Background: Colorectal cancer has increased in Middle Eastern countries and exposure to environmental pollutants such as heavy metals has been implicated. However, data linking them to this disease are generally lacking. This study aimed to explore the spatial pattern of age-standardized incidence rate (ASR) of colon cancer and its potential association with the exposure level of the amount of heavy metals existing in rice produced in north-eastern Iran. Methods: Cancer data were drawn from the Iranian population-based cancer registry of Golestan Province, north-eastern Iran. Samples of 69 rice milling factories were analysed for the concentration levels of cadmium, nickel, cobalt, copper, selenium, lead and zinc. The inverse distance weighting (IDW) algorithm was used to interpolate the concentration of this kind of heavy metals on the surface of the study area. Exploratory regression analysis was conducted to build ordinary least squares (OLS) models including every possible combination of the candidate explanatory variables and chose the most useful ones to show the association between heavy metals and the ASR of colon cancer. Results: The highest concentrations of heavy metals were found in the central part of the province and particularly counties with higher amount of cobalt were shown to be associated with higher ASR of men with colon cancer. In contrast, selenium concentrations were higher in areas with lower ASR of colon cancer in men. A significant regression equation for men with colon cancer was found (F(4,137) = 38.304, P < .000) with an adjusted R² of 0.77. The predicted ASR of men colon cancer was − 58.36 with the coefficients for cobalt = 120.33; cadmium = 80.60; selenium = − 6.07; nickel = − 3.09; and zinc = − 0.41. The association of copper and lead with colon cancer in men was not significant. We did not find a significant outcome for colon cancer in women. Conclusion: Increased amounts of heavy metals in consumed rice may impact colon cancer incidence, both positively and negatively. While there were indications of an association between high cobalt concentrations and an increased risk for colon cancer, we found that high selenium concentrations might instead decrease the risk. Further investigations are needed to clarify if there are ecological or other reasons for these discrepancies. Regular monitoring of the amount of heavy metals in consumed rice is recommended.

Objectives: The ongoing Coronavirus disease 2019 (COVID-19) pandemic has drastically impacted the global health and economy. Computed tomography (CT) is the prime imaging modality for diagnosis of lung infections in COVID-19 patients. Data-driven and Artificial intelligence (AI)-powered solutions for automatic processing of CT images predominantly rely on large-scale, heterogeneous datasets. Owing to privacy and data availability issues, open-access and publicly available COVID-19 CT datasets are difficult to obtain, thus limiting the development of AI-enabled automatic diagnostic solutions. To tackle this problem, large CT image datasets encompassing diverse patterns of lung infections are in high demand. Data description: In the present study, we provide an open-source repository containing 1000+ CT images of COVID-19 lung infections established by a team of board-certified radiologists. CT images were acquired from two main general university hospitals in Mashhad, Iran from March 2020 until January 2021. COVID-19 infections were ratified with matching tests including Reverse transcription polymerase chain reaction (RT-PCR) and accompanying clinical symptoms. All data are 16-bit grayscale images composed of 512 × 512 pixels and are stored in DICOM standard. Patient privacy is preserved by removing all patient-specific information from image headers. Subsequently, all images corresponding to each patient are compressed and stored in RAR format.

Background: Effective reduction of tuberculosis (TB) requires information on the distribution of TB incidence rate across time and location. This study aims to identify the spatio-temporal pattern of TB incidence rate in Iran between 2008 and 2018. Methods: This cross-sectional study was conducted on aggregated TB data (50,500 patients) at the provincial level provided by the Ministry of Health in Iran between 2008 and 2018. The Anselin Local Moran’s I and Getis-Ord Gi* were performed to identify the spatial variations of the disease. Furthermore, spatial scan statistic was employed for purely temporal and spatio-temporal analyses. In all instances, the null hypothesis of no clusters was rejected at p ≤ 0.05. Results: The overall incidence rate of TB decreased from 13.46 per 100,000 (95% CI: 13.19–13.73) in 2008 to 10.88 per 100,000 (95% CI: 10.65–11.11) in 2018. The highest incidence rate of TB was observed in southeast and northeast of Iran for the whole study period. Additionally, spatial cluster analysis discovered Khuzestan Province, in the West of the country, having significantly higher rates than neighbouring provinces in terms of both total TB and smear-positive pulmonary TB (SPPTB). Purely temporal analysis showed that high-rate and low-rate clusters were predominantly distributed in the time periods 2010–2014 and 2017–2018. Spatio-temporal results showed that the statistically significant clusters were mainly distributed from centre to the east during the study period. Some high-trend TB and SPPTB statistically significant clusters were found. Conclusion: The results provided an overview of the latest TB spatio-temporal status In Iran and identified decreasing trends of TB in the 2008–2018 period. Despite the decreasing incidence rate, there is still need for screening, and targeting of preventive interventions, especially in high-risk areas. Knowledge of the spatio-temporal pattern of TB can be useful for policy development as the information regarding the high-risk areas would contribute to the selection of areas needed to be targeted for the expansion of health facilities.

Background: There are only limited studies on access to COVID-19 vaccines and identifying the most appropriate health centres for performing vaccination in metropolitan areas. This study aimed to measure potential spatial access to COVID-19 vaccination centres in Mashhad, the second-most populous city in Iran. Methods: The 2021 age structure of the urban census tracts was integrated into the enhanced two-step floating catchment area model to improve accuracy. The model was developed based on three different access scenarios: only public hospitals, only public healthcare centres and both (either hospitals or healthcare centres) as potential vaccination facilities. The weighted decision-matrix and analytic hierarchy process, based on four criteria (i.e. service area, accessibility index, capacity of vaccination centres and distance to main roads), were used to choose potential vaccination centres looking for the highest suitability for residents. Global Moran’s index (GMI) was used to measure the spatial autocorrelation of the accessibility index in different scenarios and the proposed model. Results: There were 26 public hospitals and 271 public healthcare centres in the study area. Although the exclusive use of public healthcare centres for vaccination can provide the highest accessibility in the eastern and north-eastern parts of the study area, our findings indicate that including both public hospitals and public healthcare centres provide high accessibility to vaccination in central urban part. Therefore, a combination of public hospitals and public healthcare centres is recommended for efficient vaccination coverage. The value of GMI for the proposed model (accessibility to selected vaccination centres) was calculated as 0.53 (Z = 162.42, P < 0.01). Both GMI and Z-score values decreased in the proposed model, suggesting an enhancement in accessibility to COVID-19 vaccination services. Conclusions: The periphery and poor areas of the city had the least access to COVID-19 vaccination centres. Measuring spatial access to COVID-19 vaccination centres can provide valuable insights for urban public health decision-makers. Our model, coupled with geographical information systems, provides more efficient vaccination coverage by identifying the most suitable healthcare centres, which is of special importance when only few centres are available. Graphic