Geospatial Health最新文献_第2页

Risk discrepancies in COVID-19-related community environments based on spatiotemporal monitoring. 基于时空监测的covid -19相关社区环境风险差异分析

IF 1 4区医学

Geospatial Health Pub Date : 2025-01-23 Epub Date: 2025-04-28 DOI: 10.4081/gh.2025.1286

Jihong Zhang, Guohua Yin, Qiuhua Zhang, Juan Fang, Duo Jiang, Chao Yang, Na Sun

{"title":"Risk discrepancies in COVID-19-related community environments based on spatiotemporal monitoring.","authors":"Jihong Zhang, Guohua Yin, Qiuhua Zhang, Juan Fang, Duo Jiang, Chao Yang, Na Sun","doi":"10.4081/gh.2025.1286","DOIUrl":"https://doi.org/10.4081/gh.2025.1286","url":null,"abstract":"The geo-inequality of COVID-19 risk has attracted a great deal of research attention. In this study, the spatial correlation between community environment and the incidence of COVID-19 cases in 30 Chinese cities is discussed. The spread of the disease is analyzed based on timing and spatial monitoring at the km2-grid level, with the use of publicly available data relating to housing prices, Gross Deomestic Product (GDP), medical facilities, consumer sites, public green spaces, and industrial sites. The results indicate substantial geographical variations in the distribution of COVID-19 communities in all 30 cities. Significant global bivariate spatial dependence was observed between the disease and housing prices (Moran's I =0.099, p<0.01, z=488.6), medical facilities (Moran's I = 0.349, p<0.01, z=1675.0), consumer sites (Moran's I =0.369, p<0.01, z=1843.4), green space (Moran's I =0.205, p<0.01, z=1037.8), and industrial sites (Moran's I =0.234, p<0.01, z=1178.6). The risk of COVID-19 under the influence of GDP is further examined for cities with per capita GDPs from high to low ranging from 1.69 to 4.62 (1.69~3.74~4.62, 95% CI). These findings provide greater detail on the interplay between the infectious disease and community environments.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144007918","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

Sentiment analysis using a lexicon-based approach in Lisbon, Portugal. 在葡萄牙里斯本使用基于词典的方法进行情感分析。

IF 1 4区医学

Geospatial Health Pub Date : 2025-01-23 Epub Date: 2025-04-24 DOI: 10.4081/gh.2025.1344

Iuria Betco, Ana Isabel Ribeiro, David S Vale, Luis Encalada-Abarca, Cláudia M Viana, Jorge Rocha

引用次数: 0

Environmental and geographical factors influence malaria transmission in KwaZulu-Natal province, South Africa. 环境和地理因素影响南非夸祖鲁-纳塔尔省的疟疾传播。

IF 1 4区医学

Geospatial Health Pub Date : 2025-01-23 Epub Date: 2025-06-09 DOI: 10.4081/gh.2025.1370

Osadolor Ebhuoma, Michael Gebreslasie, Oswaldo Villena, Ali Arab

{"title":"Environmental and geographical factors influence malaria transmission in KwaZulu-Natal province, South Africa.","authors":"Osadolor Ebhuoma, Michael Gebreslasie, Oswaldo Villena, Ali Arab","doi":"10.4081/gh.2025.1370","DOIUrl":"https://doi.org/10.4081/gh.2025.1370","url":null,"abstract":"The malaria burden remains largely concentrated in sub- Saharan Africa. South Africa, a country within this region, has made significant progress toward malaria elimination. However, malaria continues to be endemic in three of its nine provinces: Limpopo, Mpumalanga, and KwaZulu-Natal (KZN), which are located in the northern part of the country and share borders with Botswana, Zimbabwe, and Mozambique. This study focuses on KZN, where district municipalities report monthly malaria cases ranging from zero to 8,981. Fitting Bayesian zero-inflated models in the INLA R package, we assessed the effects of various climate and environmental variables on malaria prevalence and spatio-temporal transmission dynamics from 2005-2014. Specifically, we analyzed precipitation, day and night land surface temperature, the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI) and elevation data for KZN local municipalities. Our findings indicate that the best model was the Zero- Inflated Negative Binomial (ZINB) and that at 95% Bayesian Credible Interval (CI), NDVI (0.74; CI (0.95, 3.87) is significantly related to malaria transmission in KZN, with the north-eastern part of the province exhibiting the highest risk of malaria transmission. Additionally, our model captured the reduction of malaria from 2005 to 2010 and the following resurgence. The modelling approach employed in this study represents a valuable tool for understanding and monitoring the influence of climate and environmental variables on the spatial heterogeneity of malaria. Also, this study reveals the need to strengthen the already existing crossborder collaborations to fortify KZN's malaria elimination goals.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144259461","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 future of spatial epidemiology in the AI era: enhancing machine learning approaches with explicit spatial structure. 人工智能时代空间流行病学的未来：增强具有明确空间结构的机器学习方法。

IF 1 4区医学

Geospatial Health Pub Date : 2025-01-23 Epub Date: 2025-06-04 DOI: 10.4081/gh.2025.1386

Nima Kianfar, Benn Sartorius, Colleen L Lau, Robert Bergquist, Behzad Kiani

{"title":"The future of spatial epidemiology in the AI era: enhancing machine learning approaches with explicit spatial structure.","authors":"Nima Kianfar, Benn Sartorius, Colleen L Lau, Robert Bergquist, Behzad Kiani","doi":"10.4081/gh.2025.1386","DOIUrl":"https://doi.org/10.4081/gh.2025.1386","url":null,"abstract":"Spatial epidemiology, defined as the study of spatial patterns in disease burdens or health outcomes, aims to estimate disease risk or incidence by identifying geographical risk factors and populations at risk (Morrison et al., 2024). Research in spatial epidemiology relies on both conventional approaches and Machine- Learning (ML) algorithms to explore geographic patterns of diseases and identify influential factors (Pfeiffer & Stevens, 2015). Traditional spatial techniques, including spatial autocorrelation using global Moran's I, Geary's C (Amgalan et al., 2022), and Ripley's K Function (Kan et al., 2022), Local Indicators of Spatial Association (LISA) (Sansuk et al., 2023), hotspot analysis by Getis-Ord Gi* (Lun et al., 2022), spatial lag models (Rey & Franklin, 2022), and Geographically Weighted Regression (GWR) (Kiani et al., 2024) are designed to explicitly incorporate the spatial structure of data into spatial modelling, often referred to as spatially aware models (Reich et al., 2021). Beyond these models, several other spatially aware approaches that have been widely applied in epidemiological studies include but are not limited to Bayesian spatial models that account for spatial uncertainty in disease mapping, such as Bayesian Hierarchical models, Conditional Autoregressive (CAR), and Besage, York, and Mollie' (BYM) models (Louzada et al., 2021). Bayesian methods are statistically rigorous techniques that assume neighboring regions share similar values. Kulldorff's Spatial Scan Statistic is another traditional spatial technique that uses a moving circular window to extract significant disease clusters (Tango, 2021). Moreover, geostatistical models such as Kriging and Inverse Distance Weighting (IDW) allow for continuous spatial interpolation of health data (Nayak et al., 2021). [...].","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144217643","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

Dengue risk-mapping in an Amazonian locality in Colombia based on regression and multi-criteria analysis. 基于回归和多标准分析的哥伦比亚亚马逊地区登革热风险制图

IF 1 4区医学

Geospatial Health Pub Date : 2025-01-23 Epub Date: 2025-06-04 DOI: 10.4081/gh.2025.1292

Maria Camila Lesmes, Alvaro Ávila-Díaz, Erika Santamaría, Carlos Andrés Morales, Horacio Cadena, Patricia Fuya, Nicolas Frutos, Ximena Porcasi, Catalina Marceló-Díaz

{"title":"Dengue risk-mapping in an Amazonian locality in Colombia based on regression and multi-criteria analysis.","authors":"Maria Camila Lesmes, Alvaro Ávila-Díaz, Erika Santamaría, Carlos Andrés Morales, Horacio Cadena, Patricia Fuya, Nicolas Frutos, Ximena Porcasi, Catalina Marceló-Díaz","doi":"10.4081/gh.2025.1292","DOIUrl":"10.4081/gh.2025.1292","url":null,"abstract":"The potential of dengue infection is of prime public health concern in tropical and subtropical countries. In Colombia, the management of this disease is based mainly on epidemiological monitoring and vector control. This study, covering the period 2015-2022, adds to this approach by investigating a tool that identifies dengue risk zones considering its environmental and sociodemographic determinants. For this purpose, an analytical, comparative, ecological study was carried out in three stages: i) selection of indicators associated with the occurrence of dengue through hierarchical analysis; ii) execution of a spatial-based Ordinary Least Squares (OLS) regression technique; and iii) multi-criteria analysis of the risk data obtained. Consequently, two optimal models, one for the rainy season (R2=0.5761; AIC=366.3929) and the other for the dry season (R2=0.8560; AIC=440.7557) were obtained for the Dengue Incidence Rate (DIR) during the study period mainly based on socio-demographic and environmental variables. A dengue risk map was generated, showing the impact on three neighbourhoods in the municipality of Piamonte in the Cauca Department covering both seasons. In conclusion, the dengue risk map made it possible to identify highrisk areas and also to identify the determinants of disease occurrence, which can contribute to improving disease management in tropical and subtropical regions.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144217709","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 multilevel modelling male partners' influence on women's modern contraceptive use: a study in Angola and Zambia. 男性伴侣对女性现代避孕药具使用影响的空间多层次建模：安哥拉和赞比亚的研究。

IF 1 4区医学

Geospatial Health Pub Date : 2025-01-23 Epub Date: 2025-05-12 DOI: 10.4081/gh.2025.1340

Kabeya Clement Mulamba

{"title":"Spatial multilevel modelling male partners' influence on women's modern contraceptive use: a study in Angola and Zambia.","authors":"Kabeya Clement Mulamba","doi":"10.4081/gh.2025.1340","DOIUrl":"https://doi.org/10.4081/gh.2025.1340","url":null,"abstract":"The main objective of this paper was to model the relationship between married women's contraceptive use and the influence of their male partners. The study took place in Angola and Zambia, which stems from the fact that these countries ratified the Maputo Protocol that emphasises promotion of reproductive health among women. Most previous studies investigating women's progress towards the realisation of what is advocated in this protocol have overlooked the role of the male partners. Hence, it has become imperative to reduce this gap in the literature. This paper discusses the application of spatial multilevel modelling, which incorporates two levels of information based on the nature of the data available. This approach acknowledges the hypothesis that contraceptive use is a social phenomenon occurring within the geographical space and is therefore susceptible to autocorrelation. Findings confirm that the level of influence of male partners' exertion on women's contraceptive use is dependent on the situation in the country where it takes place as shown by various study variables analysed. The results indicate that socioeconomic and education factors play a major role, a phenomenon that calls for tailor-made reproductive health policies considering these aspects.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144045072","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

Impact of climate change on dengue fever: a bibliometric analysis. 气候变化对登革热的影响：文献计量学分析。

IF 1 4区医学

Geospatial Health Pub Date : 2025-01-23 Epub Date: 2025-02-19 DOI: 10.4081/gh.2025.1301

Mai Liu, Yin Zhang

{"title":"Impact of climate change on dengue fever: a bibliometric analysis.","authors":"Mai Liu, Yin Zhang","doi":"10.4081/gh.2025.1301","DOIUrl":"10.4081/gh.2025.1301","url":null,"abstract":"Dengue is the most widespread and fastest-growing vectorborne disease worldwide. We employed bibliometric analysis to provide an overview of research on the impact of climate change on dengue fever focusing on both global and Southeast Asian regions. Using the Web of Science Core Collection (WoSCC) database, we reviewed studies on the impact of climate change on dengue fever between 1974 and 2022 taking into account study locations and international collaboration. The VOS viewer software (https://www.vosviewer.com/) and the Bibliometrix R package (https://www.bibliometrix.org/) were used to visualise country networks and keywords. We collected 2,055 relevant articles published globally between 1974 and 2022 on the impact of climate change on dengue fever, 449 of which published in Southeast Asia. Peaking in 2021, the overall number of publications showed a strong increase in the period 2000-2022. The United States had the highest number of publications (n=558) followed by China (261) and Brazil (228). Among the Southeast Asian countries, Thailand had most publications (n=123). Global and Southeast Asian concerns about the impact of climate change on dengue fever are essentially the same. They all emphasise the relationship between temperature and other climatic conditions on the one hand and the transmission of Aedes aegypti on the other. A significant positive correlation exists between the number of national publications and socioeconomic index and between international collaboration and scientific productivity in the field. Our study demonstrates the current state of research on the impact of climate change on dengue and provides a comparative analysis of the Southeast Asian region. Publication output in Southeast Asia lags behind that of major countries worldwide, and various strategies should be implemented to improve international collaboration, such as increasing the number of international collaborative projects and providing academic resources and research platforms for researchers.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460941","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

Spatio-temporal analysis of foot traffic dynamics in Charleston County, South Carolina: before, during, and after COVID-19ston County, South Carolina: Before, During, and After COVID-19. 南卡罗来纳州查尔斯顿县：2019冠状病毒病之前、期间和之后的人流量动态时空分析

IF 1 4区医学

Geospatial Health Pub Date : 2025-01-23 Epub Date: 2025-06-23 DOI: 10.4081/gh.2025.1363

Wish Shao, Abolfazl Mollalo, Navid Hashemi Tonekaboni

{"title":"Spatio-temporal analysis of foot traffic dynamics in Charleston County, South Carolina: before, during, and after COVID-19ston County, South Carolina: Before, During, and After COVID-19.","authors":"Wish Shao, Abolfazl Mollalo, Navid Hashemi Tonekaboni","doi":"10.4081/gh.2025.1363","DOIUrl":"https://doi.org/10.4081/gh.2025.1363","url":null,"abstract":"While the COVID-19 pandemic significantly disrupted urban mobility in general, its effects on spatio-temporal foot traffic patterns remain insufficiently explored. This study addresses this issue by analysing foot traffic dynamics across various regions of Charleston County, South Carolina, before, during and after the pandemic. We examined changes across nine distinct stages of the pandemic from 2018 to 2022 at the sub-county level, utilizing point of interest data and public health records. Various machine learning models, including Random Forest, were employed to predict foot traffic trends, achieving high predictive accuracy with an R2 value of 0.88. Our findings reveal varying foot traffic patterns across the county. Prior to the pandemic, foot traffic was generally consistent across county subdivisions, maintaining steady levels in each area. The onset of the pandemic led to significant decreases in foot traffic across most subdivisions, followed by gradual recovery, with some areas surpassing pre-pandemic levels. These results underscore the need for tailored crisis management and urban planning, particularly in midsized counties with similar structures to inform more effective resource allocation and improve risk management in public safety during public health crises.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144477990","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

Moran's I and Geary's C: investigation of the effects of spatial weight matrices for assessing the distribution of infectious diseases. Moran's I 和 Geary's C：调查空间权重矩阵对评估传染病分布的影响。

IF 1 4区医学

Geospatial Health Pub Date : 2025-01-23 Epub Date: 2025-04-07 DOI: 10.4081/gh.2025.1277

Sarah Isnan, Ahmad Fikri Bin Abdullah, Abdul Rashid Shariff, Iskandar Ishak, Sharifah Norkhadijah Syed Ismail, Maheshwara Rao Appanan

{"title":"Moran's I and Geary's C: investigation of the effects of spatial weight matrices for assessing the distribution of infectious diseases.","authors":"Sarah Isnan, Ahmad Fikri Bin Abdullah, Abdul Rashid Shariff, Iskandar Ishak, Sharifah Norkhadijah Syed Ismail, Maheshwara Rao Appanan","doi":"10.4081/gh.2025.1277","DOIUrl":"10.4081/gh.2025.1277","url":null,"abstract":"The COVID-19 outbreak has precipitated severe occurrences on a global scale. Hence, spatial analysis is crucial in determining the relationships and patterns of geospatial data. Moran's I and Geary's C are prominent methodologies used to measure the spatial autocorrelation of geographical data. Both measure the degree of similarity or dissimilarity between nearby locations based on attribute values in such a way that the selection of distance techniques and weight matrices significantly impact the spatial autocorrelation results. This paper aimed at carrying out the spatial epidemiological characteristics analysis of the pandemic comparing the results of Moran's I and Geary's C with different parameters to gain a comprehensive understanding of the spatial relationship of COVID-19 cases. We employed distance-based techniques, K-nearest neighbour, and Queen contiguity techniques to assess the sensitivity of the different parameter configurations for both Moran's I and Geary's C. The findings revealed that former provided more reliable and robust results compared to the latter, with consistent results of spatial autocorrelation (positive spatial autocorrelation). The distance weight of 0.05 using the Manhattan method of Moran's I is the recommended distance weight, as it outperformed other weight matrices (Moran's I = 0.0152, Z-value= 110.8844 and p-value=0.001).","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143804832","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

Land surface temperature predicts mortality due to chronic obstructive pulmonary disease: a study based on climate variables and impact machine learning. 地表温度预测慢性阻塞性肺病的死亡率：一项基于气候变量和影响机器学习的研究。

IF 1 4区医学

Geospatial Health Pub Date : 2025-01-23 Epub Date: 2025-03-26 DOI: 10.4081/gh.2025.1319

Alireza Mohammadi, Bardia Mashhoodi, Ali Shamsoddini, Elahe Pishgar, Robert Bergquist

{"title":"Land surface temperature predicts mortality due to chronic obstructive pulmonary disease: a study based on climate variables and impact machine learning.","authors":"Alireza Mohammadi, Bardia Mashhoodi, Ali Shamsoddini, Elahe Pishgar, Robert Bergquist","doi":"10.4081/gh.2025.1319","DOIUrl":"10.4081/gh.2025.1319","url":null,"abstract":"Introduction: Chronic Obstructive Pulmonary Disease (COPD) mortality rates and global warming have been in the focus of scientists and policymakers in the past decade. The long-term shifts in temperature and weather patterns, commonly referred to as climate change, is an important public health issue, especially with regard to COPD.Method: Using the most recent county-level age-adjusted COPD mortality rates among adults older than 25 years, this study aimed to investigate the spatial trajectory of COPD in the United States between 2001 and 2020. Global Moran's I was used to investigate spatial relationships utilising data from Terra satellite for night-time land surface temperatures (LSTnt), which served as an indicator of warming within the same time period across the United States. The forest-based classification and regression model (FCR) was applied to predict mortality rates.Results: It was found that COPD mortality over the 20-year period was spatially clustered in certain counties. Moran's I statistic (I=0.18) showed that the COPD mortality rates increased with LSTnt, with the strongest spatial association in the eastern and south-eastern counties. The FCR model was able to predict mortality rates based on LSTnt values in the study area with a R2 value of 0.68.Conclusion: Policymakers in the United States could use the findings of this study to develop long-term spatial and health-related strategies to reduce the vulnerability to global warming of patients with acute respiratory symptoms.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":"20 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143722936","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