Spatial heterogeneity in relationship between district patterns of HIV incidence and covariates in Zimbabwe: a multi-scale geographically weighted regression analysis.

A study was conducted to investigate the district-level patterns of incidence of the human immunodeficiency virus (HIV) in Zimbabwe in the period 2005-2015 and explore variations in the relationship between covariates and HIV incidence across different districts. Demographic health survey data were analysed using hotspot analysis, spatial autocorrelation, and multi-scale geographically weighted regression (MGWR) techniques. The analysis revealed hotspots of the HIV epidemic in the southern and western regions of Zimbabwe in contrast to the eastern and northern regions. Specific districts in Matabeleland South and Matabeleland North provinces showed clusters of HIV incidence in 2005-2006, 2010-2011 and 2015. Variables studied were multiple sex partners and sexually transmitted infections (STI) condom use and being married. Recommendations include implementing targeted HIV prevention programmes in identified hotspots, prioritising interventions addressing the factors mentioned above as well as enhancing access to HIV testing and treatment services in high-risk areas, strengthening surveillance systems, and conducting further research to tailor interventions based on contextual factors. The study also emphasizes the need for regular monitoring and evaluation at the district level to inform effective responses to the HIV epidemic over time. By addressing the unique challenges and risk factors in different districts, significant progress can be made in reducing HIV transmission and improving health outcomes in Zimbabwe. These findings should be valuable for policymakers in resource allocation and designing evidence-based interventions.

Spatial distribution of mortality from colorectal cancer in the southern region of Brazil.

Colorectal cancer (CRC) is the leading cause of death due to cancer worldwide. In Brazil, it is the second most frequent cancer in men and women, with a mortality reaching 9.4% of those diagnosed. The aim of this study was to analyze the spatial heterogeneity of CRC deaths among municipalities in south Brazil, from 2015 to 2019, in different age groups (50-59 years, 60-69 years, 70-79 years, and 80 years old or more) and identify the associated variables. Global Spatial Autocorrelation (Moran's I) and Local Spatial Autocorrelation (LISA) analyses were used to evaluate the spatial correlation between municipalities and CRC mortality. Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) were applied to evaluate global and local correlations between CRC deaths, sociodemographic, and coverage of health care services. For all age groups, our results found areas with high CRC rates surrounded by areas with similarly high rates mainly in the Rio Grande do Sul state. Even as factors associated with CRC mortality varied according to age group, our results suggested that improved access to specialized health centers, the presence of family health strategy teams, and higher rates of colonoscopies are protective factors against colorectal cancer mortality in southern Brazil.

[Determinants of Problem Drinking by Regional Variation among Adult Males in Single-Person Households: Geographically Weighted Regression Model Analysis].

PURPOSE: This study aimed to identify regional differences in problem drinking among adult males in single-person households and predict the determinants. METHODS: This study used data from the 2019 Community Health Survey. Geographically weighted regression analysis was performed on 8,625 adult males in single-person households who had been consuming alcohol for the past year. The Si-Gun-Gu was selected as the spatial unit. RESULTS: The top 10 regions for problem drinking among adult males in single-person households were located in the Jeju-do and Jeollanam-do areas near the southern coast, whereas the bottom 10 regions were located in the Incheon and northern Gyeonggi-do areas. Smoking, economic activity, and educational level were common factors affecting problem drinking among this population. Among the determinants of regional disparities in problem drinking among adult males in single-person households, personal factors included age, smoking, depression level, economic activity, educational level, and leisure activity, while regional factors included population and karaoke venue ratio. CONCLUSION: Problem drinking among adult males in single-person households varies by region, and the variables affecting each particular area differ. Therefore, it is necessary to develop interventions tailored to individuals and regions that reflect the characteristics of each region by prioritizing smoking, economic activity, and educational level as the common factors.

Government drivers of breast cancer prevention: A spatiotemporal analysis based on the association between breast cancer and macro factors.

Background: Currently, breast cancer (BC) is ranked among the top malignant tumors in the world, and has attracted widespread attention. Compared with the traditional analysis on biological determinants of BC, this study focused on macro factors, including light at night (LAN), PM2.5, per capita consumption expenditure, economic density, population density, and number of medical beds, to provide targets for the government to implement BC interventions. Methods: A total of 182 prefecture-level cities in China from 2013 to 2016 were selected as the sample of the study. The geographically and temporally weighted regression (GTWR) model was adopted to describe the spatiotemporal correlation between the scale of BC and macro factors. Results: The results showed that the GTWR model can better reveal the spatiotemporal variation. In the temporal dimension, the fluctuations of the regression coefficients of each variable were significant. In the spatial dimension, the positive impacts of LAN, per capita consumption expenditure, population density and number of medical beds gradually increased from west to east, and the positive coefficient of PM2.5 gradually increased from north to south. The negative impact of economic density gradually increased from west to east. Conclusion: The fact that the degree of effect of each variable fluctuates over time reminds the government to pay continuous attention to BC prevention. The spatial heterogeneity features also urge the government to focus on different macro indicators in eastern and western China or southern and northern China. In other words, our research helps drive the government to center on key regions and take targeted measures to curb the rapid growth of BC.

[Spatial relationship between landscape ecological risk and ecosystem service value in Fujian Province, China during 1980-2020]. / 1980­2020å¹´ç¦å»ºçœæ™¯è§‚ç”Ÿæ€é£Žé™©ä¸Žç”Ÿæ€ç³»ç»ŸæœåŠ¡ä»·å€¼çš„ç©ºé—´å ³ç³».

Understanding the relationship between landscape ecological risk and ecosystem service value (ESV) is important for building an ecological security pattern and enhancing human well-being. Taking Fujian Province as the research area, based on the remote sensing monitoring data of land use in 1980, 2000, and 2020, we carried out a grid resampling size of 5 km × 5 km in the study area, quantitatively evaluated the landscape ecological risk and ESV, and analyzed the spatio-temporal variations. The spatial correlation between landscape ecological risk and ESV was investigated by using the bivariate spatial autocorrelation analysis and the spatial regression models. The results showed that the landscape ecological risk level in Fujian Province changed from medium level to low level, with the situation being improved. The landscape ecological risk level was generally higher in the east region and lower in the west region. ESV generally declined. The functional structure of each ecosystem was relatively stable. ESV spread from high to low with high value area as the core. There was a significant negative spatial correlation between landscape ecological risk and ESV. Landscape ecological risk had a negative effect on ecosystem total ser-vice value, with the strongest impact on the supply function.

Spatial autocorrelation and stratified heterogeneity in the evaluation of breast cancer risk inequity and socioeconomic factors analysis in China: Evidence from Nanchang, Jiangxi Province.

Study of socioeconomic factors can play an important role in the spatial distribution of breast cancer by leading to a better understanding of its spatial pattern and assist breast cancer screening and early diagnosis. Taking Nanchang, a major city in central China, as an example, spatial autocorrelation and stratified heterogeneity were applied using a 10 10 km grid division to analyse breast cancer risk and socioeconomic factors. The research results showed that the median incidence rate of female breast cancer in Nanchang from 2016 to 2018 was 6.6/100,000 with a standard deviation of 12.3/100,000. Areas with higher incidence rates were mainly located in the central urban area and the major county towns. Spatial regression analysis showed that there was a statistically significant correlation between the spatial patterns of breast cancer incidence on the one hand, and on the other socioeconomic factors, such as total gross domestic product (GDP), per capita GDP and density of places of social and economic activities, i.e. points of interest. In addition, the normalized difference vegetation index also played a part in this respect. This research could serve as a reference for regional public health policy formulation and breast cancer screening.

Quantifying T2-FLAIR Mismatch Using Geographically Weighted Regression and Predicting Molecular Status in Lower-Grade Gliomas.

BACKGROUND AND PURPOSE: The T2-FLAIR mismatch sign is a validated imaging sign of isocitrate dehydrogenase-mutant 1p/19q noncodeleted gliomas. It is identified by radiologists through visual inspection of preoperative MR imaging scans and has been shown to identify isocitrate dehydrogenase-mutant 1p/19q noncodeleted gliomas with a high positive predictive value. We have developed an approach to quantify the T2-FLAIR mismatch signature and use it to predict the molecular status of lower-grade gliomas. MATERIALS AND METHODS: We used multiparametric MR imaging scans and segmentation labels of 108 preoperative lower-grade glioma tumors from The Cancer Imaging Archive. Clinical information and T2-FLAIR mismatch sign labels were obtained from supplementary material of relevant publications. We adopted an objective analytic approach to estimate this sign through a geographically weighted regression and used the residuals for each case to construct a probability density function (serving as a residual signature). These functions were then analyzed using an appropriate statistical framework. RESULTS: We observed statistically significant (P value = .05) differences between the averages of residual signatures for an isocitrate dehydrogenase-mutant 1p/19q noncodeleted class of tumors versus other categories. Our classifier predicts these cases with area under the curve of 0.98 and high specificity and sensitivity. It also predicts the T2-FLAIR mismatch sign within these cases with an under the curve of 0.93. CONCLUSIONS: On the basis of this retrospective study, we show that geographically weighted regression-based residual signatures are highly informative of the T2-FLAIR mismatch sign and can identify isocitrate dehydrogenase-mutation and 1p/19q codeletion status with high predictive power. The utility of the proposed quantification of the T2-FLAIR mismatch sign can be potentially validated through a prospective multi-institutional study.

Explainable artificial intelligence (XAI) for exploring spatial variability of lung and bronchus cancer (LBC) mortality rates in the contiguous USA.

Machine learning (ML) has demonstrated promise in predicting mortality; however, understanding spatial variation in risk factor contributions to mortality rate requires explainability. We applied explainable artificial intelligence (XAI) on a stack-ensemble machine learning model framework to explore and visualize the spatial distribution of the contributions of known risk factors to lung and bronchus cancer (LBC) mortality rates in the conterminous United States. We used five base-learners-generalized linear model (GLM), random forest (RF), Gradient boosting machine (GBM), extreme Gradient boosting machine (XGBoost), and Deep Neural Network (DNN) for developing stack-ensemble models. Then we applied several model-agnostic approaches to interpret and visualize the stack ensemble model's output in global and local scales (at the county level). The stack ensemble generally performs better than all the base learners and three spatial regression models. A permutation-based feature importance technique ranked smoking prevalence as the most important predictor, followed by poverty and elevation. However, the impact of these risk factors on LBC mortality rates varies spatially. This is the first study to use ensemble machine learning with explainable algorithms to explore and visualize the spatial heterogeneity of the relationships between LBC mortality and risk factors in the contiguous USA.

Impacto das variáveis socioeconômicas no desempenho do Enem: uma análise espacial e sociológica / Impacto de las variables socioeconómicas en el desempeño del Enem: un análisis espacial y sociológico / Impact of socioeconomic variables on Enem performance: a spatial and sociologic analysis

Resumo O objetivo deste trabalho é identificar variáveis com maior impacto no desempenho dos municípios no Exame Nacional do Ensino Médio (Enem), tanto para a prova objetiva quanto para a redação, com base em técnicas de estatística espacial, que permite analisar a dinâmica da influência territorial, e da perspectiva da sociologia da educação. Pretende-se mensurar o grau de importância de cada categoria de variáveis para a média e a variância das notas do Enem. Para isso, utilizam-se modelos estatísticos e geoespaciais, como regressão linear múltipla e regressão espacial, a partir do Enem de 2018. Para a prova objetiva, variáveis como o percentual de estudantes com bolsa, renda, raça, escolaridade e nível instrucional da mãe são fatores relevantes para o desempenho e a dispersão das notas dos estudantes de cada município. Para a redação, as variáveis são similares às da prova objetiva, mas com menor impacto na média e na dispersão das notas. Esse fator explicativo aumenta quando introduzimos um componente espacial no modelo para as notas de redação, indicando que outros fatores regionais, diferentes dos socioeconômicos, impactam o desempenho e a dispersão dos municípios. Os resultados reforçam os estudos da sociologia da educação, ao destacarem as disparidades socioeconômicas refletidas no desempenho estudantil, fundamentos da reprodução escolar das desigualdades mencionadas por Bourdieu (1998, 2008, 2009). Estudos com essa perspectiva integrada são relevantes para o entendimento da influência no nível da localidade dos municípios, podendo evidenciar lacunas específicas, direcionando e influenciando ações públicas visando à superação das desigualdades.

Resumen El objetivo de este trabajo es identificar variables con mayor impacto en el desempeño de los municipios en el Examen Nacional de Enseñanza Media (Enem), tanto para la prueba objetiva como para la prueba de redacción, a partir de técnicas de estadística espacial que permiten analizar la dinámica de la influencia territorial, y de la perspectiva de la sociología educacional. Pretendemos medir el grado de importancia de cada categoría de variables para la media y varianza de la puntuación del Enem. A tal fin, utilizamos modelos estadísticos y análisis geoespaciales, como regresión lineal y espacial, a partir del Enem 2018. Los resultados muestran que, para la prueba objetiva del Enem, variables como el porcentaje de alumnos becarios, renta, raza, escolaridad y nivel de instrucción de las madres son factores relevantes para el desempeño y dispersión de las notas de los estudiantes en cada municipio. Para el desempeño en redacción, las variables son similares a las de la prueba objetiva, no obstante, con menor impacto en la media y dispersión de la puntuación. Cuando se introduce un componente espacial en el modelo de puntuación de redacción, el factor explicativo aumenta, señalando que existen otros criterios regionales, distintos a los socioeconómicos, que impactan sobre el desempeño y dispersión de los municipios. Los resultados refuerzan los estudios de la sociología de la educación, ya que subrayan las disparidades socioeconómicas reflejadas en el desempeño de los estudiantes, que son fundamentos de la reproducción escolar de las desigualdades analizadas por Bourdieu (1998, 2008, 2009). Las investigaciones con esta perspectiva integrada son relevantes para la comprensión de la influencia en la regionalidad de los municipios, pudiendo revelar brechas específicas y, en consecuencia, dirigir e incidir en acciones públicas orientadas a la superación de desigualdades.

Abstract This study aims to identify variables with the highest impact on students' performance in the National Exam of High School (Enem), per municipality. The research adopted the educational sociology perspective and used spatial statistics to analyze the dynamics of territorial influence. We analyzed Enem 2018, measuring the degree of importance of each category of variables for the average and variance of students' grades in the exam - which is separated in an essay and an objective test - and used statistical modeling and geospatial analysis, such as linear and spatial regressions. The results indicate that, for the objective test, variables like percentage of students with scholarship, income, race, schooling, and education level of students' mothers are relevant to students' performance and dispersion of grades in each municipality. For the essay, variables were similar to the objective test but with less impact on the average and variance of the grades. This explicative factor increases when a spatial component is introduced in the model for the essay grades, indicating that there are other regional factors, besides socioeconomics, impacting the performance and dispersion per municipality. The results reinforce the sociological studies on education since the socioeconomic disparities reflected in the students' performance stand out, which are also pointed out by Bourdieu (1998, 2008, 2009) in his studies of the fundamentals of school productivity of inequalities. Studies with this integrated perspective are relevant to understand the influence of municipalities' location, evidencing specific gaps and, consequently, directing and influencing public actions to overcome inequalities.

Using geographically weighted Poisson regression to examine the association between socioeconomic factors and hysterectomy incidence in Wallonia, Belgium.

BACKGROUND: Various studies have investigated geographical variations in the incidence of hysterectomy in Western countries and analyzed socioeconomic factors to explain those variations. However, few studies have used spatial analysis to characterize them. Geographically weighted Poisson regression (GWPR) explores the spatially varying impacts of covariates across a study area and focuses attention on local variations. Given the potential of GWPR to guide decision-making, this study aimed to describe the geographical distribution of hysterectomy incidence for benign indications in women older than 15 years old (15+) at the municipal level in Wallonia (southern region of Belgium) and to analyze potential associations with socioeconomic factors ('Education/training', 'Income and purchasing power' and 'Health and care') influencing the use of this surgery. METHODS: We carried out an ecological study on data for women aged 15+ living in one of the 262 Walloon municipalities who underwent hysterectomies for benign indications between 2012 and 2014. We linked standardized hysterectomy rates to three municipal-level socioeconomic factors ('Education/training', 'Income and purchasing power' and 'Health and care'). Then, a Poisson regression model and a GWPR were applied to study the relationships between hysterectomy incidence and socioeconomic covariates in Wallonia. RESULTS: The hysterectomy rate varied across the region. The Poisson regression revealed a positive and significant association between the hysterectomy rate and 'Income and purchasing power', and a negative and significant association between hysterectomies and 'Health and care'. The same associations were seen in the GWPR model. The latter demonstrated that the association between hysterectomies and 'Education and training' ranged from negative to positive over the study area. CONCLUSIONS: Hysterectomy incidence was shown to have nonstationary relationships with socioeconomic factors. These results support the development of targeted interventions for a more appropriate use of this surgery.

Colorectal cancer screening participation: Exploring relationship heterogeneity and scale differences using multiscale geographically weighted regression.

Scotland has an organised colorectal cancer screening programme; however, despite proactively offering screening opportunities free to the at-risk population, and also despite using a screening test which may be completed at home, screening participation levels are unequal. Understanding causal pathways linking participation with other population characteristics may be aided by identifying how relationships between the two patterns vary across different localities, and such knowledge may also inform decisions regarding geographical targeting of screening promotion efforts. In this analysis, models calibrated using multiscale geographically weighted regression enabled the assessment of spatial variations of determinants of screening participation levels. The models were calibrated for localities across west central Scotland (n=409), where participation levels were relatively low, using aggregated individual-level screening records within a two-year window (2009-2011). Area deprivation was found to have a strong negative impact on participation levels across the study area, and ethnic population concentration had a significant impact on male participation levels on localities within Glasgow city. Estimates of local intercepts pointed to a systemic difference in screening participation between the two health board regions in the study area. Overall the results suggest that work to increase screening participation was necessary. They also suggest that barriers to participation could be addressed locally, and that differences between health board regions required further investigation.

Spatial inequalities of major lower limb amputation rates in Paraná state, Brazil.

OBJECTIVE: The aim of this observational cross-sectional study was to analyse the spatial distribution of major lower limb amputation (MLLA) rates and associate them to socioeconomic, demographic and public healthcare access-related variables in the State of Paraná, Brazil, from 2012 to 2017. METHOD: Data on MLLA, revascularisation surgeries, diagnostic exams and healthcare coverage were obtained from the Brazilian Public Hospital Information System. Socioeconomic data were obtained from the Brazilian Institute of Geography and Statistics. Spatial autocorrelation of the MLLA rates was tested using Moran's I method. Multivariate spatial regression models using ordinary least squares regression (OLS) and geographically weighted regression (GWR) were used to identify the variables significantly correlated with MLLA. RESULTS: A total of 5270 MLLA were included in the analysis. Mean MLLA rates were 24.32 (±18.22)/100 000 inhabitants, showing a positive global spatial autocorrelation (Moran's I=0.66; p<0.001). Queen contiguity matrix demonstrates that MLLA rates ranged from 7.6 to 46.6/100 000 with five large clusters of high MLLA rates. OLS showed that four of the nine studied variables presented significant spatial correlation with MLLA rates. Colour Doppler ultrasound showed a negative association (p<0.001), while revascularisation surgeries and illiteracy showed a positive correlation (p<0.01). GWR presented the best model (adjusted R2=0.77) showing that the predictors differentially affect the MLLA rates geographically. CONCLUSION: The high MLLA rates in some regions of the state are influenced by the high rate of illiteracy and low utilisation rate of colour Doppler, indicating a social problem and difficulty in accessing health. On the other hand, the high rates of revascularisation surgeries are related to higher MLLA rates, possibly due to delayed access to specialised hospitals. This indicates that attention must be given to population access to public healthcare in the State of Paraná in order to ensure proper and timely medical attention.

COVID-19 case-fatality rate and demographic and socioeconomic influencers: worldwide spatial regression analysis based on country-level data.

OBJECTIVE: To investigate the influence of demographic and socioeconomic factors on the COVID-19 case-fatality rate (CFR) globally. DESIGN: Publicly available register-based ecological study. SETTING: Two hundred and nine countries/territories in the world. PARTICIPANTS: Aggregated data including 10 445 656 confirmed COVID-19 cases. PRIMARY AND SECONDARY OUTCOME MEASURES: COVID-19 CFR and crude cause-specific death rate were calculated using country-level data from the Our World in Data website. RESULTS: The average of country/territory-specific COVID-19 CFR is about 2%-3% worldwide and higher than previously reported at 0.7%-1.3%. A doubling in size of a population is associated with a 0.48% (95% CI 0.25% to 0.70%) increase in COVID-19 CFR, and a doubling in the proportion of female smokers is associated with a 0.55% (95% CI 0.09% to 1.02%) increase in COVID-19 CFR. The open testing policies are associated with a 2.23% (95% CI 0.21% to 4.25%) decrease in CFR. The strictness of anti-COVID-19 measures was not statistically significantly associated with CFR overall, but the higher Stringency Index was associated with higher CFR in higher-income countries with active testing policies (regression coefficient beta=0.14, 95% CI 0.01 to 0.27). Inverse associations were found between cardiovascular disease death rate and diabetes prevalence and CFR. CONCLUSION: The association between population size and COVID-19 CFR may imply the healthcare strain and lower treatment efficiency in countries with large populations. The observed association between smoking in women and COVID-19 CFR might be due to the finding that the proportion of female smokers reflected broadly the income level of a country. When testing is warranted and healthcare resources are sufficient, strict quarantine and/or lockdown measures might result in excess deaths in underprivileged populations. Spatial dependence and temporal trends in the data should be taken into account in global joint strategy and/or policy making against the COVID-19 pandemic.

County-level variation in the long-term association between PM2.5 and lung cancer mortality in China.

INTRODUCTION: The relative risk (RR) of long-term exposure to PM2.5 in lung cancer mortality (LCM) may vary spatially in China. However, previous studies applying global regression have been unable to capture such variation. We aimed to employ a geographically weighted Poisson regression (GWPR) to estimate the RRs of LCM among the elderly (≥65 years) related to long-term exposure to PM2.5 and the LCM attributable to PM2.5 at the county level in China. METHODS: We obtained annual LCM in the elderly between 2013 and 2015 from the National Death Surveillance. We linked annual mean concentrations of PM2.5 between 2000 and 2004 with LCM using GWPR model at 148 counties across mainland China, adjusting for smoking and socioeconomic covariates. We used county-specific GWPR models to estimate annual average LCM in the elderly between 2013 and 2015 attributable to PM2.5 exposure between 2000 and 2004. RESULTS: The magnitude of the association between long-term exposure to PM2.5 and LCM varied with county. The median of county-specific RRs of LCM among elderly men and women was 1.52 (range: 0.90, 2.40) and 1.49 (range: 0.88, 2.56) for each 10 µg/m3 increment in PM2.5, respectively. The RRs were positively significant (P < 0.05) at 95% (140/148) of counties among both elderly men and women. Higher RRs of PM2.5 among elderly men were located at Southwest and South China, and higher RRs among elderly women were located at Northwest, Southwest, and South China. There were 99,967 and 54,457 lung cancer deaths among elderly men and women that could be attributed to PM2.5, with the attributable fractions of 31.4% and 33.8%, respectively. CONCLUSIONS: The relative importance of long-term exposure to PM2.5 in LCM differed by county. The results could help the government design tailored and efficient interventions. More stringent PM2.5 control is urgently needed to reduce LCM in China.

A retrospective cross-national examination of COVID-19 outbreak in 175 countries: a multiscale geographically weighted regression analysis (January 11-June 28, 2020).

OBJECTIVE: This study retrospectively examined the health and social determinants of the COVID-19 outbreak in 175 countries from a spatial epidemiological approach. METHODS: We used spatial analysis to examine the cross-national determinants of confirmed cases of COVID-19 based on the World Health Organization official COVID-19 data and the World Bank Indicators of Interest to the COVID-19 outbreak. All models controlled for COVID-19 government measures. RESULTS: The percentage of the population age between 15-64 years (Age15-64), percentage smokers (SmokTot.), and out-of-pocket expenditure (OOPExp) significantly explained global variation in the current COVID-19 outbreak in 175 countries. The percentage population age group 15-64 and out of pocket expenditure were positively associated with COVID-19. Conversely, the percentage of the total population who smoke was inversely associated with COVID-19 at the global level. CONCLUSIONS: This study is timely and could serve as a potential geospatial guide to developing public health and epidemiological surveillance programs for the outbreak in multiple countries. Removal of catastrophic medical expenditure, smoking cessation, and observing public health guidelines will not only reduce illness related to COVID-19 but also prevent unecessary deaths.

Evaluation of the Combined Effect of Heterodera glycines and Macrophomina phaseolina on Soybean Yield in Naturally Infested Fields with Spatial Regression Analysis and in Greenhouse Studies.

Heterodera glycines, the soybean cyst nematode, and Macrophomina phaseolina, causal agent of charcoal rot, are economically important soybean pathogens. The impact and effect of these pathogens on soybean yield in coinfested fields in the Midwest production region is not known. Both pathogens are soilborne, with spatially aggregated distribution and effects. Spatial regression analysis, therefore, is an appropriate method to account for the spatial dependency in either the dependent variable or regression error term from data produced in fields naturally infested with H. glycines and M. phaseolina. The objectives of this study were twofold: to evaluate the combined effect of H. glycines and M. phaseolina on soybean yield in naturally infested commercial fields with ordinary least squares and spatial regression models; and to evaluate, under environmentally controlled conditions, the combined effect of H. glycines and M. phaseolina through nematode reproduction and plant tissue fungal colonization. Six trials were conducted in fields naturally infested with H. glycines and M. phaseolina in Ohio. Systematic-grid sampling was used to determine the population densities of H. glycines and M. phaseolina, and soybean yield estimates. Though not used in any statistical analysis, M. phaseolina colony forming units from plant tissue, charcoal rot severity, and H. glycines type were also recorded and summarized. In two greenhouse experiments, treatments consisted of H. glycines alone, M. phaseolina alone, and coinfestation of soybean with both pathogens. Moran's I test indicated that the yield from five fields was spatially correlated (P < 0.05) and aggregated. In these fields, to account for spatial dependence, spatial regression models were fitted to the data. Spatial regression analyses revealed a significant interaction effect between H. glycines and M. phaseolina on soybean yield for fields with high initial population densities of both pathogens. In the greenhouse experiments, H. glycines reproduction was significantly (P < 0.05) reduced in the presence of M. phaseolina; however, soybean tissue fungal colonization was not affected by the presence of H. glycines. The direct mechanisms by which H. glycines and M. phaseolina interact were not demonstrated in this study. Future studies must be conducted in the field and greenhouse to better understand this interaction effect.

Geodemographic Disparities in Availability of Comprehensive Intimate Partner Violence Screening Services in Miami-Dade County, Florida.

This study examined geodemographic factors associated with availability of comprehensive intimate partner violence (IPV) screening services in Miami-Dade County, Florida. We geocoded 2014 survey data from 278 health facilities and created a population-normalized density surface of IPV screening comprehensiveness. We used correlation analysis and spatial regression techniques to evaluate census tract-level predictors of the mean normalized comprehensiveness score (NCS) for 505 census tracts in Miami-Dade. The population-adjusted density surface of IPV screening comprehensiveness revealed geographic disparities in the availability of screening services. Using a spatial lag regression model, we observed that race and ethnicity are associated with mean NCS by census tract after controlling for age, median gross rent, and receipt of Social Security benefits. The percentage of White non-Hispanic residents was positively associated with NCS, Black non-Hispanic was negatively associated with NCS, while Hispanic-the majority ethnicity in Miami-Dade-was not associated with NCS. This exploratory study may be the first to put IPV screening comprehensiveness on the map, and provides a starting point for addressing urban disparities in the availability of IPV screening services that are shaped by race, ethnicity, zoning, and socioeconomic status.

Emerging cancer incidence, mortality, hospitalisation and associated burden among Australian cancer patients, 1982 - 2014: an incidence-based approach in terms of trends, determinants and inequality.

OBJECTIVE: Cancer is a leading killer worldwide, including Australia. Cancer diagnosis leads to a substantial burden on the individual, their family and society. The main aim of this study is to understand the trends, determinants and inequalities associated with cancer incidence, hospitalisation, mortality and its burden over the period 1982 to 2014 in Australia. SETTINGS: The study was conducted in Australia. STUDY DESIGN: An incidence-based study design was used. METHODS: Data came from the publicly accessible Australian Institute of Health and Welfare database. This contained 2 784 148 registered cancer cases over the study period for all types of cancer. Erreygers' concentration index was used to examine the magnitude of socioeconomic inequality with regards to cancer outcomes. Furthermore, a generalised linear model was constructed to identify the influential factors on the overall burden of cancer. RESULTS: The results showed that cancer incidence (annual average percentage change, AAPC=1.33%), hospitalisation (AAPC=1.27%), cancer-related mortality (AAPC=0.76%) and burden of cancer (AAPC=0.84%) all increased significantly over the period. The same-day (AAPC=1.35%) and overnight (AAPC=1.19%) hospitalisation rates also showed an increasing trend. Further, the ratio (least-most advantaged economic resources ratio, LMR of mortality (M) and LMR of incidence (I)) was especially high for cervix (M/I=1.802), prostate (M/I=1.514), melanoma (M/I=1.325), non-Hodgkin's lymphoma (M/I=1.325) and breast (M/I=1.318), suggesting that survival inequality was most pronounced for these cancers. Socioeconomically disadvantaged people were more likely to bear an increasing cancer burden in terms of incidence, mortality and death. CONCLUSIONS: Significant differences in the burden of cancer persist across socioeconomic strata in Australia. Policymakers should therefore introduce appropriate cancer policies to provide universal cancer care, which could reduce this burden by ensuring curable and preventive cancer care services are made available to all people.

A novel approach for geographical risk mapping of morbidity and mortality rates: the case of Val D'Agri, Italy.

While associations between exposure to air pollutants and increased morbidity and mortality are well established, few rigorous studies on this issue are available. The aim of the current study is to implement a new approach to the spatial analysis of mortality and morbidity, based on testing for the presence of the same association in other areas of similar size. Additionally, we perform a case study in Val d'Agri (VA), an area of Basilicata Region, Southern Italy, where oil and natural gas extraction began in 1998. In order to examine the spatial distribution of morbidity and mortality in the region of interest, Hospital discharge (2001-2013) and mortality (2003-2014) rates for the main environment-related diseases were calculated. In addition, a comparison between the period 1980-1998 and the period 1999-2014 was performed for cardiovascular disease mortality. For the period under study, a neutral scenario emerged for cancer and respiratory diseases, where we found no differences in morbidity and mortality as compared to the national benchmark. In some cases significantly lower values (as compared to the nation-wide benchmark) were found. Conversely, a slight excess in morbidity and mortality (as compared to the nation-wide benchmark) emerged for cardiovascular diseases. Still, this excess was common to a number of municipalities in the surroundings of VA, and appeared to be already present in 1980. Higher rates of cardiovascular diseases, lower rates of neoplastic disorders no differences in mortality for respiratory causes (as compared to the nation-wide benchmark) were found in multiple areas of the region, and were therefore not specific to VA. In summary, our data do not support the hypothesis of a role of industrial activities related to oil extraction in VA in determining mortality and morbidity patterns and trends.

A geographically weighted regression approach to investigate air pollution effect on lung cancer: A case study in Portugal.

The risk of developing lung cancer might to a certain extent be attributed to tobacco. Nevertheless, the role of air pollution, both form urban and industrial sources, needs to be addressed. Numerous studies have concluded that long-term exposure to air pollution is an important environmental risk factor for lung cancer mortality. Still, there are only a few studies on air pollution and lung cancer in Portugal and none addressing its spatial dimension. The goal was to determine the influence of air pollution and urbanization rate on lung cancer mortality. A geographically weighted regression (GWR) model was performed to evaluate the relation between particle matter10 (PM10) emissions and lung cancer mortality relative risk (RR) for males and females in Portugal between 2007 and 2011. RR was computed with the BYM model. For a more in-depth analysis, the urbanization rate and the percentage of industrial area in each municipality were added. GWR efforts led to identifying three variables that were statistically significant in explaining lung cancer relative risk mortality, PM10 emissions, urbanization rate and the percentage of industrial area with an adjusted R2 of 0,63 for men and 0,59 for women. A small set of 8 municipalities with high correlation values was also identified (local R2 above 0,70). Stronger relationships were found in the north-western part of mainland Portugal. The local R2 tends to be higher when the emissions of PM10 are joined by urbanization and industrial areas. However, when assessing the industrial areas alone, it was noted that its impact was lower overall. As one of the first communications on this subject in Portugal, we have identified municipalities where possible impacts of air pollution on lung cancer mortality RR are higher thereby highlighting the role of geography and spatial analysis in explaining the associations between a disease and its determinants.