Spatial inequalities in skilled birth attendance in India: a spatial-regional model approach

Background Despite a significant increase in the skilled birth assisted (SBA) deliveries in India, there are huge gaps in availing maternity care services across social gradients - particularly across states and regions. Therefore, this study applies the spatial-regression model to examine the spatial distribution of SBA across districts of India. Furthermore, the study tries to understand the spatially associated population characteristics that influence the low coverage of SBA across districts of India and its regions. Methods The study used national representative cross-sectional survey data obtained from the fourth round of National Family Health Survey, conducted in 2015-16. The effective sample size was 259,469 for the analysis. Moran’s I statistics and bivariate Local Indicator for Spatial Association maps were used to understand spatial dependence and clustering of deliveries conducted by SBA coverage in districts of India. Ordinary least square, spatial lag and spatial error models were used to examine the correlates of deliveries conducted by SBA. Results Moran’s I value for SBA among women was 0.54, which represents a high spatial auto-correlation of deliveries conducted by SBA over 640 districts of India. There were 145 hotspots for deliveries conducted by SBA among women in India, which includes almost the entire southern part of India. The spatial error model revealed that with a 10% increase in exposure to mass media in a particular district, the deliveries conducted by SBA increased significantly by 2.5%. Interestingly, also with the 10% increase in the four or more antenatal care (ANC) in a particular district, the deliveries conducted by SBA increased significantly by 2.5%. Again, if there was a 10% increase of women with first birth order in a particular district, then the deliveries conducted by SBA significantly increased by 6.1%. If the district experienced an increase of 10% household as female-headed, then the deliveries conducted by SBA significantly increased by 1.4%. Conclusion The present study highlights the important role of ANC visits, mass media exposure, education, female household headship that augment the use of an SBA for delivery. Attention should be given in promoting regular ANC visits and strengthening women’s education.

Spatially Varying Relationships between Land Subsidence and Urbanization: A Case Study in Wuhan, China

Land subsidence has become an increasing global concern over the past few decades due to natural and anthropogenic factors. However, although several studies have examined factors affecting land subsidence in recent years, few have focused on the spatial heterogeneity of relationships between land subsidence and urbanization. In this paper, we adopted the small baseline subset-synthetic aperture radar interferometry (SBAS-InSAR) method using Sentinel-1 radar satellite images to map land subsidence from 2015 to 2018 and characterized its spatial pattern in Wuhan. The bivariate Moran’s I index was used to test and visualize the spatial correlations between land subsidence and urbanization. A geographically weighted regression (GWR) model was employed to explore the strengths and directions of impacts of urbanization on land subsidence. Our findings showed that land subsidence was obvious and unevenly distributed in the study area, the annual deformation rate varied from −42.85 mm/year to +29.98 mm/year, and its average value was −1.0 mm/year. A clear spatial pattern for land subsidence in Wuhan was mapped, and several apparent subsidence funnels were primarily located in central urban areas. All urbanization indicators were found to be significantly spatially correlated with land subsidence at different scales. In addition, the GWR model results showed that all urbanization indicators were significantly associated with land subsidence across the whole study area in Wuhan. The results of bivariate Moran’s I and GWR results confirmed that the relationships between land subsidence and urbanization spatially varied in Wuhan at multiple spatial scales. Although scale dependence existed in both the bivariate Moran’s I and GWR models for land subsidence and urbanization indicators, a “best” spatial scale could not be confirmed because the disturbance of factors varied over different sampling scales. The results can advance the understanding of the relationships between land subsidence and urbanization, and they will provide guidance for subsidence control and sustainable urban planning.

Using Moran’s I For Detection And Monitoring Of The Covid-19 Spreading Stage In Thailand During The Third Wave Of The Pandemic

Spatial distribution and spreading patterns of COVID-19 in Thailand were investigated in this study for the 1 April – 23 July 2021 period by analyzing COVID-19 incidence’s spatial autocorrelation and clustering patterns in connection to population density, adult population, mean income, hospital beds, doctors and nurses. Clustering analysis indicated that Bangkok is a significant hotspot for incidence rates, whereas other cities across the region have been less affected. Bivariate Moran’s I showed a low relationship between COVID-19 incidences and the number of adults (Moran’s I = 0.1023- 0.1985), whereas a strong positive relationship was found between COVID-19 incidences and population density (Moran’s I = 0.2776-0.6022). Moreover, the difference Moran’s I value in each parameter demonstrated the transmission level of infectious COVID-19, particularly in the Early (first phase) and Spreading stages (second and third phases). Spatial association in the early stage of the COVID-19 outbreak in Thailand was measured in this study, which is described as a spatio-temporal pattern. The results showed that all of the models indicate a significant positive spatial association of COVID-19 infections from around 10 April 2021. To avoid an exponential spread over Thailand, it was important to detect the spatial spread in the early stages. Finally, these findings could be used to create monitoring tools and policy prevention planning in future.

Spatial Clustering Analysis of the COVID-19 Pandemic: A Case Study of the Fourth Wave in Vietnam

An outbreak of the 2019 Novel Coronavirus Disease (COVID-19) in China caused by the emergence of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARSCoV2) spreads rapidly across the world and has negatively affected almost all countries including such the developing country as Vietnam. This study aimed to analyze the spatial clustering of the COVID-19 pandemic using spatial auto-correlation analysis. The spatial clustering including spatial clusters (high-high and low-low), spatial outliers (low-high and high-low), and hotspots of the COVID-19 pandemic were explored using the local Moran’s I and Getis-Ord’s G* i statistics. The local Moran’s I and Moran scatterplot were first employed to identify spatial clusters and spatial outliers of COVID-19. The Getis-Ord’s G* i statistic was then used to detect hotspots of COVID-19. The method has been illustrated using a dataset of 86,277 locally transmitted cases confirmed in two phases of the fourth COVID-19 wave in Vietnam. It was shown that significant low-high spatial outliers and hotspots of COVID-19 were first detected in the NorthEastern region in the first phase, whereas, high-high clusters and low-high outliers and hotspots were then detected in the Southern region of Vietnam. The present findings confirm the effectiveness of spatial auto-correlation in the fight against the COVID-19 pandemic, especially in the study of spatial clustering of COVID-19. The insights gained from this study may be of assistance to mitigate the health, economic, environmental, and social impacts of the COVID-19 pandemic.

Evaluation of the Operation Process of Medical Equipment to Enhance Ergonomic Reliability Based on FRAM–Moran’s I and CREAM

To improve the ergonomic reliability of medical equipment design during the operation process, a method for evaluating the operating procedure of a medical equipment interface according to functional resonance analysis method (FRAM)-Moran’s I and cognitive reliability and error analysis method (CREAM) is proposed in this study. The novelty of this research is to analyze the ergonomic reliability of medical equipment in a more systematic manner and to minimize the impact of human subjectivity and individual differences on the evaluation results of the operation process. To solve the calculation problem of functional resonance in FRAM and to make the evaluation results more objective, Moran’s I was introduced to quantify the deviation degree caused by the individual differences of the subjects. By giving weights based on Moran’s I, the influence of individual differences and subjectivity on the evaluation results can be minimized, to a certain extent. Considering the importance of a special environment, which is not fully considered by the conventional CREAM, the weighting values based on Moran’s I, Delphi survey, and technique for order preference by similarity to an ideal solution (TOPSIS) were adopted to assign weights to common performance conditions (CPCs) in CREAM. The optimal design scheme was selected more objectively than in the conventional method. The validity and practicability of this operation process evaluation method was verified by a statistical method based on ergonomic reliability experiments.

Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada

Introduction: This study aimed to produce community-level geo-spatial mapping of confirmed COVID-19 cases in Ontario Canada in near real-time to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 positive individuals. Methods: COVID-19 cases and locations were curated for geostatistical analyses from March 2020 through June 2021, corresponding to the first, second, and third waves of infections. Daily cases were aggregated according to designated forward sortation area (FSA), and postal codes (PC) in municipal regions Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, and Windsor/Essex county. Hotspots were identified with area-to-area tests including Getis-Ord Gi*, Global Moran’s I spatial autocorrelation, and Local Moran’s I asymmetric clustering and outlier analyses. Case counts were also interpolated across geographic regions by Empirical Bayesian Kriging, which localizes high concentrations of COVID-19 positive tests, independent of FSA or PC boundaries. The Geostatistical Disease Epidemiology Toolbox, which is freely-available software, automates the identification of these regions and produces digital maps for public health professionals to assist in pandemic management of contact tracing and distribution of other resources. Results: This study provided indicators in real-time of likely, community-level disease transmission through innovative geospatial analyses of COVID-19 incidence data. Municipal and provincial results were validated by comparisons with known outbreaks at long-term care and other high density residences and on farms. PC-level analyses revealed hotspots at higher geospatial resolution than public reports of FSAs, and often sooner. Results of different tests and kriging were compared to determine consistency among hotspot assignments. Concurrent or consecutive hotspots in close proximity suggested potential community transmission of COVID-19 from cluster and outlier analysis of neighboring PCs and by kriging. Results were also stratified by population based-categories (sex, age, and presence/absence of comorbidities). Conclusions: Earlier recognition of hotspots could reduce public health burdens of COVID-19 and expedite contact tracing.

Specific Urban Elements Identified for Tuberculosis Epidemic in Guangzhou by Using Geographical Detector

Background: The prevalence of tuberculosis (TB) in China has heavily affected people’s health for decades, which has been widely investigated for the rural regions and west parts. However, its spatial features in urban areas remain little understood. Thus, this study aims to identify its spatial differentiations and their influencing factors in highly urbanized region on a fine scale.Methods: Together with the TB cases in 2017 obtained from Guangzhou Institute of Tuberculosis Control and Prevention, in total 18 socioeconomic and environmental variables were included in this study. Two spatial analysis tools were respectively applied to select the relative appropriate spatial scale (global Moran’s I), and to identify specific urban factors (the Geographical detector) for this epidemic in the central four districts of Guangzhou.Results: The 2 km × 2 km grid was determined as the most appropriate spatial scale due to its relatively higher spatial autocorrelation (Moran’s I=0.33, Z=4.71). At this spatial level, the TB epidemic in the four central districts was obviously closely associated with most of socioeconomic factors (0.31<r<0.76) at the significance level of 0.01. By contrast, among environmental factors, only the concentration of fine particulate matter (PM2.5) correlated with this epidemic (r=0.21) at the significance level of 0.05. Similarly, according to the q-values derived from geographical detector analysis, socioeconomic factors posed stronger impacts (0.08<q<0.57) on the spatial differentiations of TB prevalence than those of environmental variables (0.06<q<0.27), Furthermore, 153 pairs of variables presented more powerful explanatory abilities for this epidemic’s spatial disparities due to their notable enhancements of q-values (7.3%<sq<311.6%) caused by the pairwise interactions.Conclusion: The spatial heterogeneity of TB prevalence was remarkably influenced by a series of specific urban elements and their pairwise interactions across the central region of Guangzhou. We accordingly suggest that more attentions should be paid to the areas with pairwise interactions of these specific urban elements in this city. This study would provide meaningful clues for local authorities making more targeted interventions on this disease in China’s municipal areas featured by both high urbanization and severe tuberculosis.

Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada

IntroductionThis study aimed to produce community-level geo-spatial mapping of confirmed COVID-19 cases in Ontario, Canada in near real-time to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 positive individuals.MethodsCOVID-19 cases and locations were curated for geostatistical analyses from March 2020 through June 2021, corresponding to the first, second, and third waves of infections. Daily cases were aggregated according to designated forward sortation area [FSA], and postal codes [PC] in municipal regions covering Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, and Windsor/Essex county. Hotspots were identified with area-to-area tests including Getis-Ord Gi*, Global Moran’s I spatial autocorrelation, and Local Moran’s I asymmetric clustering and outlier analyses. Case counts were also interpolated across geographic regions by Empirical Bayesian Kriging, which localizes high concentrations of COVID-19 positive tests, independent of FSA or PC boundaries. The Geostatistical Disease Epidemiology Toolbox, which is freely-available software, automates the identification of these regions and produces digital maps for public health professionals to assist in pandemic management of contact tracing and distribution of other resources.Results/DiscussionThis study provided indicators in real-time of likely, community-level disease transmission through innovative geospatial analyses of COVID-19 incidence data. Municipal and provincial results were validated by comparisons with known outbreaks at long-term care and other high density residences and on farms. PC-level analyses revealed hotspots at higher geospatial resolution than public reports of FSAs, and often sooner. Results of different tests and kriging were compared to determine consistency among hotspot assignments. Concurrent or consecutive hotspots in close proximity suggested potential community transmission of COVID-19 from cluster and outlier analysis of neighboring PCs and by kriging. Results were also stratified by population based-categories (sex, age, and presence/absence of comorbidities). Earlier recognition of hotspots could reduce public health burdens of COVID-19 and expedite contact tracing.

Untersuchung zur Ausbreitung und Entwicklung der alveolären Echinokokkose in Deutschland, 1992–2018

Zusammenfassung Ziel der Studie Die alveoläre Echinokokkose (AE) ist eine seltene Parasitose verursacht durch den Erreger Echinococcus multilocularis. In vielen Ländern wird ein Anstieg der Fallzahlen beobachtet. Ziel der Arbeit ist die Untersuchung der aktuellen Prävalenz und der Veränderung des geographische Verteilungsmusters. Methodik Die Datenerhebung erfolgte retrospektiv für den Zeitraum 1992–2018 anhand der registrierten Fälle im nationalen Erkrankungsregistern für die AE in Deutschland. Die statistische Analyse erfolgte mittels dem statistischen Auswertungssystem SAS Version 9.4 (SAS Institute, Cary, N.C., USA). Ergebnisse Das Untersuchungskollektiv von n=569 Patienten umfasste n=322 (56,59%) Frauen und n=247 (43,40%) Männer. Das mittleres Durchschnittsalter der Patienten mit alveolärer Echinokokkose bei Erstvorstellung betrug 53,90±17,54 Jahre (Median: 56,00 Jahre). Die Moran’s I Teststatistik ergab für den Zeitraum 1992–2018 eine positive räumliche Autokorrelation entsprechend einer heterogenen Verteilung der Erkrankungsfälle in Deutschland (I=0,4165; Z=10,9591, p=0,001). Für den gesamten Untersuchungszeitraum (1992–2018) konnte ein Anstieg der alters- und geschlechtsspezifischen Prävalenz ermittelt werden. Die Gesamtprävalenz im Zeitraum 1992–2018 lag bei 0,71 Erkrankungsfälle pro 100 000 Einwohner. Die Ermittlung der Prävalenz für den Zeitraum 1992–2018 ergab für Männern 0,31 Fälle, für Frauen 0,40 Fälle pro 100 000 Einwohner. Im Zeitraum von 1992–1996 waren in 11/16 (68,8%) Bundesländern (Berlin, Brandenburg, Bremen, Hamburg, Mecklenburg-Vorpommern, Rheinland-Pfalz, Saarland, Sachsen, Sachsen-Anhalt, Schleswig-Holstein und Thüringen) noch keine AE-Fälle registriert worden. Die Auswertung zeigt jüngst ein vermehrtes vorkommen von Fällen in den Bundesländern Hessen, Rheinland-Pfalz und Nordrhein-Westfalen. Schlussfolgerungen Die Analyse zeigt einen Anstieg der Prävalenz sowie zunehmend vermehrt Erkrankungsfälle außerhalb der klassischen Hauptendemiegebiete Baden-Württemberg und Bayern.

Ten years of China’s new healthcare reform: a longitudinal study on changes in health resources

Background China launched a new round of healthcare-system reform in 2009 and proposed the goal of equal and guaranteed essential medical and health services for all by 2020. We aimed to investigate the changes in China’s health resources over the past ten years after the healthcare reform. Methods Data were collected from the China Statistical Yearbook and China Health Statistics Yearbook from 2009 to 2018. Four categories and ten indicators of health resources were analyzed. A descriptive analysis was used to present the overall condition. The Health Resource Density Index was applied to showcase health-resource distribution in demographic and geographic dimensions. The global and local Moran’s I were used to assess the spatial autocorrelation of health resources. Concentration Index (CI) was used to quantify the equity of health-resource distribution. A Geo-Detector model and Geographic Weighted Regression (GWR) were applied to assess the association between gross domestic product (GDP) per capita and health resources. Results Health resources have increased over the past ten years. The global and local Moran’s I suggested spatial aggregation in the distribution of health resources. Hospital beds were concentrated in wealthier areas, but this inequity decreased yearly (from CI=0.0587 in 2009 to CI=0.0021 in 2018). Primary medical and health institutions (PMHI) and their beds were concentrated in poorer areas (CI remained negative). Healthcare employees were concentrated in wealthier areas (CI remained positive). In 2017, the q-statistics indicated that the explanatory power of GDP per capita to beds, health personnel, and health expenditure was 40.7%, 50.3%, and 42.5%, respectively. The coefficients of GWR remained positive with statistical significance, indicating the positive association between GDP per capita and health resources. Conclusions From 2009 to 2018, the total amount of health resources in China has increased substantially. Spatial aggregation existed in the health-resources distribution. Health resources tended to be concentrated in wealthier areas. When allocating health resources, the governments should take economic factors into account.