High-high Clusters Research Articles

Epidemiological features of seasonal influenza transmission among 11 climate zones in Chinese Mainland

BackgroundPrevious studies provided some evidence of meteorological factors influence seasonal influenza transmission patterns varying across regions and latitudes. However, research on seasonal influenza activities based on climate zones are still in lack. This study aims to utilize the ecological-based Köppen Geiger climate zones classification system to compare the spatial and temporal epidemiological characteristics of seasonal influenza in Chinese Mainland and assess the feasibility of developing an early warning system.MethodsWeekly influenza cases number from 2014 to 2019 at the county and city level were sourced from China National Notifiable Infectious Disease Report Information System. Epidemic temporal indices, time series seasonality decomposition, spatial modelling theories including Moran’s I and local indicators of spatial association were applied to identify the spatial and temporal patterns of influenza transmission.ResultsAll climate zones had peaks in Winter-Spring season. Arid, desert, cold (BWk) showed up the first peak. Only Tropical, savannah (Aw) and Temperate, dry winter with hot summer (Cwa) zones had unique summer peak. Temperate, no dry season and hot summer (Cfa) zone had highest average incidence rate (IR) at 1.047/100,000. The Global Moran’s I showed that average IR had significant clustered trend (z = 53.69, P < 0.001), with local Moran’s I identified high-high cluster in Cfa and Cwa. IR differed among three age groups between climate zones (0–14 years old: F = 26.80, P < 0.001; 15–64 years old: F = 25.04, P < 0.001; Above 65 years old: F = 5.27, P < 0.001). Age group 0–14 years had highest average IR in Cwa and Cfa (IR = 6.23 and 6.21) with unique dual peaks in winter and spring season showed by seasonality decomposition.ConclusionsSeasonal influenza exhibited distinct spatial and temporal patterns in different climate zones. Seasonal influenza primarily emerged in BWk, subsequently in Cfa and Cwa. Cfa, Cwa and BSk pose high risk for seasonal influenza epidemics. The research finds will provide scientific evidence for developing seasonal influenza early warning system based on climate zones.

Spatial-temporal analysis of COVID-19 cases in the state of Alagoas.

The COVID-19 pandemic has severely impacted Brazil, highlighting significant gaps in public health infrastructure. This study aims to analyze the spatial-temporal distribution of COVID-19 cases in Alagoas. The study covers all 102 municipalities in Alagoas, using official data from the Alagoas COVID-19 Panel. It is observational and retrospective with an ecological and quantitative approach. Data were collected up to July 2, 2022, totaling 305,806 cases. Spatial analysis was performed using R Statistical software, with Global Moran's Index (GMI) and Local Indicators of Spatial Association (LISAs) identifying spatial clusters. In 2020, municipalities showed significant but weak spatial autocorrelation (GMI = 0.2084; p < 0.05). High-High clusters appeared in Maceió and nearby municipalities. In 2021, spatial autocorrelation remained weak (GMI = 0.2344; p < 0.05). High-High clusters persisted in Satuba and Maceió, while Low-Low clusters expanded into northeastern Alagoas by 2022. The reduction in High-High clusters in Maceió in 2022 likely resulted from early vaccination efforts. The spatial distribution pattern of COVID-19 in Alagoas reveals significant insights into regional pandemic dynamics. Stable infection rates in the center-west and south of Alagoas may be due to lower population density and less movement. The dynamic nature of COVID-19 spread highlights the need for continuous monitoring and adaptive public health strategies. The study underscores the importance of targeted interventions and future research to refine spatial models and incorporate additional variables to enhance predictive accuracy and inform public health strategies.

Tendência e distribuição espacial da hepatite B em gestantes no Brasil

Abstract Objectives: to analyze the trend and spatial distribution of hepatitis B in pregnant women in Brazil. Methods: ecological study based on all notified cases of hepatitis B in pregnant women through the Information System for Notifiable Diseases – Sinan between 2009 and 2018. Hepatitis B virus (HBV) detection rates were calculated in all municipalities. Spatial analysis was performed using the Global Moran Index for global data and local indicators of spatial association (Lisa) for the 5,570 municipalities. For trend analysis by State, the Prais-Winsten generalized linear regression model was used. Results: 15,253 pregnant women with HBV were reported. High detection rates were observed in the municipalities of São Miguel da Boa Vista-SC (68.96/1000 live births (LB)), Araguaiana-MT (68.18/1000 LB), Reserva do Cabaçal-MT (80, 00/1,000 LB), São Geraldo da Piedade-MG (75/1000 LB), Porto Mauá-RS (111, 11/1000 LB), in the respective bienniums. Moran (I) (I=0.056) showed a positive spatial association. In Lisa, 78 municipalities were included in the high-high cluster, 51.28% in the South region and 48 in the low-low cluster with 72.91% in the Southeast. There was an increasing trend in Maranhão (p=0.004) and Pernambuco (p=0.007) and a decrease in Mato Grosso (p=0.012), Paraná (p=0.031) and Santa Catarina (p=0.008). Conclusion: the detection of hepatitis B in pregnant women was observed in most Brazilian municipalities, with an increasing trend in two states and a decrease in three others.

The impact of climate change on China's central region grain production: evidence from spatiotemporal pattern evolution

&lt;abstract&gt; &lt;p&gt;Under the influence of global climate change, the climatic conditions of China's major agricultural regions have changed significantly over the last half-century, affecting regional grain production levels. With its favorable conditions for agricultural activities, China's central region has been a strategic location for grain production since ancient times and has assumed an essential responsibility for maintaining national grain security. However, the key concerns of this study are whether the national grain security pattern is stable and whether it might be affected by global climate change (especially climate instability and increased risks in recent years). Therefore, the present study collected grain production data and used descriptive statistical and geospatial analyses to reveal the trend and spatiotemporal pattern of grain production in China's central region from 2010 to 2020. Then, a further analysis was conducted by combining meteorological data with a geographically weighted regression (GWR) model to investigate the relationship between spatial differences in the output per unit of the grain sown area (OPUGSA). The findings were as follows: (1) The overall development trend of grain production in China's central region from 2010 to 2020 revealed a positive overall trend in grain production, with notable differences in growth rates between northern and southern provinces. (2) Most regions in the southern part of the central region from 2015 to 2020 showed varying degrees of total output of grain (TOG) and OPUGSA reduction, possibly affected by the effects of the anomalies for global climate change and a strong El Niño effect in 2015. (3) Low-low (L-L) clusters of TOG and OPUGSA indicators were consistently in the northwest part (Shanxi) of the central region, and high-high (H-H) clusters of TOG were consistently in the central part (Henan and Anhui) of the central region, but H-H clusters of OPUGSA were not stably distributed. (4) The fitting results of the GWR model showed a better fit compared to the ordinary least squares (OLS) model; it was found that the annual average temperature (AAT) had the greatest impact on OPUGSA, followed by annual sunshine hours (ASH) and annual precipitation (AP) last. The spatiotemporal analysis identified distinct clusters of productivity indicators. It suggested an expanding range of climate impact possibilities, particularly in exploring climate-resilient models of grain production, emphasizing the need for targeted adaptation strategies to bolster resilience and ensure agricultural security.&lt;/p&gt; &lt;/abstract&gt;

Spatial and temporal dynamics of urban heat environment at the township scale: A case study in Jinan city, China.

The prolonged dependence on industrial development has accentuated the cumulative effects of pollutants. Simultaneously, influenced by land construction activities and green space depletion, the Urban Heat Island (UHI) effect in cities has intensified year by year, jeopardizing the foundation of sustainable urban development. Prudent urban spatial planning holds the potential to robustly ameliorate the persistent deterioration of the UHI phenomenon. This study selects Jinan City as a case study and employs spatial autocorrelation and spatial regression algorithms to explore the spatiotemporal evolution of urban-rural patterns at the township scale. The aim is to identify key factors driving the spatiotemporal differentiation of Land Surface Temperature (LST) from 2013 to 2022. The research reveals a trend of initially rising and subsequently falling LST in various townships, with low-temperature concentration areas in the southern mountainous region and the northern plain area. The "West-Central-East" main urban axis and the southeast Laiwu District exhibit high-temperature zones. Significant influences on LST are attributed to pollution levels, topographical factors, urbanization levels, and urban greenness. The global Moran's Index for LST exceeds 0.7, indicating a strong positive spatial correlation. Cluster analysis results indicate High-High (HH) clustering in the central Shizhong District and Low-Low (LL) clustering in the northern Shanghe County. Multiscale Geographically Weighted Regression (MGWR) outperforms Geographically Weighted Regression (GWR) and Ordinary Linear Regression (OLR), providing a more accurate reflection of the regression relationships between variables. By investigating the spatiotemporal evolution of LST and its driving factors at the township scale, this study contributes insights for future urban planning and sustainable development.

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

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. 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. 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. 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.

Empirical analysis of spatial heterogeneity in the development of China's National Fitness Plan.

The National Fitness Plan (NFP) is a vital initiative aimed at realizing Healthy China 2030. This study assessed spatial heterogeneity in the NFP development and the socioeconomic factors contributing to this inequality. Data from 31 administrative regions in 2021 were analyzed using four NFP development metrics. Spatial autocorrelation was evaluated using global Moran's I, followed by global and local regression models for non-random spatial patterns. National physical fitness exhibited significant clustering (z = 5.403), notably a high-high cluster in East China. The global regression model identified three socioeconomic factors in the geographically weighted regression model: per capita disposable income and the number of public buses positively affected national physical fitness, while general public budget expenditure had a negative impact. Persistent unequal NFP development is projected due to income disparities in economically backward regions. To promote the NFP effectively, a cost-efficient strategy includes creating 15-minute fitness circles, especially by establishing public sports facilities in Western China communities. These findings inform policy priorities for advancing the NFP towards Healthy China 2030.

Identification of infectious disease spatial clusters based on local Moran’s I statistic: A case study of dengue hemorrhagic fever

Background: One of the most prevalent tropical illnesses that impact people is dengue. Dengue and dengue hemorrhagic fever (DF/DHF) have become much more common within the past few decades. The purpose of this study is to use the local Moran's I statistic to identify any spatial clustering of dengue hemorrhagic fever in Ho Chi Minh City, Vietnam, during the months of June through July of 2023. Methods: The data distribution was first examined using descriptive statistics. The spatial clustering of dengue hemorrhagic fever over these four months was then examined using the global Moran's I statistic, Moran's I scatterplot, and local Moran's I statistic. More precisely, using the local Moran's I statistic, dengue hemorrhagic fever clusters (high-high and low-low) and geographical outliers (low-high and high-low) were found in Ho Chi Minh City. Results: It was discovered that DHF infection rates in Ho Chi Minh City likely to rise gradually between June and July of 2023. High-high spatial clustering of DHF infection rates was primarily found in urban areas and the city centre, despite the fact that this pattern has rapidly changed. Conclusions: The results of this investigation showed that Ho Chi Minh City has statistically significant spatial clusters of DHF. The findings of this investigation further show that local Moran's I statistic is validated in the context of studying the spatial clustering of infectious diseases in general and DHF in particular. The research findings offer valuable insights into the knowledge of the dissemination of DHF.

Carbon Emission and Annual Income in the Britain in 2019: An Intra-regional Spatial Analysis

This study conducts a comprehensive spatial analysis of carbon emissions and annual income in Great Britain in 2019 using data from the Office for National Statistics (ONS) and the Annual Survey of Hours and Earnings (ASHE). Using spatial autocorrelation techniques, especially Local Morans I, I identify significant spatial clusters, where Low - Low carbon emission areas are located in Manchester, West Yorkshire, Dorset and Wiltshire. In contrast, High-High clusters occur in Moray, the Midlands, South Yorkshire and Lancashire, highlighting areas where carbon emissions are spatially correlated. In addition, this research uses geographically weighted regression (GWR) analysis to identify any existing relationship between annual income and carbon emissions with accounting of geography. The results show a consistent trend in the strength of this relationship, decreasing from northwest to southeast across the Great Britain. The highest coefficients are concentrated in eastern regions such as the South East, London and the East of England. This research aims to contribute to the understanding of spatial heterogeneity of the local environmental economics by informing relevant stakeholders and politicians about which regions are most in demand of targeted interventions. By identifying vulnerable areas, this study provides valuable insights into the UK's pursuit of sustainable development and low-carbon economy with the global movement towards green economy.

Spatial suitability between water supply pressure and waterworks water supply capacity in the Guangdong-Hong Kong-Macau Greater Bay Area: Spatial-temporal evolution pattern, driving mechanism and Implications

The Guangdong-Hong Kong-Macao Greater Bay Area (GHMGBA) is one of the four major bay areas in the world. However, the mismatch between water resource distribution and spatial layout of population and productivity poses great challenge to water supply security of the GHMGBA. Evaluating the spatial suitability of water supply pressure (WSP) and waterworks water supply capacity (WSC) is essential to the spatial equilibrium allocation of water resources and water supply security in the GHMGBA. Therefore, the current study first investigated the spatial-temporal evolution pattern of WSP and waterworks WSC using the standard deviational ellipse and Exploratory Spatial Data Analysis (ESDA) methods, the spatial suitability of WSP and waterworks WSC was further evaluated using the coupling coordination degree model, and the driving mechanism of such spatial suitability was finally revealed using the dynamic spatial Durbin model. The results show that the WSP respectively forms high and low value aggregations in the eastern and western parts of the Pearl River estuary; the area with high waterworks WSC is mainly located in the northern part, the high-high cluster areas are in the eastern part of Zhaoqing and the western part of Huizhou, and the low-low cluster areas are mainly in Guangzhou-Foshan, Shenzhen-Hong Kong and Zhuhai-Macao. The coupling coordination of WSP and waterworks WSC was in an overall imbalance state. The eastern and western parts of the Pearl River estuary are slightly unbalanced or with better level. Demand-driven and economic efficiency have significant effects under spatial and economic proximity weights, government guidance has a significant positive effect, while market orientation has an insignificant positive effect; environmental pollution and resource allocation have a significant negative effect under geographical proximity weight, and environmental pollution has a stronger negative effect on spatial suitability. Finally, policy recommendations were proposed to improve the spatial suitability of WSP and waterworks WSC of the GHMGBA.

Fiscal policy, green finance, and low carbon transformation nexus: a novel study unleashing the synergistic effects of carbon reduction and pollution in China.

Under the simultaneous demands of combating environmental pollution and decreased carbon emissions, it is critical to investigate the combined effect of agricultural contamination reduction and fiscal policy carbon reduction to support and promote green agriculture and low-carbon transformation. Based on provincial panel data of 2007 to 2020 in China, this paper employs the spatial Dubin model to empirically examine the pollution reduction and carbon reduction effects of fiscal policies supporting agriculture, as well as calculating the synergistic effect of pollution reduction and carbon reduction. Our study's findings reveal that non-point source agricultural pollution and agricultural carbon emissions have a tendency of growing and subsequently reducing, such as increasing from 2007 to 2015 and decreasing from 2016 to 2020. Second, results demonstrate that agricultural carbon emissions and agricultural pollution have a positive geographical dependency in each province, and fiscal policies supporting agriculture have high-high and low-low spatial clustering features. Furthermore, fiscal policies that promote agriculture can lower local agricultural carbon emissions and pollution while also having a considerable beneficial spillover impact on neighboring provinces. According to the study findings, the fiscal policy for supporting agriculture has a negative pollution reduction impact and a positive synergistic effect, resulting in a synergistic effect of agricultural pollution reduction and carbon reduction. The outcomes of this study can serve to promote carbon-reduction measures and provide recommendations for future policy development.

Spatial Analysis of Bovine Anaplasmosis in Jambi Province, Indonesia: 2018-2022

Bovine anaplasmosis causes economic loss in many countries, such as Indonesia. In some provinces of Indonesia such as Jambi Provinces, cattle are one of the most important commodities, but the information on bovine anaplasmosis pattern in Jambi province is limited. This study aimed to analyze the spatial pattern and spatial autocorrelation of bovine anaplasmosis from 2018-2022 in Jambi province. The data was retrieved from active and passive surveys comprising 5,612 blood samples from eleven districts in Jambi Provinces. The blood samples were subjected for detection of Anaplasma sp. using the blood smear technique. The data were analyzed using statistical and spatial analyses in R software v.4.3.1. The results of statistical analysis showed that the incidence of bovine anaplasmosis in 2018-2022 was 16.66%, with a fluctuating pattern along the year. Among the districts, the highest incidence was found in Kerinci District (22.30%), while the lowest was presented in Kota Jambi (13.60%). A higher bovine anaplasmosis incidence was found in rainy season than dry season (median 18.2% vs 10.3%, respectively) (p=0.01). The global Moran’s I showed no significant difference in bovine anaplasmosis incidence, indicating a random pattern in the overall study period. The LISA Moran’s I identified 17 high-high clusters, 9 high-low clusters, 12 low-high clusters, and 12 low-low clusters. In conclusion, the bovine anaplasmosis incidence in Jambi Province fluctuated in 2018-2022, with a higher incidence in rainy season. The spatial analysis indicates an even distribution of the disease between eleven districts in Jambi Province. Further study and a control and prevention strategy are suggested to reduce the incidence of bovine anaplasmosis in Jambi Province.

Health impacts of spatiotemporal variation in PM2.5 concentrations from heavy-duty diesel trucks in Beijing

PM2.5 emissions from heavy-duty diesel vehicles (HDDTs) pose a serious threat to human health. Existing researches difficult to capture the spatiotemporal evolution patterns of pollutant concentrations, resulting in the health impacts of pollutant emissions under different spatiotemporal patterns remain unclear. To fill this gap, this study explores the spatiotemporal evolution patterns of PM2.5 concentrations emitted by HDDTs from a spatiotemporal integration perspective, utilizing a three-dimensional spatiotemporal cube model. On this basis, the disparities in health impacts and economic losses under various spatiotemporal patterns are quantified. The results show that: 1) Differences in spatiotemporal patterns of PM2.5 emissions from HDDTs have obvious public health impacts. 55% of the areas exhibit the Oscillating Hot Spot pattern, with health impacts and economic losses accounting for over 90% of the total. High-High Cluster pattern accounts for 26% of all spatiotemporal cluster patterns, but their health and economic consequences attribute over 80% of the total. 2) Short-term exposure to PM2.5 emissions from HDDTs have resulted in substantial human health impacts and economic losses that cannot be ignored. The results of this study can assist decision-makers in identifying spatiotemporal patterns with high health impacts and economic losses of HDDT emissions, and providing decision support for the formulation of health-oriented environmentally sustainable policies.

Did electrification of the building sector achieve carbon mitigation? A provincial retrospection in China

Electrification of end-use energy consuming sectors is becoming a widespread choice to combat climate change, yet the decarbonization of electricity determines the quality of carbon emissions reductions from electrification. This study evaluates the changes in carbon emissions from building operations due to electrification in China's 30 provinces over the period 2004–2020. Considering electricity decarbonization, electricity consumption, population and floor area, an assessment framework for carbon emissions reductions from building electrification has been built by Kaya-LMDI method. The results show that (1) The promotion of building electrification causes an increase of approximately 46.88 MtCO2 over the period 2004–2020. The urban residential buildings contribute the largest share, about 54.78%; (2) Almost half of the provinces have failed to achieve carbon emissions reductions with promotion of building electrification; (3) The change in carbon emissions caused by the promotion of building electrification shows a clustering effect in space, with the northern region characterized by high-high clusters and the southern region by low-low clusters. In addition, this study reviews the strategy for power decarbonization and building electrification in China. The overall strategy for building electrification is to accelerate the proportion of electricity consumption in existing buildings and to promote the electrification of new public buildings across the board. Overall, this study provides a reference for other economies and end-use energy sectors to evaluate electrification for carbon emissions reductions, and provides insights and strategies for policy makers on the promotion of building electrification.

Relationship between social development indicators and mortality due to Diabetes Mellitus in Brazil: a space-time analysis.

to identify the space-time pattern of mortality due to Diabetes Mellitus in Brazil, as well as its relationship with social development indicators. an ecological and time series nationwide study based on secondary data from the Unified Health System Informatics Department, with space-time analysis and inclusion of indicators in non-spatial and spatial regression models. The following was performed: overall mortality rate calculation; characterization of the sociodemographic and regional profiles of the death cases by means of descriptive and time analysis; and elaboration of thematic maps. a total of 601,521deaths related to Diabetes Mellitus were recorded in Brazil, representing a mean mortality rate of 29.5/100,000inhabitants. The states of Rio Grande do Norte, Paraíba, Pernambuco, Alagoas and Sergipe, Rio de Janeiro, Paraná and Rio Grande do Sul presented high-high clusters. By using regression models, it was verified that the Gini index (β=11.7) and the Family Health Strategy coverage (β=3.9) were the indicators that most influenced mortality due to Diabetes Mellitus in Brazil. in Brazil, mortality due to Diabetes presents an overall increasing trend, revealing itself as strongly associated with places that have worse social indicators. (1) The time-space pattern of mortality due to Diabetes presents an increasing trend. (2) The Northeast and South regions present high rates of mortality due to Diabetes. (3) Mortality due to Diabetes is associated with worse sociodemographic indicators. (4) A relationship is observed between income, access to health and mortality due toDiabetes.

Relação indicadores de desenvolvimento social e mortalidade por diabetes mellitus no Brasil: análise espacial e temporal

Objective: to identify the space-time pattern of mortality due to Diabetes Mellitus in Brazil, as well as its relationship with social development indicators. Method: an ecological and time series nationwide study based on secondary data from the Unified Health System Informatics Department, with space-time analysis and inclusion of indicators in non-spatial and spatial regression models. The following was performed: overall mortality rate calculation; characterization of the sociodemographic and regional profiles of the death cases by means of descriptive and time analysis; and elaboration of thematic maps. Results: a total of 601,521 deaths related to Diabetes Mellitus were recorded in Brazil, representing a mean mortality rate of 29.5/100,000 inhabitants. The states of Rio Grande do Norte, Paraíba, Pernambuco, Alagoas and Sergipe, Rio de Janeiro, Paraná and Rio Grande do Sul presented high-high clusters. By using regression models, it was verified that the Gini index (β=11.7) and the Family Health Strategy coverage (β=3.9) were the indicators that most influenced mortality due to Diabetes Mellitus in Brazil. Conclusion: in Brazil, mortality due to Diabetes presents an overall increasing trend, revealing itself as strongly associated with places that have worse social indicators.

Spatial distribution characteristics of the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody in Hunan Province in 2020

To investigate the spatial distribution characteristics of the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody, and to examine the correlation between the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody in Hunan Province in 2020, so as to provide insights into advanced schistosomiais control in the province. The epidemiological data of schistosomiasis in Hunan Province in 2020 were collected, including number of permanent residents in survey villages, number of advanced schistosomiasis patients, number of residents receiving serological tests and number of residents seropositive for anti-Schistosoma antibody, and the prevalence advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody were descriptively analyzed. Village-based spatial distribution characteristics of prevalence advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody were identified in Hunan Province in 2020, and the correlation between the revalence advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody was examined using Spearman correlation analysis. The prevalence of advanced schistosomiasis was 0 to 2.72% and the seroprevalence of anti-Schistosoma antibody was 0 to 20.25% in 1 153 schistosomiasis-endemic villages in Hunan Province in 2020. Spatial clusters were identified in both the prevalence of advanced schistosomiasis (global Moran's I = 0.416, P < 0.01) and the seroprevalence of anti-Schistosoma antibody (global Moran's I = 0.711, P < 0.01) in Hunan Province. Local spatial autocorrelation analysis identified 98 schistosomiasis-endemic villages with high-high clusters of the prevalence of advanced schistosomiasis, 134 endemic villages with high-high clusters of the seroprevalence of anti-Schistosoma antibody and 36 endemic villages with high-high clusters of both the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody in Hunan Province. In addition, spearman correlation analysis showed a positive correlation between the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody (rs = 0.235, P < 0.05). There were spatial clusters of the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody in Hunan Province in 2020, which were predominantly located in areas neighboring the Dongting Lake. These clusters should be given a high priority in the schistosomiasis control programs.

Epidemiology, risk areas and macro determinants of gastric cancer: a study based on geospatial analysis

BackgroundBoth incidence and mortality of gastric cancer in Gansu rank first in china, this study aimed to describe the recent prevalence of gastric cancer and explore the social and environmental determinants of gastric cancer in Gansu Province.MethodsThe incidence of gastric cancer in each city of Gansu Province was calculated by utilizing clinical data from patients with gastric cancer (2013–2021) sourced from the medical big data platform of the Gansu Province Health Commission, and demographic data provided by the Gansu Province Bureau of Statistics. Subsequently, we conducted joinpoint regression analysis, spatial auto-correlation analysis, space–time scanning analysis, as well as an exploration into the correlation between social and environmental factors and GC incidence in Gansu Province with Joinpoint_5.0, ArcGIS_10.8, GeoDa, SaTScanTM_10.1.1 and GeoDetector_2018.ResultsA total of 75,522 cases of gastric cancer were included in this study. Our findings suggested a significant upward trend in the incidence of gastric cancer over the past nine years. Notably, Wuwei, Zhangye and Jinchang had the highest incidence rates while Longnan, Qingyang and Jiayuguan had the lowest. In spatial analysis, we have identified significant high-high cluster areas and delineated two high-risk regions as well as one low-risk region for gastric cancer in Gansu. Furthermore, our findings suggested that several social and environmental determinants such as medical resource allocation, regional economic development and climate conditions exerted significant influence on the incidence of gastric cancer.ConclusionsGastric cancer remains an enormous threat to people in Gansu Province, the significant risk areas, social and environmental determinants were observed in this study, which may improve our understanding of gastric cancer epidemiology and help guide public health interventions in Gansu Province.

Spatiotemporal Evolution and Influencing Factors of Soybean Production in Heilongjiang Province, China

Heilongjiang Province, as the largest production and supply base for high-quality soybeans in China, plays a vital role in optimizing the layout of soybean production and promoting the revitalization of the soybean industry. Soybean yield is used as a key indicator of soybean production. This study integrated soybean yield data from agricultural reclamation systems and local authorities. A variety of statistical analysis methods, such as barycenter analysis, the Mann–Kendall test, the space–time cube, and grey relational analysis, were used to research the spatiotemporal evolution and influencing factors of soybean production in Heilongjiang Province from 2011 to 2021. This paper revealed the spatiotemporal evolution mechanism and explored the reasons for the differences in the effects of influencing factors. The results were as follows. (1) During the period between 2011 and 2021, the center of gravity of county-level soybean yield in Heilongjiang Province moved towards the northwest over a distance of 16.82 km. The soybean yield in the province experienced a mutation in approximately 2018, from a downward trend to an upward trend. (2) The spatiotemporal hot spots of county-level soybean yield in Heilongjiang Province were concentrated along the line from Hailun to Aihui. The types of hot spots included consecutive hot spots, intensifying hot spots, sporadic hot spots, and new hot spots. (3) The spatiotemporal agglomeration patterns of county-level soybean yield in Heilongjiang Province included only high-high clusters, only low-low clusters, only high-low outliers and multiple types. (4) The temporal changes in soybean yield in various counties of Heilongjiang Province had obvious regional characteristics. (5) Socioeconomic factors had aftereffects on soybean planting decisions. (6) Sunlight hours, the price ratio of local soybeans to local maize, average temperature, the number of soybean patents, the price ratio of imported soybeans to local soybeans, soybean cultivation income, local soybean prices, and the number of newly established soybean enterprises were primary influencing factors. Precipitation and soybean import volume were secondary influencing factors. The income difference between maize and soybeans, crops-hitting disaster area, and maize yield were general influencing factors. This study aims to offer new pathways for alleviating the structural contradiction between soybean supply and demand and to provide a reference for the formulation of national soybean industry policies and food security strategies.

The Effects of Tourism Development on Eco-Environment Resilience and Its Spatio-Temporal Heterogeneity in the Yangtze River Economic Belt, China

Tourism sustainability is a significant approach to forming a synergistic model of industry and ecology in ecologically vulnerable areas. Scientifically detecting the effect mechanism of tourism development on eco-environment resilience is important in achieving regional social-ecological system sustainability. In this work, empirical exploration is conducted on the tourism development index (TDI) and eco-environment resilience index (ERI) in the Yangtze River Economic Belt (YREB) to study the spatio-temporal heterogeneity of TDI’s effect on the ERI. The results indicate significant growth in the TDI in the YREB, with the formation of tourist clusters around Shanghai and Chongqing as the core. Although the ERI typically exhibits a declining trend, the rate of decline has notably slowed, forming a “high at the sides and low in the middle” spatial pattern. The TDI and ERI are spatially dependent in the YREB, with predominantly high-high (HH) and low-high (LH) clusters in Shanghai, Zhejiang, and Jiangsu. Conversely, upstream regions with strong eco-environmental foundations exhibit low-low (LL) and high-low (HL) clusters. In general, the TDI promotes the ERI, but there is significant spatio-temporal heterogeneity in the YREB. Positive impact regions are expanding, while negative impact regions are shrinking. These results could provide scientific evidence for differentiated classification and control policies in the YREB.