Geospatial Health最新文献_第9页

Correction. Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach.. 修正。利用共聚类方法检测任意形状和大小的时空疾病簇

IF 1.7 4区医学

Geospatial Health Pub Date : 2023-08-01 DOI: 10.4081/gh.2023.1232

The Publisher

引用次数: 0

Geospatial analysis in the United States reveals the changing roles of temperature on COVID-19 transmission. 美国的地理空间分析揭示了温度在COVID-19传播中的作用变化。

IF 1.7 4区医学

Geospatial Health Pub Date : 2023-07-20 DOI: 10.4081/gh.2023.1213

Ruiwen Xiong, Xiaolong Li

{"title":"Geospatial analysis in the United States reveals the changing roles of temperature on COVID-19 transmission.","authors":"Ruiwen Xiong, Xiaolong Li","doi":"10.4081/gh.2023.1213","DOIUrl":"https://doi.org/10.4081/gh.2023.1213","url":null,"abstract":"Environmental factors are known to affect outbreak patterns of infectious disease, but their impacts on the spread of COVID-19 along with the evolution of this relationship over time intervals and in different regions are unclear. This study utilized 3 years of data on COVID-19 cases in the continental United States from 2020 to 2022 and the corresponding weather data. We used regression analysis to investigate weather impacts on COVID-19 spread in the mainland United States and estimate the changes of these impacts over space and time. Temperature exhibited a significant and moderately strong negative correlation for most of the US while relative humidity and precipitation experienced mixed relationships. By regressing temperature factors with the spreading rate of waves, we found temperature change can explain over 20% of the spatial-temporal variation in the COVID-19 spreading, with a significant and negative response between temperature change and spreading rate. The pandemic in the continental United States during 2020-2022 was characterized by seven waves, with different transmission rates and wave peaks concentrated in seven time periods. When repeating the analysis for waves in the seven periods and nine climate zones, we found temperature impacts evolve over time and space, possibly due to virus mutation, changes in population susceptibility, social behavior, and control measures. Temperature impacts became weaker in 6 of 9 climate zones from the beginning of the epidemic to the end of 2022, suggesting that COVID-19 has increasingly adapted to wider weather conditions.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"18 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9885913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Where to place emergency ambulance vehicles: use of a capacitated maximum covering location model with real call data. 在何处放置紧急救护车辆:使用具有真实呼叫数据的可容最大覆盖位置模型。

IF 1.7 4区医学

Geospatial Health Pub Date : 2023-07-20 DOI: 10.4081/gh.2023.1198

Soheil Hashtarkhani, Stephen A Matthews, Ping Yin, Alireza Mohammadi, Shahab MohammadEbrahimi, Mahmood Tara, Behzad Kiani

{"title":"Where to place emergency ambulance vehicles: use of a capacitated maximum covering location model with real call data.","authors":"Soheil Hashtarkhani, Stephen A Matthews, Ping Yin, Alireza Mohammadi, Shahab MohammadEbrahimi, Mahmood Tara, Behzad Kiani","doi":"10.4081/gh.2023.1198","DOIUrl":"https://doi.org/10.4081/gh.2023.1198","url":null,"abstract":"This study integrates geographical information systems (GIS) with a mathematical optimization technique to enhance emergency medical services (EMS) coverage in a county in the northeast of Iran. EMS demand locations were determined through one-year EMS call data analysis. We formulated a maximal covering location problem (MCLP) as a mixed-integer linear programming model with a capacity threshold for vehicles using the CPLEX optimizer, an optimization software package from IBM. To ensure applicability to the EMS setting, we incorporated a constraint that maintains an acceptable level of service for all EMS calls. Specifically, we implemented two scenarios: a relocation model for existing ambulances and an allocation model for new ambulances, both using a list of candidate locations. The relocation model increased the proportion of calls within the 5-minute coverage standard from 69% to 75%. With the allocation model, we found that the coverage proportion could rise to 84% of total calls by adding ten vehicles and eight new stations. The incorporation of GIS techniques into optimization modelling holds promise for the efficient management of scarce healthcare resources, particularly in situations where time is of the essence.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"18 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9885916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Association of socioeconomic indicators with COVID-19 mortality in Brazil: a population-based ecological study. 巴西社会经济指标与 COVID-19 死亡率的关系：一项基于人口的生态研究。

IF 1 4区医学

Geospatial Health Pub Date : 2023-07-13 DOI: 10.4081/gh.2023.1206

João Batista Cavalcante Filho, Marco Aurélio de Oliveira Góes, Damião da Conceição Araújo, Marcus Valerius da Silva Peixoto, Marco Antônio Prado Nunes

{"title":"Association of socioeconomic indicators with COVID-19 mortality in Brazil: a population-based ecological study.","authors":"João Batista Cavalcante Filho, Marco Aurélio de Oliveira Góes, Damião da Conceição Araújo, Marcus Valerius da Silva Peixoto, Marco Antônio Prado Nunes","doi":"10.4081/gh.2023.1206","DOIUrl":"10.4081/gh.2023.1206","url":null,"abstract":"The article presents an analysis of the spatial distribution of mortality from COVID-19 and its association with socioeconomic indicators in the north-eastern region of Brazil - an area particularly vulnerable with regard to these indicators. This populationbased ecology study was carried out at the municipal level in the years 2020 and 2021, with analyses performed by spatial autocorrelation, multiple linear regression and spatial autoregressive models. The results showed that mortality from COVID-19 in this part of Brazil was higher in the most populous cities with better socioeconomic indicators. Factors such as the onset of the COVID-19 pandemic in large cities, the agglomerations existing within them, the pressure to maintain economic activities and mistakes in the management of the pandemic by the Brazilian federal Government were part of the complex scenario related to the spread of COVID-19 in the country and this study was undertaken in an attempt to understand this situation. Analysing the different scenarios is essential to face the challenges posed by the pandemic to the world's health systems.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"18 2","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9878255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatiotemporal distribution and geostatistically interpolated mapping of the melioidosis risk in an endemic zone in Thailand. 泰国某流行区类鼻疽病风险的时空分布和地理统计插值图。

IF 1.7 4区医学

Geospatial Health Pub Date : 2023-07-05 DOI: 10.4081/gh.2023.1189

Jaruwan Wongbutdee, Jutharat Jittimanee, Wacharapong Saengnill

{"title":"Spatiotemporal distribution and geostatistically interpolated mapping of the melioidosis risk in an endemic zone in Thailand.","authors":"Jaruwan Wongbutdee, Jutharat Jittimanee, Wacharapong Saengnill","doi":"10.4081/gh.2023.1189","DOIUrl":"https://doi.org/10.4081/gh.2023.1189","url":null,"abstract":"Melioidosis, a bacterial, infectious disease contracted from contaminated soil or water, is a public health problem identified in tropical regions and endemic several regions of Thailand. Surveillance and prevention are important for determining its distribution patterns and mapping its risk, which have been analysed in the present study. Case reports in Thailand were collected from 1 January 2016 to 31 December 2020. Spatial autocorrelation was analyzed using Moran's I and univariate local Moran's I. Spatial point data of melioidosis incidence were calculated, with riskmapping interpolation performed by Kriging. It was highest in 2016, at 32.37 cases per 100,000 people, and lowest in 2020, at 10.83 cases per 100,000 people. General observations revealed that its incidence decreased slightly from 2016 to 2018 and drastically in 2019 and 2020. The Moran's I values for melioidosis incidence exhibited a random spatial pattern in 2016 and clustered distribution from 2017 to 2020. The risk and variance maps show interval values. These findings may contribute to the monitoring and surveillance of melioidosis outbreaks.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"18 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10178155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Impacts of sample ratio and size on the performance of random forest model to predict the potential distribution of snail habitats. 样本比例和大小对随机森林模型预测蜗牛生境潜在分布性能的影响。

IF 1.7 4区医学

Geospatial Health Pub Date : 2023-07-03 DOI: 10.4081/gh.2023.1151

Yuanhua Liu, Jun Zhang, Michael P Ward, Wei Tu, Lili Yu, Jin Shi, Yi Hu, Fenghua Gao, Zhiguo Cao, Zhijie Zhang

{"title":"Impacts of sample ratio and size on the performance of random forest model to predict the potential distribution of snail habitats.","authors":"Yuanhua Liu, Jun Zhang, Michael P Ward, Wei Tu, Lili Yu, Jin Shi, Yi Hu, Fenghua Gao, Zhiguo Cao, Zhijie Zhang","doi":"10.4081/gh.2023.1151","DOIUrl":"https://doi.org/10.4081/gh.2023.1151","url":null,"abstract":"Few studies have considered the impacts of sample size and sample ratio of presence and absence points on the results of random forest (RF) testing. We applied this technique for the prediction of the spatial distribution of snail habitats based on a total of 15,000 sample points (5,000 presence samples and 10,000 control points). RF models were built using seven different sample ratios (1:1, 1:2, 1:3, 1:4, 2:1, 3:1, and 4:1) and the optimal ratio was identified via the Area Under the Curve (AUC) statistic. The impact of sample size was compared by RF models under the optimal ratio and the optimal sample size. When the sample size was small, the sampling ratios of 1:1, 1:2 and 1:3 were significantly better than the sample ratios of 4:1 and 3:1 at all four levels of sample sizes (p<0.01) and there was no significant difference among the ratios of 1:1, 1:2 and 1:3 (p>0.05). The sample ratio of 1:2 appeared to be optimal for a relatively large sample size with the lowest quartile deviation. In addition, increasing the sample size produced a higher AUC and a smaller slope and the most suitable sample size found in this study was 2400 (AUC=0.96). This study provides a feasible idea to select an appropriate sample size and sample ratio for ecological niche modelling (ENM) and also provides a scientific basis for the selection of samples to accurately identify and predict snail habitat distributions.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"18 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9753693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimizing allocation of colorectal cancer screening hospitals in Shanghai: a geospatial analysis. 上海市结直肠癌筛查医院优化配置的地理空间分析

IF 1.7 4区医学

Geospatial Health Pub Date : 2023-07-03 DOI: 10.4081/gh.2023.1152

Jiaqi Huang, Yichen Chen, Gu Liu, Wei Tu, Robert Bergquist, Michael P Ward, Jun Zhang, Shuang Xiao, Jie Hong, Zheng Zhao, Xiaopan Li, Zhijie Zhang

{"title":"Optimizing allocation of colorectal cancer screening hospitals in Shanghai: a geospatial analysis.","authors":"Jiaqi Huang, Yichen Chen, Gu Liu, Wei Tu, Robert Bergquist, Michael P Ward, Jun Zhang, Shuang Xiao, Jie Hong, Zheng Zhao, Xiaopan Li, Zhijie Zhang","doi":"10.4081/gh.2023.1152","DOIUrl":"https://doi.org/10.4081/gh.2023.1152","url":null,"abstract":"Screening programmes are important for early diagnosis and treatment of colorectal cancer (CRC) but they are not equally efficient in all locations. Depending on which hospital people belong to, they often are not willing to follow up even after a positive result, resulting in a lower-than-expected overall detection rate. Improved allocation of health resources would increase the program's efficiency and assist hospital accessibility. A target population exceeding 70,000 people and 18 local hospitals were included in the investigation of an optimization plan based on a locationallocation model. We calculated the hospital service areas and the accessibility for people in communities to CRC-screening hospitals using the Huff Model and the Two-Step Floating Catchment Area (2SFCA) approach. We found that only 28.2% of the residents with initially a positive screening result had chosen followup with colonoscopy and significant geographical differences in spatial accessibility to healthcare services indeed exist. The lowest accessibility was found in the Southeast, including the Zhangjiang, Jichang and Laogang communities with the best accessibility mainly distributed near the city centre of Lujiazui; the latter also had relatively a high level of what is called \"ineffective screening\" as it represents wasteful resource allocation. It is recommended that Hudong Hospital should be chosen instead of Punan Hospital as the optimization, which can improve the service population of each hospital and the populations served per colonoscope. Based on our results, changes in hospital configuration in colorectal cancer screening programme are needed to achieve adequate population coverage and equitable facility accessibility. Planning of medical services should be based on the spatial distribution trends of the population served.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"18 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9753695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Temporal and spatial analyses of colorectal cancer incidence in Yogyakarta, Indonesia: a cross-sectional study. 印度尼西亚日惹市结直肠癌发病率的时空分析:一项横断面研究。

IF 1.7 4区医学

Geospatial Health Pub Date : 2023-05-25 DOI: 10.4081/gh.2023.1186

Juan Adrian Wiranata, Herindita Puspitaningtyas, Susanna Hilda Hutajulu, Jajah Fachiroh, Nungki Anggorowati, Guardian Yoki Sanjaya, Lutfan Lazuardi, Patumrat Sripan

{"title":"Temporal and spatial analyses of colorectal cancer incidence in Yogyakarta, Indonesia: a cross-sectional study.","authors":"Juan Adrian Wiranata, Herindita Puspitaningtyas, Susanna Hilda Hutajulu, Jajah Fachiroh, Nungki Anggorowati, Guardian Yoki Sanjaya, Lutfan Lazuardi, Patumrat Sripan","doi":"10.4081/gh.2023.1186","DOIUrl":"https://doi.org/10.4081/gh.2023.1186","url":null,"abstract":"We aimed to explore the district-level temporal dynamics and sub-district level geographical variations of colorectal cancer (CRC) incidence in the Special Region of Yogyakarta Province. We performed a cross-sectional study using data from the Yogyakarta population-based cancer registry (PBCR) comprised of 1,593 CRC cases diagnosed in 2008-2019. The age-standardized rates (ASRs) were determined using 2014 population data. The temporal trend and geographical distribution of cases were analysed using joinpoint regression and Moran's I statistics. During 2008-2019, CRC incidence increased by 13.44% annually. Joinpoints were identified in 2014 and 2017, which were also the periods when annual percentage change (APC) was the highest throughout the observation periods (18.84). Significant APC changes were observed in all districts, with the highest in Kota Yogyakarta (15.57). The ASR of CRC incidence per 100,000 person- years was 7.03 in Sleman, 9.20 in Kota Yogyakarta, and 7.07 in Bantul district. We found a regional variation of CRC ASR with a concentrated pattern of hotspots in the central sub-districts of the catchment areas and a significant positive spatial autocorrelation of CRC incidence rates in the province (I=0.581, p<0.001). The analysis identified four high-high clusters sub-districts in the central catchment areas. This is the first Indonesian study reported from PBCR data, showing an increased annual CRC incidence during an extensive observation period in the Yogyakarta region. A heterogeneous distribution map of CRC incidence is included. These findings may serve as basis for CRC screening implementation and healthcare services improvement.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"18 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9932353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Understanding the spatial non-stationarity in the relationships between malaria incidence and environmental risk factors using Geographically Weighted Random Forest: A case study in Rwanda. 利用地理加权随机森林了解疟疾发病率与环境风险因素之间关系的空间非平稳性:以卢旺达为例

IF 1.7 4区医学

Geospatial Health Pub Date : 2023-05-25 DOI: 10.4081/gh.2023.1184

Gilbert Nduwayezu, Pengxiang Zhao, Clarisse Kagoyire, Lina Eklund, Jean Pierre Bizimana, Petter Pilesjo, Ali Mansourian

{"title":"Understanding the spatial non-stationarity in the relationships between malaria incidence and environmental risk factors using Geographically Weighted Random Forest: A case study in Rwanda.","authors":"Gilbert Nduwayezu, Pengxiang Zhao, Clarisse Kagoyire, Lina Eklund, Jean Pierre Bizimana, Petter Pilesjo, Ali Mansourian","doi":"10.4081/gh.2023.1184","DOIUrl":"https://doi.org/10.4081/gh.2023.1184","url":null,"abstract":"As found in the health studies literature, the levels of climate association between epidemiological diseases have been found to vary across regions. Therefore, it seems reasonable to allow for the possibility that relationships might vary spatially within regions. We implemented the geographically weighted random forest (GWRF) machine learning method to analyze ecological disease patterns caused by spatially non-stationary processes using a malaria incidence dataset for Rwanda. We first compared the geographically weighted regression (WGR), the global random forest (GRF), and the geographically weighted random forest (GWRF) to examine the spatial non-stationarity in the non-linear relationships between malaria incidence and their risk factors. We used the Gaussian areal kriging model to disaggregate the malaria incidence at the local administrative cell level to understand the relationships at a fine scale since the model goodness of fit was not satisfactory to explain malaria incidence due to the limited number of sample values. Our results show that in terms of the coefficients of determination and prediction accuracy, the geographical random forest model performs better than the GWR and the global random forest model. The coefficients of determination of the geographically weighted regression (R2), the global RF (R2), and the GWRF (R2) were 4.74, 0.76, and 0.79, respectively. The GWRF algorithm achieves the best result and reveals that risk factors (rainfall, land surface temperature, elevation, and air temperature) have a strong non-linear relationship with the spatial distribution of malaria incidence rates, which could have implications for supporting local initiatives for malaria elimination in Rwanda.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"18 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9932352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatial clustering of colorectal cancer in Malaysia. 马来西亚结直肠癌的空间聚类。

IF 1.7 4区医学

Geospatial Health Pub Date : 2023-05-25 DOI: 10.4081/gh.2023.1158

Sharifah Saffinas Syed Soffian, Azmawati Mohammed Nawi, Rozita Hod, Khairul Nizam Abdul Maulud, Ahmad Tarmizi Mohd Azmi, Mohd Hazrin Hasim Hashim, Huan-Keat Chan, Muhammad Radzi Abu Hassan

{"title":"Spatial clustering of colorectal cancer in Malaysia.","authors":"Sharifah Saffinas Syed Soffian, Azmawati Mohammed Nawi, Rozita Hod, Khairul Nizam Abdul Maulud, Ahmad Tarmizi Mohd Azmi, Mohd Hazrin Hasim Hashim, Huan-Keat Chan, Muhammad Radzi Abu Hassan","doi":"10.4081/gh.2023.1158","DOIUrl":"https://doi.org/10.4081/gh.2023.1158","url":null,"abstract":"INTRODUCTION\u0000The rise in colorectal cancer (CRC) incidence becomes a global concern. As geographical variations in the CRC incidence suggests the role of area-level determinants, the current study was designed to identify the spatial distribution pattern of CRC at the neighbourhood level in Malaysia.\u0000\u0000\u0000METHOD\u0000Newly diagnosed CRC cases between 2010 and 2016 in Malaysia were identified from the National Cancer Registry. Residential addresses were geocoded. Clustering analysis was subsequently performed to examine the spatial dependence between CRC cases. Differences in socio-demographic characteristics of individuals between the clusters were also compared. Identified clusters were categorized into urban and semi-rural areas based on the population background.\u0000\u0000\u0000RESULT\u0000Most of the 18 405 individuals included in the study were male (56%), aged between 60 and 69 years (30.3%) and only presented for care at stages 3 or 4 of the disease (71.3%). The states shown to have CRC clusters were Kedah, Penang, Perak, Selangor, Kuala Lumpur, Melaka, Johor, Kelantan, and Sarawak. The spatial autocorrelation detected a significant clustering pattern (Moran's Index 0.244, p< 0.01, Z score >2.58). CRC clusters in Penang, Selangor, Kuala Lumpur, Melaka, Johor, and Sarawak were in urbanized areas, while those in Kedah, Perak and Kelantan were in semi-rural areas.\u0000\u0000\u0000CONCLUSION\u0000The presence of several clusters in urbanized and semi-rural areas implied the role of ecological determinants at the neighbourhood level in Malaysia. Such findings could be used to guide the policymakers in resource allocation and cancer control.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"18 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9559182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0