Spatial Autocorrelation of Breast and Prostate Cancer in Slovakia

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.

The epidemiology of the most frequent cancers in Poland in 2015-2021 and the impact of the COVID-19 pandemic on cancer incidence

BACKGROUND

The late diagnosis, despite the improving availability and accessibility of diagnostic procedures during the last decade in Poland and cooperation between specialist cancer centres, remains an unsolved problem. Moreover, the accessibility to healthcare resources and diagnostic procedures has been drastically reduced because of the COVID-19 pandemic in 2019-2020. The study aimed to present the epidemiology of the most frequent cancers diagnosed in Poland as well as the impact of the COVID-19 pandemic on cancers' incidence.

METHODS

Depersonalized, epidemiological data was obtained from the National Health Fund of Poland. In this retrospective study, the epidemiological analysis was performed and divided into subregions, according to patients' domicile. For each of the subregions, we have calculated the incidence rate per 100,000 standardized to the European Standard Population 2013. The time points of providing the first healthcare service were considered as the time of cancer diagnosis.

RESULTS

In the 2015-2019 period, before the COVID-19 pandemic occurred, the nationwide incidence of analysed cancers remained stable or slightly decreased (as the lung cancer). Simultaneusly, during the same period the prevalence of the prostate cancer has increased and the large differences between subregions with the least and the highest incidence were observed. Subsequently, the incidences of all analyzed cancers decreased in 2020, compared to the period before the COVID-19 pandemic occurred. Then, in 2021 a disproportionate increase in cancers' incidence rates was noted.

CONCLUSIONS

Our results show a significant decrease in the incidence rate of the most frequent cancers diagnosed in Poland in 2020 compared to 2019. Subsequently, in 2021 the increase of the incidence ratios was noted, most likely due to the gradual reduction of epidemic restrictions.

Mapping the pandemic: a review of Geographical Information Systems-based spatial modeling of Covid-19

According to the World Health Organization (WHO), COVID-19 has caused more than 6.5 million deaths, while over 600 million people are infected. With regard to the tools and techniques of disease analysis, spatial analysis is increasingly being used to analyze the impact of COVID-19. The present review offers an assessment of research that used regional data systems to study the COVID-19 epidemic published between 2020 and 2022. The research focuses on: categories of the area, authors, methods, and procedures used by the authors and the results of their findings. This input will enable the contrast of different spatial models used for regional data systems with COVID-19. Our outcomes showed increased use of geographically weighted regression and Moran I spatial statistical tools applied to better spatial and time-based gauges. We have also found an increase in the use of local models compared to other spatial statistics models/methods.

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.

Spatiotemporal distribution of schistosomiasis transmission risk in Jiangling County, Hubei Province, P.R. China

OBJECTIVE

This study aims to explore the spatiotemporal distribution of schistosomiasis in Jiangling County, and provide insights into the precise schistosomiasis control.

METHODS

The descriptive epidemiological method and Joinpoint regression model were used to analyze the changes in infection rates of humans, livestock, snails, average density of living snails and occurrence rate of frames with snails in Jiangling County from 2005 to 2021. Spatial epidemiology methods were used to detect the spatiotemporal clustering of schistosomiasis transmission risk in Jiangling county.

RESULTS

The infection rates in humans, livestock, snails, average density of living snails and occurrence rate of frames with snails in Jiangling County decreased from 2005 to 2021 with statistically significant. The average density of living snails in Jiangling County was spatially clustered in each year, and the Moran's I varied from 0.10 to 0.26. The hot spots were mainly concentrated in some villages of Xionghe Town, Baimasi Town and Shagang Town. The mean center of the distribution of average density of living snails in Jiangling County first moved from northwest to southeast, and then returned from southeast to northwest after 2014. SDE azimuth fluctuated in the range of 111.68°-124.42°. Kernal density analysis showed that the high and medium-high risk areas of Jiangling County from 2005 to 2021 were mainly concentrated in the central and eastern of Jiangling County, and the medium-low and low risk areas were mainly distributed in the periphery of Jiangling County.

CONCLUSIONS

The epidemic situation of schistosomiasis decreased significantly in Jiangling County from 2005 to 2021, but the schistosomiasis transmission risk still had spatial clustering in some areas. After transmission interruption, targeted transmission risk intervention strategies can be adopted according to different types of schistosomiasis risk areas.

Spatial-temporal analysis of pulmonary tuberculosis among students in the Zhejiang Province of China from 2007-2020

Background

Pulmonary tuberculosis (PTB) is a serious chronic communicable disease that causes a significant disease burden in China; however, few studies have described its spatial epidemiological features in students.

Methods

Data of all notified PTB cases from 2007 to 2020 in the student population were collected in the Zhejiang Province, China using the available TB Management Information System. Analyses including time trend, spatial autocorrelation, and spatial-temporal analysis were performed to identify temporal trends, hotspots, and clustering, respectively.

Results

A total of 17,500 PTB cases were identified among students in the Zhejiang Province during the study period, accounting for 3.75% of all notified PTB cases. The health-seeking delay rate was 45.32%. There was a decreasing trend in PTB notifications throughout the period; clustering of cases was seen in the western area of Zhejiang Province. Additionally, one most likely cluster along with three secondary clusters were identified by spatial-temporal analysis.

Conclusion

Although was a downward trend in PTB notifications among students during the time period, an upward trend was seen in bacteriologically confirmed cases since 2017. The risk of PTB was higher among senior high school and above than of junior high school. The western area of Zhejiang Province was the highest PTB risk settings for students, and more comprehensive interventions should be strengthened such as admission screening and routine health monitoring to improve early identification of PTB.

The COVID-19 Mortality Rate Is Associated with Illiteracy, Age, and Air Pollution in Urban Neighborhoods: A Spatiotemporal Cross-Sectional Analysis

There are different area-based factors affecting the COVID-19 mortality rate in urban areas. This research aims to examine COVID-19 mortality rates and their geographical association with various socioeconomic and ecological determinants in 350 of Tehran's neighborhoods as a big city. All deaths related to COVID-19 are included from December 2019 to July 2021. Spatial techniques, such as Kulldorff's SatScan, geographically weighted regression (GWR), and multi-scale GWR (MGWR), were used to investigate the spatially varying correlations between COVID-19 mortality rates and predictors, including air pollutant factors, socioeconomic status, built environment factors, and public transportation infrastructure. The city's downtown and northern areas were found to be significantly clustered in terms of spatial and temporal high-risk areas for COVID-19 mortality. The MGWR regression model outperformed the OLS and GWR regression models with an adjusted R² of 0.67. Furthermore, the mortality rate was found to be associated with air quality (e.g., NO₂, PM₁₀, and O₃); as air pollution increased, so did mortality. Additionally, the aging and illiteracy rates of urban neighborhoods were positively associated with COVID-19 mortality rates. Our approach in this study could be implemented to study potential associations of area-based factors with other emerging infectious diseases worldwide.

Kharsany A, Zewotir T, North D. Spatial Variation and Factors Associated with Unsuppressed HIV Viral Load among Women in an HIV Hyperendemic Area of KwaZulu-Natal, South Africa

New HIV infections among young women remains exceptionally high and to prevent onward transmission, UNAIDS set ambitious treatment targets. This study aimed to determine the prevalence, spatial variation and factors associated with unsuppressed HIV viral load at ≥400 copies per mL. This study analysed data from women aged 15–49 years from the HIV Incidence Provincial Surveillance System (HIPSS) enrolled in two sequential cross-sectional studies undertaken in 2014 and 2015 in rural and peri-urban KwaZulu-Natal, South Africa. Bayesian geoadditive model with spatial effect for a small enumeration area was adopted using Integrated Nested Laplace Approximation (INLA) function to analyze the findings. The overall prevalence of unsuppressed HIV viral load was 45.2% in 2014 and 38.1% in 2015. Factors associated with unsuppressed viral load were no prior knowledge of HIV status, had a moderate-to-low perception of acquiring HIV, not on antiretroviral therapy (ART), and having a low CD4 cell count. In 2014, women who ever consumed alcohol and in 2015, ever ran out of money, had two or more lifetime sexual partners, ever tested for tuberculosis, and ever diagnosed with sexually transmitted infection were at higher risk of being virally unsuppressed. The nonlinear effect showed that women aged 15 to 29 years, from smaller households and had fewer number of lifetime HIV tests, were more likely to be virally unsuppressed. High viral load risk areas were the north-east and south-west in 2014, with north and west in 2015. The findings provide guidance on identifying key populations and areas for targeted interventions.

Differences in spatial patterns of long-term care depending on severity in Hokkaido, Japan

The increasing demand for long-term care (LTC) among the ageing population is a serious problem worldwide, which has greatly increased also in Japan since the introduction of the LTC insurance system there. Since there is a difference between insurers with respect to the proportion of people needing LTC, this study aimed at clarifying the spatial patterns of LTC. Insurer (n=156) LTC data for the period 2012-2019 were obtained from Ministry of Health, Labour, and Welfare and those needing LTC were classified into three classes: total, mild and severe with ageand sex-adjusted proportions needing LTC. Global and local Moran’s I statistics were calculated for each 2-year period to clarify the trends of global and local spatial clusters. From 2012 to 2019, the mean proportion of mild class cases increased (10.6% to 11.6%), whereas that of severe class cases decreased slightly (5.9% to 5.7%). The spatial pattern of the proportion of each class revealed positive spatial autocorrelation. Based on analysis by local Moran’s I, differences in spatial patterns were emphasised between the mild and severe classes. In Hokkaido, High-High clusters of mild cases were identified in the central and southern parts and severe ones in the northern and southern parts. Spatial patterns differed depending on the LTC class. Some insurers had distinctly higher or lower certification rates than those of their neighbourhoods.

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.

Epidemiological characteristics and temporal-spatial analysis of overseas imported dengue fever cases in outbreak provinces of China, 2005–2019

Background

Overseas imported dengue fever is an important factor in local outbreaks of this disease in the mainland of China. To better prevent and control such local outbreaks, the epidemiological characteristics and temporal-spatial distribution of overseas imported dengue fever cases in provincial-level administrative divisions (PLADs) where dengue fever is outbreak in the mainland of China were explored.

Methods

Using the Chinese National Notifiable Infectious Disease Reporting Information System (CNNDS), we identified overseas imported dengue fever cases in dengue fever outbreak areas in the mainland of China from 2005 to 2019 to draw the epidemic curve and population characteristic distribution of overseas imported cases in each PLAD. Based on spatial autocorrelation analysis of ArcGIS 10.5 and temporal-spatial scanning analysis of SaTScan 9.5, we analyzed the temporal-spatial distribution of overseas imported dengue fever in dengue fever outbreak areas in the mainland of China.

Results

A total of 11,407 imported cases, mainly from Southeast Asia, were recorded from 2005 to 2019 in these 13 PLADs. Of which 62.1% were imported into Yunnan and Guangdong Provinces. Among the imported cases, there were more males than females, mainly from the 21–50 age group. The hot spots were concentrated in parts of Yunnan, Guangdong and Fujian Provinces. We found the cluster of infected areas were expanding northward.

Conclusions

Based on the analysis of overseas imported dengue cases in 13 PLADs of the mainland of China from 2005 to 2019, we obtained the epidemiological characteristics and spatial distribution of imported dengue cases. Border controls need to pay attention to key population sectors, such as 21–50 years old men and education of key populations on dengue prevention. There is a need to improve the awareness of the prevention and control of imported cases in border areas. At the same time, northern regions cannot relax their vigilance.

Graphical Abstract

How to Keep University Active during COVID-19 Pandemic: Experience from Slovakia

The paper outlines the adverse consequences and challenges induced by COVID-19 pandemic for the whole world and for universities in particular. The example of Constantine the Philosopher University in Nitra illustrates the difficulties and challenges caused by the pandemic in relation to the two main activities arising from the university mission-teaching and research. It presents some particular aspects of the university activities adversely affected by COVID-19 and shares the measures to minimize the resulted damages. Furthermore, it demonstrates that, despite complications induced by COVID-19, teaching, research, and international cooperation have been successfully continued.

Evaluating spatial patterns of seasonal ozone exposure and incidence of respiratory emergency room visits in Dallas-Fort Worth

BACKGROUND

In urban environments, environmental air pollution poses significant risks to respiratory health. Moreover, the seasonal spatial variability of the air pollutant ozone, and respiratory illness within Dallas-Fort Worth (DFW) is not well understood. We examine the relationships between spatial patterns of long-term ozone exposure and respiratory illness to better understand impacts on health outcomes. We propose that this study will establish an enhanced understanding of the spatio-temporal characteristics of ozone concentrations and respiratory emergency room visits (ERV) incidence.

METHODS

Air pollution data (ozone) and ERV incidence data from DFW was used to evaluate the relationships between exposures and outcomes using three steps: (1) develop a geostatistical model to produce quarterly maps of ozone exposure for the DFW area; (2) use spatial analysis techniques to identify clusters of zip codes with high or low values of ozone exposure and respiratory ERV incidence; and (3) use concentration-response curves to evaluate the relationships between respiratory ERV incidence and ozone exposure.

RESULTS

Respiratory ERV incidence was highest in quarters 1 and 4, while ozone exposure was highest in quarters 2 and 3. Extensive statistically significant spatial clusters of ozone regions were identified. Although the maps revealed that there was no regional association between the spatial patterns of high respiratory ERV incidence and ozone exposure, the concentration-response analysis suggests that lower levels of ozone exposure may still contribute to adverse respiratory outcomes.