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Background: This article applies a variant of the Markov chain that explicitly incorporates spatial effects. It is an extension of the Markov class allowing a more complete analysis of the spatial dimensions of transition dynamics. The aim is to provide a methodology for applying the explicit model to spatial dependency analysis. Methods: Here, the question is to study and quantify whether neighborhood context affects transitional dynamics. Rather than estimating a homogeneous law, the model requires the estimation of k transition laws each dependent on spatial neighbor state. This article used published data on confirmed cases of Covid'19 in the 22 regions of Madagascar. These data were discretized to obtain a discrete state of propagation intensity. Results: The analysis gave us the transition probabilities between Covid'19 intensity states knowing the context of neighboring regions, and the propagation time laws knowing the spatial contexts. The results showed that neighboring regions had an effect on the propagation of Covid'19 in Madagascar. Conclusion: After analysis, we can say that there is spatial dependency according to these spatial transition matrices. [ABSTRACT FROM AUTHOR]

Objectives: Hepatitis B is a liver disease caused by Hepatitis B virus (HBV) infection and is highly prevalent in China. To better understand the epidemiological characteristics of hepatitis B in China and develop effective disease control strategies, we employed temporal and spatial statistical methods. Methods: We obtained HBV incidence data from the Public Health Science Data Center of the Chinese Center for Disease Control and Prevention for the years 2006 to 2018. Using Geographic Information System (GIS) and SaTScan scanning technology, we conducted spatial autocorrelation analysis and spatiotemporal scan analysis to create a map and visualize the distribution of hepatitis B incidence. Results: While hepatitis B incidence rebounded in 2011 and 2017, the overall incidence in China decreased.In the trend analysis by item, the incidence varies from high to low. The global spatial autocorrelation analysis revealed a clustered distribution, and the Moran index analysis of spatial autocorrelation within local regions identified five provinces as H-H clusters (hot spots), while one province was an L-L cluster (cold spot). Spatial scan analysis identified 11 significant spatial clusters. Conclusions: We found significant clustering in the spatial distribution of hepatitis B incidence and positive spatial correlation of hepatitis B incidence in China. We also identified high-risk times and regional clusters of hepatitis B incidence. [ABSTRACT FROM AUTHOR]

For almost everyone in the world, the last few years have been unlike any experienced in their lifetimes. The public health crisis, spawned by the SARS‐CoV‐2 virus and the outbreak of COVID‐19, presented a geospatial analysis challenge like none other as public health officials, emergency rooms, and the general public struggled to track the spread of the disease, allocate resources for testing and care, and understand the origin of this new zoonotic pathogen. During this period, the combination of rapid and open access genomic data combined with case counts and the mapping tools of geographic information systems allowed for near real‐time tracking of the pandemic. This paper describes these tools, how they were used to advise policy‐makers in the US at the height of the pandemic, and some of the lessons learned. [ABSTRACT FROM AUTHOR]

We conducted an integrative analysis to elucidate the spatial epidemiological patterns of the Vesicular Stomatitis New Jersey virus (VSNJV) during the 2014–15 epizootic cycle in the United States (US). Using georeferenced VSNJV genomics data, confirmed vesicular stomatitis (VS) disease cases from surveillance, and a suite of environmental factors, our study assessed environmental and phylogenetic similarity to compare VS cases reported in 2014 and 2015. Despite uncertainties from incomplete virus sampling and cross-scale spatial processes, patterns suggested multiple independent re-invasion events concurrent with potential viral overwintering between sequential seasons. Our findings pointed to a geographically defined southern virus pool at the US–Mexico interface as the source of VSNJV invasions and overwintering sites. Phylodynamic analysis demonstrated an increase in virus diversity before a rise in case numbers and a pronounced reduction in virus diversity during the winter season, indicative of a genetic bottleneck and a significant narrowing of virus variation between the summer outbreak seasons. Environment–vector interactions underscored the central role of meta-population dynamics in driving disease spread. These insights emphasize the necessity for location- and time-specific management practices, including rapid response, movement restrictions, vector control, and other targeted interventions. [ABSTRACT FROM AUTHOR]

Purpose: To synthesize the available evidence on the extent to which area-level socioeconomic conditions are associated with drug overdose deaths in the United States. Methods: We performed a systematic review (in MEDLINE, EMBASE, PsychINFO, Web of Science, EconLit) for papers published prior to July 2022. Eligible studies quantitatively estimated the association between an area-level measure of socioeconomic conditions and drug overdose deaths in the US, and were published in English. We assessed study quality using the Effective Public Health Practice Project Quality Assessment Tool. The protocol was preregistered at Prospero (CRD42019121317). Results: We identified 28 studies that estimated area-level effects of socioeconomic conditions on drug overdose deaths in the US. Studies were scored as having moderate to serious risk of bias attributed to both confounding and in analysis. Socioeconomic conditions and drug overdose death rates were moderately associated, and this was a consistent finding across a large number of measures and differences in study designs (e.g., cross-sectional versus longitudinal), years of data analyzed, and primary unit of analysis (e.g., ZIP code, county, state). Conclusions: This review highlights the evidence for area-level socioeconomic conditions are an important factor underlying the geospatial distribution of drug overdose deaths in the US and the need to understand the mechanisms underlying these associations to inform future policy recommendations. The current evidence base suggests that, at least in the United States, employment, income, and poverty interventions may be effective targets for preventing drug overdose mortality rates. [ABSTRACT FROM AUTHOR]

Introduction: Porcine circovirus 2 (PCV-2) is a key pathogen for the swine industry at a global level. Nine genotypes, differing in epidemiology and potentially virulence, emerged over time, with PCV-2a, -2b, and -2d being the most widespread and clinically relevant. Conversely, the distribution of minor genotypes appears geographically and temporally restricted, suggesting lower virulence and different epidemiological drivers. In 2022, PCV-2e, the most genetically and phenotypically divergent genotype, was identified in multiple rural farms in North-eastern Italy. Since rural pigs often have access to outdoor environment, the introduction from wild boars was investigated. Methods: Through a molecular and spatial approach, this study investigated the epidemiology and genetic diversity of PCV-2 in 122 wild boars across different provinces of North-eastern Italy. Results: Molecular analysis revealed a high PCV-2 frequency (81.1%, 99/122), and classified the majority of strains as PCV-2d (96.3%, 78/81), with sporadic occurrences of PCV-2a (1.2%, 1/81) and PCV-2b (2.5%, 2/81) genotypes. A viral flow directed primarily from domestic pigs to wild boars was estimated by phylogenetic and phylodynamic analyses. Discussion: These findings attested that the genotype replacement so far described only in the Italian domestic swine sector occurred also in wild boars. and suggested that the current heterogeneity of PCV-2d strains in Italian wild boars likely depends more on different introduction events from the domestic population rather than the presence of independent evolutionary pressures. While this might suggest PCV-2 circulation in wild boars having a marginal impact in the industrial sector, the sharing of PCV-2d strains across distinct wild populations, in absence of a consistent geographical pattern, suggests a complex interplay between domestic and wild pig populations, emphasizing the importance of improved biosecurity measures to mitigate the risk of pathogen transmission. [ABSTRACT FROM AUTHOR]

Abstract Background This article applies a variant of the Markov chain that explicitly incorporates spatial effects. It is an extension of the Markov class allowing a more complete analysis of the spatial dimensions of transition dynamics. The aim is to provide a methodology for applying the explicit model to spatial dependency analysis. Methods Here, the question is to study and quantify whether neighborhood context affects transitional dynamics. Rather than estimating a homogeneous law, the model requires the estimation of k transition laws each dependent on spatial neighbor state. This article used published data on confirmed cases of Covid’19 in the 22 regions of Madagascar. These data were discretized to obtain a discrete state of propagation intensity. Results The analysis gave us the transition probabilities between Covid’19 intensity states knowing the context of neighboring regions, and the propagation time laws knowing the spatial contexts. The results showed that neighboring regions had an effect on the propagation of Covid’19 in Madagascar. Conclusion After analysis, we can say that there is spatial dependency according to these spatial transition matrices.

Abstract Objectives Hepatitis B is a liver disease caused by Hepatitis B virus (HBV) infection and is highly prevalent in China. To better understand the epidemiological characteristics of hepatitis B in China and develop effective disease control strategies, we employed temporal and spatial statistical methods. Methods We obtained HBV incidence data from the Public Health Science Data Center of the Chinese Center for Disease Control and Prevention for the years 2006 to 2018. Using Geographic Information System (GIS) and SaTScan scanning technology, we conducted spatial autocorrelation analysis and spatiotemporal scan analysis to create a map and visualize the distribution of hepatitis B incidence. Results While hepatitis B incidence rebounded in 2011 and 2017, the overall incidence in China decreased.In the trend analysis by item, the incidence varies from high to low. The global spatial autocorrelation analysis revealed a clustered distribution, and the Moran index analysis of spatial autocorrelation within local regions identified five provinces as H-H clusters (hot spots), while one province was an L-L cluster (cold spot). Spatial scan analysis identified 11 significant spatial clusters. Conclusions We found significant clustering in the spatial distribution of hepatitis B incidence and positive spatial correlation of hepatitis B incidence in China. We also identified high-risk times and regional clusters of hepatitis B incidence.

Abstract Background Although road traffic injuries and deaths have decreased globally, there is substantial national and sub-national heterogeneity, particularly in low- and middle-income countries (LMICs). Ghana is one of few countries in Africa collecting comprehensive, spatially detailed data on motor vehicle collisions (MVCs). This data is a critical step towards improving roadway safety, as accurate and reliable information is essential for devising targeted countermeasures. Methods Here, we analyze 16 years of police-report data using emerging hot spot analysis in ArcGIS to identify hot spots with trends of increasing injury severity (a weighted composite measure of MVCs, minor injuries, severe injuries, and deaths), and counts of injuries, severe injuries, and deaths along major roads in urban and rural areas of Ghana. Results We find injury severity index sums and minor injury counts are significantly decreasing over time in Ghana while severe injury and death counts are not, indicating the latter should be the focus for road safety efforts. We identify new, consecutive, intensifying, and persistent hot spots on 2.65% of urban roads and 4.37% of rural roads. Hot spots are intensifying in terms of severity and frequency on major roads in rural areas. Conclusions A few key road sections, particularly in rural areas, show elevated levels of road traffic injury severity, warranting targeted interventions. Our method for evaluating spatiotemporal trends in MVC, road traffic injuries, and deaths in a LMIC includes sufficient detail for replication and adaptation in other countries, which is useful for targeting countermeasures and tracking progress.

Equine influenza (EI) is a severe infectious disease that causes huge economic losses to the horse industry. Spatial epidemiology technology can explore the spatiotemporal distribution characteristics and occurrence risks of infectious diseases, it has played an important role in the prevention and control of major infectious diseases in humans and animals. For the first time, this study conducted a systematic analysis of the spatiotemporal distribution of EI using SaTScan software and investigated the important environmental variables and suitable areas for EI occurrence using the Maxent model. A total of 517 occurrences of EI from 2005 to 2022 were evaluated, and 14 significant spatiotemporal clusters were identified. Furthermore, a Maxent model was successfully established with high prediction accuracy (AUC = 0.920 ± 0.008). The results indicated that annual average ultraviolet radiation, horse density, and precipitation of the coldest quarter were the three most important environmental variables affecting EI occurrence. The suitable areas for EI occurrence are widely distributed across all continents, especially in Asia (India, Mongolia, and China) and the Americas (Brazil, Uruguay, USA, and Mexico). In the future, these suitable areas will expand and move eastward. The largest expansion is predicted under SSP126 scenarios, while the opposite trend will be observed under SSP585 scenarios. This study presents the spatial epidemiological characteristics of EI for the first time. The results could provide valuable scientific insights that can effectively inform prevention and control strategies in regions at risk of EI worldwide. [ABSTRACT FROM AUTHOR]

Aims: Japanese encephalitis (JE) is endemic in India. Although pigs are considered important hosts and sentinels for JE outbreaks in people, limited information is available on JE virus (JEV) surveillance in pigs. Methods and Results: We investigated the spatio‐temporal distribution of JEV seroprevalence and its association with climate variables in 4451 samples from pigs in 10 districts of eastern Uttar Pradesh, India, over 10 years from 2013 to 2022. The mean seroprevalence of IgG (2013–2022) and IgM (2017–2022) was 14% (95% CI 12.8–15.2) and 10.98% (95% CI 9.8–12.2), respectively. Throughout the region, higher seroprevalence from 2013 to 2017 was observed and was highly variable with no predictable spatio‐temporal pattern between districts. Seroprevalence of up to 60.8% in Sant Kabir Nagar in 2016 and 69.5% in Gorakhpur district in 2017 for IgG and IgM was observed, respectively. IgG seroprevalence did not increase with age. Monthly time‐series decomposition of IgG and IgM seroprevalence demonstrated annual cyclicity (3–4 peaks) with seasonality (higher, broader peaks in the summer and monsoon periods). However, most variance was due to the overall trend and the random components of the time series. Autoregressive time‐series modelling of pigs sampled from Gorakhpur was insufficiently predictive for forecasting; however, an inverse association between humidity (but not rainfall or temperature) was observed. Conclusions: Detection patterns confirm seasonal epidemic periods within year‐round endemicity in pigs in eastern Uttar Pradesh. Lack of increasing age‐associated seroprevalence indicates that JEV might not be immunizing in pigs which needs further investigation because models that inform public health interventions for JEV could be inaccurate if assuming long‐term immunity in pigs. Although pigs are considered sentinels for human outbreaks, sufficient timeliness using sero‐surveillance in pigs to inform public health interventions to prevent JEV in people will require more nuanced modelling than seroprevalence and broad climate variables alone. [ABSTRACT FROM AUTHOR]

We assessed the spatiotemporal patterns of hospitalization with comorbid cancer and dementia. Using the 2013–2018 inpatient claims data for Medicare fee-for-service (FFS) beneficiaries, we calculated hospitalization rates by dividing the total admissions from individuals with the co-presence of a major cancer (breast, prostate, lung, and colorectal) and dementia diagnoses with the total counts of FFS beneficiaries aged 65 or older. We identified 22 hotspots with high hospitalization rates that showed heterogeneous spatial and temporal utilization patterns. The odds of a county being a hotspot increased significantly with the county-level percentage of dual Medicare-Medicaid beneficiaries (aOR 1.05; 95% CI: 1.04–1.07) and the prevalence of cancer (aOR 1.73; 95% CI: 1.59–1.89), while decreased significantly with increasing degree of rurality (aOR.82; 95% CI:.79–.85) and decreased yearly over time (aOR.72; 95% CI:.68–.75). The identified hotspots and factors at the county-level may help understand healthcare utilization patterns and assess resource allocation for this unique patient group. [ABSTRACT FROM AUTHOR]

The front-wave velocity of African swine fever (ASF) virus spread is depicted through a retrospective spatial and temporal analyses of wild boar outbreaks from Jan. 2014 to Jan. 2022 in Estonia, Latvia, Lithuania and Eastern Poland— regions responsible for more than 50% of all wild boar cases in the EU. The study uses empirical semivariograms in a universal kriging model to assess spatial autocorrelation in notification dates and identifies a discernable large-scale spatial trend. The critical parameter of ASF front-wave velocity was identified (Mean  =  66.33  km/month, SD  =  163.24) in the whole study area, and explored the variations across countries, wild boar habitat suitability, seasons, and the study period. Statistical differences in front-wave velocity values among countries and temporal clusters are explored, shedding light on potential factors influencing ASF transmission dynamics. The implications of these findings for surveillance and control strategies are discussed. [ABSTRACT FROM AUTHOR]

The purpose of this study was to determine whether there were any TB clusters in Aceh Province, Indonesia and their temporal distribution during the period of 2019–2021. A spatial geo-reference was conducted to 290 sub-districts coordinates by geocoding each sub-district’s offices. By using SaTScan TM v9.4.4, a retrospective space-time scan statistics analysis based on population data and annual TB incidence was carried out. To determine the regions at high risk of TB, data from 1 January 2019 to 31 December 2021 were evaluated using the Poisson model. The likelihood ratio (LLR) value was utilized to locate the TB clusters based on a total of 999 permutations were performed. A Moran’s I analysis (using GeoDa) was chosen for a study of both local and global spatial autocorrelation. The threshold for significance was fixed at 0.05. At the sub-district level, the spatial distribution of TB in Aceh Province from 2019-2021 showed 19 clusters (three most likely and 16 secondary ones), and there was a spatial autocorrelation of TB. The findings can be used to provide thorough knowledge on the spatial pattern of TB occurrence, which is important for designing effective TB interventions.

Abstract Objectives Cancer is a global health challenge with complex characteristics. Despite progress in research and treatment, a universally effective prevention strategy is lacking. Access to reliable information, especially on occurrence rates, is vital for cancer management. This study aims to create a database containing individual and spatially integrated data on commonly diagnosed cancers in Iran from 2014 to 2017, serving as a valuable resource for spatial-epidemiological approaches. Data description This database encompasses several files related to cancer data. The first file is an Excel spreadsheet, containing information on newly diagnosed cancer cases from 2014 to 2017. It provides demographic details and specific characteristics of 482,229 cancer patients. We categorized this data according to the International Agency for Research on Cancer (IARC) reporting rules to identify cancers with the highest incidence. To create a geodatabase, individual data was integrated at the county level and combined with population data. Files 2 and 3 contain gender-specific spatial data for the top cancer types and non-melanoma skin cancer. Each file includes county identifications, the number of cancer cases for each cancer type per year, and gender-specific population information. Lastly, there is a user’s guide file to help navigate through the data files.

Abstract Background Cancer is a significant public health concern and the second leading cause of death. This study aims to visualize spatial patterns of top common cancer types and identify high-risk and low-risk counties for these cancers in Iran from 2014 to 2017. Methods In this study, we analyzed 482,229 newly diagnosed cancer cases recorded by the Iranian National Population-Based Cancer Registry from 2014 to 2017. We employed a purely spatial scanning model and local Moran I analysis to explore spatial patterns across Iran. Results Approximately 53% of all cases were male. The average age of cancer diagnosis was 62.58 ± 17.42 years for males and 56.11 ± 17.33years for females. Stomach cancer was the most common cancer in men. The northern and northwestern regions of Iran were identified as high-risk areas for stomach cancer in both genders, with a relative risk (RR) ranging from 1.26 to 2.64 in males and 1.19 to 3.32 in females. These areas recognized as high-risk areas for trachea, bronchus, and lung (TBL) cancer specifically in males (RR:1.15–2.02). Central regions of Iran were identified as high-risk areas for non-melanoma skin cancers in both genders, ranking as the second most common cancer (RR:1.18–5.93 in males and 1.24–5.38 in females). Furthermore, bladder cancer in males (RR:1.32–2.77) and thyroid cancer in females (RR:1.88–3.10) showed concentration in the central part of Iran. Breast cancer, being the most common cancer among women (RR:1.23–5.54), exhibited concentration in the northern regions of the country. Also, northern regions of Iran were identified as high-risk clusters for colon cancer (RR:1.31–3.31 in males and 1.33–4.13 in females), and prostate cancer in males (RR:1.22–2.31). Brain, nervous system cancer, ranked sixth among women (RR:1.26–5.25) in central areas. Conclusions The study's revelations on the spatial patterns of common cancer incidence in Iran provide crucial insights into the distribution and trends of these diseases. The identification of high-risk areas equips policymakers with valuable information to tailor targeted screening programs, facilitating early diagnosis and effective disease control strategies.

Abstract Background Brucellosis poses a significant public health concern. This study explores the spatial and temporal dynamic evolution of human brucellosis in China and analyses the spatial heterogeneity of the influencing factors related to the incidence of human brucellosis at the provincial level. Methods The Join-point model, centre of gravity migration model and spatial autocorrelation analysis were employed to evaluate potential changes in the spatial and temporal distribution of human brucellosis in mainland China from 2005 to 2021. Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multi-scale Geographically Weighted Regression (MGWR) models were constructed to analyze the spatial and temporal correlation between the incidence rate of human brucellosis and meteorological and social factors. Results From 2005 to 2021, human brucellosis in China showed a consistent upward trend. The incidence rate rose more rapidly in South, Central, and Southwest China, leading to a shift in the center of gravity from the North to the Southwest, as illustrated in the migration trajectory diagram. Strong spatial aggregation was observed. The MGWR model outperformed others. Spatio-temporal plots indicated that lower mean annual temperatures and increased beef, mutton, and milk production significantly correlated with higher brucellosis incidence. Cities like Guangxi and Guangdong were more affected by low temperatures, while Xinjiang and Tibet were influenced more by beef and milk production. Inner Mongolia and Heilongjiang were more affected by mutton production. Importantly, an increase in regional GDP and health expenditure exerted a notable protective effect against human brucellosis incidence. Conclusions Human brucellosis remains a pervasive challenge. Meteorological and social factors significantly influence its incidence in a spatiotemporally specific manner. Tailored prevention strategies should be region-specific, providing valuable insights for effective brucellosis control measures.

Malaria remains one of the most important infectious diseases globally due to its high incidence and mortality rates. The influx of infected cases from endemic to non-endemic malaria regions like Europe has resulted in a public health concern over sporadic local outbreaks. This is facilitated by the continued presence of competent Anopheles vectors in non-endemic countries.We modelled the potential distribution of the main malaria vector across Spain using the ensemble of eight modelling techniques based on environmental parameters and the Anopheles maculipennis s.l. presence/absence data collected from 2000 to 2020. We then combined this map with the number of imported malaria cases in each municipality to detect the geographic hot spots with a higher risk of local malaria transmission.The malaria vector occurred preferentially in irrigated lands characterized by warm climate conditions and moderate annual precipitation. Some areas surrounding irrigated lands in northern Spain (e.g. Zaragoza, Logroño), mainland areas (e.g. Madrid, Toledo) and in the South (e.g. Huelva), presented a significant likelihood of A. maculipennis s.l. occurrence, with a large overlap with the presence of imported cases of malaria.While the risk of malaria re-emergence in Spain is low, it is not evenly distributed throughout the country. The four recorded local cases of mosquito-borne transmission occurred in areas with a high overlap of imported cases and mosquito presence. Integrating mosquito distribution with human incidence cases provides an effective tool for the quantification of large-scale geographic variation in transmission risk and pinpointing priority areas for targeted surveillance and prevention.

Objectives: Cancer is a global health challenge with complex characteristics. Despite progress in research and treatment, a universally effective prevention strategy is lacking. Access to reliable information, especially on occurrence rates, is vital for cancer management. This study aims to create a database containing individual and spatially integrated data on commonly diagnosed cancers in Iran from 2014 to 2017, serving as a valuable resource for spatial-epidemiological approaches. Data description: This database encompasses several files related to cancer data. The first file is an Excel spreadsheet, containing information on newly diagnosed cancer cases from 2014 to 2017. It provides demographic details and specific characteristics of 482,229 cancer patients. We categorized this data according to the International Agency for Research on Cancer (IARC) reporting rules to identify cancers with the highest incidence. To create a geodatabase, individual data was integrated at the county level and combined with population data. Files 2 and 3 contain gender-specific spatial data for the top cancer types and non-melanoma skin cancer. Each file includes county identifications, the number of cancer cases for each cancer type per year, and gender-specific population information. Lastly, there is a user's guide file to help navigate through the data files. [ABSTRACT FROM AUTHOR]

Spatial epidemiology investigates the patterns and determinants of health outcomes over both space and time. Within this field, Bayesian spatiotemporal models have gained popularity due to their capacity to incorporate spatial and temporal dependencies, uncertainties, and intricate interactions. However, the complexity of modelling and computations associated with Bayesian spatiotemporal models vary across different diseases. Presently, there is a limited comprehensive overview of Bayesian spatiotemporal models and their applications in epidemiology. This article aims to address this gap through a thorough review. The review commences by delving into the historical development of Bayesian spatiotemporal models concerning disease mapping, prediction, and regression analysis. Subsequently, the article compares these models in terms of spatiotemporal data distribution, general spatiotemporal data models, environmental covariates, parameter estimation methods, and model fitting standards. Following this, essential preparatory processes are outlined, encompassing data acquisition, data preprocessing, and available statistical software. The article further categorizes and summarizes the application of Bayesian spatiotemporal models in spatial epidemiology. Lastly, a critical examination of the advantages and disadvantages of these models, along with considerations for their application, is provided. This comprehensive review aims to enhance comprehension of the dynamic spatiotemporal distribution and prediction of epidemics. By facilitating effective disease scrutiny, especially in the context of the global COVID-19 pandemic, the review holds significant academic merit and practical value. It also aims to contribute to the development of improved ecological and epidemiological prevention and control strategies. [ABSTRACT FROM AUTHOR]

Background: Cancer is a significant public health concern and the second leading cause of death. This study aims to visualize spatial patterns of top common cancer types and identify high-risk and low-risk counties for these cancers in Iran from 2014 to 2017. Methods: In this study, we analyzed 482,229 newly diagnosed cancer cases recorded by the Iranian National Population-Based Cancer Registry from 2014 to 2017. We employed a purely spatial scanning model and local Moran I analysis to explore spatial patterns across Iran. Results: Approximately 53% of all cases were male. The average age of cancer diagnosis was 62.58 ± 17.42 years for males and 56.11 ± 17.33years for females. Stomach cancer was the most common cancer in men. The northern and northwestern regions of Iran were identified as high-risk areas for stomach cancer in both genders, with a relative risk (RR) ranging from 1.26 to 2.64 in males and 1.19 to 3.32 in females. These areas recognized as high-risk areas for trachea, bronchus, and lung (TBL) cancer specifically in males (RR:1.15–2.02). Central regions of Iran were identified as high-risk areas for non-melanoma skin cancers in both genders, ranking as the second most common cancer (RR:1.18–5.93 in males and 1.24–5.38 in females). Furthermore, bladder cancer in males (RR:1.32–2.77) and thyroid cancer in females (RR:1.88–3.10) showed concentration in the central part of Iran. Breast cancer, being the most common cancer among women (RR:1.23–5.54), exhibited concentration in the northern regions of the country. Also, northern regions of Iran were identified as high-risk clusters for colon cancer (RR:1.31–3.31 in males and 1.33–4.13 in females), and prostate cancer in males (RR:1.22–2.31). Brain, nervous system cancer, ranked sixth among women (RR:1.26–5.25) in central areas. Conclusions: The study's revelations on the spatial patterns of common cancer incidence in Iran provide crucial insights into the distribution and trends of these diseases. The identification of high-risk areas equips policymakers with valuable information to tailor targeted screening programs, facilitating early diagnosis and effective disease control strategies. [ABSTRACT FROM AUTHOR]

Background Central nervous system (CNS) cancers including gliomas have low incidence but high mortality. The age-adjusted incidence rate for CNS cancers is higher in Nebraska than nationally. This exploratory study was motivated by glioma patient inquiries about possible clustering of cases within the state to see if more in-depth investigation was warranted. Methods Using electronic health records from Nebraska Medicine, we identified Nebraska adult (age ≥19) glioma patients diagnosed between January 1, 2009 and November 1, 2019. Patient residential addresses were geocoded, mapped, and combined with annual US Census data to compute age-adjusted incidence rates (AAIR) at the county level. Counties with fewer than five cases were excluded to protect patient identity. ArcGIS software was used for geocoding and mapping. Results Of the 285 cases included in the analysis, 53.2% were geocoded with exact match and the remainder were processed manually. Cases occurred in 47 of the 93 counties. After data suppression, 11 counties (228 cases) visually clustered in eastern and central Nebraska with AAIR ranging from 0.85 to 5.66 per 100 000. Conclusions Many counties in the state were excluded from analysis of this rare cancer due to the small number of cases leading to unstable rates and the need to suppress data to protect patient privacy. However, this preliminary study suggests that glioma incidence is highest in central and eastern Nebraska. Next steps include analysis of state cancer registry data to ensure more complete case ascertainment. [ABSTRACT FROM AUTHOR]

Background: Brucellosis poses a significant public health concern. This study explores the spatial and temporal dynamic evolution of human brucellosis in China and analyses the spatial heterogeneity of the influencing factors related to the incidence of human brucellosis at the provincial level. Methods: The Join-point model, centre of gravity migration model and spatial autocorrelation analysis were employed to evaluate potential changes in the spatial and temporal distribution of human brucellosis in mainland China from 2005 to 2021. Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multi-scale Geographically Weighted Regression (MGWR) models were constructed to analyze the spatial and temporal correlation between the incidence rate of human brucellosis and meteorological and social factors. Results: From 2005 to 2021, human brucellosis in China showed a consistent upward trend. The incidence rate rose more rapidly in South, Central, and Southwest China, leading to a shift in the center of gravity from the North to the Southwest, as illustrated in the migration trajectory diagram. Strong spatial aggregation was observed. The MGWR model outperformed others. Spatio-temporal plots indicated that lower mean annual temperatures and increased beef, mutton, and milk production significantly correlated with higher brucellosis incidence. Cities like Guangxi and Guangdong were more affected by low temperatures, while Xinjiang and Tibet were influenced more by beef and milk production. Inner Mongolia and Heilongjiang were more affected by mutton production. Importantly, an increase in regional GDP and health expenditure exerted a notable protective effect against human brucellosis incidence. Conclusions: Human brucellosis remains a pervasive challenge. Meteorological and social factors significantly influence its incidence in a spatiotemporally specific manner. Tailored prevention strategies should be region-specific, providing valuable insights for effective brucellosis control measures. [ABSTRACT FROM AUTHOR]

Rape is an underreported violent crime that frequently remains uncleared (open) in the legal system. Rape disproportionately affects women, with 91% of rape victim-survivors estimated to be female. However, law enforcement agencies, the entry point into the criminal justice system, are predominantly comprised of male officers. According to the theory of representative bureaucracy, groups with greater representation in a bureaucratic system are more likely to have their interests protected. This study aims to determine if California law enforcement agencies with a higher percentage of female officers are more likely to have higher rates of rape reporting, clearances, and arrests. No previous study has examined this relationship using statewide data. Crimes and Clearances, Monthly Arrest and Citation Register, and Uniform Crime Reporting data for California (2013–2016) were aggregated into 499 Law Enforcement Reporting Areas (LERA). Bayesian space-time Poisson regressions controlling for LERA demographics and crime produced scaled relative rates for three outcomes: (a) rape report rate: number of reports relative to population ages 18+; (b) rape clearance rate: number of clearances relative to reports; and (c) rape arrest rate: number of arrests for rape relative to reports. A 5% increase in the percentage of female officers within an agency was associated with a 6.2% increase in the rape report rate (ARR: 1.062, 95% credible interval (CI) [1.048, 1.077]), a 2.9% decrease in the clearance rate (ARR: 0.971 95% CI [0.950, 0.993]), and no change in the rape arrest rates (ARR: 1.010; 95% CI [0.981, 1.039]) across all LERA. Thus, increased female officer representation was associated with an increase in rape reporting rates but associated with a decrease in rape clearance rates. The theory of representative bureaucracy was only partially supported, and these relationships may not be causal. The quantity of rape reports received by an agency, employment and promotion practices of agencies, and victim-survivor's attitudes toward officer's gender should also be considered. [ABSTRACT FROM AUTHOR]

Geographical epidemiology has been description of geographical patterns of mortality rates as part of descriptive epidemiological investigations, with the goal of developing theories regarding disease causation. Disease mapping, disease clustering, and ecological analysis are the predominant methods of geographical epidemiology, having close relationships between them. For describing the transmission of an illness within a geographically dispersed population, many models incorporating frameworks based on individuals, networks, stochastic processes, as well as partial derivative equations have been made. However, these models need a large amount of information and even a large amount of computational performance. Keeping this in mind, we have tried to create deterministic models formulated as partial differential equations to model spatial epidemics in spatial domains. This has been by assuming two types of population, the susceptible population, and the infective population, considering the functions of space and time. COVID-19 is a global tragedy, with India likely to be among the most hit. The fluctuation in the dispersion of COVID-19-related well-being results is most likely connected with numerous basic factors, like segment, financial, or natural poisons related factors. [ABSTRACT FROM AUTHOR]

Background: Fujian Province has one of the highest reported incidences of hepatitis B virus infection in China. This study aimed to provide a theoretical framework for preventing and controlling hepatitis B in Fujian Province, and to assess the trends and the spatial-temporal distribution patterns of hepatitis B in this region. Methods: Data on hepatitis B cases were extracted from the National Notifiable Infectious Disease Surveillance System. Spatial autocorrelation analysis, trend surface analysis, and spatial-temporal scanning statistics were used to identify the spatial and aggregation patterns at the county level. The Joinpoint was used to assess the reported incidence trends. Results: The average reported incidence of hepatitis B in Fujian from 2012 to 2021 was 14.46/10,000 population, with 583,262 notified cases. The age-adjusted reported incidence of hepatitis B decreased from 17.44/10,000 population in 2012 to 11.88/10,000 population in 2021, with an average reduction in the annual percentage change of 4.5%. There were obvious spatial-temporal aggregation characteristics in hepatitis B cases, and a high-incidence area was located in eastern Fujian. Spatio-temporal scanning statistics revealed four levels of aggregation of hepatitis B reporting rates. The first level of aggregation area included Minhou, Gulou, Jin'an, Taijiang, and nine other districts and counties. Conclusions: The incidence of hepatitis B is declining in Fujian Province. Spatial clusters of hepatitis B cases in Fujian Province were identified, and high-risk areas in eastern Fujian still exist. Closely monitoring the general patterns in the occurrence of hepatitis B and implementing focused control and preventative strategies are important.

IntroductionPorcine circovirus 2 (PCV-2) is a key pathogen for the swine industry at a global level. Nine genotypes, differing in epidemiology and potentially virulence, emerged over time, with PCV-2a, -2b, and -2d being the most widespread and clinically relevant. Conversely, the distribution of minor genotypes appears geographically and temporally restricted, suggesting lower virulence and different epidemiological drivers. In 2022, PCV-2e, the most genetically and phenotypically divergent genotype, was identified in multiple rural farms in North-eastern Italy. Since rural pigs often have access to outdoor environment, the introduction from wild boars was investigated.MethodsThrough a molecular and spatial approach, this study investigated the epidemiology and genetic diversity of PCV-2 in 122 wild boars across different provinces of North-eastern Italy.ResultsMolecular analysis revealed a high PCV-2 frequency (81.1%, 99/122), and classified the majority of strains as PCV-2d (96.3%, 78/81), with sporadic occurrences of PCV-2a (1.2%, 1/81) and PCV-2b (2.5%, 2/81) genotypes. A viral flow directed primarily from domestic pigs to wild boars was estimated by phylogenetic and phylodynamic analyses.DiscussionThese findings attested that the genotype replacement so far described only in the Italian domestic swine sector occurred also in wild boars. and suggested that the current heterogeneity of PCV-2d strains in Italian wild boars likely depends more on different introduction events from the domestic population rather than the presence of independent evolutionary pressures. While this might suggest PCV-2 circulation in wild boars having a marginal impact in the industrial sector, the sharing of PCV-2d strains across distinct wild populations, in absence of a consistent geographical pattern, suggests a complex interplay between domestic and wild pig populations, emphasizing the importance of improved biosecurity measures to mitigate the risk of pathogen transmission.

Abstract Identifying clusters or hotspots from disease maps is critical in research and practice. Hotspots have been shown to have a higher potential for transmission risk and may be the source of infections, making them a priority for controlling epidemics. However, the role of edge areas of hotspots in disease transmission remains unclear. This study aims to investigate the role of edge areas in disease transmission by examining whether disease incidence rate growth is higher in the edges of disease hotspots during outbreaks. Our data is based on the three most severe dengue epidemic years in Kaohsiung city, Taiwan, from 1998 to 2020. We employed conditional autoregressive (CAR) models and Bayesian areal Wombling methods to identify significant edge areas of hotspots based on the extent of risk difference between adjacent areas. The difference-in-difference (DID) estimator in spatial panel models measures the growth rate of risk by comparing the incidence rate between two groups (hotspots and edge areas) over two time periods. Our results show that in years characterized by exceptionally large-scale outbreaks, the edge areas of hotspots have a more significant increase in disease risk than hotspots, leading to a higher risk of disease transmission and potential disease foci. This finding explains the geographic diffusion mechanism of epidemics, a pattern mixed with expansion and relocation, indicating that the edge areas play an essential role. The study highlights the importance of considering edge areas of hotspots in disease transmission. Furthermore, it provides valuable insights for policymakers and health authorities in designing effective interventions to control large-scale disease outbreaks.

We conducted an integrative analysis to elucidate the spatial epidemiological patterns of the Vesicular Stomatitis New Jersey virus (VSNJV) during the 2014–15 epizootic cycle in the United States (US). Using georeferenced VSNJV genomics data, confirmed vesicular stomatitis (VS) disease cases from surveillance, and a suite of environmental factors, our study assessed environmental and phylogenetic similarity to compare VS cases reported in 2014 and 2015. Despite uncertainties from incomplete virus sampling and cross-scale spatial processes, patterns suggested multiple independent re-invasion events concurrent with potential viral overwintering between sequential seasons. Our findings pointed to a geographically defined southern virus pool at the US–Mexico interface as the source of VSNJV invasions and overwintering sites. Phylodynamic analysis demonstrated an increase in virus diversity before a rise in case numbers and a pronounced reduction in virus diversity during the winter season, indicative of a genetic bottleneck and a significant narrowing of virus variation between the summer outbreak seasons. Environment–vector interactions underscored the central role of meta-population dynamics in driving disease spread. These insights emphasize the necessity for location- and time-specific management practices, including rapid response, movement restrictions, vector control, and other targeted interventions.

Identifying clusters or hotspots from disease maps is critical in research and practice. Hotspots have been shown to have a higher potential for transmission risk and may be the source of infections, making them a priority for controlling epidemics. However, the role of edge areas of hotspots in disease transmission remains unclear. This study aims to investigate the role of edge areas in disease transmission by examining whether disease incidence rate growth is higher in the edges of disease hotspots during outbreaks. Our data is based on the three most severe dengue epidemic years in Kaohsiung city, Taiwan, from 1998 to 2020. We employed conditional autoregressive (CAR) models and Bayesian areal Wombling methods to identify significant edge areas of hotspots based on the extent of risk difference between adjacent areas. The difference-in-difference (DID) estimator in spatial panel models measures the growth rate of risk by comparing the incidence rate between two groups (hotspots and edge areas) over two time periods. Our results show that in years characterized by exceptionally large-scale outbreaks, the edge areas of hotspots have a more significant increase in disease risk than hotspots, leading to a higher risk of disease transmission and potential disease foci. This finding explains the geographic diffusion mechanism of epidemics, a pattern mixed with expansion and relocation, indicating that the edge areas play an essential role. The study highlights the importance of considering edge areas of hotspots in disease transmission. Furthermore, it provides valuable insights for policymakers and health authorities in designing effective interventions to control large-scale disease outbreaks. [ABSTRACT FROM AUTHOR]

South Africa is faced with a high HIV and STI prevalence and incidence, respectively, with pockets of high burden areas driving these diseases. Localised monitoring of the HIV epidemic and STI endemic would enable more effective targeted prevention strategies. We assessed spatial variations in curable STI incidence among a cohort of women enrolled in HIV prevention clinical trials between 2002 and 2012. STI incidence rates from 7557 South African women enrolled in five HIV prevention trials were geo-mapped using participant household GPS coordinates. Age and period standardised incidence rates were calculated for 43 recruitment areas and Bayesian conditional autoregressive areal spatial regression (CAR) was used to identify significant patterns and spatial patterns of STI infections in recruitment communities. Overall age and period standardised STI incidence rate were estimated as 15 per 100 PY and ranged from 6 to 24 per 100 PY. We identified five significant STI high risk areas with higher-than-expected incidence of STIs located centrally (three-locations) and southern neighbouring areas of Durban (two-locations). Younger age (<25), not married/cohabitating, parity <3 and poor education were all significant correlates of high STI communities. Findings demonstrate sustained STI incidence rates across the greater Durban area. The role of STI incidence in HIV acquisition in high HIV endemic areas need to be revisited as current highly effective PrEP interventions do not protect from STI acquisition. In these settings there is an urgent need for integrative HIV and STI prevention and treatment services. [ABSTRACT FROM AUTHOR]

Peste des petits ruminants (PPR) is a highly contagious viral disease of small ruminants that threatens livelihoods and food security in developing countries and, in some cases, wild ungulate species conservation. The Greater Serengeti-Mara Ecosystem (GSME) encompasses one of the major wildlife populations of PPR virus (PPRV)-susceptible species left on earth, although no clinical disease has been reported so far. This study aimed to gain further knowledge about PPRV circulation in the GSME by identifying which factors predict PPRV seropositivity in African buffalo (Syncerus caffer). Following an ecological niche modeling framework to map host-pathogen distribution, two models of PPRV exposure and buffalo habitat suitability were performed using serological data and buffalo censuses. Western Maasai Mara National Reserve and Western Serengeti National Park were identified as high-risk areas for PPRV exposure in buffalo. Variables related to wildlife-livestock interaction contributed to the higher risk of PPRV seropositivity in buffalo, providing supportive evidence that buffalo acquire the virus through contact with infected livestock. These findings can guide the design of cost-effective PPRV surveillance using buffalo as a sentinel species at the identified high-risk locations. As more intensive studies have been carried out in Eastern GSME, this study highlights the need for investigating PPRV dynamics in Western GSME. [ABSTRACT FROM AUTHOR]

Background: Given that the pathogenetic process of ALS begins many years prior to its clinical onset, examining patients' residential histories may offer insights on the disease risk factors. Here, we analyzed the spatial distribution of a large ALS cohort in the 50 years preceding the disease onset. Methods: Data from the PARALS register were used. A spatial cluster analysis was performed at the time of disease onset and at 1-year intervals up to 50 years prior to that. Results: A total of 1124 patients were included. The analysis revealed a higher-incidence cluster in a large area (435,000 inhabitants) west of Turin. From 9 to 2 years before their onset, 105 cases were expected and 150 were observed, resulting in a relative risk of 1.49 (P = 0.04). We also found a surprising high number of patients pairs (51) and trios (3) who lived in the same dwelling while not being related. Noticeably, these occurrences were not observed in large dwellings as we would have expected. The probability of this occurring in smaller buildings only by chance was very low (P = 0.01 and P = 0.04 for pairs and trios, respectively). Conclusions: We identified a higher-incidence ALS cluster in the years preceding the disease onset. The cluster area being densely populated, many exposures could have contributed to the high incidence ALS cluster, while we could not find a shared exposure among the dwellings where multiple patients had lived. However, these findings support that exogenous factors are likely involved in the ALS pathogenesis. [ABSTRACT FROM AUTHOR]

Background: Research examining the effects of historical redlining on present-day health outcomes is often complicated by the misalignment of contemporary census boundaries with the neighborhood boundaries drawn by the US Home Owners' Loan Corporation (HOLC) in the 1930s. Previous studies have used different approaches to assign historical HOLC grades to contemporary geographies, but how well they capture redlining exposure is unknown. Methods: Our analysis included 7711 residences identified in the Multiple Listing Service database in Atlanta, Georgia (2017-2022). We evaluated the classification of HOLC grade assignment (A, B, C, D, or ungraded) when assigning exposure under four area-level approaches (centroid, majority land area, weighted score, and highest HOLC) compared with using complete address data (gold standard). We additionally compared approaches across three 2020 census geographies (tract, block group, and block). Results: When comparing the use of census tracts to complete address data, sensitivity was highest for the weighted score approach, which correctly identified 77% of residences in truly A-D graded neighborhoods as compared with the majority land area (44%), centroid (54%), and highest HOLC (59%) approaches. Regarding specificity, the majority land area approach best-classified residences in truly ungraded neighborhoods (93%) as compared with the weighted score (65%), centroid (81%), and highest HOLC (54%) approaches. Classification improved regardless of approach when using census block compared with the census tract. Conclusions: Misclassification of historical redlining exposure is inevitable when using contemporary census geographies rather than complete address data. This study provides a framework for assessing spatial misalignment and selecting an approach for classification. [ABSTRACT FROM AUTHOR]

Despite the amount of research on disease mapping in recent years, the use of multivariate models for areal spatial data remains limited due to difficulties in implementation and computational burden. These problems are exacerbated when the number of areas is very large. In this paper, we introduce an order-free multivariate scalable Bayesian modelling approach to smooth mortality (or incidence) risks of several diseases simultaneously. The proposal partitions the spatial domain into smaller subregions, fits multivariate models in each subdivision and obtains the posterior distribution of the relative risks across the entire spatial domain. The approach also provides posterior correlations among the spatial patterns of the diseases in each partition that are combined through a consensus Monte Carlo algorithm to obtain correlations for the whole study region. We implement the proposal using integrated nested Laplace approximations (INLA) in the R package bigDM and use it to jointly analyse colorectal, lung, and stomach cancer mortality data in Spanish municipalities. The new proposal allows for the analysis of large datasets and yields superior results compared to fitting a single multivariate model. Additionally, it facilitates statistical inference through local homogeneous models, which may be more appropriate than a global homogeneous model when dealing with a large number of areas. [ABSTRACT FROM AUTHOR]

The front-wave velocity of African swine fever (ASF) virus spread is depicted through a retrospective spatial and temporal analyses of wild boar outbreaks from Jan. 2014 to Jan. 2022 in Estonia, Latvia, Lithuania and Eastern Poland—regions responsible for more than 50% of all wild boar cases in the EU. The study uses empirical semivariograms in a universal kriging model to assess spatial autocorrelation in notification dates and identifies a discernable large-scale spatial trend. The critical parameter of ASF front-wave velocity was identified (Mean = 66.33 km/month, SD = 163.24) in the whole study area, and explored the variations across countries, wild boar habitat suitability, seasons, and the study period. Statistical differences in front-wave velocity values among countries and temporal clusters are explored, shedding light on potential factors influencing ASF transmission dynamics. The implications of these findings for surveillance and control strategies are discussed.

BackgroundLimited information is available on geographic disparities of COVID-19 vaccination in Missouri and yet this information is essential for guiding efforts to improve vaccination coverage. Therefore, the objectives of this study were to (a) investigate geographic disparities in the proportion of the population vaccinated against COVID-19 in Missouri and (b) identify socioeconomic and demographic predictors of the identified disparities.MethodsThe COVID-19 vaccination data for time period January 1 to December 31, 2021 were obtained from the Missouri Department of Health. County-level data on socioeconomic and demographic factors were downloaded from the 2020 American Community Survey. Proportions of county population vaccinated against COVID-19 were computed and displayed on choropleth maps. Global ordinary least square regression model and local geographically weighted regression model were used to identify predictors of proportions of COVID-19 vaccinated population.ResultsCounties located in eastern Missouri tended to have high proportions of COVID-19 vaccinated population while low proportions were observed in the southernmost part of the state. Counties with low proportions of population vaccinated against COVID-19 tended to have high percentages of Hispanic/Latino population (p = 0.046), individuals living below the poverty level (p = 0.049), and uninsured (p = 0.015) populations. The strength of association between proportion of COVID-19 vaccinated population and percentage of Hispanic/Latino population varied by geographic location.ConclusionThe study findings confirm geographic disparities of proportions of COVID-19 vaccinated population in Missouri. Study findings are useful for guiding programs geared at improving vaccination coverage and uptake by targeting resources to areas with low proportions of vaccinated individuals.

Geographically weighted regression (GWR) takes a prominent role in spatial regression analysis, providing a nuanced perspective on the intricate interplay of variables within geographical landscapes (Brunsdon et al., 1998). However, it is essential to have a strong rationale for employing GWR, either as an addition to, or a complementary analysis alongside, non-spatial (global) regression models (Kiani, Mamiya et al., 2023). Moreover, the proper selection of bandwidth, weighting function or kernel types, and variable choices constitute the most critical configurations in GWR analysis (Wheeler, 2021). [...]

Abstract Background Spatial epidemiology plays an important role in public health. Yet, it is unclear whether the current university education in spatial epidemiology in China could meet the competency-oriented professional demands. This study aimed to understand the current situation of education and training, practical application, and potential demands in spatial epidemiology among public health postgraduates in China, and to assess the critical gaps in a future emerging infectious diseases (EID) pandemic preparedness and response. Methods This study was divided into three parts. The first part was a comparative study on spatial epidemiology education in international public health postgraduate training. The second part was a cross-sectional survey conducted among public health professionals. The third part was a nationwide cross-sectional survey conducted among public health postgraduates at Chinese universities from October 2020 to February 2021. Data was collected by the WeChat-based questionnaire star survey system and analyzed using the SPSS software. Results International education institutions had required public health postgraduates to master the essential knowledge and capacity of spatial epidemiology. A total of 198 public health professionals were surveyed, and they had a median of 4.00 (IQR 3.13–4.53) in demand degree of spatial epidemiology. A total of 1354 public health postgraduates were surveyed from 51 universities. Only 29.41% (15/51) of universities offered spatial epidemiology course. Around 8.05% (109/1354) of postgraduates had learned spatial epidemiology, and had a median of 1.05 (IQR 1.00–1.29) in learning degree and a median of 1.91 (IQR 1.05–2.78) in practical application degree of spatial epidemiology. To enhance professional capacity, 65.95% (893/1354) of postgraduates hoped that universities would deliver a credit-course of spatial epidemiology. Conclusions A huge unmet education and training demand in spatial epidemiology existed in the current education system of public health postgraduates in China. To enhance the competency-oriented professional capacity in preparedness and response to a future pandemic, it is urgent to incorporate the teaching and training of spatial epidemiology into the compulsory curriculum system of public health postgraduates in China.

Abstract Objective: The purpose of this study is to determine if healthier neighbourhood food environments are associated with healthier diet quality. Design: This was a cross-sectional study using linear regression models to analyse data from the Maastricht Study. Diet quality was assessed using data collected with a FFQ to calculate the Dutch Healthy Diet (DHD). A buffer zone encompassing a 1000 m radius was created around each participant home address. The Food Environment Healthiness Index (FEHI) was calculated using a Kernel density analysis within the buffers of available food outlets. The association between the FEHI and the DHD score was analysed and adjusted for socio-economic variables. Setting: The region of Maastricht including the surrounding food retailers in the Netherlands. Participants: 7367 subjects aged 40–75 years in the south of the Netherlands. Results: No relationship was identified between either the FEHI (B = 0·62; 95 % CI = –2·54, 3·78) or individual food outlets, such as fast food (B = –0·07; 95 % CI = –0·20, 0·07) and diet quality. Similar null findings using the FEHI were identified at the 500 m (B = 0·95; 95 % CI = –0·85, 2·75) and 1500 m (B = 1·57; 95 % CI = –3·30, 6·44) buffer. There was also no association between the food environment and individual items of the DHD including fruits, vegetables and sugar-sweetened beverages. Conclusion: The food environment in the Maastricht area appeared marginally unhealthy, but the differences in the food environment were not related to the quality of food that participants reported as intake.

BackgroundDengue is one of the newest emerging diseases in Nepal with increasing burden and geographic spread over the years. The main objective of this study was to explore the epidemiological patterns of dengue since its first outbreak (2006) to 2019 in Nepal.MethodsThis study is a retrospective analysis that covers the last 14 years (2006-2019) of reported dengue cases from Epidemiology Diseases Control Division (EDCD), Ministry of Health and Population, Government of Nepal. Reported cases were plotted over time and maps of reported case incidence were generated (from 2016 through 2019). An ecological analysis of environmental predictors of case incidence was conducted using negative binomial regression.ResultsWhile endemic dengue has been reported in Nepal since 2006, the case load has increased over time and in 2019 a total of 17 992 dengue cases were reported from 68 districts (from all seven provinces). Compared to the case incidence in 2016, incidence was approximately five times higher in 2018 [incidence rate ratio (IRR): 4.8; 95% confidence interval (CI) 1.5-15.3] and over 140 times higher in 2019 (IRR: 141.6; 95% CI 45.8-438.4). A one standard deviation increase in elevation was associated with a 90% decrease in reported case incidence (IRR: 0.10; 95% CI 0.01-0.20). However, the association between elevation and reported cases varied across the years. In 2018 there was a cluster of cases reported from high elevation Kaski District of Gandaki Province. Our results suggest that dengue infections are increasing in magnitude and expanding out of the lowland areas to higher elevations over time.ConclusionsThere is a high risk of dengue outbreak in the lowland Terai region, with increasing spread towards the mid-mountains and beyond as seen over the last 14 years. Urgent measures are required to increase the availability of diagnostics and resources to mitigate future dengue epidemics.

Abstract Background Cutaneous leishmaniasis (CL) is a wide-reaching infection of major public health concern. Iran is one of the six most endemic countries in the world. This study aims to provide a spatiotemporal visualization of CL cases in Iran at the county level from 2011 to 2020, detecting high-risk zones, while also noting the movement of high-risk clusters. Methods On the basis of clinical observations and parasitological tests, data of 154,378 diagnosed patients were obtained from the Iran Ministry of Health and Medical Education. Utilizing spatial scan statistics, we investigated the disease’s purely temporal, purely spatial, spatial variation in temporal trends and spatiotemporal patterns. At P = 0.05 level, the null hypothesis was rejected in every instance. Results In general, the number of new CL cases decreased over the course of the 9-year research period. From 2011 to 2020, a regular seasonal pattern, with peaks in the fall and troughs in the spring, was found. The period of September–February of 2014–2015 was found to hold the highest risk in terms of CL incidence rate in the whole country [relative risk (RR) = 2.24, P

Abstract Background COVID-19 is an important public health concern due to its high morbidity, mortality and socioeconomic impact. Its burden varies by geographic location affecting some communities more than others. Identifying these disparities is important for guiding health planning and service provision. Therefore, this study investigated geographical disparities and temporal changes of the percentage of positive COVID-19 tests and COVID-19 incidence risk in North Dakota. Methods COVID-19 retrospective data on total number of tests and confirmed cases reported in North Dakota from March 2020 to September 2021 were obtained from the North Dakota COVID-19 Dashboard and Department of Health, respectively. Monthly incidence risks of the disease were calculated and reported as number of cases per 100,000 persons. To adjust for geographic autocorrelation and the small number problem, Spatial Empirical Bayesian (SEB) smoothing was performed using queen spatial weights. Identification of high-risk geographic clusters of percentages of positive tests and COVID-19 incidence risks were accomplished using Tango’s flexible spatial scan statistic. ArcGIS was used to display and visiualize the geographic distribution of percentages of positive tests, COVID-19 incidence risks, and high-risk clusters. Results County-level percentages of positive tests and SEB incidence risks varied by geographic location ranging from 0.11% to 13.67% and 122 to 16,443 cases per 100,000 persons, respectively. Clusters of high percentages of positive tests were consistently detected in the western part of the state. High incidence risks were identified in the central and south-western parts of the state, where significant high-risk spatial clusters were reported. Additionally, two peaks (August 2020-December 2020 and August 2021-September 2021) and two non-peak periods of COVID-19 incidence risk (March 2020-July 2020 and January 2021-July 2021) were observed. Conclusion Geographic disparities in COVID incidence risks exist in North Dakota with high-risk clusters being identified in the rural central and southwest parts of the state. These findings are useful for guiding intervention strategies by identifying high risk communities so that resources for disease control can be better allocated to communities in need based on empirical evidence. Future studies will investigate predictors of the identified disparities so as to guide planning, disease control and health policy.

ABSTRACTSouth Africa is faced with a high HIV and STI prevalence and incidence, respectively, with pockets of high burden areas driving these diseases. Localised monitoring of the HIV epidemic and STI endemic would enable more effective targeted prevention strategies. We assessed spatial variations in curable STI incidence among a cohort of women enrolled in HIV prevention clinical trials between 2002 and 2012. STI incidence rates from 7557 South African women enrolled in five HIV prevention trials were geo-mapped using participant household GPS coordinates. Age and period standardised incidence rates were calculated for 43 recruitment areas and Bayesian conditional autoregressive areal spatial regression (CAR) was used to identify significant patterns and spatial patterns of STI infections in recruitment communities. Overall age and period standardised STI incidence rate were estimated as 15 per 100 PY and ranged from 6 to 24 per 100 PY. We identified five significant STI high risk areas with higher-than-expected incidence of STIs located centrally (three-locations) and southern neighbouring areas of Durban (two-locations). Younger age (

Introduction: Several low-and middle-income countries are undergoing rapid epidemiological transition with a rising burden of non-communicable diseases (NCDs). South Africa (SA) is a country with one of the largest HIV epidemics worldwide and a growing burden of NCDs where the collision of these epidemics poses a major public health challenge. Methods: Using data from a large nationally representative survey, the South Africa Demographic and Health Survey (SADHS 2016), we conducted a geospatial analysis of several diseases including HIV, tuberculosis (TB), cardiovascular, respiratory, and metabolic diseases to identify areas with a high burden of co-morbidity within the country. We explored the spatial structure of each disease and associations between diseases using different spatial and visual data methodologies. We also assessed the individual level co-occurrence of HIV and the other diseases included in the analysis. Results: The spatial distribution for HIV prevalence showed that this epidemic is most intense in the eastern region of the country, mostly within the Gauteng, Mpumalanga, and Kwazulu-Natal provinces. In contrast, chronic diseases had their highest prevalence rates the southern region of the country, particularly in the Eastern and Western Cape provinces. Individual-level analyses were consistent with the spatial correlations and found no statistically significant associations between HIV infection and the presence of any NCDs. Conclusions: We found no evidence of geospatial overlap between the HIV epidemic and NCDs in SA. These results evidence the complex epidemiological landscape of the country, characterized by geographically distinct areas exhibiting different health burdens. The detailed description of the heterogenous prevalence of HIV and NCDs in SA reported in this study could be a useful tool to inform and direct policies to enhance targeted health service delivery according to the local health needs of each community. [ABSTRACT FROM AUTHOR]

Background: Spatial epidemiology plays an important role in public health. Yet, it is unclear whether the current university education in spatial epidemiology in China could meet the competency-oriented professional demands. This study aimed to understand the current situation of education and training, practical application, and potential demands in spatial epidemiology among public health postgraduates in China, and to assess the critical gaps in a future emerging infectious diseases (EID) pandemic preparedness and response. Methods: This study was divided into three parts. The first part was a comparative study on spatial epidemiology education in international public health postgraduate training. The second part was a cross-sectional survey conducted among public health professionals. The third part was a nationwide cross-sectional survey conducted among public health postgraduates at Chinese universities from October 2020 to February 2021. Data was collected by the WeChat-based questionnaire star survey system and analyzed using the SPSS software. Results: International education institutions had required public health postgraduates to master the essential knowledge and capacity of spatial epidemiology. A total of 198 public health professionals were surveyed, and they had a median of 4.00 (IQR 3.13–4.53) in demand degree of spatial epidemiology. A total of 1354 public health postgraduates were surveyed from 51 universities. Only 29.41% (15/51) of universities offered spatial epidemiology course. Around 8.05% (109/1354) of postgraduates had learned spatial epidemiology, and had a median of 1.05 (IQR 1.00–1.29) in learning degree and a median of 1.91 (IQR 1.05–2.78) in practical application degree of spatial epidemiology. To enhance professional capacity, 65.95% (893/1354) of postgraduates hoped that universities would deliver a credit-course of spatial epidemiology. Conclusions: A huge unmet education and training demand in spatial epidemiology existed in the current education system of public health postgraduates in China. To enhance the competency-oriented professional capacity in preparedness and response to a future pandemic, it is urgent to incorporate the teaching and training of spatial epidemiology into the compulsory curriculum system of public health postgraduates in China. [ABSTRACT FROM AUTHOR]