Pub Date : 2025-08-22DOI: 10.1016/j.sste.2025.100745
Charlotte K․ Bainomugisa , Paramita Dasgupta , Jessica K. Cameron , Ben Tran , Susanna M. Cramb , Peter D. Baade
Aim
To investigate the spatial patterns of the incidence rates of testicular cancer, and broad regional differences in survival, between 2010 and 2019 in Australia using national population-based cancer registry data.
Methods
Incidence data including residential location at diagnosis were obtained from the Australian Cancer Database, with mortality followed-up until end of 2019. Incidence spatial patterns were modelled using Bayesian spatial Leroux models and spatial heterogeneity tested using the maximised excess events test. Relative survival rates by broad region were modelled using flexible parametric relative survival models.
Results
From all the notifications of testicular cancer (n = 8217), the age-standardized incidence rate was 8.9 cases per 100,000 males each year. We found evidence of significant spatial variation in the incidence of testicular cancer across small geographical areas, with some areas including those in Tasmania showing standardised incidence ratios above the national average. The 5-year relative survival estimate was 97.5 % [95 % CI: 97.1–97.9].
Conclusion
There is a need to raise awareness of testicular cancer in high-risk geographical areas and age groups, and to conduct further research into drivers of localised spatial patterns.
{"title":"Spatial patterns of testicular cancer diagnosis in Australia, 2010-2019","authors":"Charlotte K․ Bainomugisa , Paramita Dasgupta , Jessica K. Cameron , Ben Tran , Susanna M. Cramb , Peter D. Baade","doi":"10.1016/j.sste.2025.100745","DOIUrl":"10.1016/j.sste.2025.100745","url":null,"abstract":"<div><h3>Aim</h3><div>To investigate the spatial patterns of the incidence rates of testicular cancer, and broad regional differences in survival, between 2010 and 2019 in Australia using national population-based cancer registry data.</div></div><div><h3>Methods</h3><div>Incidence data including residential location at diagnosis were obtained from the Australian Cancer Database, with mortality followed-up until end of 2019. Incidence spatial patterns were modelled using Bayesian spatial Leroux models and spatial heterogeneity tested using the maximised excess events test. Relative survival rates by broad region were modelled using flexible parametric relative survival models.</div></div><div><h3>Results</h3><div>From all the notifications of testicular cancer (<em>n</em> = 8217), the age-standardized incidence rate was 8.9 cases per 100,000 males each year. We found evidence of significant spatial variation in the incidence of testicular cancer across small geographical areas, with some areas including those in Tasmania showing standardised incidence ratios above the national average. The 5-year relative survival estimate was 97.5 % [95 % CI: 97.1–97.9].</div></div><div><h3>Conclusion</h3><div>There is a need to raise awareness of testicular cancer in high-risk geographical areas and age groups, and to conduct further research into drivers of localised spatial patterns.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"55 ","pages":"Article 100745"},"PeriodicalIF":1.7,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1016/j.sste.2025.100736
Nicholas B. Link , Anuraag Gopaluni , Isabel Fulcher , Emma Jean Boley , Rachel C. Nethery , Bethany Hedt-Gauthier
Syndromic surveillance monitors infectious diseases, especially in situations where direct disease monitoring is unavailable. However, conventional syndromic surveillance methods face challenges in handling missing data, particularly when the missing completely at random (MCAR) assumption is violated. Additionally, these methods often do not leverage spatio-temporal techniques that can reduce bias and improve their performance. This study addresses both of these limitations by comparing a baseline syndromic surveillance model with a frequentist spatio-temporal model used in infectious diseases and a Bayesian spatio-temporal conditional autoregressive (CAR) model.
Drawing inspiration from COVID-19 symptom data collected via routine health systems in Liberia, we conduct simulations with various data generating processes, spatio-temporal correlation structures, and missing data mechanisms. Across the diverse simulations for outbreak detection, the baseline model and the Bayesian CAR model had high specificity, thus limiting outbreak false alarms. The findings underscore the importance of considering spatio-temporal models for syndromic surveillance.
{"title":"Spatio-temporal methods to handle missing data in syndromic surveillance with applications to health management information system data","authors":"Nicholas B. Link , Anuraag Gopaluni , Isabel Fulcher , Emma Jean Boley , Rachel C. Nethery , Bethany Hedt-Gauthier","doi":"10.1016/j.sste.2025.100736","DOIUrl":"10.1016/j.sste.2025.100736","url":null,"abstract":"<div><div>Syndromic surveillance monitors infectious diseases, especially in situations where direct disease monitoring is unavailable. However, conventional syndromic surveillance methods face challenges in handling missing data, particularly when the missing completely at random (MCAR) assumption is violated. Additionally, these methods often do not leverage spatio-temporal techniques that can reduce bias and improve their performance. This study addresses both of these limitations by comparing a baseline syndromic surveillance model with a frequentist spatio-temporal model used in infectious diseases and a Bayesian spatio-temporal conditional autoregressive (CAR) model.</div><div>Drawing inspiration from COVID-19 symptom data collected via routine health systems in Liberia, we conduct simulations with various data generating processes, spatio-temporal correlation structures, and missing data mechanisms. Across the diverse simulations for outbreak detection, the baseline model and the Bayesian CAR model had high specificity, thus limiting outbreak false alarms. The findings underscore the importance of considering spatio-temporal models for syndromic surveillance.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"54 ","pages":"Article 100736"},"PeriodicalIF":1.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144779656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1016/j.sste.2025.100744
C. Edson Utazi , Ortis Yankey , Somnath Chaudhuri , Iyanuloluwa D. Olowe , M. Carolina Danovaro-Holliday , Attila N. Lazar , Andrew J. Tatem
Recently, there has been a growing interest in the production of high-resolution maps of vaccination coverage. These maps have been useful for uncovering geographic inequities in coverage and improving targeting of interventions to reach marginalized populations. Different methodological approaches have been developed for producing these maps using mostly geolocated household survey data and geospatial covariate information. However, it remains unclear how much the predicted coverage maps produced by the various methods differ, and which methods yield more reliable estimates. Here, we explore the predictive performance of these methods and resulting implications for spatial prioritization to fill this gap. Using Nigeria Demographic and Health Survey as a case study, we generate 1 × 1 km and district level maps of indicators of vaccination coverage using geostatistical, machine learning (ML) and hybrid methods and evaluate predictive performance via cross-validation. Our results show similar predictive performance for five of the seven methods investigated, although two geostatistical approaches are the best performing methods. The worst-performing methods are two ML approaches. We find marked differences in spatial prioritization using these methods, which could potentially result in missing important underserved populations, although broad similarities exist. Our study can help guide map production for other health and development metrics.
{"title":"Geostatistical and machine learning approaches for high-resolution mapping of vaccination coverage","authors":"C. Edson Utazi , Ortis Yankey , Somnath Chaudhuri , Iyanuloluwa D. Olowe , M. Carolina Danovaro-Holliday , Attila N. Lazar , Andrew J. Tatem","doi":"10.1016/j.sste.2025.100744","DOIUrl":"10.1016/j.sste.2025.100744","url":null,"abstract":"<div><div>Recently, there has been a growing interest in the production of high-resolution maps of vaccination coverage. These maps have been useful for uncovering geographic inequities in coverage and improving targeting of interventions to reach marginalized populations. Different methodological approaches have been developed for producing these maps using mostly geolocated household survey data and geospatial covariate information. However, it remains unclear how much the predicted coverage maps produced by the various methods differ, and which methods yield more reliable estimates. Here, we explore the predictive performance of these methods and resulting implications for spatial prioritization to fill this gap. Using Nigeria Demographic and Health Survey as a case study, we generate 1 × 1 km and district level maps of indicators of vaccination coverage using geostatistical, machine learning (ML) and hybrid methods and evaluate predictive performance via cross-validation. Our results show similar predictive performance for five of the seven methods investigated, although two geostatistical approaches are the best performing methods. The worst-performing methods are two ML approaches. We find marked differences in spatial prioritization using these methods, which could potentially result in missing important underserved populations, although broad similarities exist. Our study can help guide map production for other health and development metrics.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"54 ","pages":"Article 100744"},"PeriodicalIF":1.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144890105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1016/j.sste.2025.100742
Tahmina Akter , Rob Deardon
The conditional logistic individual-level model is a recently developed infectious disease model, particularly suited for modeling spatial-based infection risk. It is designed to reduce computational complexity and expand the range of available statistical software for data analysis (Akter & Deardon, 2025). This study aims to apply and evaluate different variable selection techniques for the newly introduced conditional logistic individual-level models (CL-ILMs). These variable selection methods include forward and backward stepwise Akaike information criterion (AIC), least absolute shrinkage and selection operator (Lasso), spike-and-slab prior (SS prior), and two-stage screening methods. The ultimate goal is to boost model performance and interpretability, and to reduce the risk of overfitting ultimately leading to more robust and effective models. We examine and compare the performance of these methods using simulated data and real-life data from the outbreak of foot-and-mouth disease in the UK in 2001.
{"title":"Variable Screening Methods in Conditional Logistic Individual Level Models of Disease Spread","authors":"Tahmina Akter , Rob Deardon","doi":"10.1016/j.sste.2025.100742","DOIUrl":"10.1016/j.sste.2025.100742","url":null,"abstract":"<div><div>The conditional logistic individual-level model is a recently developed infectious disease model, particularly suited for modeling spatial-based infection risk. It is designed to reduce computational complexity and expand the range of available statistical software for data analysis (Akter & Deardon, 2025). This study aims to apply and evaluate different variable selection techniques for the newly introduced conditional logistic individual-level models (CL-ILMs). These variable selection methods include forward and backward stepwise Akaike information criterion (AIC), least absolute shrinkage and selection operator (Lasso), spike-and-slab prior (SS prior), and two-stage screening methods. The ultimate goal is to boost model performance and interpretability, and to reduce the risk of overfitting ultimately leading to more robust and effective models. We examine and compare the performance of these methods using simulated data and real-life data from the outbreak of foot-and-mouth disease in the UK in 2001.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"54 ","pages":"Article 100742"},"PeriodicalIF":1.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1016/j.sste.2025.100739
Peter Congdon
There is considerable evidence of elevated psychosis rates in more urban settings. However, the urbanicity effect is confounded with other neighbourhood contextual effects, such as from deprivation and crime. To assess the nature of the underlying urbanicity effect, removing distorting effects of confounders, we consider a novel method to assessing causality in spatial applications: a propensity weight approach, with weights obtained by entropy optimization, and adjusting for the spatial overlap in the urbanicity effect via a bivariate exposure approach. The application is to the effect of urbanicity on psychosis prevalence in 6856 English neighbourhoods. We use a measure of urbanicity adapted to represent aspects of urban form, rather than simply population density or a binary indicator. The overlap effect in the psychosis outcome model is shown to outweigh the local effect, and we find a clear urbanicity gradient with a relative risk of 1.91 comparing the most and least urban areas, after adjustment for confounding through propensity weighting.
{"title":"The causal impact of urbanicity on neighbourhood psychosis prevalence","authors":"Peter Congdon","doi":"10.1016/j.sste.2025.100739","DOIUrl":"10.1016/j.sste.2025.100739","url":null,"abstract":"<div><div>There is considerable evidence of elevated psychosis rates in more urban settings. However, the urbanicity effect is confounded with other neighbourhood contextual effects, such as from deprivation and crime. To assess the nature of the underlying urbanicity effect, removing distorting effects of confounders, we consider a novel method to assessing causality in spatial applications: a propensity weight approach, with weights obtained by entropy optimization, and adjusting for the spatial overlap in the urbanicity effect via a bivariate exposure approach. The application is to the effect of urbanicity on psychosis prevalence in 6856 English neighbourhoods. We use a measure of urbanicity adapted to represent aspects of urban form, rather than simply population density or a binary indicator. The overlap effect in the psychosis outcome model is shown to outweigh the local effect, and we find a clear urbanicity gradient with a relative risk of 1.91 comparing the most and least urban areas, after adjustment for confounding through propensity weighting.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"54 ","pages":"Article 100739"},"PeriodicalIF":1.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144772549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Overweight/obesity and hypertension pose significant global health challenges. This study examines the spatial distribution, sociodemographic determinants, and clustering patterns of these conditions in Nepal.
Methods
We conducted a comprehensive spatial-epidemiological analysis of 136,235 participants from the 2022 Nepal Demographic and Health Survey. Outcome variables in this study were overweight/obesity (present/absent) and hypertension (present/absent). A weighted descriptive and inferential analysis addressed the complex survey design and non-response rate. We used spatial scan statistics to identify areas with higher or lower-than-expected cases, and geospatial mapping to illustrate the distribution of cases and the significant spatial clusters. Multivariable logistic regression models determine the association between the outcome variables and respondents’ age, gender, marital status, education level, and wealth.
Findings
Overall, 42.5 % of respondents were obese, and 38.5 % had hypertension. Respondents who were women, middle-aged, married, educated, wealthy, and living in cities had higher odds of being overweight. Similarly, respondents who were male, older, single, poor, uneducated, and lived in cities had higher odds of having hypertension. A spatial scan statistic using the Bernoulli model identified twelve (seven low and five high rate) significant clusters for obesity and eleven (five low and six high rate) for hypertension.
Conclusion
This study showed the utility of health risk mapping across Nepal, emphasizing the complex interaction between sociodemographic and geographic factors impacting the prevalence of obesity and hypertension. The findings highlighted the need for targeted interventions in the high-risk regions of Nepal based on the identified risk factors to mitigate the impact.
{"title":"Spatial clustering and sociodemographic factors impacting obesity and hypertension in Nepal: Analysis of a national demographic and health survey, 2022","authors":"Biraj Neupane , Bikram Adhikari , Niharika Jha , Ian Brooks , Csaba Varga","doi":"10.1016/j.sste.2025.100743","DOIUrl":"10.1016/j.sste.2025.100743","url":null,"abstract":"<div><h3>Background</h3><div>Overweight/obesity and hypertension pose significant global health challenges. This study examines the spatial distribution, sociodemographic determinants, and clustering patterns of these conditions in Nepal.</div></div><div><h3>Methods</h3><div>We conducted a comprehensive spatial-epidemiological analysis of 136,235 participants from the 2022 Nepal Demographic and Health Survey. Outcome variables in this study were overweight/obesity (present/absent) and hypertension (present/absent). A weighted descriptive and inferential analysis addressed the complex survey design and non-response rate. We used spatial scan statistics to identify areas with higher or lower-than-expected cases, and geospatial mapping to illustrate the distribution of cases and the significant spatial clusters. Multivariable logistic regression models determine the association between the outcome variables and respondents’ age, gender, marital status, education level, and wealth.</div></div><div><h3>Findings</h3><div>Overall, 42.5 % of respondents were obese, and 38.5 % had hypertension. Respondents who were women, middle-aged, married, educated, wealthy, and living in cities had higher odds of being overweight. Similarly, respondents who were male, older, single, poor, uneducated, and lived in cities had higher odds of having hypertension. A spatial scan statistic using the Bernoulli model identified twelve (seven low and five high rate) significant clusters for obesity and eleven (five low and six high rate) for hypertension.</div></div><div><h3>Conclusion</h3><div>This study showed the utility of health risk mapping across Nepal, emphasizing the complex interaction between sociodemographic and geographic factors impacting the prevalence of obesity and hypertension. The findings highlighted the need for targeted interventions in the high-risk regions of Nepal based on the identified risk factors to mitigate the impact.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"54 ","pages":"Article 100743"},"PeriodicalIF":1.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144886714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01Epub Date: 2025-08-05DOI: 10.1016/j.sste.2025.100738
Guowen Huang, Patrick E Brown, Marta Blangiardo
Our study explores the roles of precipitation and temperature in snakebite fatalities in India, with a focus on short-term effects and different lagged exposures. We propose the use of a spatial case-crossover model that accounts for spatially varying coefficients to assess these environmental exposures. While the spatial case-crossover model has primarily been applied to small area data, we extend its use to continuous spatial fields, allowing for more detailed regional analysis. The spatial model is implemented using MCMC (Markov Chain Monte Carlo) methods, allowing us to capture regional variations in the impacts of environmental factors on snakebite mortality. Our findings indicate that snakebite fatalities are primarily influenced by seasonality rather than precipitation or temperature, with notable spatial heterogeneity in these effects. This emphasizes the importance of spatially explicit models in understanding snakebite-related fatalities and the complexities of this public health challenge.
{"title":"Investigating the impact of precipitation and temperature on snakebite mortality in India: A spatial case-crossover study.","authors":"Guowen Huang, Patrick E Brown, Marta Blangiardo","doi":"10.1016/j.sste.2025.100738","DOIUrl":"10.1016/j.sste.2025.100738","url":null,"abstract":"<p><p>Our study explores the roles of precipitation and temperature in snakebite fatalities in India, with a focus on short-term effects and different lagged exposures. We propose the use of a spatial case-crossover model that accounts for spatially varying coefficients to assess these environmental exposures. While the spatial case-crossover model has primarily been applied to small area data, we extend its use to continuous spatial fields, allowing for more detailed regional analysis. The spatial model is implemented using MCMC (Markov Chain Monte Carlo) methods, allowing us to capture regional variations in the impacts of environmental factors on snakebite mortality. Our findings indicate that snakebite fatalities are primarily influenced by seasonality rather than precipitation or temperature, with notable spatial heterogeneity in these effects. This emphasizes the importance of spatially explicit models in understanding snakebite-related fatalities and the complexities of this public health challenge.</p>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"54 ","pages":"100738"},"PeriodicalIF":1.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145041806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1016/j.sste.2025.100740
Fernando Henrique Antunes Murata , Jéssica Priscilla Barboza , Fernanda Follis Tasso , Tainara Souza Pinho , Tiago Henrique , Janine Fusco Alves , FAMERP Toxoplasma Research Group , Carlos Alexandre Guimarães de Souza , Daniel Abrahão , Ubirajara Leoncy de Lavor , Chunlei Su , Luiz Carlos de Mattos , Cinara Cássia Brandão
Toxoplasmosis is a zoonotic disease caused by the apicomplexan parasite Toxoplasma gondii, that can infect any warm-blooded animal, including mammals and birds. Felids are the definitive hosts, with infected cats capable of shedding millions of resistant oocysts into the environment. This study aimed to evaluate the seroprevalence and geospatial distribution of T. gondii infection in pet and stray cats attended at the Zoonosis Control Center in São José do Rio Preto, northwest São Paulo, Brazil. Anti-T. gondii antibodies were detected in 36 (25.2 %) of 143 pet cats and 85 (27.8 %) of 306 stray cats, with an overall prevalence of 26.9 %. Male pet cats exhibited a significantly higher risk of infection compared to females (19.5 % vs 34.5 %; p = 0.045). Regional analysis revealed significant difference in seroprevalence between four regions (HB vs Bosque for pet cats, p = 0.035, and Cidade da Criança vs Central for stray cats, p = 0.040). Spatial cluster analysis identified 27 significant hotspots and 70 coldspots (p ≤ 0.05) throughout the municipality. This study represents the first investigation of the seroprevalence and geospatial distribution of T. gondii infection in domestic and stray cats within this region, providing valuable information on the epidemiology of T. gondii. These findings contribute to a better understanding of the transmission dynamics of T. gondii, supporting the development of effective prevention strategies and reinforcing the importance of a One Health approach.
弓形虫病是一种由弓形虫引起的人畜共患疾病，它可以感染任何温血动物，包括哺乳动物和鸟类。猫科动物是最终宿主，受感染的猫能够向环境中释放数百万个具有抗性的卵囊。本研究旨在评估巴西圣保罗西北部 o joss do里约热内卢Preto人畜共患病控制中心接待的宠物和流浪猫中弓形虫感染的血清阳性率和地理空间分布。Anti-T。143只宠物猫中检出弓形虫抗体36只（25.2%），306只流浪猫中检出弓形虫抗体85只（27.8%），总体检出率为26.9%。与雌性相比，雄性宠物猫的感染风险明显更高（19.5% vs 34.5%; p = 0.045）。区域分析显示，四个地区（宠物猫HB vs博斯克，p = 0.035，流浪猫Cidade da criana vs Central， p = 0.040）的血清患病率存在显著差异。空间聚类分析发现全市有27个显著热点和70个显著冷点（p≤0.05）。本研究首次调查了该地区家猫和流浪猫中弓形虫感染的血清流行率和地理空间分布，为弓形虫流行病学研究提供了有价值的信息。这些发现有助于更好地了解弓形虫的传播动态，支持制定有效的预防战略，并加强“同一个健康”方针的重要性。
{"title":"Prevalence and spatial distribution of Toxoplasma gondii infection in domestic and stray cats (Felis catus) in Northwestern São Paulo, Brazil","authors":"Fernando Henrique Antunes Murata , Jéssica Priscilla Barboza , Fernanda Follis Tasso , Tainara Souza Pinho , Tiago Henrique , Janine Fusco Alves , FAMERP Toxoplasma Research Group , Carlos Alexandre Guimarães de Souza , Daniel Abrahão , Ubirajara Leoncy de Lavor , Chunlei Su , Luiz Carlos de Mattos , Cinara Cássia Brandão","doi":"10.1016/j.sste.2025.100740","DOIUrl":"10.1016/j.sste.2025.100740","url":null,"abstract":"<div><div>Toxoplasmosis is a zoonotic disease caused by the apicomplexan parasite <em>Toxoplasma gondii</em>, that can infect any warm-blooded animal, including mammals and birds. Felids are the definitive hosts, with infected cats capable of shedding millions of resistant oocysts into the environment. This study aimed to evaluate the seroprevalence and geospatial distribution of <em>T. gondii</em> infection in pet and stray cats attended at the Zoonosis Control Center in São José do Rio Preto, northwest São Paulo, Brazil. Anti-<em>T. gondii</em> antibodies were detected in 36 (25.2 %) of 143 pet cats and 85 (27.8 %) of 306 stray cats, with an overall prevalence of 26.9 %. Male pet cats exhibited a significantly higher risk of infection compared to females (19.5 % vs 34.5 %; <em>p</em> = 0.045). Regional analysis revealed significant difference in seroprevalence between four regions (HB vs Bosque for pet cats, <em>p</em> = 0.035, and Cidade da Criança vs Central for stray cats, <em>p</em> = 0.040). Spatial cluster analysis identified 27 significant hotspots and 70 coldspots (<em>p</em> ≤ 0.05) throughout the municipality. This study represents the first investigation of the seroprevalence and geospatial distribution of <em>T. gondii</em> infection in domestic and stray cats within this region, providing valuable information on the epidemiology of <em>T. gondii</em>. These findings contribute to a better understanding of the transmission dynamics of <em>T. gondii</em>, supporting the development of effective prevention strategies and reinforcing the importance of a One Health approach.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"54 ","pages":"Article 100740"},"PeriodicalIF":1.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144866637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1016/j.sste.2025.100741
Abdul Joseph Fofanah , Alpha Alimamy Kamara , Albert Patrick Sankoh , Tiegang Gao , Ibrahim Dumbuya , Zachariyah Bai Conteh
The paper introduces DeepEVD, an innovative framework that integrates human mobility data to forecast Ebola Virus Disease (EVD) outbreaks. Traditional epidemiological models often struggle to account for the dynamic nature of human movement, which is crucial for understanding EVD transmission. DeepEVD leverages diverse mobility data sources, including phone records, GPS traces, and social media posts, to extract significant spatio-temporal features. It utilises Graph Convolutional Networks (GCN) and Long Short Term Memory (LSTM) networks to establish connections between mobility patterns and EVD cases across both space and time. The framework was tested on real-world datasets from the 2014–2016 West Africa outbreak and the 2015–2016 Sierra Leone outbreak, demonstrating a 5%–10% reduction in forecasting errors compared to baseline methods. Ablation studies reveal the impact of various data sources and feature extraction methods on accuracy. DeepEVD not only delivers state-of-the-art performance, but it also provides actionable insights for EVD prevention and control. Implementation of the proposed DeepEVD can be accessed here https://github.com/afofanah/DeepEVDMob.
{"title":"DeepEVD: Integrating Epidemiological data into deep learning frameworks based on spatio-temporal feature learning for EVD forecasting","authors":"Abdul Joseph Fofanah , Alpha Alimamy Kamara , Albert Patrick Sankoh , Tiegang Gao , Ibrahim Dumbuya , Zachariyah Bai Conteh","doi":"10.1016/j.sste.2025.100741","DOIUrl":"10.1016/j.sste.2025.100741","url":null,"abstract":"<div><div>The paper introduces DeepEVD, an innovative framework that integrates human mobility data to forecast Ebola Virus Disease (EVD) outbreaks. Traditional epidemiological models often struggle to account for the dynamic nature of human movement, which is crucial for understanding EVD transmission. DeepEVD leverages diverse mobility data sources, including phone records, GPS traces, and social media posts, to extract significant spatio-temporal features. It utilises Graph Convolutional Networks (GCN) and Long Short Term Memory (LSTM) networks to establish connections between mobility patterns and EVD cases across both space and time. The framework was tested on real-world datasets from the 2014–2016 West Africa outbreak and the 2015–2016 Sierra Leone outbreak, demonstrating a 5%–10% reduction in forecasting errors compared to baseline methods. Ablation studies reveal the impact of various data sources and feature extraction methods on accuracy. DeepEVD not only delivers state-of-the-art performance, but it also provides actionable insights for EVD prevention and control. Implementation of the proposed DeepEVD can be accessed here <span><span>https://github.com/afofanah/DeepEVDMob</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"54 ","pages":"Article 100741"},"PeriodicalIF":1.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-21DOI: 10.1016/j.sste.2025.100735
Melissa J. Smith , Emily K. Roberts , Mary E. Charlton , Jacob J. Oleson
Various methods have been employed in the medical literature to conduct mediation analyses with areal datasets. These analyses are typically performed to understand why age-adjusted incidence or mortality rates vary by county or ZIP code-level characteristics. Two primary approaches are commonly used: the “Calculation before mediation” (C-BM) approach, where age-adjusted rates are calculated from the raw data for each areal unit and used as the outcome in the mediation analysis, and the “Small-area estimation before mediation” (SAE-BM) approach, which uses pre-existing small-area estimates as the outcome in the mediation analysis. However, these approaches have significant limitations that can impact the inferences around mediation effects and the overall conclusions of a mediation analysis. In this paper, we propose a new method, the “Small-area estimation within mediation” (SAE-WM) approach, for conducting mediation analyses with areal datasets. This method integrates Bayesian small-area estimation techniques into the mediation analysis outcome model, allowing for precise estimation of mediation effects with areal datasets. We conduct a simulation study to demonstrate the advantages of the SAE-WM method for estimating mediation effects with areal datasets, while highlighting the pitfalls and potential problems with the C-BM and SAE-BM methods. We also illustrate an application of the SAE-WM method to assess whether healthcare access mediates the relationship between ZIP code-level socioeconomic environment and age-adjusted colorectal cancer incidence rates in Iowa.
{"title":"Incorporating small-area estimation into mediation analyses with areal datasets","authors":"Melissa J. Smith , Emily K. Roberts , Mary E. Charlton , Jacob J. Oleson","doi":"10.1016/j.sste.2025.100735","DOIUrl":"10.1016/j.sste.2025.100735","url":null,"abstract":"<div><div>Various methods have been employed in the medical literature to conduct mediation analyses with areal datasets. These analyses are typically performed to understand why age-adjusted incidence or mortality rates vary by county or ZIP code-level characteristics. Two primary approaches are commonly used: the “Calculation before mediation” (C-BM) approach, where age-adjusted rates are calculated from the raw data for each areal unit and used as the outcome in the mediation analysis, and the “Small-area estimation before mediation” (SAE-BM) approach, which uses pre-existing small-area estimates as the outcome in the mediation analysis. However, these approaches have significant limitations that can impact the inferences around mediation effects and the overall conclusions of a mediation analysis. In this paper, we propose a new method, the “Small-area estimation within mediation” (SAE-WM) approach, for conducting mediation analyses with areal datasets. This method integrates Bayesian small-area estimation techniques into the mediation analysis outcome model, allowing for precise estimation of mediation effects with areal datasets. We conduct a simulation study to demonstrate the advantages of the SAE-WM method for estimating mediation effects with areal datasets, while highlighting the pitfalls and potential problems with the C-BM and SAE-BM methods. We also illustrate an application of the SAE-WM method to assess whether healthcare access mediates the relationship between ZIP code-level socioeconomic environment and age-adjusted colorectal cancer incidence rates in Iowa.</div></div>","PeriodicalId":46645,"journal":{"name":"Spatial and Spatio-Temporal Epidemiology","volume":"54 ","pages":"Article 100735"},"PeriodicalIF":2.1,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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