Associations between gentrification, census tract-level socioeconomic status, and cycling infrastructure expansions in Montreal, Canada.

Background: Cycling infrastructure investments support active transportation, improve population health, and reduce health inequities. This study examines the relationship between changes in cycling infrastructure (2011-2016) and census tract (CT)-level measures of material deprivation, visible minorities, and gentrification in Montreal. Methods: Our outcomes are the length of protected bike lanes, cyclist-only paths, multi-use paths, and on-street bike lanes in 2011, and change in total length of bike lanes between 2011 and 2016 at the CT level. Census data provided measures of the level of material deprivation and of the percentage of visible minorities in 2011, and if a CT gentrified between 2011 and 2016. Using a hurdle modeling approach, we explore associations among these CT-level socioeconomic measures, gentrification status, baseline cycling infrastructure (2011), and its changes (2011-2016). We further tested if these associations varied depending on the baseline level of existing infrastructure, to assess if areas with originally less resources benefited less or more. Results: In 2011, CTs with higher level of material deprivation or greater percentages of visible minorities had less cycling infrastructure. Overall, between 2011 and 2016, cycling infrastructure increased from 7.0% to 10.9% of the road network, but the implementation of new cycling infrastructure in CTs with no pre-existing cycling infrastructure in 2011 was less likely to occur in CTs with a higher percentage of visible minorities. High-income CTs that were ineligible for gentrification between 2011 and 2016 benefited less from new cycling infrastructure implementations compared to low-income CTs that were not gentrified during the same period. Conclusion: Montreal's municipal cycling infrastructure programs did not exacerbate socioeconomic disparities in cycling infrastructure from 2011 to 2016 in CTs with pre-existing infrastructure. However, it is crucial to prioritize the implementation of cycling infrastructure in CTs with high populations of visible minorities, particularly in CTs where no cycling infrastructure currently exists.

Mastering geographically weighted regression: key considerations for building a robust model.

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). [...].

A gender-specific geodatabase of five cancer types with the highest frequency of occurrence in Iran.

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.

Cancer patterns in Iran: a gender-specific spatial modelling of cancer incidence during 2014-2017.

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.

From Snow's map of cholera transmission to dynamic catchment boundary delineation: current front lines in spatial analysis.

The history of mapping infectious diseases dates back to the 19th century when Dr John Snow utilised spatial analysis to pinpoint the source of the 1854 cholera outbreak in London, a ground-breaking work that laid the foundation for modern epidemiology and disease mapping (Newsom, 2006). As technology advanced, so did mapping techniques. In the late 20th century, geographic information systems (GIS) revolutionized disease mapping by enabling researchers to overlay diverse datasets to visualise and analyse complex spatial patterns (Bergquist & Manda 2019; Hashtarkhani et al., 2021). The COVID-19 pandemic showed that disease mapping is particularly valuable for optimising prevention and control strategies of infectious diseases by prioritising geographical targeting interventions and containment strategies (Mohammadi et al., 2021). Today, with the aid of highresolution satellite imagery, geo-referenced electronic data collection systems, real-time data feeds, and sophisticated modelling algorithms, disease mapping has become a feasible and accessible tool for public health officials in tracking, managing, and mitigating the spread of infectious diseases at global, regional and local scales (Hay et al., 2013). [...].

Where to place emergency ambulance vehicles: use of a capacitated maximum covering location model with real call data.

This study integrates geographical information systems (GIS) with a mathematical optimization technique to enhance emergency medical services (EMS) coverage in a county in the northeast of Iran. EMS demand locations were determined through one-year EMS call data analysis. We formulated a maximal covering location problem (MCLP) as a mixed-integer linear programming model with a capacity threshold for vehicles using the CPLEX optimizer, an optimization software package from IBM. To ensure applicability to the EMS setting, we incorporated a constraint that maintains an acceptable level of service for all EMS calls. Specifically, we implemented two scenarios: a relocation model for existing ambulances and an allocation model for new ambulances, both using a list of candidate locations. The relocation model increased the proportion of calls within the 5-minute coverage standard from 69% to 75%. With the allocation model, we found that the coverage proportion could rise to 84% of total calls by adding ten vehicles and eight new stations. The incorporation of GIS techniques into optimization modelling holds promise for the efficient management of scarce healthcare resources, particularly in situations where time is of the essence.

High-risk spatiotemporal patterns of cutaneous leishmaniasis: a nationwide study in Iran from 2011 to 2020.

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 < 0.001)]. In terms of location, six significant high-risk CL clusters covering 40.6% of the total area of the country were observed, with the RR ranging from 1.87 to 9.69. In addition, spatial variation in the temporal trend analysis found 11 clusters as potential high-risk areas that highlighted certain regions with an increasing tendency. Finally, five space-time clusters were found. The geographical displacement and spread of the disease followed a moving pattern over the 9-year study period affecting many regions of the country. CONCLUSIONS: Our study has revealed significant regional, temporal, and spatiotemporal patterns of CL distribution in Iran. Over the years, there have been multiple shifts in spatiotemporal clusters, encompassing many different parts of the country from 2011 to 2020. The results reveal the formation of clusters across counties that cover certain parts of provinces, indicating the importance of conducting spatiotemporal analyses at the county level for studies that encompass entire countries. Such analyses, at a finer geographical scale, such as county level, might provide more precise results than analyses at the scale of the province.

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

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

A Mixed Model Approach for Estimating the Optimal Food Fortification of Vitamin D: Experiment Based on Mashhad Cohort Study in Iran.

BACKGROUND: Vitamin D deficiency is a prevalent problem in worldwide healthcare related to several system disorders. Food fortification as a solution is associated with several challenges including insufficient coverage of the entire population, required degree of fortification, the vehicles used for fortification and potential toxicity. This study aimed to determine the optimal amount of vitamin D for fortification without surpassing the upper intake level (UL) of intake at the 95th percentile of the Iranian population and compare two methods of food fortification. METHODS: This study is aimed to develop a model of two different fortifying approaches related to an available dataset called MASHAD cohort study. The dataset comprised demographic and nutritional data of 9704 Iranian individuals living in the Greater Mashhad region. The first approach was a computational method necessary to implement a range of eight foods and calculate the optimal approach. In the second case, we used the European formula method called ILSI. RESULTS: To find the appropriate value for fortification, we calculated the consumption of 400 IU and 1000 IU supplements of vitamin D. Three micrograms per 100 g in each food was the optimal output. We also used Flynn and Rasmussen's formula on our data. Using these methods, we found that 2.1 micrograms per 100 kcal provides the best result. Hence, using the two different approaches, the results appear to be consistent and promising. CONCLUSION: One interesting finding was that supplement consumption did not greatly affect the impact of fortification. This observation may support the hypothesis to determine the amount of fortification, and we can ignore the study population's supplement consumption.

Geospatial epidemiology of hospitalized patients with a positive influenza assay: A nationwide study in Iran, 2016-2018.

INTRODUCTION: Seasonal influenza is a significant public health challenge worldwide. This study aimed to investigate the epidemiological characteristics and spatial patterns of severe hospitalized influenza cases confirmed by polymerase chain reaction (PCR) in Iran. METHODS: Data were obtained from Iran's Ministry of Health and Medical Education and included all hospitalized lab-confirmed influenza cases from January 1, 2016, to December 30, 2018 (n = 9146). The Getis-Ord Gi* and Local Moran's I statistics were used to explore the hotspot areas and spatial cluster/outlier patterns of influenza. We also built a multivariable logistic regression model to identify covariates associated with patients' mortality. RESULTS: Cumulative incidence and mortality rate were estimated at 11.44 and 0.49 (per 100,000), respectively, and case fatality rate was estimated at 4.35%. The patients' median age was 40 (interquartile range: 22-63), and 55.5% (n = 5073) were female. The hotspot and cluster analyses revealed high-risk areas in northern parts of Iran, especially in cold, humid, and densely populated areas. Moreover, influenza hotspots were more common during the colder months of the year, especially in high-elevated regions. Mortality was significantly associated with older age (adjusted odds ratio [aOR]: 1.01, 95% confidence interval [CI]: 1.01-1.02), infection with virus type-A (aOR: 1.64, 95% CI: 1.27-2.15), male sex (aOR: 1.77, 95% CI: 1.44-2.18), cardiovascular disease (aOR: 1.71, 95% CI: 1.33-2.20), chronic obstructive pulmonary disease (aOR: 1.82, 95% CI: 1.40-2.34), malignancy (aOR: 4.77, 95% CI: 2.87-7.62), and grade-II obesity (aOR: 2.11, 95% CI: 1.09-3.74). CONCLUSIONS: We characterized the spatial and epidemiological heterogeneities of severe hospitalized influenza cases confirmed by PCR in Iran. Detecting influenza hotspot clusters could inform prioritization and geographic specificity of influenza prevention, testing, and mitigation resource management, including vaccination planning in Iran.

Homicide rates are spatially associated with built environment and socio-economic factors: a study in the neighbourhoods of Toronto, Canada.

OBJECTIVES: Homicide rate is associated with a large variety of factors and therefore unevenly distributed over time and space. This study aims to explore homicide patterns and their spatial associations with different socioeconomic and built-environment conditions in 140 neighbourhoods of the city of Toronto, Canada. METHODS: A homicide dataset covering the years 2012 to 2021 and neighbourhood-based indicators were analysed using spatial techniques such as Kernel Density Estimation, Global/Local Moran's I and Kulldorff's SatScan spatio-temporal methodology. Geographically weighted regression (GWR) and multi-scale GWR (MGWR) were used to analyse the spatially varying correlations between the homicide rate and independent variables. The latter was particularly suitable for manifested spatial variations between explanatory variables and the homicide rate and it also identified spatial non-stationarities in this connection. RESULTS: The adjusted R2 of the MGWR was 0.53, representing a 4.35 and 3.74% increase from that in the linear regression and GWR models, respectively. Spatial and spatio-temporal high-risk areas were found to be significantly clustered in downtown and the north-western parts of the city. Some variables (e.g., the population density, material deprivation, the density of commercial establishments and the density of large buildings) were significantly associated with the homicide rate in different spatial ways. CONCLUSION: The findings of this study showed that homicide rates were clustered over time and space in certain areas of the city. Socioeconomic and the built environment characteristics of some neighbourhoods were found to be associated with high homicide rates but these factors were different for each neighbourhood.

The role of geographic information system and global positioning system in dementia care and research: a scoping review.

BACKGROUND: Geographic Information System (GIS) and Global Positioning System (GPS), vital tools for supporting public health research, provide a framework to collect, analyze and visualize the interaction between different levels of the health care system. The extent to which GIS and GPS applications have been used in dementia care and research is not yet investigated. This scoping review aims to elaborate on the role and types of GIS and GPS applications in dementia care and research. METHODS: A scoping review was conducted based on Arksey and O'Malley's framework. All published articles in peer-reviewed journals were searched in PubMed, Scopus, and Web of Science, subject to involving at least one GIS/GPS approach focused on dementia. Eligible studies were reviewed, grouped, and synthesized to identify GIS and GPS applications. The PRISMA standard was used to report the study. RESULTS: Ninety-two studies met our inclusion criteria, and their data were extracted. Six types of GIS/GPS applications had been reported in dementia literature including mapping and surveillance (n = 59), data preparation (n = 26), dementia care provision (n = 18), basic research (n = 18), contextual and risk factor analysis (n = 4), and planning (n = 1). Thematic mapping and GPS were most frequently used techniques in the dementia field. CONCLUSIONS: Even though the applications of GIS/GPS methodologies in dementia care and research are growing, there is limited research on GIS/GPS utilization in dementia care, risk factor analysis, and dementia policy planning. GIS and GPS are space-based systems, so they have a strong capacity for developing innovative research based on spatial analysis in the area of dementia. The existing research has been summarized in this review which could help researchers to know the GIS/GPS capabilities in dementia research.

Geospatial analysis of cesarean section in Iran (2016-2020): exploring clustered patterns and measuring spatial interactions of available health services.

BACKGROUND: The lives of babies and mothers are at risk due to the uneven distribution of healthcare facilities required for emergency cesarean sections (CS). However, CS without medical indications might cause complications for mothers and babies, which is a global health problem. Identifying spatiotemporal variations of CS rates in each geographical area could provide helpful information to understand the status of using CS services. METHODS: This cross-sectional study explored spatiotemporal patterns of CS in northeast Iran from 2016 to 2020. Space-time scan statistics and spatial interaction analysis were conducted using geographical information systems to visualize and explore patterns of CS services. RESULTS: The temporal analysis identified 2017 and 2018 as the statistically significant high clustered times in terms of CS rate. Five purely spatial clusters were identified that were distributed heterogeneously in the study region and included 14 counties. The spatiotemporal analysis identified four clusters that included 13 counties as high-rate areas in different periods. According to spatial interaction analysis, there was a solid spatial concentration of hospital facilities in the political center of the study area. Moreover, a high degree of inequity was observed in spatial accessibility to CS hospitals in the study area. CONCLUSIONS: CS Spatiotemporal clusters in the study area reveal that CS use in different counties among women of childbearing age is significantly different in terms of location and time. This difference might be studied in future research to identify any overutilization of CS or lack of appropriate CS in clustered counties, as both put women at risk. Hospital capacity and distance from population centers to hospitals might play an essential role in CS rate variations and spatial interactions among people and CS facilities. As a result, some healthcare strategies, e.g., building new hospitals and empowering the existing local hospitals to perform CS in areas out of service, might be developed to decline spatial inequity.

Geographical patterns and mechanisms of Cimex lectularius Linnaeus, 1758, and Cimex hemipterus Fabricius, 1803 (Hemiptera: Cimicidae) resistance to insecticides: a systematic review and meta-analysis.

This study aimed to review published scientific literature on bed bugs in countries where insecticide resistance has been reported worldwide from 2000 to 2021. Electronic databases, including Scopus, PubMed, and WOS, were searched. Out of 606 articles found in the initial search, we selected 57 articles, of which 40 articles had reported on Cimex lectularius (C. lectularius), and 22 papers had reported on Cimex hemipterus (C. hemipterus). Most studies on insecticide resistance were carried out on C. lectularius in North America (14, 35%) and C. hemipterus in Asia (16, 72.7%). The most common method used to detect bed bug resistance to insecticides was toxicological bioassay with an overall random pooled effect size of 0.38 (95% CI: 0.23-0.53) in C. lectularius and 0.46 (95% CI: 0.27-0.65) in C. hemipterus. Resistance to pyrethroids was reported against C. lectularius with an overall pooled effect size of 0.75 (95% CI: 0.56-0.94) and C. hemipterus with an overall pooled effect size of 0.81 (95% CI: 0.57-0.93) in 33.40 (82.5%) and 19.22 (86.3%) published articles, respectively. A very high resistance level to pyrethroids in both studied species was observed, and resistance ratios at the highest level were 76389.3 and 315.5 in C. lectularius and C. hemipterus, respectively. Resistance mechanisms against pyrethroids were reported from most locations except Iran and Thailand, but these mechanisms were not studied in other insecticide groups. These reports indicate that chemical control options for bed bugs are limited. Therefore, a combination of chemical and non-chemical strategies is recommended for bed bug control.

Access to the COVID-19 services during the pandemic - a scoping review.

Appropriate accessibility to coronavirus disease 2019 (COVID-19) services is essential in the efficient management of the pandemic. Different geospatial methods and approaches have been used to measure accessibility to COVID-19 health-related services. This scoping review aimed to summarize and synthesize the geospatial studies conducted to measure accessibility to COVID-19 healthcare services. Web of Science, Scopus, and PubMed were searched to find relevant studies. From 1113 retrieved unique citations, 26 articles were selected to be reviewed. Most of the studies were conducted in the USA and floating catchment area methods were mostly used to measure the spatial accessibility to COVID-19 services including vaccination centres, Intensive Care Unit beds, hospitals and test sites. More attention is needed to measure the accessibility of COVID-19 services to different types of users especially with combining different non-spatial factors which could lead to better allocation of resources especially in populations with limited resources.

A decision support system for prioritised COVID-19 two-dosage vaccination allocation and distribution.

This study proposes a decision support system (DSS) that integrates GIS, analytics, and simulation methods to help develop a priority-based distribution of COVID-19 vaccines in a large urban setting. The methodology applies novel hierarchical heuristic-simulation procedures to create a holistic algorithm for prioritising the process of demand allocation and optimising vaccine distribution. The Melbourne metropolitan area in Australia with a population of over five million is used as a case study. Three vaccine supply scenarios, namely limited, excessive, and disruption, were formulated to operationalise a two-dose vaccination program. Vaccine distribution with hard constraints were simulated and then further validated with sensitivity analyses. The results show that vaccines can be prioritised to society's most vulnerable segments and distributed using the current logistics network with 10 vehicles. Compared with other vaccine distribution plans with no prioritisation, such as equal allocation of vaccines to local government areas based on population size or one on a first-come-first-serve basis, the plans generated by the proposed DSS ensure prioritised vaccination of the most needed and vulnerable population. The aim is to curb the spread of the infection and reduce mortality rate more effectively. They also achieve vaccination of the entire population with less logistical resources required. As such, this study contributes to knowledge and practice in pandemic vaccine distribution and enables governments to make real-time decisions and adjustments in daily distribution plans. In this way any unforeseen disruptions in the vaccine supply chain can be coped with.

One-year spatiotemporal database of Emergency Medical Service (EMS) calls in Mashhad, Iran: data on 224,355 EMS calls.

OBJECTIVES: Emergency Medical Services (EMS) is the first point of service for the people who are in critical condition and in need of urgent health care. In Iran, as in other countries, people in need of emergency services often die or are left with a permanent injury due to the poor EMS-related infrastructure. It has been shown that a detailed examination of the response times and the spatiotemporal pattern of EMS calls for service can lead to improvements in time-sensitive patient outcomes. We performed a spatiotemporal study in city of Mashhad, the second-most populous city of Iran, to investigate the pattern of the EMS calls and now wish to release a comprehensive dataset resulting from this study. DATA DESCRIPTION: The data include three data files plus a data dictionary file. Data file 1 contains the characteristics of EMS requests including sex, age group, date of call, different time periods of each EMS missions, the census tracts' ID of callers, the chief complaint, and the EMS mission result. Two spatial data files include the boundaries of the census tracts in Mashhad and the point location of all EMS stations, respectively. A data dictionary file defines all fields and values across the data files.

Spatial epidemiology of skin cancer in Iran: separating sun-exposed and non-sun-exposed parts of the body.

BACKGROUND: Skin cancer is among the most common cancer types with an increasing global trend of incidence rate. This study explores the spatial distribution of skin cancer, considering body sites exposed and not exposed to sunshine separately. METHODS: We used 4302 skin cancer cases recorded by Fars Cancer Registry in south-western Iran for over 6 years (2011-2017). The variables included in the study were patients' residence address, gender, age, report date, and final topographical code. The patients' addresses were geocoded to the counties of the study area. Skin cancer sites were categorized based on sun exposure in male and female cases. We used the empirical Bayesian smoothing approach to smooth the skin cancer incidence rate at the county level to remove any potential population size bias. Finally, Anselin's Local Moran's Index and Getis Ord G* were used to identify the clustered and high-risk skin cancer geographical areas. RESULTS: The incidence rates had an increasing trend from 14.28 per 100,000 people in 2011 to 17.87 per 100,000 people in 2016, however, it was decreased to 13.05 per 100,000 people in 2017. Out of 4302 patients with skin cancer, 2602 cases (60%) were male. The cancer cumulative incidence rate in males and females who were not exposed to sunshine was 7.80 and 14.18 per 100,000, respectively. The rates increased to 86.22 and 48.20 in males and females who were exposed to the sun. There were some high-risk spatial clusters of skin cancer in the study area. Further investigations are required to identify the underlying cause of the formation of these clusters. CONCLUSIONS: Patients exposed to sunshine, especially among the male group, experienced much higher rates of cancer occurrence as compared to unexposed individuals. With a heterogeneous spatial pattern, hotspots were identified in non-sun-exposed and sun-exposed categories in the study area. Researchers and policymakers can significantly benefit from the spatial analyses of skin cancer incidence. These analyses can provide useful and timely prevention policies as well as tailored monitoring techniques in high-risk regions.

Spatio-temporal visualisation of cutaneous leishmaniasis in an endemic, urban area in Iran.

INTRODUCTION: Cutaneous Leishmaniasis (CL) is a significant public health concern worldwide. Iran is among the most CL-affected countries, being one of the six most endemic countries in the world. This study aimed to provide a spatio-temporal visualisation of CL cases in an endemic urban area in north-eastern Iran identifying high-risk and low-risk areas during the period 2016-2019. METHODS: This ecological study was conducted in the city of Mashhad, north-eastern Iran. All cases (n=2425) were diagnosed based on clinical findings and parasitological tests. The patient data were aggregated at the census tract level (the highest resolution available). CL incidence rates were subjected to Empirical Bayesian smoothing across the census tracts followed by spatial autocorrelation analysis to identify clusters and outliers. Spatial scan statistic was used to explore the purely temporal, purely spatial and spatio-temporal trend of the disease. In all instances, the null hypothesis of no clusters was rejected at p ≤0.05. RESULTS: The overall crude incidence rate decreased from 34.6 per 100,000 individuals in 2016 to 19.9 per 100,000 in 2019. Cluster analysis identified high-risk areas in south-western Mashhad and low-risk areas in the north-eastern areas. Purely time scan statistics identified March to July as the time period with highest risk for CL occurrence. One most likely purely high-risk spatial cluster and six secondary purely high-risk spatial clusters were identified. Further, two spatio-temporal high-risk clusters, one in the north of the city from April to August and a second in the south-western part from March to September were observed. CONCLUSIONS: Significant spatial, temporal and spatio-temporal patterns of CL distribution were observed in the study area, which should be considered when designing tailored interventions, such as effective resource allocation models, informed control plans and implementation of efficient surveillance systems. Furthermore, this study generated new hypotheses to test potential relationships between socio-economic and environmental risk factors and incidence of CL in high-risk areas.

The prevalence of foodborne parasites in raw vegetables in Iran: a comprehensive systematic review and meta-analysis.

Foodborne parasites in raw vegetables could cause parasitic infections in humans. An effective approach to the reduction of pathogenic microorganisms in vegetables involves identifying the main sources of contamination and the ecology of the involved microorganisms. This review aimed to evaluate the prevalence of foodborne parasites in raw vegetables in Iran. According to the reviewed studies, the prevalence rate of Ascaris spp., Giardia spp., Toxocara spp., Fasciola, Trichuris, Cryptosporidium, Dicrocoeliasis, Taenia spp., and Entamoeba histolytica was 6 % (95 % CI: 5-8 %), 6 % (95 % CI: 5-7 %), 6 % (95 % CI: 4-7 %), 2 % (95 % CI: 1-2 %), 1 % (95 % CI: 1-2 %), 10 % (95 % CI: 6-15 %), 2 % (95 % CI: 1-3 %), 5 % (95 % CI: 4-6 %), and 3 % (95 % CI: 2-4 %), respectively. According to the standard deviation map, Ilam province was one of the high-risk areas in terms of food-borne parasites in raw vegetables (3 SD < prevalence < 4 SD). Given the key role of raw vegetables in human parasitic contamination, governments must implement more stringent programs for effective wastewater treatment, preventing domestic and wild animals from entering farms, not using human and animal manure on farms, using ready-to-eat packaged vegetables, food safety training for households, implementation of GMP and HACCP in the factory vegetable packaging are required in this regard, especially in the high-risk areas of Iran, such as Ilam province. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40201-021-00714-w.