Geospatial Health最新文献

To answer the question if the Geohealth domain requires a body of knowledge (BoK), we need a general understanding of concepts associated with this field. Two years ago, the United Nation (UN) committee of experts on global geospatial information management identified "semantic- and ontology-linked data" as something that "will become essential to support the next generation of autonomous systems" (UN-GGIM, 2020). The term ontology is closely related to BoK. Unlike data models, ontologies are independent of application; they are generic, can be used in different ways and have clear advantages but they are challenging to create and even more difficult to maintain. The brief description below summarizes what ontologies are, why they are needed to support linked data, what the role of the semantic web is, what is already going on within the Geohealth domain on ontologies and how a BoK can assist.

Tuberculosis (TB) infection continues to present as a leading cause of morbidity and mortality in North Aceh District, Aceh Province, Indonesia. Local TB spatial risk factors have been investigated but space-time clusters of TB in the district have not yet been the subject of study. To that end, research was undertaken to detect clusters of TB incidence during 2019-2021 in this district. First, the office of each of the 27 sub-districts wasgeocoded by collecting data of their geographical coordinates. Then, a retrospective space-time scan statistics analysis based on population data and annual TB incidence was performed using SaTScan TM v9.4.4. The Poisson model was used to identify the areas at high risk of TB and the clusters found were ranked by their likelihood ratio (LLR), with the significance level set at 0.05.There were 2,266 TB cases reported in North Aceh District and the annualized average incidence was 122.91 per 100,000 population. The SaTScan analysis identified that there were three most like clusters and ten secondary clusters, while Morans'Ishowed that there was spatial autocorrelation of TB in the district. The sub-district of GeureudongPase was consistently the location of most likely clusters. The indicators showed that there were significant differences between TB data before the COVID-19 pandemic and those found during the study period. These findings may assist health authorities to improve the TB preventive strategies and develop public health interventions, with special reference to the areas where the clusters were found.

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According to the Substance Abuse and Mental Health Services Administration, about 21 million adults in the US experience a major depressive episode. Depression is considered a primary risk factor for suicide. In the US, about 19.5% of adults are reported to be experiencing a depressive disorder, leading to over 45,000 deaths (14.0 deaths per 100,000) due to suicides. To our knowledge, no previous spatial analysis study of depression relative to the social vulnerability index has been performed across the nation. In this study, county-level depression prevalence and indicators were compiled. We analysed the geospatial distribution of depression prevalence based on ordinary least squares, geographically weighted regression, and multiscale geographically weighted regression models. Our findings indicated that the multiscale model could explain over 86% of the local variance of depression prevalence across the US based on per capita income, age 65 and older, belonging to a minority group (predominantly negative impacts), and disability (mainly positive effect). This study can provide valuable insights for public health professionals and policymakers to address depression disparities.

Assessment of personal exposure in the external environment commonly relies on global positioning system (GPS) measurements. However, it has been challenging to determine exposures accurately due to missing data in GPS trajectories. In environmental health research using GPS, missing data are often discarded or are typically imputed based on the last known location or linear interpolation. Imputation is said to mitigate bias in exposure measures, but methods used are hardly evaluated against ground truth. Widely used imputation methods assume that a person is either stationary or constantly moving during the missing interval. Relaxing this assumption, we propose a method for imputing locations as a function of a person's likely movement state (stop, move) during the missing interval. We then evaluate the proposed method in terms of the accuracy of imputed location, movement state, and daily mobility measures such as the number of trips and time spent on places visited. Experiments based on real data collected by participants (n=59) show that the proposed approach outperforms existing methods. Imputation to the last known location can lead to large deviation from the actual location when gap distance is large. Linear interpolation is shown to result in large errors in mobility measures. Researchers should be aware that the different treatment of missing data can affect the spatiotemporal accuracy of GPS-based exposure assessments.

Equitable allocation of resources targeting the human immunodeficiency virus (HIV) at the local level requires focusing interventions in areas of the greatest need. Understanding the geographical variation in the HIV epidemic and uptake of selected HIV prevention and treatment programmes are necessary to identify such areas. Individual-level HIV data were obtained from a 2012 national HIV survey in South Africa. Spatial regression models on each outcome measure (HIV infection, sub-optimal condom use or non-anti-retroviral treatment (ART) adjusted for spatial random effects at the ward level were fitted using WINBUGS software. In addition, ward-level data was utilized to estimate condom use coverage and ART initiation rates which were obtained from routinely collected data in 2012. Ordinary Kriging was used to produce smoothed maps of HIV infection, condom use coverage and ART initiation rates. HIV infection was associated with individuals undertaking tertiary education [posterior odds ratio (POR): 19.53; 95% credible intervals (CrI): 3.22- 84.93]. Sub-optimal condom use increased with age (POR: 1.09; 95%CrI: 1.06-1.11) and was associated with being married (POR: 4.14; 95%CrI: 1.23-4.28). Non-ART use was associated with being married (POR: 6.79; 95%CrI: 1.43-22.43). There were clusters with high HIV infection, sub-optimal condom use, and non- ART use in Ekurhuleni, an urban and semi-urban district in Gauteng province, South Africa. Findings show the need for expanding condom programmes and/or strengthening other HIV prevention programmes such as pre-exposure prophylaxis and encouraging sustained engagement in HIV care and treatment in the identified areas with the greatest need in Ekurhuleni Metropolitan Municipality.

Due to a mistake, the authors' affiliations were incorrectly reported in this article, published in Geospatial Health in 2022 (DOI: 10.4081/gh.2022.1077 - PMID: 35579241). The correct affiliations appear above. Geospatial Health DOI: 10.4081/gh.2022.1137.

The risk of coronavirus disease 2019 (COVID-19) may vary by age, biological, socioeconomic, behavioural and logistical reasons may be attributed to these variations. In Toronto, Canada, the aging population has been severely impacted, accounting for 92% of all COVID-19 deaths. Four age groups: 60-69 years, 70-79 years, 80-89 years and ≥90 years in Toronto neighbourhoods were investigated for clustering tendencies using space-time statistics. Cohen's Kappa coefficient was computed to assess variations in risk by neighbourhood between different age groups. The findings suggest that knowledge of health risks and health behaviour varied by age across neighbourhoods in Toronto. Therefore, understanding the socioecological context of the communities and targeting age-appropriate intervention strategies is important for planning an effective mechanism for controlling the disease.

Although two years have passed since the coronavirus disease 2019 (COVID-19) outbreak, various variants are still rampant across the globe. The Omicron variant, in particular, is rapidly gained dominance through its ability to spread. In this study, we elucidated the spatial distribution pattern of Omicron from a global perspective. We used the cumulative number of notified COVID-19 cases per country spanning four weeks up to February 10, 2022, and the proportion of the Omicron variant genomic sequences from the Global Initiative on Sharing Avian Influenza Data (GISAID). The global spatial distribution of Omicron was investigated by analyzing Global & Local Moran's I and Getis- Ord General G. The spatial weight matrix was defined by combining K-Nearest neighbour and flight connectivity between countries. The results showed that the epidemic is relatively severe in Europe, countries with a high number of Omicron cases and incidence tended to be clustered spatially. In contrast, there are relatively fewer Omicron cases in Asia and Africa, with few hotspots identified. Furthermore, some noted spatial outliers, such as a lowvalue area surrounded by high-value areas, deserve special attention. This study has improved our awareness of the global distribution of Omicron. The findings can provide helpful information for deploying targeted epidemic preparedness for the subsequent COVID-19 variant and future epidemics.