Geospatial Health最新文献

Lung cancer is the most common cause of cancer-related death in Michigan. Most patients are diagnosed at advanced stages of the disease. There is a need to detect clusters of lung cancer incidence over time, to generate new hypotheses about causation and identify high-risk areas for screening and treatment. The Michigan Cancer Surveillance database of individual lung cancer cases, 1985 to 2018 was used for this study. Spatial and spatiotemporal clusters of lung cancer and level of disease (localized, regional and distant) were detected using discrete Poisson spatial scan statistics at the zip code level over the study time period. The approach detected cancer clusters in cities such as Battle Creek, Sterling Heights and St. Clair County that occurred prior to year 2000 but not afterwards. In the northern area of the lower peninsula and the upper peninsula clusters of late-stage lung cancer emerged after year 2000. In Otter Lake Township and southwest Detroit, late-stage lung cancer clusters persisted. Public and patient education about lung cancer screening programs must remain a health priority in order to optimize lung cancer surveillance. Interventions should also involve programs such as telemedicine to reduce advanced stage disease in remote areas. In cities such as Detroit, residents often live near industry that emits air pollutants. Future research should therefore, continue to focus on the geography of lung cancer to uncover place-based risks and in response, the need for screening and health care services.

Rabies is a zoonotic disease that affects livestock worldwide. The distribution of rabies is highly correlated with the distribution of the vampire bat Desmodus rotundus, the main vector of the disease. In this study, climatic, topographic, livestock population, vampire distribution and urban and rural zones were used to estimate the risk for presentation of cases of rabies in Mexico by co- Kriging interpolation. The highest risk for the presentation of cases is in the endemic areas of the disease, i.e. the States of Yucatán, Chiapas, Campeche, Quintana Roo, Tabasco, Veracruz, San Luis Potosí, Nayarit and Baja California Sur. A transition zone for cases was identified across northern Mexico, involving the States of Sonora, Sinaloa, Chihuahua, and Durango. The variables topography, vampire distribution, bovine population and rural zones are the most important to explain the risk of cases in livestock. This study provides robust estimates of risk and spread of rabies based on geostatistical methods. The information presented should be useful for authorities responsible of public and animal health when they plan and establish strategies preventing the spread of rabies into rabies-free regions of México.

Chronic kidney disease (CKD) is a persistent, progressive condition characterized by gradual decline of kidney functions leading to a range of health issues. This research used recent data from the Ministry of Public Health in Thailand and applied spatial regression and local indicators of spatial association (LISA) to examine the spatial associations with night-time light, Internet access and the local number of health personnel per population. Univariate Moran's I scatter plot for CKD in Thailand's provinces revealed a significant positive spatial autocorrelation with a value of 0.393. High-High (HH) CKD clusters were found to be predominantly located in the North, with Low-Low (LL) ones in the South. The LISA analysis identified one HH and one LL with regard to Internet access, 15 HH and five LL clusters related to night-time light and eight HH and five LL clusters associated with the number of health personnel in the area. Spatial regression unveiled significant and meaningful connections between various factors and CKD in Thailand. Night-time light displayed a positive association with CKD in both the spatial error model (SEM) and the spatial lag model (SLM), with coefficients of 3.356 and 2.999, respectively. Conversely, Internet access exhibited corresponding negative CKD associations with a SEM coefficient of - 0.035 and a SLM one of -0.039. Similarly, the health staff/population ratio also demonstrated negative associations with SEM and SLM, with coefficients of -0.033 and -0.068, respectively. SEM emerged as the most suitable spatial regression model with 54.8% according to R2. Also, the Akaike information criterion (AIC) test indicated a better performance for this model, resulting in 697.148 and 698.198 for SEM and SLM, respectively. These findings emphasize the complex interconnection between factors contributing to the prevalence of CKD in Thailand and suggest that socioeconomic and health service factors are significant contributing factors. Addressing this issue will necessitate concentrated efforts to enhance access to health services, especially in urban areas experiencing rapid economic growth.

This study described spatiotemporal changes in health insurance coverage, healthcare access, and reasons for non-insurance among racial/ethnic minority populations in the United States during the COVID-19 pandemic using four national survey datasets. Getis-Ord Gi* statistic and scan statistics were used to analyze geospatial clusters of health insurance coverage by race/ethnicity. Logistic regression was used to estimate odds of reporting inability to access healthcare across two pandemic time periods by race/ethnicity. Racial/ethnic differences in insurance were observed from 2010 through 2019, with the lowest rates being among Hispanic/Latino, African American, American Indian/Alaska Native, and Native Hawaiian/Pacific Islander populations. Pre-pandemic insurance coverage rates were geographically clustered. The percentage of adults citing change in employment status as the reason for non-insurance increased by about 7% after the start of the pandemic, with a small decrease observed among African American adults. Almost half of adults reported reduced healthcare access in June 2020, with 38.7% attributing reduced access to the pandemic; however, by May 2021, the percent of respondents reporting reduced access for any reason and due to the pandemic fell to 26.9% and 12.7%, respectively. In general, racial/ethnic disparities in health insurance coverage and healthcare access worsened during the pandemic. Although coverage and access improved over time, pre-COVID disparities persisted with African American and Hispanic/Latino populations being the most affected by insurance loss and reduced healthcare access. Cost, unemployment, and eligibility drove non-insurance before and during the pandemic.

The issue of reducing spatial disparities is one of the most pressing concerns for policymakers and planners, which consider a crucial focus in planning and public service, especially accessibility to healthcare. Accessibility and proximity are the principal keys to providing good public service. Therefore, a healthcare system that meets the requirements of availability and affordability will be useless if spatial accessibility is not provided equally to all demands (population). Many technics and methods exist to quantify accessibility, including the two-step floating catchment area (2SFCA) method, its widely used to measure healthcare accessibility based on the travel distance threshold. This research paper aims to use the 2SFCA method to measure the spatial healthcare accessibility in Batna City because the 2SFCA method offers to measure accessibility on both spatial and functional levels. The spatial level will consider the threshold distances between the health demand (population) and the health provider location (healthcare facilities); moreover, functional accessibility is measured based on facility to population ratio that will give a better overview of Batna's healthcare provider. As a result, the optimal threshold distance that offers balanced results between the spatial accessibility score and other WHO ratios will be a distance between 1000- and 1500-meters travel distance. In addition, the central census districts have a higher access score than the rest of the city's districts; most census districts that do not have accessibility (12% of the population) to healthcare facilities are concentrated in the southwest of Batna city.

A study was conducted to investigate the district-level patterns of incidence of the human immunodeficiency virus (HIV) in Zimbabwe in the period 2005-2015 and explore variations in the relationship between covariates and HIV incidence across different districts. Demographic health survey data were analysed using hotspot analysis, spatial autocorrelation, and multi-scale geographically weighted regression (MGWR) techniques. The analysis revealed hotspots of the HIV epidemic in the southern and western regions of Zimbabwe in contrast to the eastern and northern regions. Specific districts in Matabeleland South and Matabeleland North provinces showed clusters of HIV incidence in 2005-2006, 2010-2011 and 2015. Variables studied were multiple sex partners and sexually transmitted infections (STI) condom use and being married. Recommendations include implementing targeted HIV prevention programmes in identified hotspots, prioritising interventions addressing the factors mentioned above as well as enhancing access to HIV testing and treatment services in high-risk areas, strengthening surveillance systems, and conducting further research to tailor interventions based on contextual factors. The study also emphasizes the need for regular monitoring and evaluation at the district level to inform effective responses to the HIV epidemic over time. By addressing the unique challenges and risk factors in different districts, significant progress can be made in reducing HIV transmission and improving health outcomes in Zimbabwe. These findings should be valuable for policymakers in resource allocation and designing evidence-based interventions.

The occurrence of floods has the potential to escalate the transmission of infectious diseases. To enhance our comprehension of the health impacts of flooding and facilitate effective planning for mitigation strategies, it is necessary to explore the flood risk management. The variability present in hydrological records is an important and neglecting non-stationary patterns in flood data can lead to significant biases in estimating flood quantiles. Consequently, adopting a non-stationary flood frequency analysis appears to be a suitable approach to challenge the assumption of independent and identically distributed observations in the sample. This research employed the generalized extreme value (GEV) distribution to examine annual maximum flood series. To estimate non-stationary models in the flood data, several statistical tests, including the TL-moment method was utilized on the data from ten stream-flow stations in Johor, Malaysia, which revealed that two stations, namely Kahang and Lenggor, exhibited non-stationary behaviour in their annual maximum streamflow. Two non-stationary models efficiently described the data series from these two specific stations, the control of which could reduce outbreak of infectious diseases when used for controlling the development measures of the hydraulic structures. Thus, the application of these models may help prevent biased prediction of flood occurrences leading to lower number of cases infected by disease.

Addressing the presence of periodontal disease requires a high level of expertise to detect the disease as well as effective communication to understand patients' problems. Based on basic health data from 2018, the prevalence of this problem in Indonesia is approximately 74%. This study examined the distribution of periodontal conditions in March 2021 in Makassar City, the capital of South Sulawesi Province. To determine the distribution of periodontal disease, a questionnaire was used to find out the severity of this issue. A descriptive observational method, used with a cross-sectional design and a web-based geospatial information system (GIS) application linked to ArcGIS, was conducted. The results showed thatout of the 15 districts in Makassar City, the island district of Sangkarranghad had the highest presence of periodontal disease. Three other districts were classified as also belonging to this low category, while 11 other ones exhibited a medium disease incidence score.

The history of mapping infectious diseases dates back to the 19th century when Dr John Snow utilised spatial analysis to pinpoint the source of the 1854 cholera outbreak in London, a ground-breaking work that laid the foundation for modern epidemiology and disease mapping (Newsom, 2006). As technology advanced, so did mapping techniques. In the late 20th century, geographic information systems (GIS) revolutionized disease mapping by enabling researchers to overlay diverse datasets to visualise and analyse complex spatial patterns (Bergquist & Manda 2019; Hashtarkhani et al., 2021). The COVID-19 pandemic showed that disease mapping is particularly valuable for optimising prevention and control strategies of infectious diseases by prioritising geographical targeting interventions and containment strategies (Mohammadi et al., 2021). Today, with the aid of highresolution satellite imagery, geo-referenced electronic data collection systems, real-time data feeds, and sophisticated modelling algorithms, disease mapping has become a feasible and accessible tool for public health officials in tracking, managing, and mitigating the spread of infectious diseases at global, regional and local scales (Hay et al., 2013). [...].

New approaches to the study of cardiometabolic disease (CMD) distribution include analysis of built environment (BE), with spatial tools as suitable instruments. We aimed to characterize the spatial dissemination of CMD and the associated risk factors considering the BE for people attending the Non-Invasive Cardiology Service of Hospital Nacional de Clinicas in Córdoba City, Argentina during the period 2015-2020. We carried out an observational, descriptive, cross-sectional study performing non-probabilistic convenience sampling. The final sample included 345 people of both sexes older than 35 years. The CMD data were collected from medical records and validated techniques and BE information was extracted from Landsat-8 satellite products. A geographic information system (GIS) was constructed to assess the distribution of CMD and its risk factors in the area. Out of the people sampled, 41% showed the full metabolic syndrome and 22.6% only type-2 diabetes mellitus (DM2), a cluster of which was evidenced in north-western Córdoba. The risk of DM2 showed an association with high values of the normalized difference vegetation index (NDVI) (OR= 0.81; 95% CI: - 0.30 to 1.66; p=0.05) and low normalized difference built index (NDBI) values that reduced the probability of occurrence of DM2 (OR= -1.39; 95% CI: -2.62 to -0.17; p=0.03). Considering that the results were found to be linked to the environmental indexes, the study of BE should include investigation of physical space as a fundamental part of the context in which people develop medically within society. The novel collection of satellite-generated information on BE proved efficient.