Geospatial Health最新文献_第6页

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":null,"pages":null},"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

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":null,"pages":null},"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

The geographic environment and the frequency of falling: a study of mortality outcomes in elderly people in China. 地理环境与跌倒频率:中国老年人死亡结果的研究。

IF 1.7 4区医学

Geospatial Health Pub Date : 2023-05-25 DOI: 10.4081/gh.2023.1180

Yi Huang, Chen Li, Xianjing Lu, Yue Wang

{"title":"The geographic environment and the frequency of falling: a study of mortality outcomes in elderly people in China.","authors":"Yi Huang, Chen Li, Xianjing Lu, Yue Wang","doi":"10.4081/gh.2023.1180","DOIUrl":"https://doi.org/10.4081/gh.2023.1180","url":null,"abstract":"Falling has become the first and second cause of death due to injury among urban and rural residents in China. This mortality is considerably higher in the southern part of the country than in the North. We collected the rate of mortality due to falling for 2013 and 2017 by province, age structure and population density, taking topography, precipitation and temperature into account. 2013 was used as the first year of the study since this year marks the expansion of the mortality surveillance system from 161 counties to 605 counties making available data more representative. A geographically weighted regression was used to evaluate the relationship between mortality and the geographic risk factors. High levels of precipitation, steep topography and uneven land surfaces as well as a higher quantile of the population aged above 80 years in southern China are believed to have led to the significantly higher number of falling compared with that in the North. Indeed, when evaluated by geographically weighted regression, the factors mentioned found a difference between the South and the North with regard to falling of 81% and 76% for the years 2013 and 2017, respectively. Interaction effects were observed between geographic risk factors and falling that, apart from the age factor, could be explained by topographic and climatic differences. The roads in the South are more difficult to negotiate on foot, particularly when it rains, which increases the probability of falling. In summary, the higher mortality due to falling in southern China emphasizes the need to apply more adaptive and effective measures in rainy and mountainous region to reduce this kind of risk.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9559179","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

Investigating local variation in disease rates within high-rate regions identified using smoothing. 在使用平滑法确定的高发病率区域内调查疾病发病率的局部变化。

IF 1.7 4区医学

Geospatial Health Pub Date : 2023-05-25 DOI: 10.4081/gh.2023.1144

Matthew Tuson, Matthew Yap, David Whyatt

{"title":"Investigating local variation in disease rates within high-rate regions identified using smoothing.","authors":"Matthew Tuson, Matthew Yap, David Whyatt","doi":"10.4081/gh.2023.1144","DOIUrl":"https://doi.org/10.4081/gh.2023.1144","url":null,"abstract":"Exploratory disease maps are designed to identify risk factors of disease and guide appropriate responses to disease and helpseeking behaviour. However, when produced using aggregatelevel administrative units, as is standard practice, disease maps may mislead users due to the Modifiable Areal Unit Problem (MAUP). Smoothed maps of fine-resolution data mitigate the MAUP but may still obscure spatial patterns and features. To investigate these issues, we mapped rates of Mental Health- Related Emergency Department (MHED) presentations in Perth, Western Australia, in 2018/19 using Australian Bureau of Statistics (ABS) Statistical Areas Level 2 (SA2) boundaries and a recent spatial smoothing technique: the Overlay Aggregation Method (OAM). Then, we investigated local variation in rates within high-rate regions delineated using both approaches. The SA2- and OAM-based maps identified two and five high-rate regions, respectively, with the latter not conforming to SA2 boundaries. Meanwhile, both sets of high-rate regions were found to comprise a select number of localised areas with exceptionally high rates. These results demonstrate how, due to the MAUP, disease maps that are produced using aggregate-level administrative units are unreliable as a basis for delineating geographic regions of interest for targeted interventions. Instead, reliance on such maps to guide responses may compromise the efficient and equitable delivery of healthcare. Detailed investigation of local variation in rates within high-rate regions identified using both administrative units and smoothing is required to improve hypothesis generation and the design of healthcare responses.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9552806","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 pattern and heterogeneity of chronic respiratory diseases and relationship to socio-demographic factors in Thailand in the period 2016 to 2019. 2016 - 2019年泰国慢性呼吸道疾病的空间格局、异质性及其与社会人口因素的关系

IF 1.7 4区医学

Geospatial Health Pub Date : 2023-05-25 DOI: 10.4081/gh.2023.1203

Zar Chi Htwe, Wongsa Laohasiriwong, Kittipong Sornlorm, Roshan Mahato

{"title":"Spatial pattern and heterogeneity of chronic respiratory diseases and relationship to socio-demographic factors in Thailand in the period 2016 to 2019.","authors":"Zar Chi Htwe, Wongsa Laohasiriwong, Kittipong Sornlorm, Roshan Mahato","doi":"10.4081/gh.2023.1203","DOIUrl":"https://doi.org/10.4081/gh.2023.1203","url":null,"abstract":"Chronic respiratory diseases (CRDs) constitute 4% of the global disease burden and cause 4 million deaths annually. This cross-sectional study used QGIS and GeoDa to explore the spatial pattern and heterogeneity of CRDs morbidity and spatial autocorrelation between socio-demographic factors and CRDs in Thailand from 2016 to 2019. We found an annual, positive, spatial autocorrelation (Moran's I >0.66, p<0.001) showing a strong clustered distribution. The local indicators of spatial association (LISA) identified hotspots mostly in the northern region, while coldspots were mostly seen in the central and north-eastern regions throughout the study period. Of the socio-demographic factors, the density of population, households, vehicles, factories and agricultural areas, correlated with the CRD morbidity rate, with statistically significant negative spatial autocorrelations and coldspots in the north-eastern and central areas (except for agricultural land) and two hotspots between farm household density and CRD in the southern region in 2019. This study identified vulnerable provinces with high risk of CRDs and can guide prioritization of resource allocation and provide target interventions for policy makers.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9932350","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

A spatiotemporal analysis of the social determinants of health for COVID-19. COVID-19健康社会决定因素时空分析

IF 1.7 4区医学

Geospatial Health Pub Date : 2023-05-25 DOI: 10.4081/gh.2023.1153

Claire Bonzani, Peter Scull, Daisaku Yamamoto

{"title":"A spatiotemporal analysis of the social determinants of health for COVID-19.","authors":"Claire Bonzani, Peter Scull, Daisaku Yamamoto","doi":"10.4081/gh.2023.1153","DOIUrl":"https://doi.org/10.4081/gh.2023.1153","url":null,"abstract":"This research aims to uncover how the association between social determinants of health and COVID-19 cases and fatality rate have changed across time and space. To begin to understand these associations and show the benefits of analysing temporal and spatial variations in COVID-19, we utilized Geographically Weighted Regression (GWR). The results emphasize the advantages for using GWR in data with a spatial component, while showing the changing spatiotemporal magnitude of association between a given social determinant and cases or fatalities. While previous research has demonstrated the merits of GWR for spatial epidemiology, our study fills a gap in the literature, by examining a suite of variables across time to reveal how the pandemic unfolded across the US at a county-level spatial scale. The results speak to the importance of understanding the local effects that a social determinant may have on populations at the county level. From a public health perspective, these results can be used for an understanding of the disproportionate disease burden felt by different populations, while upholding and building upon trends observed in epidemiological literature.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9552805","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

Prehistoric human migrations: a prospective subject for modelling using geographical information systems. 史前人类迁徙:利用地理信息系统建模的前瞻性课题。

IF 1.7 4区医学

Geospatial Health Pub Date : 2023-05-25 DOI: 10.4081/gh.2023.1210

Robert Bergquist

引用次数: 0

Understanding COVID-19: comparison of spatio-temporal analysis methods used to study epidemic spread patterns in the United States. 了解COVID-19:用于研究美国流行病传播模式的时空分析方法的比较。

IF 1.7 4区医学

Geospatial Health Pub Date : 2023-05-25 DOI: 10.4081/gh.2023.1200

Chunhui Liu, Xiaodi Su, Zhaoxuan Dong, Xingyu Liu, Chunxia Qiu

{"title":"Understanding COVID-19: comparison of spatio-temporal analysis methods used to study epidemic spread patterns in the United States.","authors":"Chunhui Liu, Xiaodi Su, Zhaoxuan Dong, Xingyu Liu, Chunxia Qiu","doi":"10.4081/gh.2023.1200","DOIUrl":"https://doi.org/10.4081/gh.2023.1200","url":null,"abstract":"This article examines three spatiotemporal methods used for analyzing of infectious diseases, with a focus on COVID-19 in the United States. The methods considered include inverse distance weighting (IDW) interpolation, retrospective spatiotemporal scan statistics and Bayesian spatiotemporal models. The study covers a 12-month period from May 2020 to April 2021, including monthly data from 49 states or regions in the United States. The results show that the spread of COVID-19 pandemic increased rapidly to a high value in winter of 2020, followed by a brief decline that later reverted into another increase. Spatially, the COVID-19 epidemic in the United States exhibited a multi-centre, rapid spread character, with clustering areas represented by states such as New York, North Dakota, Texas and California. By demonstrating the applicability and limitations of different analytical tools in investigating the spatiotemporal dynamics of disease outbreaks, this study contributes to the broader field of epidemiology and helps improve strategies for responding to future major public health events.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9932351","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

Prediction of dengue cases using the attention-based long short-term memory (LSTM) approach. 利用基于注意的长短期记忆方法预测登革热病例。

IF 1.7 4区医学

Geospatial Health Pub Date : 2023-05-25 DOI: 10.4081/gh.2023.1176

Mokhalad A Majeed, Helmi Z M Shafri, Aimrun Wayayok, Zed Zulkafli

{"title":"Prediction of dengue cases using the attention-based long short-term memory (LSTM) approach.","authors":"Mokhalad A Majeed, Helmi Z M Shafri, Aimrun Wayayok, Zed Zulkafli","doi":"10.4081/gh.2023.1176","DOIUrl":"https://doi.org/10.4081/gh.2023.1176","url":null,"abstract":"This research proposes a 'temporal attention' addition for long-short term memory (LSTM) models for dengue prediction. The number of monthly dengue cases was collected for each of five Malaysian states i.e. Selangor, Kelantan, Johor, Pulau Pinang, and Melaka from 2011 to 2016. Climatic, demographic, geographic and temporal attributes were used as covariates. The proposed LSTM models with temporal attention was compared with several benchmark models including a linear support vector machine (LSVM), a radial basis function support vector machine (RBFSVM), a decision tree (DT), a shallow neural network (SANN) and a deep neural network (D-ANN). In addition, experiments were conducted to analyze the impact of look-back settings on each model performance. The results showed that the attention LSTM (A-LSTM) model performed best, with the stacked, attention LSTM (SA-LSTM) one in second place. The LSTM and stacked LSTM (S-LSTM) models performed almost identically but with the accuracy improved by the attention mechanism was added. Indeed, they were both found to be superior to the benchmark models mentioned above. The best results were obtained when all attributes were included in the model. The four models (LSTM, S-LSTM, A-LSTM and SA-LSTM) were able to accurately predict dengue presence 1-6 months ahead. Our findings provide a more accurate dengue prediction model than previously used, with the prospect of also applying this approach in other geographic areas.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9559178","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: 1

A geospatial study of the coverage of catheterization laboratory facilities (cath labs) and the travel time required to reach them in East Java, Indonesia. 对印度尼西亚东爪哇导尿实验室设施(导管室)的覆盖范围和到达这些设施所需的旅行时间的地理空间研究。

IF 1.7 4区医学

Geospatial Health Pub Date : 2023-05-25 DOI: 10.4081/gh.2023.1164

Andrianto Andrianto, Farizal Rizky Muharram, Chaq El Chaq Zamzam Multazam, Wigaviola Socha, Doni Firman, Ahmad Chusnu Romdhoni, Senitza Anisa Salsabilla

{"title":"A geospatial study of the coverage of catheterization laboratory facilities (cath labs) and the travel time required to reach them in East Java, Indonesia.","authors":"Andrianto Andrianto, Farizal Rizky Muharram, Chaq El Chaq Zamzam Multazam, Wigaviola Socha, Doni Firman, Ahmad Chusnu Romdhoni, Senitza Anisa Salsabilla","doi":"10.4081/gh.2023.1164","DOIUrl":"https://doi.org/10.4081/gh.2023.1164","url":null,"abstract":"Coronary heart disease is a non-communicable disease whose treatment is closely related to infrastructure, such as diagnostic imaging equipment visualizing arteries and chambers of the heart (cath lab) and infrastructure that supports access to healthcare. This research is intended as a preliminary geospatial study to carry out initial measurements of health facility coverage at the regional level, survey available supporting data and provide input on problems in future research. Data on cath lab presence was gathered through direct survey, while population data was taken from an open-source geospatial system. The cath lab service coverage was obtained by analysis based on a Geographical Information System (GIS) specific tool to evaluate travel time from the sub-district centre to the nearest cath lab facility. The number of cath labs in East Java has increased from 16 to 33 in the last six years and the 1-hour access time increased from 24.2% to 53.8%. However, accessibility remains a problem as16.5% of the total population of East Java cannot access a cath lab even within 2 hours. Thus, additional cath lab facilities are required to provide ideal healthcare coverage. Geospatial analysis is the tool to determine the optimal cath lab distribution.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9559183","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