Geospatial Health最新文献

Disease surveillance remains important for early detection of new COVID-19 variants. For this purpose, the World Health Organization (WHO) recommends integrating of COVID-19 surveillance with other respiratory diseases. This requires knowledge of areas with elevated risk, which in developing countries is lacking from the routine analyses. Focusing on Ghana, this study employed scan-statistic cluster analysis to uncover the spatial patterns of incidence and Case Fatality Rates (CFR) of COVID-19 based on reports covering the four pandemic waves in Ghana between 12 March 2020 and 28 February 2022. Applying flexible spatial scan statistic with restricted likelihood ratio, we examined the incidence and CFR clusters before and after adjustment for covariates. We used distance to the epicentre, proportion of the population aged ≥ 65, male proportion of the population and urban proportion of the population as the covariates. We identified 56 significant spatial clusters for incidence and 26 for CFR for all four waves of the pandemic. The Most Likely Clusters (MLCs) of incidence occurred in the districts in south-eastern Ghana, while the CFR ones occurred in districts in the central and the northeastern parts of the country. These districts could serve as sites for sentinel or genomic surveillance. Spatial relationships were identified between COVID-19 incidence covariates and the CFR. We observed closeness to the epicentre and high proportions of urban populations increased COVID-19 incidence, whiles high proportions of those aged ≥ 65 years increased the CFR. Accounting for the covariates resulted in changes in the distribution of the clusters. Both incidence and CFR due to COVID-19 were spatially clustered, and these clusters were affected by high proportions of the urban population, high proportions of the male population, high proportions of the population aged ≥ 65 years and closeness to the epicentre. Surveillance should target districts with elevated risk. Long-term control measures for COVID-19 and other contagious diseases should consider improving quality healthcare access and measures to reduce growth rates of urban populations.

This study used data from the second Nigeria Malaria Indicator Survey (NMIS) conducted in 2015 to investigate the spatial distribution of malaria prevalence in the country and identify its associated factors. Nigeria is divided into 36 states with 109 senatorial districts, most of which are affected by malaria, a major cause of morbidity and mortality in children under five years of age. We carried out an ecological study with analysis at the senatorial district level. A malaria prevalence map was produced combining geographic information systems data from the Nigeria Malaria Indicator Survey (NMIS) of 2015 with shape files from an open data-sharing platform. Spatial autoregressive models were fitted using a set of key covariates. Malaria prevalence in children under-five was highest in Kebbi South senatorial district (70.6%). It was found that poorest wealth index (β = 0.10 (95% CI: 0.01, 0.20), p = 0.04), mothers having only secondary level of education (β = 0.78 (95% CI: 0.05, 1.51), p = 0.04) and households without mosquito bed nets (β = 0.21 (95% CI: 0.02, 0.39), p = 0.03) were all significantly associated with higher malaria prevalence. Moran's I (54.81, p<0.001) showed spatial dependence of malaria prevalence across contiguous districts and spatial autoregressive modelling demonstrated significant spill-over effect of malaria prevalence. Maps produced in this study provide a useful graphical representation of the spatial distribution of malaria prevalence based on NMIS-2015 data. Clustering of malaria prevalence in certain areas further highlights the need for sustained malaria elimination interventions across affected regions in order to break the chain of transmission.

Access to healthcare is influenced by various socioeconomic factors such as income, population group, educational attainment and health insurance. This study used Geographically Weighted Regression (GWR) to investigate spatial variations in the association between socioeconomic factors and access to public healthcare facilities in the City of Tshwane, South Africa based on data from the Gauteng City-Region Observatory Quality of Life Survey (2020/2021). Socioeconomic predictors included population group, income, health insurance status and health satisfaction. The GWR model revealed that all socioeconomic factors combined explained the variation in access to healthcare facilities (R²=0.77). Deviance residuals, ranging from -2.67 to 1.83, demonstrated a good model fit, indicating the robustness of the GWR model in predicting access to healthcare facilities. Black African, low-income and uninsured populations had each a relatively strong association with access to healthcare facilities (R²=0.65). Additionally, spatial patterns revealed that socioeconomic relationships with access to health care facilities are not homogeneous, with significance of the relationships varying with space. This study highlights the need for a spatially nuanced approach to improving healthcare facilities access and emphasizes the need for targeted policy interventions that address local socio-environmental conditions.

Objects for Targeted Surveillance (OTS) are infrastructure entities that can be considered as focal points and conduits for transmitting infectious animal diseases, necessitating ongoing epidemiological surveillance. These entities encompass slaughterhouses, meat processing plants, animal markets, burial sites, veterinary laboratories, etc. Currently, in Kazakhstan, a funded research project is underway to establish a Geographic Information System (GIS) database of OTSs and investigate their role in the emergence and dissemination of infectious livestock diseases. This initial investigation examined the correlation between brucellosis outbreaks in cattle and small ruminant farms in the southeastern region of Kazakhstan and the presence of OTSs categorized as "slaughterhouses," "cattle markets," and "meat processing plants. The study area (namely Qyzylorda, Turkestan, Zhambyl, Almaty, Zhetysu, Abay and East Kazakhstan oblasts), characterized by the highest livestock density in the country, covers 335 slaughterhouses (with varying levels of biosecurity), 45 livestock markets and 15 meat processing plants. Between 2020 and 2023, 338 cases of brucellosis were reported from livestock farms in this region. The findings of the regression model reveal a statistically significant (p<0.05) positive association between the incidence of brucellosis cases and the number of OTSs in the region. Conversely, meat processing plants and livestock markets did not exhibit a significant influence on the prevalence of brucellosis cases. These results corroborate the hypothesis of an elevated risk of brucellosis transmission in regions with slaughterhouses, likely attributable to increased animal movements within and across regions, interactions with vehicles and contact with slaughterhouse staff. These outcomes mark a pivotal advancement in the national agricultural development agenda. The research will be extended to encompass the entire country, compiling a comprehensive OTS database.

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.