disparities were found in late-stage cervical cancer diagnosis in Texas. These findings suggest that these communities may benefit from aggressive cervical cancer interventions.

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“…It can provide public health authorities with important information about the pandemic to enable better management under the situation [8][9][10]. Among diverse spatiotemporal methods, space-time scanning is one of the most popular methods adopted by many studies to explore spatiotemporal clusters in different regions worldwide, such as Mainland China [11], the United States [12], Mexico [13], Spain [14], Malaysia [15], Bangladesh [16], Brazil [17], and South Korea [18]. In SEA, previous studies have applied this analysis to investigate the first wave of COVID-19 cases [19,20].…”

Section: Introductionmentioning

confidence: 99%

Investigating Linkages Between Spatiotemporal Patterns of the COVID-19 Delta Variant and Public Health Interventions in Southeast Asia: Prospective Space-Time Scan Statistical Analysis Method

Luo¹,

Liu²,

Zhou³

et al. 2022

JMIR Public Health Surveill

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Background The COVID-19 Delta variant has presented an unprecedented challenge to countries in Southeast Asia (SEA). Its transmission has shown spatial heterogeneity in SEA after countries have adopted different public health interventions during the process. Hence, it is crucial for public health authorities to discover potential linkages between epidemic progression and corresponding interventions such that collective and coordinated control measurements can be designed to increase their effectiveness at reducing transmission in SEA. Objective The purpose of this study is to explore potential linkages between the spatiotemporal progression of the COVID-19 Delta variant and nonpharmaceutical intervention (NPI) measures in SEA. We detected the space-time clusters of outbreaks of COVID-19 and analyzed how the NPI measures relate to the propagation of COVID-19. Methods We collected district-level daily new cases of COVID-19 from June 1 to October 31, 2021, and district-level population data in SEA. We adopted prospective space-time scan statistics to identify the space-time clusters. Using cumulative prospective space-time scan statistics, we further identified variations of relative risk (RR) across each district at a half-month interval and their potential public health intervention linkages. Results We found 7 high-risk clusters (clusters 1-7) of COVID-19 transmission in Malaysia, the Philippines, Thailand, Vietnam, and Indonesia between June and August, 2021, with an RR of 5.45 (P<.001), 3.50 (P<.001), 2.30 (P<.001), 1.36 (P<.001), 5.62 (P<.001), 2.38 (P<.001), 3.45 (P<.001), respectively. There were 34 provinces in Indonesia that have successfully mitigated the risk of COVID-19, with a decreasing range between –0.05 and –1.46 due to the assistance of continuous restrictions. However, 58.6% of districts in Malaysia, Singapore, Thailand, and the Philippines saw an increase in the infection risk, which is aligned with their loosened restrictions. Continuous strict interventions were effective in mitigating COVID-19, while relaxing restrictions may exacerbate the propagation risk of this epidemic. Conclusions The analyses of space-time clusters and RRs of districts benefit public health authorities with continuous surveillance of COVID-19 dynamics using real-time data. International coordination with more synchronized interventions amidst all SEA countries may play a key role in mitigating the progression of COVID-19.

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Spatial cluster analysis of COVID-19 in Malaysia (Mar-Sep, 2020)

Cited by 12 publications

References 14 publications

Assessing the Spatiotemporal Spread Pattern of the COVID-19 Pandemic in Malaysia

Assessing the Spatiotemporal Spread Pattern of the COVID-19 Pandemic in Malaysia

Geographic Variation in Late-Stage Cervical Cancer Diagnosis

Investigating Linkages Between Spatiotemporal Patterns of the COVID-19 Delta Variant and Public Health Interventions in Southeast Asia: Prospective Space-Time Scan Statistical Analysis Method

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