Geospatial Health最新文献

As extreme weather events increase in frequency and intensity, the health system faces significant challenges, not only from shifting patterns of climate-sensitive diseases but also from disruptions to healthcare infrastructure, supply chains and the physical systems essential for delivering care. This necessitates the strategic use of geospatial tools to guide the delivery of healthcare services and make evidence-informed priorities, especially in contexts with scarce human and financial resources. In this article, we highlight several published papers that have been used throughout the phases of the disaster management cycle in relation to health service delivery. We complement the findings from these publications with a rapid scoping review to present the body of knowledge for using spatial methods for health service delivery in the context of disasters. The main aim of this article is to demonstrate the benefits and discuss the challenges associated with the use of geospatial methods throughout the disaster management cycle. Our scoping review identified 48 articles employing geospatial techniques in the disaster management cycle. Most of them focused on geospatial tools employed for preparedness, anticipatory action and mitigation, particularly for targeted health service delivery. We note that while geospatial data analytics are effectively deployed throughout the different phases of disaster management, important challenges remain, such as ensuring timely availability of geospatial data during disasters, developing standardized and structured data formats, securing pre-disaster data for disaster preparedness, addressing gaps in health incidence data, reducing underreporting of cases and overcoming limitations in spatial and temporal coverage and granularity. Overall, existing and novel geospatial methods can bridge specific evidence gaps in all phases of the disaster management cycle. Improvement and 'operationalization' of these methods can provide opportunities for more evidence-informed decision making in responding to health crises during climate change.

In order to effectively cope with the situation caused by the COVID-19 pandemic, cases should be concentrated in designated medical institutions with full capability to deal with patients infected by this virus. We studied the location of such hospitals dividing the patients into two categories: ordinary and severe. Genetic algorithms were constructed to achieve a three-phase dynamic approach for the location of hospitals designated to receive and treat COVID-19 cases based on the goal of minimizing the cost of construction and operation isolation wards as well as the transportation costs involved. A dynamic location model was established with the decision variables of the corresponding 'chromosome' of the genetic algorithms designed so that this goal could be reached. In the static location model, 15 hospitals were required throughout the treatment cycle, whereas the dynamic location model found a requirement of only 11 hospitals. It further showed that hospital construction costs can be reduced by approximately 13.7% and operational costs by approximately 26.7%. A comparison of the genetic algorithm and the Gurobi optimizer gave the genetic algorithm several advantages, such as great convergence and high operational efficiency.

An expert panel discussion on achievements, current areas of rapid scientific progress, prospects, and critical gaps in geospatial health was organized as part of the 16thsymposium of the global network of public health and earth scientists dedicated to the development of geospatial health (GnosisGIS), held at the Faculty of Geo-Information Science and Earth Observation (ITC) of the University of Twente in The Netherlands in November 2023. The symposium consisted of a three-day scientific event that brought together an interdisciplinary group of researchers and health professionals from across the globe. The aim of the panel session was threefold: firstly, to reflect on the main achievements of the scientific discipline of geospatial health in the past decade; secondly, to identify key innovation areas where rapid scientific progress is currently made and thirdly, to identify critical gaps and associated research and education priorities to move the discipline forward. [...].

Cartography, or geographical visualization of disease is an essential aspect of the field of GeoHealth, yet there is limited guidance on the visualization of spatiotemporal disease maps. In order to adequately contribute to understanding disease outbreaks, disease maps should be crafted carefully and according to relevant cartographic guidelines. This article aims to increase the understanding of space-time visualization techniques that are relevant to the field of GeoHealth, by providing a step-by-step framework for the creation of space-time disease visualizations. This study introduces a systematic approach to spatiotemporal disease mapping by integrating operations from the Generalized Space Time Cube (GSTC) Framework with established cartographic symbology guidelines. This resulted in an overview table that contains both the relevant GSTC operations and cartographic guidelines, as well as a step-by-step procedure that guides users through the process of creating informative spatiotemporal disease maps. The practical application of this step-by-step procedure is demonstrated with an example using Dutch COVID-19 data. By providing a clear, practical step by step procedure, this study enhances the capacity of public health professionals, policymakers, and researchers to monitor, understand, and respond to the spatial and temporal dynamics of diseases.

According to World Trade Organization (WTO) statistics, the incidence of seasonal influenza in China has been on the rise since 2018. The aim of this study was to identify and investigate the influence of factors related to the incidence of four common types of influenza viruses. Data of patients with common cold and associated virus infections are described, and a logistic regression model based on gender, age and season was established. The relationship between virus type and the above three factors was analyzed in depth and significant (p<0.05) associations noted. We noted a fluctuation trend, with the infection rate of influenza virus showing an upward trend from 2018 to 2019 and from 2021 to 2022 and a downward trend from 2019 to 2021. The total number of cases in adolescents aged 18-30 years was higher than that in the elderly. The impact of different types of influenza virus on the population ranked from large to small, with special roles played by Influenza B/Victoria, H3N2, Influenza A/H1N1 pdm and Influenza B/Yamagata.

Stunting continues to be a significant health issue, particularly in developing nations, with Indonesia ranking third in prevalence in Southeast Asia. This research examined the risk of stunting and influencing factors in Indonesia by implementing various Bayesian spatial conditional autoregressive (CAR) models that include covariates. A total of 750 models were run, including five different Bayesian spatial CAR models (Besag-York-Mollie (BYM), CAR Leroux and three forms of localised CAR), with 30 covariate combinations and five different hyperprior combinations for each model. The Poisson distribution was employed to model the counts of stunting cases. After a comprehensive evaluation of all model selection criteria utilized, the Bayesian localised CAR model with three covariates were preferred, either allowing up to 2 clusters with a variance hyperprior of inverse-gamma (1, 0.1) or allowing 3 clusters with a variance hyperprior of inverse-gamma (1, 0.01). Poverty and recent low birth weight (LBW) births are significantly associated with an increased risk of stunting, whereas child diet diversity is inversely related to the risk of stunting. Model results indicated that Sulawesi Barat Province has the highest risk of stunting, with DKI Jakarta Province the lowest. These areas with high stunting require interventions to reduce poverty, LBW births and increase child diet diversity.

During the COVID-19 pandemic, a system was established in China that required testing of all residents for COVID-19. It consisted of sampling stations, laboratories capable of carrying out DNA investigations and vehicles carrying out immediate transfer of all samples from the former to the latter. Using Beilin District, Xi'an City, Shaanxi Province, China as example, we designed a genetic algorithm based on a two-stage location coverage model for the location of the sampling stations with regard to existing residencies as well as the transfer between the sampling stations and the laboratories. The aim was to estimate the minimum transportation costs between these units. In the first stage, the model considered demands for testing in residential areas, with the objective of minimizing the costs related to travel between residencies and sampling stations. In the second stage, this approach was extended to cover the location of the laboratories doing the DNAinvestigation, with the aim of minimizing the transportation costs between them and the sampling stations as well as the estimating the number of laboratories needed. Solutions were based on sampling stations and laboratories existing in 2022, with the results visualized by geographic information systems (GIS). The results show that the genetic algorithm designed in this paper had a better solution speed than the Gurobi algorithm. The convergence was better and the larger the network size, the more efficient the genetic algorithm solution time.

Cardiovascular Disease (CVD) is currently the major challenge to people's health and the world's top cause of death. In Tanzania, deaths due to CVD account for about 13% of the total deaths caused by the non-communicable diseases. This study examined the spatio-temporal clustering of CVDs from 2010 to 2019 in Tanzania for retrospective spatio-temporal analysis using the Bernoulli probability model on data sampled from four selected hospitals. Spatial scan statistics was performed to identify CVD clusters and the effect of covariates on the CVD incidences was examined using multiple logistic regression. It was found that there was a comparatively high risk of CVD during 2011-2015 followed by a decline during 2015-2019. The spatio-temporal analysis detected two high-risk disease clusters in the coastal and lake zones from 2012 to 2016 (p<0.001), with similar results produced by purely spatial analysis. The multiple logistic model showed that sex, age, blood pressure, body mass index (BMI), alcohol intake and smoking were significant predictors of CVD incidence.

The purpose of this study was to determine whether there were any TB clusters in Aceh Province, Indonesia and their temporal distribution during the period of 2019-2021. A spatial geo-reference was conducted to 290 sub-districts coordinates by geocoding each sub-district's offices. By using SaTScan TM v9.4.4, a retrospective space-time scan statistics analysis based on population data and annual TB incidence was carried out. To determine the regions at high risk of TB, data from 1 January 2019 to 31 December 2021 were evaluated using the Poisson model. The likelihood ratio (LLR) value was utilized to locate the TB clusters based on a total of 999 permutations were performed. A Moran's I analysis (using GeoDa) was chosen for a study of both local and global spatial autocorrelation. The threshold for significance was fixed at 0.05. At the sub-district level, the spatial distribution of TB in Aceh Province from 2019-2021 showed 19 clusters (three most likely and 16 secondary ones), and there was a spatial autocorrelation of TB. The findings can be used to provide thorough knowledge on the spatial pattern of TB occurrence, which is important for designing effective TB interventions.

Spatial cluster analyses of health events are useful for enabling targeted interventions. Spatial scan statistic is the stateof- the-art method for this kind of analysis and the Poisson Generalized Linear Model (GLM) approach to the spatial scan statistic can be used for count data for spatial cluster detection with covariate adjustment. However, its use for modelling is limited due to data over-dispersion. A Generalized Linear Mixed Model (GLMM) has recently been proposed for modelling this kind of over-dispersion by incorporating random effects to model area-specific intrinsic variation not explained by other covariates in the model. However, these random effects may exhibit a geographical correlation, which may lead to a potential spatial cluster being undetected. To handle the over-dispersion in the count data, this study aimed to evaluate the performance of a negative binomial- GLM in spatial scan statistic on real-world data of low birth weights in Khyber-Pakhtunkhwa Province, Pakistan, 2019. The results were compared with the Poisson-GLM and GLMM, showing that the negative binomial-GLM is an ideal choice for spatial scan statistic in the presence of over-dispersed data. With a covariate (maternal anaemia) adjustment, the negative binomial-GLMbased spatial scan statistic detected one significant cluster covering Dir lower district. Without the covariate adjustment, it detected two clusters, each covering one district. The district of Peshawar was seen as the most likely cluster and Battagram as the secondary cluster. However, none of the clusters were detected by GLMM spatial scan statistic, which might be due to the spatial correlation of the random effects in GLMM.