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.

Spatial variations and determinants of iron containing foods consumption among 6-23 months old children in Ethiopia: spatial, and multilevel analysis.

Consuming foods high in iron benefits metabolic processes as well as the development of the neonatal and fetal brain. Despite the significance of eating foods high in iron for public health, Ethiopian practices are still limited when compared to the World Health Organization's (WHO) assessment of its consumption of such foods. This study used the Ethiopia Demographic and Health Survey (EDHS) to evaluate the consumption of iron-rich foods, regional clustering, and related characteristics among children aged 6-23 months. The information was taken from the typical EDHS 2019 dataset, which included a weighted sample of 1572 young children aged 6-23 months old in total. Utilizing Kuldorff's SaTScan version 9.6 software, spatial scan statistics were produced. Software from ArcGIS 10.8 was used to display the regional distribution of inadequate consumption of foods high in iron. Utilizing multilevel or mixed effects logistic regression analysis, the associated determinants for a healthy diet rich in foods containing iron were found. In the final model, a P-value of < 0.05 was announced as a statistical significance variable. Overall, in Ethiopia, children aged 6-23 months consumed iron-rich foods at a rate of 27.14% (95% CI 24.99-29.39). Poor intake of foods heavy in iron is concentrated in Ethiopia's regional states of Afar, a sizable portion of Amhara, Oromia, Tigray, Somali, Gambela, and SNNPS. Primary and secondary education (AOR = 1.73, CI 95%: 1.23, 2.41), and (AOR = 1.97,CI 95%: 1.25, 3.10), having ≥ 2 under five children, and current status of breastfeeding (AOR = 0.62 (CI 95%: 0.45, 0.84), and (AOR = 0.32, CI 95%: 0.23, 0.44), giving birth at health facilities (AOR = 1.51, CI 95%: 1.06, 2.13),being from Afar and Somali regions (AOR = 0.39, 95%: 0.17, 0.93), and (AOR = 0.26, CI 95%: 0.10, 0.69) have shown statistically significant association with the outcome variable respectively. In Ethiopia, providing high-iron meals and supplements to under-2-year-old children represents minimal, but persistent, public health expenses. Based on the identified determinants, the Ethiopian federal ministry of health and other stakeholders should pay special attention to the locations designated as hot spots for maternal and child health service enhancement to promote the consumption of iron-rich meals among children aged 6-23 months.

Sub-regional variation in atmospheric and land variables regulates tea yield in the Dooars region of West Bengal, India.

Climatic variables can have localized variations within a region and these localized climate patterns can have significant effect on production of climate-sensitive crops such as tea. Even though tea cultivation and industries significantly contribute to employment generation and foreign earnings of several South Asian nations including India, sub-regional differences in the effects of climatic and soil variables on tea yield have remained unexplored since past studies focused on a tea-producing region as a whole and did not account for local agro-climatic conditions. Here, using a garden-level panel dataset based on tea gardens of Dooars region, a prominent tea-producing region in India, we explored how sub-regional variations in climatic and land variables might differently affect tea yield within a tea-producing region. Our analysis showed that the Dooars region harboured significant spatial variability for different climatic (temperature, precipitation, surface solar radiation) and soil temperature variables. Using graph-based Louvain clustering of tea gardens, we identified four spatial sub-regions which varied in terms of topography, annual and seasonal distribution of climatic and land variables and tea yield. Our sub-region-specific panel regression analyses revealed differential effects of climatic and land variables on tea yield of different sub-regions. Finally, for different emission scenario, we also projected future (2025-2100) tea yield in each sub-region based on predictions of climatic variables from three GCMs (MIROC5, CCSM4 and CESM1(CAM5)). A large variation in future seasonal production changes was projected across sub-regions (-23.4-35.7% changes in premonsoon, -4.2-3.1% changes in monsoon and -10.9-10.7% changes in postmonsoon tea production, respectively).

Spatiotemporal Epidemiology of the Gonorrhea Epidemic in Relation to Neighborhood-level Structural Factors in an Eastern Province of China, 2016-2020.

Context: Gonorrhea, a highly communicable, sexually transmitted infection, remains a major public-health concern globally. In recent years, Zhejiang province, an eastern province, has had the highest incidence of gonorrhea in China. Objective: The study intended to identify the geographic distribution patterns and spaciotemporal clustering characteristics of the disease's incidence in Zhejiang between 2016 and 2020, to understand the spatial epidemiology of gonorrhea and to pinpoint the locations with relatively high risks of gonorrhea, the hotspots, which could be the key areas for disease prevention and control. Design: The research team performed a retrospective, spaciotemporal-clustering analysis of data about newly reported gonorrhea cases from January 2016 to December 2020 in Zhejiang province, using the China Information System for Disease Control and Prevention. Setting: The study took place at the Zhejiang Provincial Institute of Dermatology in Huzhou, China. Outcome Measures: The research team: (1) used the Geographic Information System software-ArcGIS 10.8 software to draw statistical maps; (2) conducted a spatial-pattern clustering analysis at the district or county level; (3) performed an autocorrelation analysis using Getis-Ord (Gi*) statistics to detect spatial patterns and the hotspots of gonorrhea incidence; and (4) used SaTScan9.7 to analyze the space-time clusters. Results: Zhejiang province reported 85 904 gonorrhea cases from 2016 to 2020, with a male to female ratio of 3.81:1. The average annual incidence rate of gonorrhea was 30.50 per 100 000 individuals in the population, ranging from 22.73 cases to 39.65 cases, and the annual incidence showed a significant downward trend over the five years (χ2 = 16.142, P < .001). The northern and central areas had a higher incidence than the southern area did. Autocorrelation analysis showed that the gonorrhea incidence had a significantly clustered distribution (Moran's I from 0.197 to 0.295, Z score from 4.749 to 6.909, P < .001). The high-high cluster areas were mainly in the urban districts of Hangzhou and some counties and districts of Jiaxing. The Gi* statistics further indicated that the hotspots of gonorrhea were mainly in Hangzhou, Jiaxing, and Huzhou. The Kuldorff's scan identified two clusters, mainly composed of 36 counties or districts in northern Zhejiang, such as Hangzhou and Jiaxing, and central Zhejiang, such as Jinhua and Shaoxing. Conclusions: The gonorrhea incidence rates in northern and central Zhejiang from 2016 to 2020 were higher than those in southern Zhejiang. An area of relatively higher risk for gonorrhea existed mainly in the urban districts of Hangzhou and some counties and districts of Jiaxing, Jinhua, and Shaoxing. In the future, the research team plans to focus on strengthening the prevention and control measures against gonorrhea in those areas.

Undernutrition among the children below five years of age in Uganda: a spatial analysis approach.

BACKGROUND: Undernutrition is a health condition caused by a lack of enough food intake, not having enough of the right combination of food nutrients, or the body's failure to utilize the food eaten resulting in either, stunting, being underweight, or wasting. Globally, undernutrition affects more than 149 million under-five children, while in Uganda about 3 in every 10 children suffer from undernutrition. Undernutrition and its risk factors among under-five children in Uganda were unevenly distributed across the country and a study that focused on spatial distribution was prudent to examine the nature of the problem and salient factors associated with it. The current study addressed the issues of spatial heterogeneity of undernutrition and its determinants with the goal to identify hot spots and advise policymakers on the best actions to be taken to address the problem. METHODS: Data were obtained from the 2016 Uganda Demographic and Health Survey. Prevalence rates and percentages of risk factors were combined with the Uganda district shape file to allow spatial analysis. Moran's I, Getis-Ord (GI*), and Geographically Weighted Regressions were respectively used to establish the local, global, and geographically weighted regressions across the country. Stata 15 and ArcGIS 10. 7 soft wares were used. RESULTS: The results indicate that undernutrition in Uganda shows varies spatially across regions. Evidence of hot spots exists in the Karamoja and Arua regions, cold spot areas exist around the central part of the country while the greatest part of Western Uganda, Northern, and Eastern were not significant. CONCLUSION: The study reveals that a variation in the distribution of undernutrition throughout the country. Significant spatial patterns associated with undernutrition as identified through the hotspot and cold spot analysis do exist in Uganda. Programs targeting to reduce the undernutrition of under-five children in Uganda should consider the spatial distribution of undernutrition and its determinants whereby priority should be given to hotspot areas. The spatial intensity of undernutrition and its determinants indicate that focus should be tailored to meet the local needs as opposed to a holistic national approach.

Area-to-point regression Kriging approach fusion of Landsat 8 OLI and Sentinel 2 data for assessment of soil macronutrients at Anaimalai, Coimbatore.

Spectral indices-based soil prediction models derived from multispectral datasets are too intricate in terms of accuracy as well as resolution. Complications arise while incorporating multispectral datasets for regional-scale spatial assessment of soil macronutrients. Sporadically satellite image fusion techniques have been used for soil nutrient interpolation to circumvent the complications. The fusion of multispectral bands encompasses precise soil information that cannot be observed as accurate with single satellite dataset. In this study, fusion of near infrared regions of Landsat 8 Operational Land Imager and Sentinel 2 has been observed for its contribution on soil macronutrient assessments. Area-to-point regression Kriging (ATPRK) approach is followed in fusing the two satellite imagery and in situ soil spectral have used for the validation of the resultant. Comparative statistical analysis on Landsat 8 OLI band 5 (wavelength: 845-885 nm), Sentine-2 band 8,8A (wavelength: 785-900 nm) datasets and fused satellite bands provides R2 values of 0.8209, 0.8436, and 0.8763 respectively. Regression models y = (0.25006 ± 0.00754) + (0.0000313)x, y = (0.25252 ± 0.0062) + (0.0000810)x, and y = (0.23715 ± 0.0062) + (0.0001210)x for nitrogen, phosphorus, and potassium respectively aids for soil macronutrient interpolation and assessments. Computations reveals the ranges of nitrogen, phosphorus and potassium that floats from 48 to 295 kg/ha, 5.0 to 37 kg/ha, and 32 to 455 kg/ha in the study area. Fusion of satellite imagery by ATPRK approaches in soil macronutrient study at regional scale brings the novelty of the study.

A Broad Study to Develop Maternity Units Design Knowledge Combining Spatial Analysis and Mothers' and Midwives' Perception of the Birth Environment.

OBJECTIVES: This article investigates how the physical birth environment is perceived by the users (women and midwives) in different settings, a midwife-led unit and an obstetric-led unit, placed in Italy. BACKGROUND: In the field of birth architecture research, there is a gap in the description of the spatial and physical characteristics of birth environments that impact users' health, specifically for what concerns the perception by women. METHODS: The study focuses on multi-centered mixed methods design, employing both quantitative and qualitative research methods (questionnaire, spatial analysis) and covering different disciplines (architecture, environmental psychology, and midwifery). RESULTS: The results revealed significant differences between the two settings and some associations between perceived and spatial data concerning: calm atmosphere, greater intimacy, spacious birth room, clarity of service points, clarity in finding midwives, sufficient space for labor, noise, privacy, and the birth room adaptability. CONCLUSIONS: The findings confirm the importance of the spatial layout and indicate documented knowledge as an input to consider when designing birth spaces in order to promote user well-being.

Multi-Scale Spatial Analysis of the Tumor Microenvironment Reveals Features of Cabozantinib and Nivolumab Efficacy in Hepatocellular Carcinoma.

Background: Concomitant inhibition of vascular endothelial growth factor (VEGF) and programmed cell death protein 1 (PD-1) or its ligand PD-L1 is a standard of care for patients with advanced hepatocellular carcinoma (HCC), but only a minority of patients respond, and responses are usually transient. Understanding the effects of therapies on the tumor microenvironment (TME) can provide insights into mechanisms of therapeutic resistance. Methods: 14 patients with HCC were treated with the combination of cabozantinib and nivolumab through the Johns Hopkins Sidney Kimmel Comprehensive Cancer Center. Among them, 12 patients (5 responders + 7 non-responders) underwent successful margin negative resection and are subjects to tissue microarray (TMA) construction containing 37 representative tumor region cores. Using the TMAs, we performed imaging mass cytometry (IMC) with a panel of 27-cell lineage and functional markers. All multiplexed images were then segmented to generate a single-cell dataset that enables (1) tumor-immune compartment analysis and (2) cell community analysis based on graph-embedding methodology. Results from these hierarchies are merged into response-associated biological process patterns. Results: Image processing on 37 multiplexed-images discriminated 59,453 cells and was then clustered into 17 cell types. Compartment analysis showed that at immune-tumor boundaries from NR, PD-L1 level on tumor cells is significantly higher than remote regions; however, Granzyme B expression shows the opposite pattern. We also identify that the close proximity of CD8+ T cells to arginase 1hi (Arg1hi) macrophages, rather than CD4+ T cells, is a salient feature of the TME in non-responders. Furthermore, cell community analysis extracted 8 types of cell-cell interaction networks termed cellular communities (CCs). We observed that in non-responders, macrophage-enriched CC (MCC) and lymphocyte-enriched CC (LCC) strongly communicate with tumor CC, whereas in responders, such communications were undermined by the engagement between MCC and LCC. Conclusion: These results demonstrate the feasibility of a novel application of multiplexed image analysis that is broadly applicable to quantitative analysis of pathology specimens in immuno-oncology and provides further evidence that CD163-Arg1hi macrophages may be a therapeutic target in HCC. The results also provide critical information for the development of mechanistic quantitative systems pharmacology models aimed at predicting outcomes of clinical trials.

[Responses of spatial distribution pattern of the dominant population Artemisia ordosica to enclosure restoration in desert steppe.] / è’æ¼ è‰åŽŸä¼˜åŠ¿ç§ç¾¤æ²¹è’¿ç©ºé—´åˆ†å¸ƒæ ¼å±€å¯¹å°è‚²æ¢å¤çš„å“åº”.

Shrubs play an important role in maintaining biodiversity, stability and ecological service in grassland. Exploring the effects of enclosure on dominant shrub population can provide scientific guidance for grassland restoration and tending management. In this study, we investigated main growth characteristics and spatial distribution pattern of Artemisia ordosica population in four enclosed grasslands with duration of 0, 5, 15, and 25 years. The results showed that population density increased first and then decreased with time extension, and peaked after enclosed for 15 years, which was 3.7 times that of unenclosed plot. The crown and projected area showed opposite responses trend to that of density, which decreased by 31.7% and 52.3% after enclosed 15 years, respectively. The height decreased by 25.3% after 5 years of enclosure, and then increased gradually. Semi-variance function analysis showed that population distribution in all grasslands conformed to Gaussian model. The spatial variation decreased gradually in the early stage of enclosure, and then increased after enclosed for 15 years. Structure ratio in each plot was higher than 0.75, but nugget was relatively small, indicating that spatial autocorrelation of population was mainly affected by structural factors rather than random factors. Spatial distribution of A. ordosica population was patchy and striped. Enclosure reduced spatial variation of population at small scale. However, spatial heterogeneity and scale dependence of population enhanced after enclosed 25 years as plaque dissociating. Our findings suggest that enclosure duration is the key factor affecting plant growth and spatial distribution of dominant population in desert steppe. Long-term fencing enhances the spatial heterogeneity of dominant population. Appropriate human intervention should be carried out after 15 years of enclosure.

Spatial variability of soil physical properties in longitudinal profiles.

The spatial variability of physical properties, such as bulk density, penetration resistance and gravimetric moisture, obtained by applying geostatistics in precision agriculture, can effectively indicate the physical behavior of agricultural soils in longitudinal profiles. In this way, the spatial dependence of physical properties in streets of coffee plantations with different lengths was evaluated in the southern Minas Gerais, Brazil. For this purpose, five longitudinal profiles were measured in streets, each one with depths ranging from 0 to 0.60 m, in six layers of 0.10 m, being the database composed of 432 property, 144 by property, submitted to the ordinary kriging geostatistical method in order to obtain spatial variability maps using the R software. They were evaluated by the lower mean cross-validation error of theoretical models fitted by ordinary least squares (OLS), being detected in higher superficial layers, from 0 to 0.30 m, lower bulk density and lower penetration resistance, with variable gravimetric moisture in the length direction of some streets of coffee plantations, being that these properties presented different structures of spatial dependence for each street.

Geographical variations and determinants of iron and folic acid supplementation during pregnancy in Ethiopia: analysis of 2019 mini demographic and health survey.

BACKGROUND: One of the packages of critical antenatal care treatments for pregnant women includes iron and folic acid (Fe/FA) supplementation. Using recently available and nationwide representative survey data, this study aimed to determine the spatial patterns and drivers of Fe/FA supplementation during pregnancy. METHOD: The data for this study was obtained from Ethiopia's 2019 Mini Demographic and Health Survey (EMDHS). We used the Kid's Record (KR) dataset, and a total weighted sample of 3926 reproductive-age women who gave birth within the previous 5 years was used as the study's final sample size. To analyze the spatial distributions (geographic variation of Fe/FA supplementation) different statistical software like Excel, ArcGIS, and Stata 14 were used. A two-level multilevel binary logistic regression model was fitted to identify both individual and community-level factors associated with Fe/FA supplementation during pregnancy. RESULT: This study found that there were significant geographical variations of iron and folic acid supplementation across Ethiopia, eastern and southern parts of the country were predicted to have low Fe/FA supplementation coverage. Advanced maternal age (AOR = 0.75: 95%CI: 0.59 0.96), resides in developing region (AOR = 0.57, 95%CI: 0.43 0.74), not attended formal education (AOR = 0.60, 95%CI: 0.39 0.92), middle (AOR = 1.51, 95%CI: 18 1.93) and rich wealth status (AOR = 1.48, 95%CI: 1.15 1.91), and four and above ANC visits (AOR = 4.35 95%CI: 3.64 5.21) were determinants of iron and folic acid supplementation among pregnant women. CONCLUSION: Our research found that there were geographical variations across the country, with low coverage seen in Ethiopia's eastern and southern regions. Iron and folic acid supplementation coverage were inadequate among pregnant women with low education, advanced maternal age, and those from underdeveloped countries. Conversely, increasing iron and folic acid uptake was associated with higher socioeconomic class and four or more ANC visits. The findings of this study highlight the importance of increasing maternal health care, such as iron and folic acid supplements, for underserved populations.

[Identifying Relationship Between Nutrient Contents in Road-Deposited Sediment and Urban Basic Elements Based on Kernel Density Estimation].

Understanding the spatial connection between urban basic elements and pollutants in road-deposited sediment (RDS) is beneficial to the quantitative explanation of source areas and the precise management of urban nonpoint source pollution. The objective of this study was to explore the spatial connection between urban basic elements and nutrient contents in RDS, using Hanyang district of Wuhan city as a case study area. Total nitrogen (TN), total phosphorus (TP), exchangeable phosphorus (Ex-P), aluminum-bound phosphorus (Al-P), iron-bound phosphorus (Fe-P), occluded phosphorus (Oc-P), apatite phosphorus (Ca-P), detrital apatite phosphorus (De-P), and organophosphorus (Or-P) were chosen as representative pollutants, using the kernel density of the road network and seven groups of points of interest to represent the spatial distribution of urban basic elements. Through correlation analysis, the relationship between urban basic elements and nutrient contents in RDS was examined. The results showed that nutrient contents in RDS from Hanyang varied significantly with location. Specifically, Al-P, Oc-P, and Or-P had greater spatial variability in contrast with that of Ex-P and Fe-P, and the spatial variabilities of Ca-P and De-P both varied considerably with the particle size of the RDS. All types of urban basic elements and nutrient contents in RDS were correlated to different extents. Ex-P, Al-P, Oc-P, and Or-P were negatively correlated with the kernel density of most urban basic elements, whereas TN, TP, Fe-P, Ca-P, and De-P showed a positive correlation with the kernel density of most urban basic elements. TN, Al-P, Fe-P, Ca-P, and Or-P appeared to have much stronger correlation with the kernel density of urban basic elements compared to other selected pollutants, whereas the extent of correlation between them was influenced by the particle size of the RDS. In general, the correlation between nutrient contents in RDS and the kernel density of urban basic elements was more significant when the particle size of the RDS was under 450 µm and more robust when the particle size of the RDS was under 150 µm.

Geographical disparities and determinants of adherence to iron folate supplementation among pregnant women in Ethiopia: spatial and multilevel analysis of the Ethiopian Mini Demographic and Health Survey of 2019.

OBJECTIVE: This study aimed to investigate geographic disparities and determinants of adherence to iron and folate supplementation among pregnant women in Ethiopia. METHOD: A secondary data analysis was performed using data from the Ethiopian Mini Demographic and Health Survey 2019. A total of 2235 pregnant women aged 15-49 years were included in the analysis. ArcGIS V.10.8 and SaTScan V.9.6 were used for spatial analysis. Multilevel logistic regression analysis was used to determinants. RESULT: Of the total number of participants, 80.3% of pregnant mothers took iron and folate supplements for less than the recommended days. Adherence to iron folate supplementation among pregnant women in Ethiopia was spatially clustered with Moran's global I=0.15868. The SaTScan analysis identified the most likely significant clusters found in the eastern Tigray, northeast Amhara and northwest Afar regions. Multivariable multilevel analysis showed that mothers who were living apart from their partner (adjusted OR (AOR)=10.05, 95% CI 1.84 to 55.04), had antenatal care (ANC) visits at least four times (AOR=0.53, 95% CI 0.41 to 0.69), a higher education level (AOR=0.39, 95% CI 0.25 to 0.63), big distance from health facilities (AOR=1.7, 95% CI 1.51 to 1.97) were significant factors of adherence to iron-folate supplementation. Mothers living in the Amhara and Addis Ababa regions were 0.35 (AOR=0.35, 95% CI 0.19 to 0.621), and 0.29 (AOR=0.29, 95% CI 0.15 to 0.7) times lower iron-folate supplementation intake than mother's in Tigray region. CONCLUSION: In this study, 8 out of 10 pregnant women did not take iron and folate supplements during the recommended period. As a result, health education activities were necessary to raise awareness among women and the community about the importance of iron folate supplementation during pregnancy, and public health programmes should increase iron folate supplementation through women's education, ANC visits and mothers living in low-iron areas.

Investigation on spatial variability and influencing factors of drinking water iodine in Xinjiang, China.

BACKGROUND AND OBJECTIVES: Xinjiang is one of the areas in China with extremely severe iodine deficiency. The health of Xinjiang residents has been endangered for a long time. In order to provide reasonable suggestions for scientific iodine supplementation and improve the health and living standards of the people in Xinjiang, it is necessary to understand the spatial distribution of iodine content in drinking water and explore the influencing factors of spatial heterogeneity of water iodine content distribution. METHODS: The data of iodine in drinking water arrived from the annual water iodine survey in Xinjiang in 2017. The distribution of iodine content in drinking water in Xinjiang is described from three perspectives: sampling points, districts/counties, and townships/streets. ArcGIS was used for spatial auto-correlation analysis, mapping the distribution of iodine content in drinking water and visualizing the distribution of Geographically Weighted Regression (GWR) model parameter. Kriging method is used to predict the iodine content in water at non-sampling points. GWR software was used to build GWR model in order to find the factors affecting the distribution of iodine content in drinking water. RESULTS: There are 3293 sampling points in Xinjiang. The iodine content of drinking water ranges from 0 to 128 µg/L, the median is 4.15 µg/L. The iodine content in 78.6% of total sampling points are less than 10 µg/L, and only that in the 3.4% are more than 40 µg/L. Among 1054 towns' water samples in Xinjiang, 88.9% of the samples' water iodine content is less than 10 µg/L. Among the 94 studied areas, the median iodine content in drinking water in 87 areas was less than 10 µg/L, those values in 7 areas were between 10-40 µg/L, and the distribution of water iodine content in Xinjiang shows clustered. The GWR model established had found that the effects of soil type and precipitation on the distribution of iodine content in drinking water were statistically significant. CONCLUSIONS: The iodine content of drinking water in Xinjiang is generally low, but there are also some areas which their drinking water has high iodine content. Soil type and precipitation are the factors affecting the distribution of drinking water iodine content, and are statistically significant (P<0.05).

Geographical variation and associated factors of vitamin A supplementation among 6-59-month children in Ethiopia.

INTRODUCTION: Vitamin A has been one of the most important micronutrients which are necessary for the health of the children. In developing countries, the supplementation of vitamins under a regular schedule had different constraints. Awareness, access, and resource limitations were usually the problem. In the current study, we analyzed the data from the demographic health survey (EDHS) 2016 to uncover the spatial distribution, predictors, and to provide additional information for policymaking and interventions. METHODS: In this analysis, we applied intra-community correlation to measure the random effect; global Moran's I to test the nature of variance in the null model; proportional change in variance to check the variance of null and neighborhood in subsequent models. We used STATA 15 for prediction; ArcGIS 10.7 for the spatial distribution of vitamin A supplementation; SaTscan 9.6.1 to specify location of clustering were the applied soft wares. After confirming that the traditional logistic regression cannot explore the variances, we applied multilevel logistic regression to examine predictors where p-value <0.25 was used to include variables into the model and p-value<0.05 was used to declare associations. We presented the result using means, standard deviations, numbers, and proportions or percent, and AOR with 95% CI. RESULT: The vitamin A coverage was 4,029.22 (44.90%) in Ethiopia in 2016. The distribution followed some spatial geo-locations where Afar, Somali were severely affected (RR = 1.46, P-value < 0.001), some pockets of Addis Ababa (RR = 1.47, p-value <0.001), and the poor distribution also affected all other regions partially. Place of delivery 1.2(1-1.34), primary and secondary education 1.3 (1-1.6), media exposure 1.2(1.1-1.4), having work 1.4(1.2-1.5), and all visits of ANC were positively influenced the distribution. CONCLUSION: The distribution of vitamin A coverage was not random as per the EDHS 2016 data. Regions like Afar, Somali, and some pocket areas in Addis inquires immediate interventions. Pastoralist, agrarian, and city administrations were all involved from severe to the lesser coverage in order. Since factors like Place of delivery, education, ANC, media exposure, and having work were showed positive associations, interventions considering awareness, access, and availability of service need more attention than ever.

Quantifying clinic transfers among people living with HIV in the Western Cape, South Africa: a retrospective spatial analysis.

OBJECTIVES: For persons living with HIV (PLWH) in long-term care, clinic transfers are common and influence sustained engagement in HIV care, as they are associated with significant time out-of-care, low CD4 count, and unsuppressed viral load on re-entry. Despite the geospatial nature of clinic transfers, there exist limited data on the geospatial trends of clinic transfers to guide intervention development. In this study, we investigate the geospatial characteristics and trends of clinic transfers among PLWH on antiretroviral therapy (ART) in the Western Cape Province of South Africa. DESIGN: Retrospective spatial analysis. SETTING: PLWH who initiated ART treatment between 2012 and 2016 in South Africa's Western Cape Province were followed from ART initiation to their last visit prior to 2017. Deidentified electronic medical records from all public clinical, pharmacy, and laboratory visits in the Western Cape were linked across space and time using a unique patient identifier number. PARTICIPANTS: 4176 ART initiators in South Africa (68% women). METHODS: We defined a clinic transfer as any switch between health facilities that occurred on different days and measured the distance between facilities using geodesic distance. We constructed network flow maps to evaluate geospatial trends in clinic transfers over time, both for individuals' first transfer and overall. RESULTS: Two-thirds of ART initiators transferred health facilities at least once during follow-up. Median distance between all clinic transfer origins and destinations among participants was 8.6 km. Participant transfers were heavily clustered around Cape Town. There was a positive association between time on ART and clinic transfer distance, both among participants' first transfers and overall. CONCLUSION: This study is among the first to examine geospatial trends in clinic transfers over time among PLWH. Our results make clear that clinic transfers are common and can cluster in urban areas, necessitating better integrated health information systems and HIV care.

Geostatistical analysis of uranium concentrations in North-Western part of Ogun State, Nigeria.

Several statistical techniques have been adopted in the analysis and interpretation of radiometric survey data set with little attention to the geostatistical approach. In this study, the geostatistical technique was used to analyse an airborne uranium data set in the North-western part of Ogun state to deduce the autocorrelation level of uranium with the corresponding geological composition through the geostatistical model fitting. Twelve uranium lithological (UL) units were identified and analysed using variogram and kriging. Three variogram models namely; spherical, exponential and gaussian, were utilised. The variogram results showed that the three models fit in the interpretation of uranium data in the order of Spherical < Gaussian < Exponential models for all the UL units except at UL6 which had range (R) values of 4227.3 m, 5318.2 m and 5636.4 m for Gaussian, Spherical and Exponential models, respectively. The Nugget-Sill ratios (NSR) were in the range of 3.57-18.97% for the three models across the 12 UL units. These results indicate strong spatial autocorrelation/dependency of uranium concentration on geological composition. The ordinary kriging estimator revealed that the weathered basement formation has the highest uranium concentration compared to basement and sedimentary formations across the study area. The coefficient of variation results also revealed homogeneity in the uranium source across the UL units. The geostatistics technique is a useful mathematical tool in the analysis and interpretation of uranium concentration in the North-western part of Ogun State.

Spatial variation in direct and indirect contact rates at the wildlife-livestock interface for informing disease management.

Little is known about disease transmission relevant contact rates at the wildlife-livestock interface and the factors shaping them. Indirect contact via shared resources is thought to be important but remains unquantified in most systems, making it challenging to evaluate the impact of livestock management practices on contact networks. Free-ranging wild pigs (Sus scrofa) in North America are an invasive, socially-structured species with an expanding distribution that pose a threat to livestock health given their potential to transmit numerous livestock diseases, such as pseudorabies, brucellosis, trichinellosis, and echinococcosis, among many others. Our objective in this study was to quantify the spatial variations in direct and indirect contact rates among wild pigs and cattle on a commercial cow-calf operation in Florida, USA. Using GPS data from 20 wild pigs and 11 cattle and a continuous-time movement model, we extracted three types of spatial contacts between wild pigs and cattle, including direct contact, indirect contact in the pastoral environment (unknown naturally occurring resources), and indirect contact via anthropogenic cattle resources (feed supplements and water supply troughs). We examined the effects of sex, spatial proximity, and cattle supplement availability on contact rates at the species level and characterized wild pig usage of cattle supplements. Our results suggested daily pig-cattle direct contacts occurred only occasionally, while a significant number of pig-cattle indirect contacts occurred via natural resources distributed heterogeneously across the landscape. At cattle supplements, more indirect contacts occurred at liquid molasses than water troughs or molasses-mineral block tubs due to higher visitation rates by wild pigs. Our results can be directly used for parameterizing epidemiological models to inform risk assessment and optimal control strategies for controlling transmission of shared diseases.

Factorial kriging for estimating and mapping the geochemical background from in situ gamma dose rate measurements downstream of a former uranium mine.

A geostatistical approach is applied for extracting the geochemical background from gamma dose rate data acquired downstream of a former French uranium mining area. The exploratory data analysis shows that the spatial structure of the gamma dose rate consists of two components: a short isotropic component (10 m-range) that corresponds to the geochemical background; and a long anisotropic component (30-60 m-range) that corresponds to the drainage network features previously fed by the mine discharge water. The gamma dose rate on the whole area of interest was estimated and simulated (to deal with uncertainties) through a kriging of the measured values. The spatial component related to the geochemical background was then extracted through factorial kriging. The proportion of the gamma dose rate explained by the geochemical background according to factorial kriging is consistent within uncertainties with geochemical analyses performed on soil and sediment samples. This study thus highlights the potential of such geostatistical approaches for better exploiting radiometric data.

Corona and coffee on your commute: a spatial analysis of COVID-19 mortality and commuting flows in England in 2020.

BACKGROUND: The COVID-19 pandemic forced governments to implement lockdown policies to curb the spread of the disease. These policies explicitly encouraged homeworking, hence reducing the number of commuters with the implicit assumption that restricting peoples' movement reduces risk of infection for travellers and other people in their areas of residence and work. Yet, the spatial interrelation of different areas has been rarely addressed both in the public discourse and in early accounts of the various consequences of COVID-19. METHODS: Our study proposes a spatial analysis of the association between commuting flows and COVID-19 mortality in England between March and June 2020, using a range of publicly available area-level data. To account for spatial correlation, we used a structural mobility gravity model to analyze commuting flows between Local Authority Districts. By accounting for these spatial dependencies, we temper concerns of bias and inefficiency affecting simple linear estimates. Additionally, we disentangle the direct and indirect (from other areas) influence of commuting on COVID-19 mortality. RESULTS: The results of our spatial regression models suggest that higher commuting flows-in general and particularly by public transport-are associated with higher COVID-19 mortality. Our results are consistent with a reduction in COVID-related mortality after the introduction of a national lockdown in March. The spatial-lag term is statistically significant, highlighting the importance of accounting for spatial dependencies. CONCLUSION: We suggest that considering spatial interactions through commuting or travel motivations may offer interesting perspectives on the trade-off between health and economic activity during lockdowns.