Temporal and spatial analysis of over 7,000 measles cases outbreak from 2018 to 2019 in the Brazilian Amazon.

OBJECTIVE: This study aimed to present a temporal and spatial analysis of the 2018 measles outbreak in Brazil, particularly in the metropolitan city of Manaus in the Amazon region, and further introduce a new tool for spatial analysis. METHODS: We analyzed the geographical data of the residences of over 7,000 individuals with measles in Manaus during 2018 and 2019. Spatial and temporal analyses were conducted to characterize various aspects of the outbreak, including the onset and prevalence of symptoms, demographics, and vaccination status. A visualization tool was also constructed to display the geographical and temporal distribution of the reported measles cases. RESULTS: Approximately 95% of the included participants had not received vaccination within the past decade. Heterogeneity was observed across all facets of the outbreak, including variations in the incubation period and symptom presentation. Age distribution exhibited two peaks, occurring at one year and 18 years of age, and the potential implications of this distribution on predictive analysis were discussed. Additionally, spatial analysis revealed that areas with the highest case densities tended to have the lowest standard of living. CONCLUSION: Understanding the spatial and temporal spread of measles outbreaks provides insights for decision-making regarding measures to mitigate future epidemics.

Spatial analysis of modern contraceptive use among women who need it in Ethiopia: Using geo-referenced data from performance monitoring for action.

INTRODUCTION: The challenge of achieving maternal and neonatal health-related goals in developing countries is significantly impacted by high fertility rates, which are partly attributed to limited access to family planning and access to the healthcare systems. The most widely used indicator to monitor family planning coverage is the proportion of women in reproductive age using contraception (CPR). However, this metric does not accurately reflect the true family planning coverage, as it fails to account for the diverse needs of women in reproductive age. Not all women in this category require contraception, including those who are pregnant, wish to become pregnant, sexually inactive, or infertile. To effectively address the contraceptive needs of those who require it, this study aims to estimate family planning coverage among this specific group. Further, we aimed to explore the geographical variation and factors influencing contraceptive uptake of contraceptive use among those who need. METHOD: We used data from the Performance Monitoring for Action Ethiopia (PMA Ethiopia) survey of women of reproductive age and the service delivery point (SDP) survey conducted in 2019. A total of 4,390 women who need contraception were considered as the analytical sample. To account for the study design, sampling weights were considered to compute the coverage of modern contraceptive use disaggregated by socio-demographic factors. Bayesian geostatistical modeling was employed to identify potential factors associated with the uptake of modern contraception and produce spatial prediction to unsampled locations. RESULT: The overall weighted prevalence of modern contraception use among women who need it was 44.2% (with 95% CI: 42.4%-45.9%). Across regions of Ethiopia, contraceptive use coverage varies from nearly 0% in Somali region to 52.3% in Addis Ababa. The average nearest distance from a woman's home to the nearest SDP was high in the Afar and Somali regions. The spatial mapping shows that contraceptive coverage was lower in the eastern part of the country. At zonal administrative level, relatively high (above 55%) proportion of modern contraception use coverage were observed in Adama Liyu Zone, Ilu Ababor, Misrak Shewa, and Kefa zone and the coverage were null in majority of Afar and Somali region zones. Among modern contraceptive users, use of the injectable dominated the method-mix. The modeling result reveals that, living closer to a SDP, having discussions about family planning with the partner, following a Christian religion, no pregnancy intention, being ever pregnant and being young increases the likelihood of using modern contraceptive methods. CONCLUSION: Areas with low contraceptive coverage and lower access to contraception because of distance should be prioritized by the government and other supporting agencies. Women who discussed family planning with their partner were more likely to use modern contraceptives unlike those without such discussion. Thus, to improve the coverage of contraceptive use, it is very important to encourage/advocate women to have discussions with their partner and establish movable health systems for the nomadic community.

Spatial variations and determinants of timely completion of vaccination in Ethiopia using further analysis of EDHS 2019 data: Spatial and multilevel analysis.

BACKGROUND: Timely vaccination is the practice of administering the vaccine within the first birthday of the child. Not vaccinating the child at the appropriate age is the cause of improper protection of diseases and can be a possible factor in death. The problem of not completing the vaccine in the scheduled period is a globally distributed problem, but especially in sub-Saharan African countries, it is a bottleneck to child health. Even if timely vaccination is crucial for reducing the impact of VPDs, there are no current national-level studies to generate conclusive and tangible evidence in Ethiopia. OBJECTIVE: To assess spatial variations and determinants of timely completion of vaccination in Ethiopia using further analysis of EDHS 2019 data. METHOD: The secondary data analysis of a community-based cross-sectional study design was employed among 3094 participants. Stata-14 software was used for data cleaning, recording, and analysis. Arc GIS version 10.3 and Kuldorff SAT scan version 9.6 software are used for spatial and SAT scan statistics. A multilevel mixed-effect binary logistic regression analysis was used to identify the predictors of timely vaccination. The clustering effect was also evaluated by Moran's I statistics and intra class correlation. RESULTS: The timely completion of vaccination among Ethiopian women who had a child aged 12-35 months was 19.5% (95%CI: 18.2-20.8), and the spatial distribution of timely completion of vaccinations in Ethiopia was non-randomly distributed. A statistically significant high proportion of timely completion areas were clustered in the eastern part of Amhara, the south part of Afar, Addis Ababa, and Oromia. The primary cluster was located at a 13.11 km radius in Diredawa, which was 3.68 times higher than outside the window (RR = 3.68, LLR = 68.76, p-value < 0.001). History of antenatal care follow-up (AOR = 1.63, 95% CI: 1.3-2.04), giving birth at health facilities (AOR = 1.63, 95% CI: 1.25-2.13), age ≥ 35 years (AOR = 186, 95% CI: 1.35-2.63), age 25-34 years (AOR = 1.72, 95% CI: 1.33-2.21), and being richest (AOR = 2.71, 95% CI: 1.86-3.94) were the factors contributing to the timely completion of vaccination. CONCLUSION: The prevalence of timely completion of vaccination was low in Ethiopia, and the spatial distribution of timely completion of vaccination in Ethiopia was non-randomly distributed across the regions. The factors associated with the timely completion of vaccinations were ANC follow-up, place of delivery, age of the participant, and wealth index. We recommend expanding facility delivery, antenatal care services, and empowering women to scale up timely vaccination in Ethiopia.

Epidemiological Characteristics of Varicella in Anhui Province, China, 2012-2021: Surveillance Study.

BACKGROUND: Varicella is a mild, self-limited disease caused by varicella-zoster virus (VZV) infection. Recently, the disease burden of varicella has been gradually increasing in China; however, the epidemiological characteristics of varicella have not been reported for Anhui Province. OBJECTIVE: The aim of this study was to analyze the epidemiology of varicella in Anhui from 2012 to 2021, which can provide a basis for the future study and formulation of varicella prevention and control policies in the province. METHODS: Surveillance data were used to characterize the epidemiology of varicella in Anhui from 2012 to 2021 in terms of population, time, and space. Spatial autocorrelation of varicella was explored using the Moran index (Moran I). The Kulldorff space-time scan statistic was used to analyze the spatiotemporal aggregation of varicella. RESULTS: A total of 276,115 cases of varicella were reported from 2012 to 2021 in Anhui, with an average annual incidence of 44.8 per 100,000, and the highest incidence was 81.2 per 100,000 in 2019. The male-to-female ratio of cases was approximately 1.26, which has been gradually decreasing in recent years. The population aged 5-14 years comprised the high-incidence group, although the incidence in the population 30 years and older has gradually increased. Students accounted for the majority of cases, and the proportion of cases in both home-reared children (aged 0-7 years who are not sent to nurseries, daycare centers, or school) and kindergarten children (aged 3-6 years) has changed slightly in recent years. There were two peaks of varicella incidence annually, except for 2020, and the incidence was typically higher in the winter peak than in summer. The incidence of varicella in southern Anhui was higher than that in northern Anhui. The average annual incidence at the county level ranged from 6.61 to 152.14 per 100,000, and the varicella epidemics in 2018-2021 were relatively severe. The spatial and temporal distribution of varicella in Anhui was not random, with a positive spatial autocorrelation found at the county level (Moran I=0.412). There were 11 districts or counties with high-high clusters, mainly distributed in the south of Anhui, and 3 districts or counties with high-low or low-high clusters. Space-time scan analysis identified five possible clusters of areas, and the most likely cluster was distributed in the southeastern region of Anhui. CONCLUSIONS: This study comprehensively describes the epidemiology and changing trend of varicella in Anhui from 2012 to 2021. In the future, preventive and control measures should be strengthened for the key populations and regions of varicella.

Malaria risk mapping among children under five in Togo.

Malaria is a major health threat in sub-Sahara Africa, especially for children under five. However, there is considerable heterogeneity between areas in malaria risk reported, associated with environmental and climatic. We used data from Togo to explore spatial patterns of malaria incidence. Geospatial covariate datasets, including climatic and environmental variables from the 2017 Malaria Indicator Survey in Togo, were used for this study. The association between malaria incidence and ecological predictors was assessed using three regression techniques, namely the Ordinary Least Squares (OLS), spatial lag model (SLM), and spatial error model (SEM). A total of 171 clusters were included in the survey and provided data on environmental and climate variables. Spatial autocorrelation showed that the distribution of malaria incidence was not random and revealed significant spatial clustering. Mean temperature, precipitation, aridity and proximity to water bodies showed a significant and direct association with malaria incidence rate in the SLM model, which best fitted the data according to AIC. Five malaria incidence hotspots were identified. Malaria incidence is spatially clustered in Togo associated with climatic and environmental factors. The results can contribute to the development of specific malaria control plans taking geographical variation into consideration and targeting transmission hotspots.

Spatial variation and associated factors of inadequate counselling regarding pregnancy danger signs during antenatal care visits among pregnant women in Ethiopia: a Geographically Weighted Regression Model.

INTRODUCTION: Inadequate counselling of pregnant women regarding pregnancy danger signs contributes to a delay in deciding to seek care, which causes up to 77% of all maternal deaths in developing countries. However, its spatial variation and region-specific predictors have not been studied in Ethiopia. Hence, the current study aimed to model its predictors using geographically weighted regression analysis. METHODS: The 2019 Ethiopian Mini Demographic and Health Survey data were used. A total weighted sample of 2922 women from 283 clusters was included in the final analysis. The analysis was performed using ArcGIS Pro, STATA V.14.2 and SaTScan V.10.1 software. The spatial variation of inadequate counselling was examined using hotspot analysis. Ordinary least squares regression was used to identify factors for geographical variations. Geographically weighted regression was used to explore the spatial heterogeneity of selected variables to predict inadequate counselling. RESULTS: Significant hotspots of inadequate counselling regarding pregnancy danger signs were found in Gambella region, the border between Amhara and Afar regions, Somali region and parts of Oromia region. Antenatal care provided by health extension workers, late first antenatal care initiation and antenatal care follow-up at health centres were spatially varying predictors. The geographically weighted regression model explained about 66% of the variation in the model. CONCLUSION: Inadequate counselling service regarding pregnancy danger signs in Ethiopia varies across regions and there exists within country inequality in the service provision and utilisation. Prioritisation and extra efforts should be made by concerned actors for those underprivileged areas and communities (as shown in the maps), and health extension workers, as they are found in the study.

Spatial and temporal differences and convergence analysis of multidimensional relative poverty in ethnic areas.

Reducing multidimensional relative poverty is one of the important issues in the current global poverty governance field. This article takes 12 ethnic regions in China as the research object and constructs a multidimensional relative poverty measurement system. The calculated multidimensional relative poverty index is decomposed according to provinces, cities, dimensions, and indicators. Then, the Dagum Gini coefficient and convergence analysis are used to analyze spatiotemporal heterogeneity and convergence characteristics. The results show that the multi-dimensional relative poverty situation of various provinces in ethnic minority areas has improved from 2012 to 2021, among which Tibet province is the most serious and Shaanxi is the best. According to the analysis of convergence, it was observed that there is no σ-convergence of multidimensional relative poverty in ethnic areas in general, and there is absolute ß-convergence in general and in the southwest and northwest regions, and there is no absolute ß-convergence in the northeast region. Based on this, policy recommendations for reducing multidimensional relative poverty are proposed at the end of the article. Compared with previous studies, this article focuses on ethnic regions that are easily overlooked. Starting from the dimensions of economy, social development, and ecological environment, the poverty measurement system has been enriched.

How do taxi drivers expose to fine particulate matter (PM2.5) in a Chinese megacity: a rapid assessment incorporating with satellite-derived information and urban mobility data.

BACKGROUND: Taxi drivers in a Chinese megacity are frequently exposed to traffic-related particulate matter (PM2.5) due to their job nature, busy road traffic, and urban density. A robust method to quantify dynamic population exposure to PM2.5 among taxi drivers is important for occupational risk prevention, however, it is limited by data availability. METHODS: This study proposed a rapid assessment of dynamic exposure to PM2.5 among drivers based on satellite-derived information, air quality data from monitoring stations, and GPS-based taxi trajectory data. An empirical study was conducted in Wuhan, China, to examine spatial and temporal variability of dynamic exposure and compare whether drivers' exposure exceeded the World Health Organization (WHO) and China air quality guideline thresholds. Kernel density estimation was conducted to further explore the relationship between dynamic exposure and taxi drivers' activities. RESULTS: The taxi drivers' weekday and weekend 24-h PM2.5 exposure was 83.60 µg/m3 and 55.62 µg/m3 respectively, 3.4 and 2.2 times than the WHO's recommended level of 25 µg/m3. Specifically, drivers with high PM2.5 exposure had a higher average trip distance and smaller activity areas. Although major transportation interchanges/terminals were the common activity hotspots for both taxi drivers with high and low exposure, activity hotspots of drivers with high exposure were mainly located in busy riverside commercial areas within historic and central districts bounded by the "Inner Ring Road", while hotspots of drivers with low exposure were new commercial areas in the extended urbanized area bounded by the "Third Ring Road". CONCLUSION: These findings emphasized the need for air quality management and community planning to mitigate the potential health risks of taxi drivers.

Spatial analysis toolkits for RNA in situ sequencing.

Spatial transcriptomics (ST) is featured by high-throughput gene expression profiling within their native cell and tissue context, offering a means to investigate gene regulatory networks in tissue microenvironment. In situ sequencing (ISS) is an imaging-based ST technology that simultaneously detects hundreds to thousands of genes at subcellular resolution. As a highly reproducible and robust technique, ISS has been widely adapted and undergone a series of technical iterations. As the interest in ISS-based spatial transcriptomic analysis grows, scalable and integrated data analysis workflows are needed to facilitate the applications of ISS in different research fields. This review presents the state-of-the-art bioinformatic toolkits for ISS data analysis, which covers the upstream and downstream analysis workflows, including image analysis, cell segmentation, clustering, functional enrichment, detection of spatially variable genes and cell clusters, spatial cell-cell interactions, and trajectory inference. To assist the community in choosing the right tools for their research, the application of each tool and its compatibility with ISS data are reviewed in detailed. Finally, future perspectives and challenges concerning how to integrate heterogeneous tools into a user-friendly analysis pipeline are discussed. This article is categorized under: RNA Methods > RNA Analyses In Vitro and In Silico.

Unveiling spatial disparities in basic medical and health services: insights from China's provincial analysis.

Based on the panel data of 31 provinces (municipalities and autonomous regions) in China from 2012 to 2019, this paper constructs the evaluation index system of basic medical and health services in China from seven dimensions: medical and health facilities, health expenditure, medical services, traditional Chinese medicine hospital services, maternal and child health care, people's health and medical security, disease control and public health. The entropy method was used to measure the level of basic medical and health services in China, and its spatial differences and convergence characteristics were further investigated. In this study, we employ the entropy weight method, σ convergence, and ß convergence as our primary methodologies. The entropy weight method is used to evaluate the variability of each indicator, determine the weights of indicators, and quantify the information content of the data. σ convergence illustrates the process by which the variance of a sample decreases over time. ß convergence refers to the gradual approach of variables within an economic system towards their long-term equilibrium level over time. The results show that: (1) The scores of basic medical and health services in China's four major regions (including Northeast, East, Central and West) remain in a relatively stable state, with small fluctuations and great room for improvement; (2) There are significant regional differences in the level of basic medical and health services in China, and the intra-regional differences are much greater than the inter-regional differences; (3) There is no significant σ convergence observed in China and its four major regions; however, there is a notable presence of ß convergence.

Healthcare equity in focus: bridging gaps through a spatial analysis of healthcare facilities in Irbid, Jordan.

In the Irbid Governorate, Jordan, equitable healthcare facility distribution is vital to ensuring healthcare accessibility and improving public health outcomes. This study investigated the spatial distribution, accessibility, and conformity of healthcare facilities to the Ministry of Health standards to identify areas requiring improvement. Using geographic information systems (GIS), three spatial analyses were conducted: nearest neighbor analysis, buffer analysis, and service area analysis. These analyses comprehensively assessed the healthcare landscape, revealing a random spatial distribution pattern of healthcare facilities; and indicating an absence of structured organization. The buffer analysis revealed concentrations in specific regions, while others were underserved. The Service Area Analysis revealed significant healthcare access challenges, especially in remote areas. The healthcare resource distribution of the Irbid governorate fell short of national and international standards, emphasizing the need for improvements. To address these disparities, policymakers and healthcare authorities should focus on equitably redistributing resources, tailoring allocation to local needs, improving remote area infrastructure, and refining government policies. Continuous monitoring and evaluation are imperative to ensure alignment with international standards and achieve healthcare equity. The insights from this case study provide valuable guidance for regions facing similar healthcare distribution challenges.

Spatio-temporal evolution and driving factors of new urbanization in central China based on multi-source data.

Urbanization is an inevitable outcome of the development of human society to a certain stage, and it is also an irreversible pattern of the concentration degree of human society. Based on multi-source data such as remote sensing images, ecological environment and socio-economic data, the evaluation index system of new urbanization is constructed from multi-dimensions of population, economy, society, space and ecology. To explore the spatio temporal evolution and driving factors of urbanization in 80 prefecture-level cities in central China from 2013 to 2021 by using entropy method, spatial autocorrelation model and geographic detector. The results show that: (1) The level of new urbanization continues to grow, with the average value rising from 0.1562 in 2013 to 0.2557 in 2021, and the regional differences are obvious, forming a circle structure with Wuhan, Zhengzhou and other provincial capitals as the center and weakening radiation to surrounding cities. (2) The agglomeration of ecological urbanization is significant, and the agglomeration trend is gradually enhanced. The high-high agglomeration areas tend to Xinzhou City, most prefecture-level cities in Hubei Province and some prefecture-level cities in Southern Hunan Province, while the low-low agglomeration areas tend to Changzhi City, most prefecture level cities in Henan Province and some prefecture-level cities in Northern Anhui Province. (3) The night light index, total retail sales of consumer goods, investment in fixed assets, proportion of built-up areas and urban economic density are the main driving factors affecting the level of new urbanization. (4) The interaction of driving factors shows double factor enhancement and nonlinear enhancement effects.

Chronic Disease Prevalence in the US: Sociodemographic and Geographic Variations by Zip Code Tabulation Area.

Introduction: We examined the geographic distribution and sociodemographic and economic characteristics of chronic disease prevalence in the US. Understanding disease prevalence and its impact on communities is crucial for effective public health interventions. Methods: Data came from the American Community Survey, the American Hospital Association Survey, and the Centers for Disease Control and Prevention's PLACES. We used quartile thresholds for 10 chronic diseases to assess chronic disease prevalence by Zip Code Tabulation Areas (ZCTAs). ZCTAs were scored from 0 to 20 based on their chronic disease prevalence quartile. Three prevalence categories were established: least prevalent (score ≤6), moderately prevalent (score 7-13), and highest prevalence (score ≥14). Community characteristics were compared across categories and spatial analyses to identify clusters of ZCTAs with high disease prevalence. Results: Our study showed a high prevalence of chronic disease in the southeastern region of the US. Populations in ZCTAs with the highest prevalence showed significantly greater socioeconomic disadvantages (ie, lower household income, lower home value, lower educational attainment, and higher uninsured rates) and barriers to health care access (lower percentage of car ownership and longer travel distances to hospital-based intensive care units, emergency departments, federally qualified health centers, and pharmacies) compared with ZCTAs with the lowest prevalence. Conclusion: Socioeconomic disparities and health care access should be addressed in communities with high chronic disease prevalence. Carefully directed resource allocation and interventions are necessary to reduce the effects of chronic disease on these communities. Policy makers and clinicians should prioritize efforts to reduce chronic disease prevalence and improve the overall health and well-being of affected communities throughout the US.

Geographically weighted machine learning for modeling spatial heterogeneity in traffic crash frequency and determinants in US.

Spatial analyses of traffic crashes have drawn much interest due to the nature of the spatial dependence and spatial heterogeneity in the crash data. This study makes the best of Geographically Weighted Random Forest (GW-RF) model to explore the local associations between crash frequency and various influencing factors in the US, including road network attributes, socio-economic characteristics, and land use factors collected from multiple data sources. Special emphasis is put on modeling the spatial heterogeneity in the effects of a factor on crash frequency in different geographical areas in a data-driven way. The GW-RF model outperforms global models (e.g. Random Forest) and conventional geographically weighted regression, demonstrating superior predictive accuracy and elucidating spatial variations. The GW-RF model reveals spatial distinctions in the effects of certain factors on crash frequency. For example, the importance of intersection density varies significantly across regions, with high significance in the southern and northeastern areas. Low-grade road density emerges as influential in specific cities. The findings highlight the significance of different factors in influencing crash frequency across zones. Road network factors, particularly intersection density, exhibit high importance universally, while socioeconomic variables demonstrate moderate effects. Interestingly, land use variables show relatively lower importance. The outcomes could help to allocate resources and implement tailored interventions to reduce the likelihood of crashes.

Spatio-Temporal analysis of exports of cultural products and their affecting factors for spatial distribution.

Cultural products constitute a significant portion of global trade, and understanding their export patterns can shed light on economic trends, trade dynamics, and market opportunities. This study conducted the spatio-temporal analysis of exports of cultural products, exploring the relationship between various influencing factors and their impact on the spatial distribution of these exports. Leveraging a diverse dataset encompassing 55 BRI countries for the period of 2005-2022, this research employs advanced spatial analysis techniques, including spatial autocorrelation and spatial regression models, to examine the spatial patterns and determinants of exports if cultural product exports. Moreover, this study delves into the multifaceted determinants affecting the spatial distribution of these exports. The findings of this study reveal significant spatio-temporal variations in the exports of cultural products. Spatial autocorrelation analysis indicates the presence of spatial clustering, suggesting that regions with high cultural product exports tend to be geographically close to each other. The spatial regression models further identify several key factors like economic development, productive capacities, cultural tourism, information development and human capital influence the spatial distribution of these exports. The findings of the study reveal that there is strong spatial relationship for exports of cultural products in BRI countries. The findings of this research contribute valuable insights for policymakers, businesses, and stakeholders regarding a deeper comprehension of the driving forces behind the spatial distribution of these cultural products, facilitating informed decision-making processes to optimize strategies for promoting and sustaining the trade of cultural products in an increasingly interconnected world.

Factors associated with different types of freight crashes: A macro-level analysis.

INTRODUCTION: Despite evidence showing higher fatality rates in freight-related crashes, there has been limited exploration of their spatial distribution and factors associated with such distribution. This gap in the literature primarily stems from the focus of existing studies on micro-level factors predicting the frequency or severity of injuries in freight crashes. The present study delves into the factors contributing to freight crashes at the neighborhood level, particularly focusing on different types of freight crashes: collisions involving a freight vehicle and a passenger vehicle, crashes between freight vehicles, and freight vehicle-non-motorized crashes. METHOD: This study analyzes traffic crash data from the urbanized region of Seoul, collected between 2016 and 2019. To effectively deal with spatial autocorrelation and model different types of crashes in a unified framework, a Bayesian multivariate conditional autoregressive model was employed. RESULTS: Findings show substantial differences in the factors associated with various types of freight crashes. The predictors for crashes between freight vehicles diverge significantly from those for freight vehicle-non-motorized crashes. Crashes between freight vehicles are relatively more influenced by road network structure, while freight crashes involving non-motorized users are relatively more affected by the built environment and freight facilities than the other crash types examined. Freight vehicle-passenger vehicle crashes fall into an intermediate category, sharing most predictors with either of the other two types of freight crashes. CONCLUSIONS AND PRACTICAL APPLICATIONS: The findings of this study offer valuable lessons for transportation practitioners and policymakers. They can guide the formulation of effective land use policies and infrastructure planning, specifically designed to address the unique characteristics of different types of freight crashes.

Mapping the spatial disparities of HIV prevalence in Ethiopian zones using the generalized additive model.

HIV is a worldwide social and health pandemic that poses a significant problem. This study contributes to the 2030 global agenda of reducing HIV prevalence. The study analyzed HIV prevalence using the 2016 Ethiopian Demographic and Health Survey data. The study included men aged 15-54 years and women aged 15-49 years who responded to questions about HIV tests. A generalized geo-additive model (GAM) was fitted to HIV data using nonparametric smooth terms for geolocations. Two smoothing techniques were used in GAMs to evaluate spatial disparities and the probable effects of variables on HIV risk. There were certain areas in Ethiopia that were identified as hot spot zones for HIV, including Nuer and Agnuak in Gambella, West Wollega and Illubabor in Oromia, Benchi Maji and Shaka in SNNPR, Awsi, Fantana, Kilbet, and Gabi in the Afar region, Shinilie of the Somalia region, North and South Wollo, Oromia special zones of the Amhara region, Central Ethiopia, and Addis Ababa city. On the other hand, the eastern parts of Ethiopia, particularly most zones in the Somalia region, were identified as cold spot zones with the lowest HIV odds ratio. The odds of HIV+ were higher for those who reside in rural areas than in urban areas. Furthermore, people who have STIs, who used contraceptive methods, and who learned at the secondary level of education were more likely to be infected with HIV. After adjusting for confounding variables, the results indicated that there are substantially significant spatial variations in HIV prevalence across Ethiopian zones. These results provide essential information to strategically target geographic areas to allocate resources and policy interventions at zonal level administrations.

Is low-dose computed tomography for lung cancer screening conveniently accessible in China? A spatial analysis based on cross-sectional survey.

BACKGROUND: Regular Low-Dose Computed Tomography (LDCT) for lung cancer high-risk population has been proved to improve health outcomes and relieve disease burden efficiently for both individual and society. With geographical impedance becoming the major barrier preventing patients from getting timely healthcare service, this study incorporated health seeking behavior in estimating spatial accessibility of relative scarce LDCT resource in China, thus to provide real-world evidence for future government investment and policy making. METHODS: Taking Sichuan Province in southwest China as the study area, a cross-sectional survey was first carried out to collect actual practice and preferences for seeking LDCT services. Using Computed Tomography (CT) registration data reported by owner institutions representing LDCT services capacity, and grided town-level high-risk population as demand, the Nearest Neighbor Method was then utilized to calculate spatial accessibility of LDCT services. RESULTS: A total of 2,529 valid questionnaires were collected, with only 34.72% of the high-risk populations (746 individuals) followed the recommended annual screening. Participants preferred to travel to municipal-level and above institutions within 60 min for LDCT services. Currently, every thousand high-risk populations own 0.0845 CT scanners in Sichuan Province, with 96.95% able to access LDCT within 60 min and over half within 15 min. Urban areas generally showed better accessibility than rural areas, and the more developed eastern regions were better than the western regions with ethnic minority clusters. CONCLUSIONS: Spatial access to LDCT services is generally convenient in Sichuan Province, but disparity exists between different regions and population groups. Improving LDCT capacity in county-level hospitals as well as promoting health education and policy guidance to the public can optimize efficiency of existing CT resources. Implementing mobile CT services and improving rural public transportation may alleviate emerging disparities in accessing early lung cancer detection.

Mastering geographically weighted regression: key considerations for building a robust model.

Geographically weighted regression (GWR) takes a prominent role in spatial regression analysis, providing a nuanced perspective on the intricate interplay of variables within geographical landscapes (Brunsdon et al., 1998). However, it is essential to have a strong rationale for employing GWR, either as an addition to, or a complementary analysis alongside, non-spatial (global) regression models (Kiani, Mamiya et al., 2023). Moreover, the proper selection of bandwidth, weighting function or kernel types, and variable choices constitute the most critical configurations in GWR analysis (Wheeler, 2021). [...].

Spatiotemporal modeling of under-five mortality and associated risk factors in Ethiopia using 2000-2016 EDHS data.

BACKGROUND: The under-five mortality rate serves as a key indicator of the performance of a country's healthcare system. Despite a minor decline, Ethiopia continues to face a persistently high under-five mortality rate across different zones. Thus, this study aimed to identify the risk factors of under-five mortality and the spatiotemporal variation in Ethiopian administrative zones. METHOD: This study used the 2000-2016 Ethiopian Demographic and Health Survey (EDHS) data which were collected using a two-stage sampling method. A total of 43,029 (10,873 in 2000, 9,861 in 2005, 11,654 in 2011, and 10,641 in 2016) weighted sample under-five child mortality were used. The space-time dynamic model was employed to account for spatial and time effects in 65 administrative zones in Ethiopia. RESULTS: From the result of a general nesting spatial-temporal dynamic model, there was a significant space-time interaction effect [γ = -0.1444, 95% CI(-0.6680, -0.1355)] for under-five mortality. The increase in the percentages of mothers illiteracy [ß = 0.4501, 95% CI (0.2442, 0.6559)], not vaccinated[ß= 0.7681, 95% CI (0.5683, 0.9678)], unimproved water[ß= 0.5801, CI (0.3793, 0.7808)] were increased death rates for under five children while increased percentage of contraceptive use [ß= -0.6609, 95% CI (-0.8636, -0.4582)] and antenatal care unit visit > 4 times [ß= -0.1585, 95% CI(-0.1812, -0.1357)] were contributed to the decreased under-five mortality rate at the zone in Ethiopia. CONCLUSIONS: Even though the mortality rate for children under five has decreased over time, still there is higher in different zones of Ethiopia. There exists spatial and temporal variation in under-five mortality among zones. Therefore, it is very important to consider spatial neighborhood's and temporal context when aiming to avoid under-five mortality.