Geospatial Health最新文献_第7页

Spatial autocorrelation and heterogenicity of demographic and healthcare factors in the five waves of COVID-19 epidemic in Thailand. 泰国五波新冠肺炎疫情中人口与卫生保健因素的空间自相关及异质性

IF 1.7 4区医学

Geospatial Health Pub Date : 2023-05-25 DOI: 10.4081/gh.2023.1183

Ei Sandar U, Wongsa Laohasiriwong, Kittipong Sornlorm

{"title":"Spatial autocorrelation and heterogenicity of demographic and healthcare factors in the five waves of COVID-19 epidemic in Thailand.","authors":"Ei Sandar U, Wongsa Laohasiriwong, Kittipong Sornlorm","doi":"10.4081/gh.2023.1183","DOIUrl":"https://doi.org/10.4081/gh.2023.1183","url":null,"abstract":"A study of 2,569,617 Thailand citizens diagnosed with COVID-19 from January 2020 to March 2022 was conducted with the aim of identifying the spatial distribution pattern of incidence rate of COVID-19 during its five main waves in all 77 provinces of the country. Wave 4 had the highest incidence rate (9,007 cases per 100,000) followed by the Wave 5, with 8,460 cases per 100,000. We also determined the spatial autocorrelation between a set of five demographic and health care factors and the spread of the infection within the provinces using Local Indicators of Spatial Association (LISA) and univariate and bivariate analysis with Moran's I. The spatial autocorrelation between the variables examined and the incidence rates was particularly strong during the waves 3-5. All findings confirmed the existence of spatial autocorrelation and heterogenicity of COVID-19 with the distribution of cases with respect to one or several of the five factors examined. The study identified significant spatial autocorrelation with regard to the COVID-19 incidence rate with these variables in all five waves. Depending on which province that was investigated, strong spatial autocorrelation of the High-High pattern was observed in 3 to 9 clusters and of the Low-Low pattern in 4 to 17 clusters, whereas negative spatial autocorrelation was observed in 1 to 9 clusters of the High-Low pattern and in 1 to 6 clusters of Low-High pattern. These spatial data should support stakeholders and policymakers in their efforts to prevent, control, monitor and evaluate the multidimensional determinants of the COVID-19 pandemic.","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":"9559177","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: 2

Dynamic effect of economic growth on the persistence of suicide rates. 经济增长对自杀率持续性的动态影响。

IF 1.7 4区医学

Geospatial Health Pub Date : 2023-05-25 DOI: 10.4081/gh.2023.1201

Tzu-Yi Yang, Yu-Tai Yang, Ssu-Han Chen, Yu-Ting Lan, Chia-Jui Peng

引用次数: 0

Spatial analysis of the relationship between out-of-pocket expenditure and socioeconomic status in South Korea. 韩国自费支出与社会经济地位关系的空间分析。

IF 1.7 4区医学

Geospatial Health Pub Date : 2023-05-25 DOI: 10.4081/gh.2023.1175

Young-Gyu Kwon, Man-Kyu Choi

{"title":"Spatial analysis of the relationship between out-of-pocket expenditure and socioeconomic status in South Korea.","authors":"Young-Gyu Kwon, Man-Kyu Choi","doi":"10.4081/gh.2023.1175","DOIUrl":"https://doi.org/10.4081/gh.2023.1175","url":null,"abstract":"The rapid increase in out-of-pocket expenditures regressively raises the issue of equity in medical access opportunities according to income class and negatively affects public health. Factors related to out-of-pocket expenses have been analyzed in previous studies using an ordinary regression model (Ordinary Least Squares [OLS]). However, as OLS assumes equal error variance, it does not consider spatial variation due to spatial heterogeneity and dependence. Accordingly, this study presents a spatial analysis of outpatient out-of-pocket expenses from 2015 to 2020, targeting 237 local governments nationwide, excluding islands and island regions. R (version 4.1.1) was used for statistical analysis, and QGIS (version 3.10.9), GWR4 (version 4.0.9), and Geoda (version 1.20.0.10) were used for the spatial analysis. As a result, in OLS, it was found that the aging rate and number of general hospitals, clinics, public health centers, and beds had a positive (+) significant effect on outpatient out-of-pocket expenses. The Geographically Weighted Regression (GWR) suggests regional differences exist concerning out-of-pocket payments. As a result of comparing the OLS and GWR models through the Adj. R² and Akaike's Information Criterion indices, the GWR model showed a higher fit. This study provides public health professionals and policymakers with insights that could inform effective regional strategies for appropriate out-of-pocket cost management.","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":"9559184","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 and temporal clustering analysis of pulmonary tuberculosis and its associated risk factors in southwest China. 西南地区肺结核及其相关危险因素时空聚类分析

IF 1.7 4区医学

Geospatial Health Pub Date : 2023-05-25 DOI: 10.4081/gh.2023.1169

Jianjiao Wang, Xiaoning Liu, Zhengchao Jing, Jiawai Yang

{"title":"Spatial and temporal clustering analysis of pulmonary tuberculosis and its associated risk factors in southwest China.","authors":"Jianjiao Wang, Xiaoning Liu, Zhengchao Jing, Jiawai Yang","doi":"10.4081/gh.2023.1169","DOIUrl":"https://doi.org/10.4081/gh.2023.1169","url":null,"abstract":"Pulmonary tuberculosis (PTB) remains a serious public health problem, especially in areas of developing countries. This study aimed to explore the spatial-temporal clusters and associated risk factors of PTB in south-western China. Space-time scan statistics were used to explore the spatial and temporal distribution characteristics of PTB. We collected data on PTB, population, geographic information and possible influencing factors (average temperature, average rainfall, average altitude, planting area of crops and population density) from 11 towns in Mengzi, a prefecture-level city in China, between 1 January 2015 and 31 December 2019. A total of 901 reported PTB cases were collected in the study area and a spatial lag model was conducted to analyse the association between these variables and the PTB incidence. Kulldorff's scan results identified two significant space-time clusters, with the most likely cluster (RR = 2.24, p < 0.001) mainly located in northeastern Mengzi involving five towns in the time frame June 2017 - November 2019. A secondary cluster (RR = 2.09, p < 0.05) was located in southern Mengzi, covering two towns and persisting from July 2017 to December 2019. The results of the spatial lag model showed that average rainfall was associated with PTB incidence. Precautions and protective measures should be strengthened in high-risk areas to avoid spread of the disease.","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":"9552804","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 analysis of antimicrobial resistance in the environment. A systematic review. 环境中抗菌素耐药性的空间分析。系统回顾。

IF 1.7 4区医学

Geospatial Health Pub Date : 2023-05-25 DOI: 10.4081/gh.2023.1168

Patrick Spets, Karin Ebert, Patrik Dinnétz

{"title":"Spatial analysis of antimicrobial resistance in the environment. A systematic review.","authors":"Patrick Spets, Karin Ebert, Patrik Dinnétz","doi":"10.4081/gh.2023.1168","DOIUrl":"https://doi.org/10.4081/gh.2023.1168","url":null,"abstract":"Antimicrobial resistance (AMR) is a global major health concern. Spatial analysis is considered an invaluable method in health studies. Therefore, we explored the usage of spatial analysis in Geographic Information Systems (GIS) in studies on AMR in the environment. This systematic review is based on database searches, a content analysis, ranking of the included studies according to the preference ranking organization method for enrichment evaluations (PROMETHEE) and estimation of data points per km2. Initial database searches resulted in 524 records after removal of duplicates. After the last stage of full text screening, 13 greatly heterogeneous articles with diverse study origins, methods and design remained. In the majority of studies, the data density was considerably less than one sampling site per km2 but exceeded 1,000 sites per km2 in one study. The results of the content analysis and ranking showed a variation between studies that primarily used spatial analysis and those that used spatial analysis as a sec ondary method. We identified two distinct groups of GIS methods. The first was focused on sample collection and laboratory testing, with GIS as supporting method. The second group used overlay analysis as the primary method to combine datasets in a map. In one case, both methods were combined. The low number of articles that met our inclusion criteria highlights a research gap. Based on the findings of this study we encourage application of GIS to its full potential in studies of AMR in the environment.","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":"9559181","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

Does mobility restriction significantly control infectious disease transmission? Accounting for non-stationarity in the impact of COVID-19 based on Bayesian spatially varying coefficient models. 限制行动是否能有效控制传染病的传播?基于贝叶斯空间变系数模型的新冠肺炎影响非平稳性分析

IF 1.7 4区医学

Geospatial Health Pub Date : 2023-05-25 DOI: 10.4081/gh.2023.1161

I Gede Nyoman Mindra Jaya, Anna Chadidjah, Farah Kristiani, Gumgum Darmawan, Jane Christine Princidy

{"title":"Does mobility restriction significantly control infectious disease transmission? Accounting for non-stationarity in the impact of COVID-19 based on Bayesian spatially varying coefficient models.","authors":"I Gede Nyoman Mindra Jaya, Anna Chadidjah, Farah Kristiani, Gumgum Darmawan, Jane Christine Princidy","doi":"10.4081/gh.2023.1161","DOIUrl":"https://doi.org/10.4081/gh.2023.1161","url":null,"abstract":"COVID-19 is the most severe health crisis of the 21st century. COVID-19 presents a threat to almost all countries worldwide. The restriction of human mobility is one of the strategies used to control the transmission of COVID-19. However, it has yet to be determined how effective this restriction is in controlling the rise in COVID-19 cases, particularly in small areas. Using Facebook's mobility data, our study explores the impact of restricting human mobility on COVID-19 cases in several small districts in Jakarta, Indonesia. Our main contribution is showing how the restriction of human mobility data can give important information about how COVID-19 spreads in different small areas. We proposed modifying a global regression model into a local regression model by accounting for the spatial and temporal interdependence of COVID-19 transmission across space and time. We applied Bayesian hierarchical Poisson spatiotemporal models with spatially varying regression coefficients to account for non-stationarity in human mobility. We estimated the regression parameters using an Integrated Nested Laplace Approximation. We found that the local regression model with spatially varying regression coefficients outperforms the global regression model based on DIC, WAIC, MPL, and R2 criteria for model selection. In Jakarta's 44 districts, the impact of human mobility varies significantly. The impacts of human mobility on the log relative risk of COVID-19 range from -4.445 to 2.353. The prevention strategy involving the restriction of human mobility may be beneficial in some districts but ineffective in others. Therefore, a cost-effective strategy had to be adopted.","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":"9552803","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

Exploring the distribution of risk factors for drop-out from Ponseti treatment for clubfoot across Bangladesh using geospatial cluster analysis. 使用地理空间聚类分析探讨孟加拉国各地因俱乐部足Ponseti治疗而辍学的风险因素分布。

IF 1.7 4区医学

Geospatial Health Pub Date : 2023-05-25 DOI: 10.4081/gh.2023.1174

Manon Pigeolet, Tarinee Kucchal, Matthew T Hey, Marcia C Castro, Angela Margaret Evans, Tarsicio Uribe-Leitz, Mohommad Mamun Hossen Chowhury, Sabrina Juran

{"title":"Exploring the distribution of risk factors for drop-out from Ponseti treatment for clubfoot across Bangladesh using geospatial cluster analysis.","authors":"Manon Pigeolet, Tarinee Kucchal, Matthew T Hey, Marcia C Castro, Angela Margaret Evans, Tarsicio Uribe-Leitz, Mohommad Mamun Hossen Chowhury, Sabrina Juran","doi":"10.4081/gh.2023.1174","DOIUrl":"10.4081/gh.2023.1174","url":null,"abstract":"Clubfoot is a congenital anomaly affecting 1/1,000 live births. Ponseti casting is an effective and affordable treatment. About 75% of affected children have access to Ponseti treatment in Bangladesh, but 20% are at risk of drop-out. We aimed to identify the areas in Bangladesh where patients are at high or low risk for drop-out. This study used a cross-sectional design based on publicly available data. The nationwide clubfoot program: 'Walk for Life' identified five risk factors for drop-out from the Ponseti treatment, specific to the Bangladeshi setting: household poverty, household size, population working in agriculture, educational attainment and travel time to the clinic. We explored the spatial distribution and clustering of these five risk factors. The spatial distribution of children <5 years with clubfoot and the population density differ widely across the different sub-districts of Bangladesh. Analysis of risk factor distribution and cluster analysis showed areas at high risk for dropout in the Northeast and the Southwest, with poverty, educational attainment and working in agriculture as the most prevalent driving risk factor. Across the entire country, twenty-one multivariate high-risk clusters were identified. As the risk factors for drop-out from clubfoot care are not equally distributed across Bangladesh, there is a need in regional prioritization and diversification of treatment and enrolment policies. Local stakeholders and policy makers can identify high-risk areas and allocate resources effectively.","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":"9559180","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 heterogeneity of SARS-CoV-2 diffusion at the city level using geographically weighted Poisson regression model: The case of Bologna, Italy. 基于地理加权泊松回归模型的SARS-CoV-2城市扩散时空异质性研究——以意大利博洛尼亚为例

IF 1.7 4区医学

Geospatial Health Pub Date : 2022-12-01 DOI: 10.4081/gh.2022.1145

Addisu Jember Zeleke, Rossella Miglio, Pierpaolo Palumbo, Paolo Tubertini, Lorenzo Chiari

{"title":"Spatiotemporal heterogeneity of SARS-CoV-2 diffusion at the city level using geographically weighted Poisson regression model: The case of Bologna, Italy.","authors":"Addisu Jember Zeleke, Rossella Miglio, Pierpaolo Palumbo, Paolo Tubertini, Lorenzo Chiari","doi":"10.4081/gh.2022.1145","DOIUrl":"https://doi.org/10.4081/gh.2022.1145","url":null,"abstract":"This paper aimed to analyse the spatio-temporal patterns of the diffusion of SARS-CoV-2, the virus causing coronavirus 2019 (COVID-19, in the city of Bologna, the capital and largest city of the Emilia-Romagna Region in northern Italy. The study took place from February 1st, 2020 to November 20th, 2021 and accounted for space, sociodemographic characteristics and health conditions of the resident population. A second goal was to derive a model for the level of risk of being infected by SARS-CoV-2 and to identify and measure the place-specific factors associated with the disease and its determinants. Spatial heterogeneity was tested by comparing global Poisson regression (GPR) and local geographically weighted Poisson regression (GWPR) models. The key findings were that different city areas were impacted differently during the first three epidemic waves. The area-to-area influence was estimated to exert its effect over an area with 4.7 km radius. Spatio-temporal heterogeneity patterns were found to be independent of the sociodemographic and the clinical characteristics of the resident population. Significant single-individual risk factors for detected SARS-CoV-2 infection cases were old age, hypertension, diabetes and co-morbidities. More specifically, in the global model, the average SARS-CoV-2 infection rate decreased 0.93-fold in the 21-65 years age group compared to the >65 years age group, whereas hypertension, diabetes, and any other co-morbidities (present vs absent), increased 1.28-, 1.39- and 1.15-fold, respectively. The local GWPR model had a better fit better than GPR. Due to the global geographical distribution of the pandemic, local estimates are essential for mitigating or strengthening security measures.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10549419","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

Comparison of complete and spatial sampling frames for estimation of the prevalence of hypertension and diabetes mellitus. 估算高血压和糖尿病患病率的完整和空间抽样框架的比较。

IF 1.7 4区医学

Geospatial Health Pub Date : 2022-11-30 DOI: 10.4081/gh.2022.1097

Vasna Joshua, Kamaraj Pattabi, Yuvaraj Jeyaraman, Prabhdeep Kaur, Tarun Bhatnagar, Suresh Arunachalam, Sabarinathan Ramasamy, Venkateshprabhu Janagaraj, Manoj V Murhekar

{"title":"Comparison of complete and spatial sampling frames for estimation of the prevalence of hypertension and diabetes mellitus.","authors":"Vasna Joshua, Kamaraj Pattabi, Yuvaraj Jeyaraman, Prabhdeep Kaur, Tarun Bhatnagar, Suresh Arunachalam, Sabarinathan Ramasamy, Venkateshprabhu Janagaraj, Manoj V Murhekar","doi":"10.4081/gh.2022.1097","DOIUrl":"https://doi.org/10.4081/gh.2022.1097","url":null,"abstract":"A complete sampling frame (CSF) is needed for the development of probability sampling structures; utilisation of a spatial sampling frame (SSF) was the objective of the present study. We used two sampling methods, simple random sampling (SRS) and stratified random sampling (STRS), to compare the prevalence estimates delivered by a CSF to that by a SSF when applied to self-reported hypertension and diabetes mellitus in a semi-urban setting and in a rural one. A CSF based on Geodatabase of all households and all individuals was available for our study that focused on adults aged 18-69 years in the two settings. A single digitized shapefile of solely household regions/structures as SSF was developed using Google Earth and employed for the study. The results from the two sampling frames were similar and not significantly different. All 95%CI calculations contained the prevalence rates of the two medical conditions except for one occasion based on STRS and CSF. The SRS based on CSF showed a minimum 95% CI width for diabetes mellitus, whereas SSF showed a minimum 95% CI width for hypertension. The coefficient of variation exceeded 10.0% on six occasions for CSF but only once for SSF, which was found to be as efficient as CSF.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10492802","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 distribution and identifying biochemical factors affecting haemoglobin levels among women of reproductive age for each province in Indonesia: A geospatial analysis. 空间分布和确定影响印度尼西亚各省育龄妇女血红蛋白水平的生化因素:地理空间分析。

IF 1.7 4区医学

Geospatial Health Pub Date : 2022-11-30 DOI: 10.4081/gh.2022.1118

Muhammad Nur Aidi, Fitrah Ernawati, Efriwati Efriwati, Nunung Nurjanah, Rika Rachmawati, Elisa Diana Julianti, Dian Sundari, Fifi Retiaty, Anwar Fitrianto, Khalilah Nurfadilah, Aya Yuriestia Arifin

{"title":"Spatial distribution and identifying biochemical factors affecting haemoglobin levels among women of reproductive age for each province in Indonesia: A geospatial analysis.","authors":"Muhammad Nur Aidi, Fitrah Ernawati, Efriwati Efriwati, Nunung Nurjanah, Rika Rachmawati, Elisa Diana Julianti, Dian Sundari, Fifi Retiaty, Anwar Fitrianto, Khalilah Nurfadilah, Aya Yuriestia Arifin","doi":"10.4081/gh.2022.1118","DOIUrl":"https://doi.org/10.4081/gh.2022.1118","url":null,"abstract":"Anaemia is still a public health problem in Indonesia. The iron supplement program, known as Tablet Tambah Darah (Blood Add Tablet) has not yet produced optimal results. This study aimed to identify the cause of anaemia and the factors that influence it. Biochemical indicator data are haemoglobin (Hb), C-reactive protein (CRP), ferritin and serum transferrin receptor (sTfR) from 9,463 women of reproduction age. Data from the Basic Health Research (Riskesdas) project of 2013 were used for the study. ANOVA as well as global and local regression approaches (classical regression and geo-weighted regression) were used to compare the mean Hb and CRP values between provinces and to determine the factors that influence Hb concentrations. The results showed that the distribution of anaemia in Indonesia is uneven and not always caused by iron deficiency. The lowest Hb mean coupled with the highest iron deficiency was found in Papua, where there are high rates of parasitic infections. In contrast, the highest mean Hb coupled with low iron deficiency, and also low infection rates, was found in North Sulawesi. The Hb concentrations were significantly associated by ferritin, CRP and sTfR and there were varying magnitudes between provinces. Although anaemia is mainly influenced by the iron concentration, CRP, ferritin and sTfR can also affect it through their association with inflammatory reactions. Identification of all causes of anaemia in each province needs to be done in the future, while blanket iron supplementation should be reviewed.","PeriodicalId":56260,"journal":{"name":"Geospatial Health","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10492796","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