Geospatial pattern of HIV seropositivity and its predictors among women in Ethiopia. A spatial and multiscale geographically weighted regression analysis.

BACKGROUND: Although promising efforts have been made so far, HIV remains a public health concern. Women in Ethiopia are disproportionately affected by HIV, accounting for a majority of new infections and AIDS-related deaths. However, the geospatial distribution of HIV among women in Ethiopia is not well understood, making it challenging to develop geographically targeted measures. Besides, to accelerate the pathway of decreasing HIV prevalence and plan geographically specific interventions, understanding the geospatial distribution of HIV seropositivity and its predictors among women plays a significant role. METHODS: A spatial and multiscale geographically weighted regression analysis was conducted using the 2016 EDHS dataset, comprising 14,778 weighted samples of women in the reproductive age group. The EDHS sample underwent two-stage stratification and selection. The data were extracted between October 18 and 30, 2023. Non-spatial analysis was carried out using STATA version 17. Additionally, ArcGIS Pro and Sat Scan version 9.6 were used to visually map HIV seropositivity. Global Moran's I was computed to evaluate the distribution of HIV seropositivity. The Getis-Ord Gi* spatial statistic was utilized to identify significant spatial clusters of cold and hot spot areas. Geographically weighted regression analysis was subsequently performed to identify significant predictors of HIV seropositivity. Significance was established at a P-value <0.05 throughout all statistical analyses. RESULTS: HIV seropositivity among women in Ethiopia is distributed non-randomly (Global Moran's I = 0.16, p-value <0.001 and Z-score = 7.12). Significant hotspot clustering of HIV seropositivity was found in the Addis Ababa, Harari, Dire Dawa, and Gambela region. Poor wealth index, being divorced and widowed, having more than one sexual partner, and early first sexual experience (<15 years) were found to be predictors of geographical variation of HIV seropositivity among women. CONCLUSION: HIV seropositivity among women in Ethiopia varies geographically. Thus, deploying additional resources in high hotspot regions is recommended. Programs should focus on improving the economic empowerment of women to prevent the from engaging in risky sexual behaviors. Furthermore, comprehensive sex education programs in schools and community settings regarding the consequences of early first sexual debut might play a role in reducing HIV seropositivity among women in Ethiopia.

Geospatial pattern of level of minimum acceptable diet and its determinants among children aged 6-23 months in Ethiopia. Spatial and multiscale geographically weighted regression analysis.

Background: Despite prior progress and the proven benefits of optimal feeding practices, improving child dietary intake in developing countries like Ethiopia remains challenging. In Ethiopia, over 89% of children fail to meet the minimum acceptable diet. Understanding the geographical disparity and determinants of minimum acceptable diet can enhance child feeding practices, promoting optimal child growth. Methods: Spatial and multiscale geographically weighted regression analysis was conducted among 1,427 weighted sample children aged 6-23 months. ArcGIS Pro and SatScan version 9.6 were used to map the visual presentation of geographical distribution failed to achieve the minimum acceptable diet. A multiscale geographically weighted regression analysis was done to identify significant determinants of level of minimum acceptable diet. The statistical significance was declared at P-value <0.05. Results: Overall, 89.56% (95CI: 87.85-91.10%) of children aged 6-23 months failed to achieve the recommended minimum acceptable diet. Significant spatial clustering was detected in the Somali, Afar regions, and northwestern Ethiopia. Children living in primary clusters were 3.6 times more likely to be unable to achieve the minimum acceptable diet (RR = 3.61, LLR =13.49, p < 0.001). Mother's with no formal education (Mean = 0.043, p-value = 0.000), family size above five (Mean = 0.076, p-value = 0.005), No media access (Mean = 0.059, p-value = 0.030), home delivery (Mean = 0.078, p-value = 0.002), and no postnatal checkup (Mean = 0.131, p-value = 0.000) were found to be spatially significant determinants of Inadequate minimum acceptable diet. Conclusion: Level of minimum acceptable diet among children in Ethiopia varies geographically. Therefore, to improve child feeding practices in Ethiopia, it is highly recommended to deploy additional resources to high-need areas and implement programs that enhance women's education, maternal healthcare access, family planning, and media engagement.

Spatial variation and geographical weighted regression analysis to explore open defecation practice and its determinants among households in Ethiopia.

BACKGROUND: In Ethiopia, recent evidence revealed that over a quarter (27%) of households (HHs) defecated openly in bush or fields, which play a central role as the source of many water-borne infectious diseases, including cholera. Ethiopia is not on the best track to achieve the SDG of being open-defecation-free by 2030. Therefore, this study aimed to explore the spatial variation and geographical inequalities of open defecation (OD) among HHs in Ethiopia. METHODS: This was a country-wide community-based cross-sectional study among a weighted sample of 8663 HHs in Ethiopia. The global spatial autocorrelation was explored using the global Moran's-I, and the local spatial autocorrelation was presented by Anselin Local Moran's-I to evaluate the spatial patterns of OD practice in Ethiopia. Hot spot and cold spot areas of OD were detected using ArcGIS 10.8. The most likely high and low rates of clusters with OD were explored using SaTScan 10.1. Geographical weighted regression analysis (GWR) was fitted to explore the geographically varying coefficients of factors associated with OD. RESULTS: The prevalence of OD in Ethiopia was 27.10% (95% CI: 22.85-31.79). It was clustered across enumeration areas (Global Moran's I = 0.45, Z-score = 9.88, P-value ≤ 0.001). Anselin Local Moran's I analysis showed that there was high-high clustering of OD at Tigray, Afar, Northern Amhara, Somali, and Gambela regions, while low-low clustering of OD was observed at Addis Ababa, Dire-Dawa, Harari, SNNPR, and Southwest Oromia. Hotspot areas of OD were detected in the Tigray, Afar, eastern Amhara, Gambela, and Somali regions. Tigray, Afar, northern Amhara, eastern Oromia, and Somali regions were explored as having high rates of OD. The GWR model explained 75.20% of the geographical variation of OD among HHs in Ethiopia. It revealed that as the coefficients of being rural residents, female HH heads, having no educational attainment, having no radio, and being the poorest HHs increased, the prevalence of OD also increased. CONCLUSION: The prevalence of OD in Ethiopia was higher than the pooled prevalence in sub-Saharan Africa. Tigray, Afar, northern Amhara, eastern Oromia, and Somali regions had high rates of OD. Rural residents, being female HH heads, HHs with no educational attainment, HHs with no radio, and the poorest HHs were spatially varying determinants that affected OD. Therefore, the government of Ethiopia and stakeholders need to design interventions in hot spots and high-risk clusters. The program managers should plan interventions and strategies like encouraging health extension programs, which aid in facilitating basic sanitation facilities in rural areas and the poorest HHs, including female HHs, as well as community mobilization with awareness creation, especially for those who are uneducated and who do not have radios.

Geographically and Temporally Weighted Regression in Assessing Dengue Fever Spread Factors in YunnanBorder Regions.

Objective: This study employs the Geographically and Temporally Weighted Regression (GTWR) model to assess the impact of meteorological elements and imported cases on dengue fever outbreaks, emphasizing the spatial-temporal variability of these factors in border regions. Methods: We conducted a descriptive analysis of dengue fever's temporal-spatial distribution in Yunnan border areas. Utilizing annual data from 2013 to 2019, with each county in the Yunnan border serving as a spatial unit, we constructed a GTWR model to investigate the determinants of dengue fever and their spatio-temporal heterogeneity in this region. Results: The GTWR model, proving more effective than Ordinary Least Squares (OLS) analysis, identified significant spatial and temporal heterogeneity in factors influencing dengue fever's spread along the Yunnan border. Notably, the GTWR model revealed a substantial variation in the relationship between indigenous dengue fever incidence, meteorological variables, and imported cases across different counties. Conclusion: In the Yunnan border areas, local dengue incidence is affected by temperature, humidity, precipitation, wind speed, and imported cases, with these factors' influence exhibiting notable spatial and temporal variation.

Mapping and predicting open defecation in Ethiopia: 2021 PMA-ET study.

INTRODUCTION: There has been extensive research conducted on open defecation in Ethiopia, but a notable gap persists in comprehensively understanding the spatial variation and predictors at the household level. This study utilizes data from the 2021 Performance Monitoring for Action Ethiopia (PMA-ET) to address this gap by identifying hotspots and predictors of open defecation. Employing geographically weighted regression analysis, it goes beyond traditional models to account for spatial heterogeneity, offering a nuanced understanding of geographical variations in open defecation prevalence and its determinants. This research pinpoints hotspot areas and significant predictors, aiding policymakers and practitioners in tailoring interventions effectively. It not only fills the knowledge gap in Ethiopia but also informs global sanitation initiatives. METHODS: The study comprised a total weighted sample of 24,747 household participants. ArcGIS version 10.7 and SaT Scan version 9.6 were used to handle mapping, hotspots, ordinary least squares, Bernoulli model analysis, and Spatial regression. Bernoulli-based model was used to analyze the purely spatial cluster detection of open defecation at the household level in Ethiopia. Ordinary Least Square (OLS) analysis and geographically weighted regression analysis were employed to assess the association between an open defecation and explanatory variables. RESULTS: The spatial distribution of open defecation at the household level exhibited clustering (global Moran's I index value of 4.540385, coupled with a p-value of less than 0.001), with significant hotspots identified in Amhara, Afar, Harari, and parts of Dire Dawa. Spatial analysis using Kuldorff's Scan identified six clusters, with four showing statistical significance (P-value < 0.05) in Amhara, Afar, Harari, Tigray, and southwest Ethiopia. In the geographically weighted regression model, being male [coefficient = 0.87, P-value < 0.05] and having no media exposure (not watching TV or listening to the radio) [coefficient = 0.47, P-value < 0.05] emerged as statistically significant predictors of household-level open defecation in Ethiopia. CONCLUSION: The study revealed that open defecation at the household level in Ethiopia varies across the regions, with significant hotspots identified in Amhara, Afar, Harari, and parts of Dire Dawa. Geographically weighted regression analysis highlights male participants lacking media exposure as substantial predictors of open defecation. Targeted interventions in Ethiopia should improve media exposure among males in hotspot regions, tailored sanitation programs, and region-specific awareness campaigns. Collaboration with local communities is crucial.

Using a spatial autoregressive model with spatial autoregressive disturbances to investigate origin-destination trip flows.

Spatial interaction models with spatial origin-destination (OD) filters are powerful tools to characterize trip flows in space, which is a classic and important problem in regional science. To the authors' knowledge, existing studies adopting OD filters mostly specify the spatial dependence as an autoregressive process, which may not be the full picture of spatial effects. To examine the problem, this paper proposes the hypotheses that 1) spatial OD dependences can take place in both the spatial autoregressive term and the spatial error term in a spatial interaction model. 2) Estimating a spatial autoregressive model with spatial autoregressive disturbances (SARAR) model with OD filters would disentangle where the spatial dependence exists and by how much. 3) The marginal effects obtained from SARAR models would be preferred to analysts when SARAR models outperform spatial autoregressive (SAR) models and spatial error models (SEM) from the statistical point of view. To assess these hypotheses, this paper specifies, estimates, and applies SARAR models with OD filters to investigate trip distributions. By comparing against alternative models, this paper investigates the estimation results in SAR, SEM and SARAR models using an empirical data collected from Hangzhou, China. The contribution of this paper is to be the first in developing an SARAR model with OD filters for trip distribution analyses and examining its performance.

Geographical variation in hotspots of stunting among under-five children in Ethiopia: A geographically weighted regression and multilevel robust Poisson regression analysis.

INTRODUCTION: Childhood stunting is a global public health concern, associated with both short and long-term consequences, including high child morbidity and mortality, poor development and learning capacity, increased vulnerability for infectious and non-infectious disease. The prevalence of stunting varies significantly throughout Ethiopian regions. Therefore, this study aimed to assess the geographical variation in predictors of stunting among children under the age of five in Ethiopia using 2019 Ethiopian Demographic and Health Survey. METHOD: The current analysis was based on data from the 2019 mini Ethiopian Demographic and Health Survey (EDHS). A total of 5,490 children under the age of five were included in the weighted sample. Descriptive and inferential analysis was done using STATA 17. For the spatial analysis, ArcGIS 10.7 were used. Spatial regression was used to identify the variables associated with stunting hotspots, and adjusted R2 and Corrected Akaike Information Criteria (AICc) were used to compare the models. As the prevalence of stunting was over 10%, a multilevel robust Poisson regression was conducted. In the bivariable analysis, variables having a p-value < 0.2 were considered for the multivariable analysis. In the multivariable multilevel robust Poisson regression analysis, the adjusted prevalence ratio with the 95% confidence interval is presented to show the statistical significance and strength of the association. RESULT: The prevalence of stunting was 33.58% (95%CI: 32.34%, 34.84%) with a clustered geographic pattern (Moran's I = 0.40, p<0.001). significant hotspot areas of stunting were identified in the west and south Afar, Tigray, Amhara and east SNNPR regions. In the local model, no maternal education, poverty, child age 6-23 months and male headed household were predictors associated with spatial variation of stunting among under five children in Ethiopia. In the multivariable multilevel robust Poisson regression the prevalence of stunting among children whose mother's age is >40 (APR = 0.74, 95%CI: 0.55, 0.99). Children whose mother had secondary (APR = 0.74, 95%CI: 0.60, 0.91) and higher (APR = 0.61, 95%CI: 0.44, 0.84) educational status, household wealth status (APR = 0.87, 95%CI: 0.76, 0.99), child aged 6-23 months (APR = 1.87, 95%CI: 1.53, 2.28) were all significantly associated with stunting. CONCLUSION: In Ethiopia, under-five children suffering from stunting have been found to exhibit a spatially clustered pattern. Maternal education, wealth index, birth interval and child age were determining factors of spatial variation of stunting. As a result, a detailed map of stunting hotspots and determinants among children under the age of five aid program planners and decision-makers in designing targeted public health measures.

The impact of Chinese foreign direct investment and environmental tax revenues on air degradation in Europe: a spatial regression approach, 2000-2020.

This study analyzes air pollution through the effects of China's FDI in 27 European countries over a 20-year period, with a focus on the impact of environmental tax revenues (ETRs) and the environmental context in China. The relationship is estimated through spatial regressions that account for the presence of air pollutants in neighboring countries. The findings suggest that China's FDI in Europe does not contribute to air pollution but rather has a positive impact. The presence of environmental charges filters out non-polluting investments, which has a non-linear relationship with PM2.5 pollution rates. The study also concludes that air pollution is closely linked to the global environmental context, highlighting the positive effects of international agreements in the fight against climate change. Specifically, the study finds a link between China's efforts to address its polluting activities and their impact on European air quality.

Geospatial variation of exclusive breastfeeding and its determinants among mothers of infants under 6 months in Ethiopia: spatial and geographical weighted regression analysis.

BACKGROUND: Exclusive breastfeeding (EBF) is a major public health problem in Ethiopia. However, the spatial variation of EBF and the associated factors have not been studied as much as we have searched. This study aimed at assessing geospatial variation and the predictors of EBF using geographically weighted regression. METHODS: A cross-sectional study was conducted using the 2019 Mini-Ethiopian Demographic and Health Survey data set. The study used a total weighted sample of 548 infants. Hotspot spatial analysis showed the hotspot and cold spot areas of EBF. The spatial distribution of EBF was interpolated for the target population using spatial interpolation analysis. SaTScan V.9.6 software was used to detect significant clusters. Ordinary least squares regression analysis identified significant spatial predictors. In geographically weighted regression analysis, the effect of predictor variables on the spatial variation of EBF was detected using local coefficients. RESULTS: The weighted prevalence of EBF in Ethiopia was 58.97% (95% CI 52.67% to 64.99%), and its spatial distribution was found to be clustered (global Moran's I=0.56, p<0.001). Significant hotspot areas were located in Amhara, Tigray, Southern Nations, Nationalities, and Peoples' Region, and Somali regions, while significant cold spots were located in Dire Dawa, Addis Ababa and Oromia regions. Kulldorff's SaTScan V.9.6 was used to detect significant clusters of EBF using a 50% maximum cluster size per population. The geographically weighted regression model explained 35.75% of the spatial variation in EBF. The proportions of households with middle wealth index and married women were significant spatial predictors of EBF. CONCLUSION: Middle wealth index and married women were significant spatial predictors of EBF. Our detailed map of EBF hotspot areas will help policymakers and health programmers encourage the practice of EBF in hotspot areas and set national and regional programmes focused on improving EBF in cold spots by considering significant predictor variables.

When everyone's doing it: The relative effects of geographical context and social determinants of health on teen birth rates.

Geographic disparities in teen birth rates in the U.S. persist, despite overall reductions over the last two decades. Research suggests these disparities might be driven by spatial variations in social determinants of health (SDOH). An alternative view is that "place" or "geographical context" affects teen birth rates so that they would remain uneven across the U.S. even if all SDOH were constant. We use multiscale geographically weighted regression (MGWR) to quantify the relative effects of geographical context, independent of SDOH, on county-level teen birth rates across the U.S. Findings indicate that even if all counties had identical compositions with respect to SDOH, strong geographic disparities in teen birth rates would still persist. Additionally, local parameter estimates show the relationships between several components of SDOH and teen birth rates vary over space in both direction and magnitude, confirming that global regression techniques commonly employed to examine these relationships likely obscure meaningful contextual differences in these relationships. Findings from this analysis suggest that reducing geographic disparities in teen birth rates will require not only ameliorating differences in SDOH across counties but also combating community norms that contribute to high rates of teen birth, particularly in the southern U.S. Further, the results suggest that if geographical context is not incorporated into models of SDOH, the effects of such determinants may be interpreted incorrectly.

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.

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). [...].

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.

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.

Urban green and blue infrastructure: unveiling the spatiotemporal impact on carbon emissions in China's Yangtze River Delta.

Blue-green infrastructure (BGI) plays a crucial role in regulating urban carbon cycles. Nonetheless, the spatiotemporal effect of BGI on carbon emissions has not received extensive attention. This study used the Yangtze River Delta (YRD) region as the study area and quantified the landscape patterns of BGI. Using a spatiotemporal geographically weighted regression model, we analyzed the impact of evolving spatiotemporal characteristics of BGI on carbon emissions. Additionally, we constructed a spatiotemporal weight matrix using the Moran index ratio to examine the spillover effects of BGI among different regions. Our results show that the aggregation effect of carbon emissions in the YRD region is gradually increasing while BGI has a dynamic impact on carbon emissions. In terms of spatial and temporal spillovers, under the influence of economic connections between regions, patch fragmentation and distance exert a persistent positive influence on carbon emissions, while shape complexity has a negative impact, with area and layout characteristics showing no significant effects. However, area and patch distance have a persistent positive influence on carbon emissions in adjacent areas, while shape complexity exhibits a negative impact. Therefore, optimizing urban BGI through a regional synergistic governance system is important to promote low-carbon urban development.

Spatial regression models to assess variations of composite index for anthropometric failure across the administrative zones in Ethiopia.

BACKGROUND: There are a number of previous studies that investigated undernutrition and its determinants in Ethiopia. However, the national average in the level of undernutrition conceals large variation across administrative zones of Ethiopia. Hence, this study aimed to determine the geographic distribution of composite index for anthropometric failure (CIAF) and identify the influencing factors it' might be more appropriate. METHODS: We used the zonal-level undernutrition data for the under-five children in Ethiopia from the Ethiopian Demographic and Health Survey (EDHS) dataset. Different spatial models were applied to explore the spatial distribution of the CIAF and the covariates. RESULTS: The Univariate Moran's I statistics for CIAF showed spatial heterogeneity of undernutrition in Ethiopian administrative zones. The spatial autocorrelation model (SAC) was the best fit based on the AIC criteria. Results from the SAC model suggested that the CIAF was positively associated with mothers' illiteracy rate (0.61, pvalue 0.001), lower body mass index (0.92, pvalue = 0.023), and maximum temperature (0.2, pvalue = 0.0231) respectively. However, the CIAF was negatively associated with children without any comorbidity (-0.82, pvalue = 0.023), from families with accessibility of improved drinking water (-0.26, pvalue = 0.012), and minimum temperature (-0.16). CONCLUSION: The CIAF across the administrative zones of Ethiopia is spatially clustered. Improving women's education, improving drinking water, and improving child breast feeding can reduce the prevalence of undernutrition (CIAF) across Ethiopian administrative zones. Moreover, targeted intervention in the geographical hotspots of CIAF can reduce the burden of CIAF across the administrative zones.

Toward tuberculosis elimination by understanding epidemiologic characteristics and risk factors in Hainan Province, China.

BACKGROUND: The disease burden of tuberculosis (TB) was heavy in Hainan Province, China, and the information on transmission patterns was limited with few studies. This atudy aims to further explore the epidemiological characteristics and influencing factors of TB in Hainan Province, and thereby contribute valuable scientific evidences for TB elimination in Hainan Province. METHODS: The TB notification data in Hainan Province from 2013 to 2022 were collected from the Chinese National Disease Control Information System Tuberculosis Surveillance System, along with socio-economic data. The spatial-temporal and population distributions were analyzed, and spatial autocorrelation analysis was conducted to explore TB notification rate clustering. In addition, the epidemiological characteristics of the cases among in-country migrants were described, and the delay pattern in seeking medical care was investigated. Finally, a geographically and temporally weighted regression (GTWR) model was adopted to analyze the relationship between TB notification rate and socio-economic indicators. The tailored control suggestions in different regions for TB elimination was provided by understanding epidemiological characteristics and risk factors obtained by GTWR. RESULTS: From 2013 to 2022, 64,042 cases of TB were notified in Hainan Province. The estimated annual percentage change of TB notification rate in Hainan Province from 2013 to 2020 was - 6.88% [95% confidence interval (CI): - 5.30%, - 3.69%], with higher rates in central and southern regions. The majority of patients were males (76.33%) and farmers (67.80%). Cases among in-country migrants primarily originated from Sichuan (369 cases), Heilongjiang (267 cases), Hunan (236 cases), Guangdong (174 cases), and Guangxi (139 cases), accounting for 53%. The majority (98.83%) of TB cases were notified through passive case finding approaches, with delay in seeking care. The GTWR analysis showed that gross domestic product per capita, the number of medical institutions and health personnel per 10,000 people were main factors affecting the high TB notification rates in some regions in Hainan Province. Different regional tailored measures such as more TB specialized hospitals were proposed based on the characteristics of each region. CONCLUSIONS: The notification rate of TB in Hainan Province has been declining overall but still remained high in central and southern regions. Particular attention should be paid to the prevalence of TB among males, farmers, and out-of-province migrant populations. The notification rate was also influenced by economic development and medical conditions, indicating the need of more TB specialized hospitals, active surveillance and other tailored prevention and control measures to promote the progress of TB elimination in Hainan Province.

Spatial associations between chronic kidney disease and socio-economic factors in Thailand.

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.

Explaining the role of land use changes on land surface temperature in an arid and semi-arid metropolitan area with multi-scale spatial regression analysis.

Urban Heat Islands (UHIs), Land Surface Temperature (LST), and Land Use Land Cover (LULC) changes are critical environmental concerns that require continuous monitoring and assessment, especially in cities within arid and semi-arid (ASA) climates. Despite the abundance of research in tropical, Mediterranean, and cold climates, there is a significant knowledge gap for cities in the Middle East with ASA climates. This study aimed to examine the effects of LULC change, population, and wind speed on LST in the Mashhad Metropolis, a city with an ASA climate, over a 30-year period. The research underscores the importance of environmental monitoring and assessment in understanding and mitigating the impacts of urbanization and climate change. Our research combines spatial regression models, multi-scale and fine-scale analyses, seasonal and city outskirts considerations, and long-term change assessments. We used Landsat satellite imagery, a crucial tool for environmental monitoring, to identify LULC changes and their impact on LST at three scales. The relationships were analyzed using Ordinary Least Squares (OLS) and Spatial Error Model (SEM) regressions, demonstrating the value of these techniques in environmental assessment. Our findings highlight the role of environmental factors in shaping LST. A decrease in vegetation and instability of water bodies significantly increased LST over the study period. Bare lands and rocky terrains had the most substantial effect on LST. At the same time, built-up areas resulted in Urban Cooling Islands (UCIs) due to their lower temperatures compared to surrounding bare lands. The Normalized Difference Vegetation Index (NDVI) and Dry Bare-Soil Index (DBSI) were the most effective indices impacting LST in ASA regions, and the 30×30 m2 micro-scale provides more precise results in regression models, underscoring their importance in environmental monitoring. Our study provided a comprehensive understanding of the relationship between LULC changes and LST in an ASA environment, contributing significantly to the literature on environmental change in arid regions and the methodologies for monitoring such changes. Future research should aim to validate and expand additional LST-affecting factors and test our approach and findings in other ASA regions, considering the unique characteristics of these areas and the importance of tailored environmental monitoring and assessment approaches.

Spatiotemporal dynamics and influencing factors of human brucellosis in Mainland China from 2005-2021.

BACKGROUND: Brucellosis poses a significant public health concern. This study explores the spatial and temporal dynamic evolution of human brucellosis in China and analyses the spatial heterogeneity of the influencing factors related to the incidence of human brucellosis at the provincial level. METHODS: The Join-point model, centre of gravity migration model and spatial autocorrelation analysis were employed to evaluate potential changes in the spatial and temporal distribution of human brucellosis in mainland China from 2005 to 2021. Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multi-scale Geographically Weighted Regression (MGWR) models were constructed to analyze the spatial and temporal correlation between the incidence rate of human brucellosis and meteorological and social factors. RESULTS: From 2005 to 2021, human brucellosis in China showed a consistent upward trend. The incidence rate rose more rapidly in South, Central, and Southwest China, leading to a shift in the center of gravity from the North to the Southwest, as illustrated in the migration trajectory diagram. Strong spatial aggregation was observed. The MGWR model outperformed others. Spatio-temporal plots indicated that lower mean annual temperatures and increased beef, mutton, and milk production significantly correlated with higher brucellosis incidence. Cities like Guangxi and Guangdong were more affected by low temperatures, while Xinjiang and Tibet were influenced more by beef and milk production. Inner Mongolia and Heilongjiang were more affected by mutton production. Importantly, an increase in regional GDP and health expenditure exerted a notable protective effect against human brucellosis incidence. CONCLUSIONS: Human brucellosis remains a pervasive challenge. Meteorological and social factors significantly influence its incidence in a spatiotemporally specific manner. Tailored prevention strategies should be region-specific, providing valuable insights for effective brucellosis control measures.