Spatiotemporal analysis and seasonality of tuberculosis in Pudong New Area of Shanghai, China, 2014-2023.

BACKGROUND: Spatiotemporal analysis is a vital method that plays an indispensable role in monitoring epidemiological changes in diseases and identifying high-risk clusters. However, there is still a blank space in the spatial and temporal distribution of tuberculosis (TB) incidence rate in Pudong New Area, Shanghai. Consequently, it is crucial to comprehend the spatiotemporal distribution of TB in this district, this will guide the prevention and control of TB in the district. METHODS: Our research used Geographic Information System (GIS) visualization, spatial autocorrelation analysis, and space-time scan analysis to analyze the TB incidence reported in the Pudong New Area of Shanghai from 2014 to 2023, and described the spatiotemporal clustering and seasonal hot spot distribution of TB incidence. RESULTS: From 2014 to 2023, the incidence of TB in the Pudong New Area decreased, and the mortality was at a low level. The incidence of TB in different towns/streets has declined. The spatial autocorrelation analysis revealed that the incidence of TB was spatially clustered in 2014, 2016-2018, and 2022, with the highest clusters in 2014 and 2022. The high clustering area was mainly concentrated in the northeast. The space-time scan analysis indicated that the most likely cluster was located in 12 towns/streets, with a period of 2014-2018 and a radiation radius of 15.74 km. The heat map showed that there was a correlation between TB incidence and seasonal variations. CONCLUSIONS: From 2014 to 2023, the incidence of TB in the Pudong New Area of Shanghai declined, but there were spatiotemporal clusters and seasonal correlations in the incidence area. Local departments should formulate corresponding intervention measures, especially in high-clustering areas, to achieve accurate prevention and control of TB within the most effective time and scope.

Spatiotemporal Evolution and Spatial Analysis of Ecological Environmental Quality in the Longyangxia to Lijiaxia Basin in China Based on GEE.

The upper reaches of the Yellow River are critical ecological barriers within the Yellow River Basin (YRB) that are crucial for source conservation. However, environmental challenges in this area, from Longyangxia to Lijiaxia, have emerged in recent years. To assess the ecological environment quality (EEQ) evolution from 1991 to 2021, we utilized remote sensing ecological indices (RSEIs) on the Google Earth Engine (GEE) platform. Spatial autocorrelation and heterogeneity impacting EEQ changes were examined. The results of this study show that the mean value of the RSEIs fluctuated over time (1991: 0.70, 1996: 0.77, 2001: 0.67, 2006: 0.71, 2011: 0.68, 2016: 0.65, and 2021: 0.66) showing an upward, downward, and then upward trend. The mean values of the overall RSEI are all at 0.65 and above. Most regions showed no significant EEQ change during 1991-2021 (68.59%, 59.23%, and 55.78%, respectively). Global Moran's I values (1991-2021) ranged from 0.627 to 0.412, indicating significant positive correlation between EEQ and spatial clustering, and the LISA clustering map (1991-2021) shows that the area near Longyangxia Reservoir shows a pattern of aggregation, dispersion, and then aggregation again. The factor detection results showed that heat was the most influential factor, and the interaction detection results showed that greenness and heat had a significant effect on regional ecosystem distribution. Our study integrates spatial autocorrelation and spatial heterogeneity and combines them with reality to provide an in-depth discussion and analysis of the Longyangxia to Lijiaxia Basin. These findings offer guidance for ecological governance, vegetation restoration, monitoring, and safeguarding the upper Yellow River's ecological integrity.

Spatio-temporal assessment of landscape ecological risk using spatial statistical analysis in a basin of Turkiye.

Monitoring the land use/land cover (LU/LC) changes that have occurred with rapid population growth and urbanization since the Industrial Revolution is important for the optimal configuration of landscape patterns and ensuring the sustainability of ecological functions. Spatiotemporal dynamic pattern of LU/LC change using high-resolution land use data is an indicator to evaluate the landscape ecological risk through landscape pattern index analysis. In this study, the landscape ecological risk index (LERi) based on LU/LC change was calculated using remote sensing images of Landsat TM (Thematic Mapper) and OLI (Operational Land Imager) Rdata of a Gediz Mainstream Sub-basin in Turkiye between 1992 and 2022, and the spatial distribution regularity of LERi values was determined with spatial statistical analysis. According to the results, it was determined that the LERi values of the study area changed by 45% in 30 years. The highest change is in the very high-risk class, with an increase of 10.96%, and the least change occurred in the very low-risk class, with a decrease of 1.29%. According to the obtained statistical analysis results, it was determined that the global spatial autocorrelation values analyzed at different grain levels showed positive autocorrelation for both years and that the LERi values tended to have strong spatial clustering. As a result, it is emphasized that strict control measures should be taken for areas showing High-High (HH) autocorrelation type located in the southeast and north-southwest line of the study area at the local level, and ecological restoration applications should be given priority in these areas.

Global incidence and death estimates of chronic kidney disease due to hypertension from 1990 to 2019, an ecological analysis of the global burden of diseases 2019 study.

BACKGROUND: The intricate relationship between hypertension and chronic kidney disease (CKD) presents a global challenge for prevention of hypertension-related CKD. This study's objective is to analyze age, gender, regional disparities, and evolving trends in the disease burden of hypertension-related CKD. We aim to estimate changing spatial and temporal trends in incidence and mortality rates, considering the socio-demographic index (SDI), to inform health strategies effectively. METHOD: Age-standardized incidence rates (ASIR) and death rates (ASDR) were collected from the GBD 2019. Trend analysis was conducted by Joinpoint regression of ASRs from 1990 to 2019. Spatial autocorrelation analysis was performed to obtain spatial patterns. The association between SDI and burden of CKD due to hypertension was estimated using a Pearson correlation analysis. RESULTS: The global ASIR and ASDR due to hypertension-related CKD were 19.45 (95% CI, 17.85 to 21.09) and 5.88 (95% CI, 4.95 to 6.82) per 100 K population in 2019, representing increases of 17.89% and 13.29% compared to 1990, respectively. The elderly population and males were found the highest ASIR and ASDR. The high SDI region had the highest ASIRs, while low SDI regions experienced the highest ASDRs. Joinpoint regression found both global ASIR and ASDR showed increasing trends, with the highest increases observed in middle- and high-SDI regions, respectively. The SDI exhibited a positive association with ASIRs but displayed an inverse V-shaped correlation with the average annual percentage change (AAPC) of ASIRs. Spatial autocorrelation analysis revel significant positive spatial autocorrelation for the AAPC of ASDRs and ASIRs, from 1990 to 2019. CONCLUSIONS: Results met the objectives, and demonstrated a rising global burden of hypertension-related CKD. Factors such as aging, gender, and regional variations should be considered when designing control measures and developing healthcare systems to effectively address the burden of this complex condition.

Spatial and temporal patterns of prostate cancer burden and their association with Socio-Demographic Index in Asia, 1990-2019.

BACKGROUND: Prostate cancer is the second most frequently diagnosed cancer for males worldwide, but the spatial and temporal trends of prostate cancer burden remain unknown in Asia. This study aimed to investigate the changing spatial and temporal trends of incidence, prevalence, mortality, disability-adjusted life year (DALY), and mortality-to-incidence ratio (MIR) of prostate cancer, and their association with the Socio-Demographic Index (SDI) in 48 Asian countries from 1990 to 2019. METHODS: Data were extracted from the Global Health Data Exchange query tool, covering 48 Asian countries from 1990 to 2019. The average annual percent change was calculated to evaluate temporal trends. Spatial autocorrelation analysis was used to obtain spatial patterns, and the association between SDI and prostate cancer burden was estimated using a spatial panel model. RESULTS: In Asia, the age-standardized incidence and prevalence of prostate cancer increased in almost all countries, and its mortality and DALY also increased in over half of the countries. Significantly regional disparities were found in Asia, and the hot spots for incidence, prevalence, mortality, and DALY were all located in Western Asia, the hot spots of percent change also occurred in Western Asia for incidence and DALY. Furthermore, SDI had a positive association with mortality (coef = 2.51, 95% confidence interval [CI]: 2.13-2.90) and negative association with DALY (coef = -14.99, 95% CI: -20.37 to -9.60) and MIR (coef = -0.95, 95%CI: -0.99 to -0.92). CONCLUSIONS: Prostate cancer burden increased rapidly throughout Asia and substantial disparities had persisted between countries. Geographically targeted interventions are needed to reduce the prostate cancer burden throughout Asia and in specific countries.

The application of geostatistical analysis and receptor model for the spatial distribution and sources of potentially toxic elements in soils.

This study aimed to identify the spatial patterns of potentially toxic elements (PTEs), including the spatial distribution, spatial autocorrelation, and risk probability, and to quantify the sources of PTEs, to provide guidelines for soil management. Spatial distributions and probabilities of PTEs were determined by empirical Bayesian kriging (EBK), while spatial autocorrelation was estimated by Moran's I. Positive matrix factorization (PMF) was adopted for the quantitative source contributions of PTEs. More than 64.6% of Co, Cr, Mn, and Ni were derived from geogenic sources, with high regions and high-high clusters both correlated to sandstone. Thus, it can be deduced that parent materials dominated the spatial patterns of these PTEs. In addition, some hotspots were situated in urban areas, and the influence of human activities on these four PTEs should be considered. Industry-traffic discharge and parent materials both influenced As, Cu, Pb, and Zn. Nonetheless, the spatial patterns of As, Cu, Pb, and Zn were formed by anthropogenic emissions since hotspots and high-high clusters were contiguously situated in urban areas. 58.5% of Hg originated from atmospheric deposition related to industrial emissions, and 47.2% of Cd was controlled by the application of chemical fertilizers. High levels of Hg and Cd mainly corresponded with industrial sites and cultivated land, suggesting that industrial and geoponic production played major roles in the generation of spatial patterns for Hg and Cd, respectively. Furthermore, the Cd and Hg posed a severe risk to soils, with a high probability to surpass 1.5 times the backgrounds. The EBK, Moran's I, and PMF results showed that all ten PTEs were enriched to some degree due to natural or anthropogenic factors. The results of geostatistical analysis and the receptor model can be mutually verified, indicating the reliability of these methods.

The spatiotemporal distribution of human brucellosis in mainland China from 2007-2016.

BACKGROUND: Despite the considerable efforts made to address the issue of brucellosis worldwide, its prevalence in dairy products continues to be difficult to estimate and represents a key public health issue around the world today. The aim of the present study was to better understand the epidemiology of this disease in mainland China. We set out to investigate the yearly spatial distribution and possible hotspots of the disease. METHODS: Human brucellosis data from mainland China between 2007 and 2016 were collected from the China Information System for Disease Control and Prevention. A geographic information system ArcGIS10.3 (ESRI, Redlands) was used to identify potential changes in the spatial and temporal distribution of human brucellosis in mainland China during the study period. These distributions were evaluated using three-dimensional trend analysis and spatial autocorrelation analyse. A gravity-center was used to analyse the migration track of human brucellosis. RESULTS: A total of 399,578 cases of human brucellosis were reported during the 10-year study period. The monthly incidence of brucellosis in China demonstrates clear seasonality. Spring and summer are the peak seasons, while May is the peak month for brucellosis. Three-dimensional trend analysis suggests that brucellosis is on the rise from south to north, and that the epidemic situation in northern China is more severe. Between 2007 and 2016, the overall migration distance of the brucellosis incidence gravity-center was 906.43 km, and the direction was southwest. However, the overall gravity center of brucellosis was still in the northern part of China. In the global autocorrelation analysis, brucellosis in China demonstrated a non-random distribution between 2013 and 2014, with spatial autocorrelation (Z > 1.96, P < 0.05) and a clustering trend, while no clustering trend was found from 2007 to 2012 or from 2015 to 2016. In the local autocorrelation analysis, a Low-Low cluster phenomenon was found in the south of China in 2013 and 2014. CONCLUSION: Human brucellosis remains a widespread challenge, particularly in northern China. The hotspots highlight potential high-risk areas which may require special plans and resources for monitoring and controlling the disease.

Spatial variation and health risk assessment of fluoride in drinking water in the Chongqing urban areas, China.

Fluoride is an essential trace element for humans, and its deficiency or excess in the environment could lead to disease. To investigate the spatial distribution and health risk assessment of fluoride (F-) in drinking water, 302 tap water samples from Chongqing urban areas, China, were collected to analyze F- using an ion chromatograph. The results showed that (1) F- concentration in drinking water ranged from 0.100 to 0.503 mg/L, with an average of 0.238 ± 0.045 mg/L. (2) The spatial autocorrelation analysis showed that high-low clusters were mostly located in Dadukou District and Beibei District, while low-low clusters were mainly in southern Banan District. (3) The fluoride average daily doses of children, teens and adults were 0.030, 0.029 and 0.031 mg/(kg day). (4) Hazard quotients of excessive fluoride (HQe) of children, teens and adults were 0.51 ± 09, 0.49 ± 0.09 and 0.52 ± 0.10, respectively (inferior to 1.00), whereas hazard quotients of inadequate fluoride (HQi) of those groups were 1.21 ± 0.26, 1.23 ± 0.26 and 1.15 ± 0.25, respectively (superior to 1.00). Therefore, average daily fluoride intake of residents with drinking water was inadequate. This could pose dental caries and osteoporosis threats for residents from Chongqing urban areas.

[Spatial autocorrelation and its application in public health field].

The prevalence and distribution of infectious diseases and occupational diseases in specific time and space is the result of the interaction between natural environment, social economy and other factors, its distribution pattern has spatial properties. Based on the assumption of independence, the traditional statistical methods ignore the spatial attributes of diseases and cannot analyze the spatial characteristics of diseases. On the basis of geographic information system, the spatial autocorrelation analysis can simultaneously analyze the spatial relationship and attribute value of diseases, explore the spatial dependence of disease data in different spatial units, and provide decision-making basis for the prediction and early warning of diseases such as occupational and infectious diseases, and the formulation and evaluation of prevention and control measures.

The epidemic characteristics and spatial autocorrelation analysis of hand, foot and mouth disease from 2010 to 2015 in Shantou, Guangdong, China.

BACKGROUND: Hand, foot and mouth disease (HFMD) is the highest incidence of infectious diseases in China. Shantou is one of the most infected cities. Therefore, it is necessary for us to understand the epidemic characteristics and distribution trend of HFMD in Shantou. The purpose of this study is to investigate the spatial epidemiological characteristics of HFMD and analyse its spatial autocorrelation. METHOD: We collated and summarised the data of HFMD in Shantou from 2010 to 2015. SaTScan software and Moran's I were used to analyse the spatial correlation of HFMD, and the results were presented in ArcMap. RESULTS: The distribution of HFMD in Shantou was of a seasonal trend, mainly concentrating during May and June. Children under 5-years-old were the main group of cases of HFMD, accounting for 92.46%. The proportion of infected children, especially those aged zero to 1, was the largest in each year, accounting for 45.62%, meaning that smaller children were more susceptible to HFMD. The number of male patients with HFMD was greater than that of females (1.78:1, male: female). With regard to the potential impact of patients' living style on the incidence rate of HFMD, this study revealed that scattered children were the dominant infected population, accounting for as much 84.49% of cases. The incidence of HFMD was unevenly distributed among streets. The incidence interval of streets was in a range of 13.76/100,000 to 1135.19/100,000. Spatial autocorrelation analysis showed that there was no global spatial correlation in Shantou, except in 2013. The results of local spatial autocorrelation analysis showed that H-H correlation existed in the high incidence local area of Shantou. CONCLUSIONS: The incidence of HFMD across the various streets in Shantou not only varied widely but also represented local autocorrelation. Attention, as well as prevention and control measures, should be focused on those high-incidence areas, such as the Queshi street, Zhuchi street and Xinjin street.