1
|
Mehri S, Alesheikh AA, Lotfata A. Abiotic factors impact on oak forest decline in Lorestan Province, Western Iran. Sci Rep 2024; 14:3973. [PMID: 38368502 PMCID: PMC10874411 DOI: 10.1038/s41598-024-54551-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 02/14/2024] [Indexed: 02/19/2024] Open
Abstract
The Zagros oak forests in Iran are facing a concerning decline due to prolonged and severe drought conditions over several decades, compounded by the simultaneous impact of temperature on oak populations. This study in oak woodlands of central Zagros forests in Lorestan province analyzed abiotic factors such as climate properties, topographic features, land use, and soil properties from 1958 to 2022. We found that higher elevation areas with steeper slopes and diverse topography show significant potential for enhancing oak tree resilience in the face of climate change. Additionally, traditional land use practices like livestock keeping and dryland farming contribute to a widespread decline in oak populations. Preserving forest biodiversity and ensuring ecological sustainability requires immediate attention. Implementing effective land-use management strategies, such as protecting and regulating human-forest interaction, and considering meteorological factors to address this issue is crucial. Collaborative efforts from stakeholders, policymakers, and local communities are essential to oppose destructive suburban sprawl and other developments. Sustainable forestry practices should be implemented to improve the living standards of local communities that rely on forests and traditional livestock keeping, offer forestry-related jobs, and ensure social security. Such efforts are necessary to promote conservation awareness and sustainable practices, safeguarding this unique and vital ecosystem for future generations.
Collapse
Affiliation(s)
- Saeed Mehri
- Department of Geospatial Information Systems, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Ali Asghar Alesheikh
- Department of Geospatial Information Systems, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran.
| | - Aynaz Lotfata
- Department of Pathology, Microbiology, and Immunology, School of Veterinary Medicine, University of California, Davis, USA
| |
Collapse
|
2
|
Romadhon YA, Kurniati YP, Jumadi J, Alesheikh AA, Lotfata A. Analyzing socio-environmental determinants of bone and soft tissue cancer in Indonesia. BMC Cancer 2024; 24:206. [PMID: 38350928 PMCID: PMC10865616 DOI: 10.1186/s12885-024-11974-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 02/06/2024] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND This study is designed to explore the potential impact of individual and environmental residential factors as risk determinants for bone and soft tissue cancers, with a particular focus on the Indonesian context. While it is widely recognized that our living environment can significantly influence cancer development, there has been a notable scarcity of research into how specific living environment characteristics relate to the risk of bone and soft tissue cancers. METHODS In a cross-sectional study, we analyzed the medical records of oncology patients treated at Prof. Suharso National Referral Orthopedic Hospital. The study aimed to assess tumor malignancy levels and explore the relationships with socio-environmental variables, including gender, distance from the sea, sunrise time, altitude, and population density. Data were gathered in 2020 from diverse sources, including medical records, Google Earth, and local statistical centers. The statistical analyses employed Chi-square and logistic regression techniques with the support of Predictive Analytics SoftWare (PASW) Statistics 18. RESULTS Both bivariate and multivariate analyses revealed two significant factors associated with the occurrence of bone and soft tissue cancer. Age exhibited a statistically significant influence (OR of 5.345 and a p-value of 0.000 < 0.05), indicating a robust connection between cancer development and age. Additionally, residing within a distance of less than 14 km from the sea significantly affected the likelihood of bone and soft tissue cancers OR 5.604 and p-value (0.001 < 0.05). CONCLUSIONS The study underscores the strong association between age and the development of these cancers, emphasizing the need for heightened vigilance and screening measures in older populations. Moreover, proximity to the sea emerges as another noteworthy factor influencing cancer risk, suggesting potential environmental factors at play. These results highlight the multifaceted nature of cancer causation and underscore the importance of considering socio-environmental variables when assessing cancer risk factors. Such insights can inform more targeted prevention and early detection strategies, ultimately contributing to improved cancer management and patient outcomes.
Collapse
Affiliation(s)
- Yusuf Alam Romadhon
- Faculty of Medicine, Universitas Muhammadiyah Surakarta, Surakarta, 57162, Indonesia
- Centre for Chronical Disease, Universitas Muhammadiyah Surakarta, Surakarta, 57162, Indonesia
| | - Yuni Prastyo Kurniati
- Faculty of Medicine, Universitas Muhammadiyah Surakarta, Surakarta, 57162, Indonesia
| | - Jumadi Jumadi
- Centre for Chronical Disease, Universitas Muhammadiyah Surakarta, Surakarta, 57162, Indonesia
- Faculty of Geography, Universitas Muhammadiyah Surakarta, Surakarta, 57162, Indonesia
| | - Ali Asghar Alesheikh
- Department of Geospatial Information Systems, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran.
| | - Aynaz Lotfata
- Department of Pathology, Microbiology, and Immunology, School of Veterinary Medicine, University of California, Davis, USA
| |
Collapse
|
3
|
Rahmatinejad Z, Dehghani T, Hoseini B, Rahmatinejad F, Lotfata A, Reihani H, Eslami S. A comparative study of explainable ensemble learning and logistic regression for predicting in-hospital mortality in the emergency department. Sci Rep 2024; 14:3406. [PMID: 38337000 PMCID: PMC10858239 DOI: 10.1038/s41598-024-54038-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 02/07/2024] [Indexed: 02/12/2024] Open
Abstract
This study addresses the challenges associated with emergency department (ED) overcrowding and emphasizes the need for efficient risk stratification tools to identify high-risk patients for early intervention. While several scoring systems, often based on logistic regression (LR) models, have been proposed to indicate patient illness severity, this study aims to compare the predictive performance of ensemble learning (EL) models with LR for in-hospital mortality in the ED. A cross-sectional single-center study was conducted at the ED of Imam Reza Hospital in northeast Iran from March 2016 to March 2017. The study included adult patients with one to three levels of emergency severity index. EL models using Bagging, AdaBoost, random forests (RF), Stacking and extreme gradient boosting (XGB) algorithms, along with an LR model, were constructed. The training and validation visits from the ED were randomly divided into 80% and 20%, respectively. After training the proposed models using tenfold cross-validation, their predictive performance was evaluated. Model performance was compared using the Brier score (BS), The area under the receiver operating characteristics curve (AUROC), The area and precision-recall curve (AUCPR), Hosmer-Lemeshow (H-L) goodness-of-fit test, precision, sensitivity, accuracy, F1-score, and Matthews correlation coefficient (MCC). The study included 2025 unique patients admitted to the hospital's ED, with a total percentage of hospital deaths at approximately 19%. In the training group and the validation group, 274 of 1476 (18.6%) and 152 of 728 (20.8%) patients died during hospitalization, respectively. According to the evaluation of the presented framework, EL models, particularly Bagging, predicted in-hospital mortality with the highest AUROC (0.839, CI (0.802-0.875)) and AUCPR = 0.64 comparable in terms of discrimination power with LR (AUROC (0.826, CI (0.787-0.864)) and AUCPR = 0.61). XGB achieved the highest precision (0.83), sensitivity (0.831), accuracy (0.842), F1-score (0.833), and the highest MCC (0.48). Additionally, the most accurate models in the unbalanced dataset belonged to RF with the lowest BS (0.128). Although all studied models overestimate mortality risk and have insufficient calibration (P > 0.05), stacking demonstrated relatively good agreement between predicted and actual mortality. EL models are not superior to LR in predicting in-hospital mortality in the ED. Both EL and LR models can be considered as screening tools to identify patients at risk of mortality.
Collapse
Affiliation(s)
- Zahra Rahmatinejad
- Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Toktam Dehghani
- Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
- Toos Institute of Higher Education, Mashhad, Iran
| | - Benyamin Hoseini
- Pharmaceutical Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Fatemeh Rahmatinejad
- Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Aynaz Lotfata
- Department of Pathology, Microbiology, and Immunology, School of Veterinary Medicine, University of California, Davis, CA, USA
| | - Hamidreza Reihani
- Department of Emergency Medicine, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
| | - Saeid Eslami
- Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
- Pharmaceutical Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
- Department of Medical Informatics, Amsterdam UMC - Location AMC, University of Amsterdam, Amsterdam, The Netherlands.
| |
Collapse
|
4
|
Shirzad R, Alesheikh AA, Asgharzadeh M, Hoseini B, Lotfata A. Spatio-temporal modeling of human leptospirosis prevalence using the maximum entropy model. BMC Public Health 2023; 23:2521. [PMID: 38104062 PMCID: PMC10724969 DOI: 10.1186/s12889-023-17391-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 12/01/2023] [Indexed: 12/19/2023] Open
Abstract
BACKGROUND Leptospirosis, a zoonotic disease, stands as one of the prevailing health issues in some tropical areas of Iran. Over a decade, its incidence rate has been estimated at approximately 2.33 cases per 10,000 individuals. Our research focused on analyzing the spatiotemporal clustering of Leptospirosis and developing a disease prevalence model as an essential focal point for public health policymakers, urging targeted interventions and strategies. METHODS The SaTScan and Maximum Entropy (MaxEnt) modeling methods were used to find the spatiotemporal clusters of the Leptospirosis and model the disease prevalence in Iran. We incorporated nine environmental covariates by employing a spatial resolution of 1 km x 1 km, the finest resolution ever implemented for modeling Human Leptospirosis in Iran. These covariates encompassed the Digital Elevation Model (DEM), slope, displacement areas, water bodies, and land cover, monthly recorded Normalized Difference Vegetation Index (NDVI), monthly recorded precipitation, monthly recorded mean and maximum temperature, contributing significantly to our disease modeling approach. The analysis using MaxEnt yielded the Area Under the Receiver Operating Characteristic Curve (AUC) metrics for the training and test data, to evaluate the accuracy of the implemented model. RESULTS The findings reveal a highly significant primary cluster (p-value < 0.05) located in the western regions of the Gilan province, spanning from July 2013 to July 2015 (p-value < 0.05). Moreover, there were four more clusters (p-value < 0.05) identified near Someh Sara, Neka, Gorgan and Rudbar. Furthermore, the risk mapping effectively illustrates the potential expansion of the disease into the western and northwestern regions. The AUC metrics of 0.956 and 0.952 for the training and test data, respectively, underscoring the robust accuracy of the implemented model. Interestingly, among the variables considered, the influence of slope and distance from water bodies appears to be minimal. However, altitude and precipitation stand out as the primary determinants that significantly contribute to the prevalence of the disease. CONCLUSIONS The risk map generated through this study carries significant potential to enhance public awareness and inform the formulation of impactful policies to combat Leptospirosis. These maps also play a crucial role in tracking disease incidents and strategically directing interventions toward the regions most susceptible.
Collapse
Affiliation(s)
- Reza Shirzad
- Department of Geospatial Information System, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Ali Asghar Alesheikh
- Department of Geospatial Information System, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran.
| | - Mojtaba Asgharzadeh
- Department of Geospatial Information System, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Benyamin Hoseini
- Pharmaceutical Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
- Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
| | - Aynaz Lotfata
- School Of Veterinary Medicine, Department of Pathology, Microbiology, and Immunology, University of California, Davis, USA
| |
Collapse
|
5
|
Mohammadzadeh Z, Saeidnia HR, Lotfata A, Hassanzadeh M, Ghiasi N. Smart city healthcare delivery innovations: a systematic review of essential technologies and indicators for developing nations. BMC Health Serv Res 2023; 23:1180. [PMID: 37904181 PMCID: PMC10614321 DOI: 10.1186/s12913-023-10200-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 10/23/2023] [Indexed: 11/01/2023] Open
Abstract
BACKGROUND In recent times, the concept of smart cities has gained remarkable traction globally, driven by the increasing interest in employing technology to address various urban challenges, particularly in the healthcare domain. Smart cities are proving to be transformative, utilizing an extensive array of technological tools and processes to improve healthcare accessibility, optimize patient outcomes, reduce costs, and enhance overall efficiency. METHODS This article delves into the profound impact of smart cities on the healthcare landscape and discusses its potential implications for the future of healthcare delivery. Moreover, the study explores the necessary infrastructure required for developing countries to establish smart cities capable of providing intelligent health and care services. To ensure a comprehensive analysis, we employed a well-structured search strategy across esteemed databases, including PubMed, OVID, EMBASE, Web of Science, and Scopus. The search scope encompassed articles published up to November 2022, resulting in a meticulous review of 22 relevant articles. RESULTS Our findings provide compelling evidence of the pivotal role that smart city technology plays in elevating healthcare delivery, forging a path towards improved accessibility, efficiency, and quality of care for communities worldwide. By harnessing the power of data analytics, Internet of Things (IoT) sensors, and mobile applications, smart cities are driving real-time health monitoring, early disease detection, and personalized treatment approaches. CONCLUSION Smart cities possess the transformative potential to reshape healthcare practices, providing developing nations with invaluable opportunities to establish intelligent and adaptable healthcare systems customized to their distinct requirements and limitations. Moreover, the implementation of smart healthcare systems in developing nations can lead to enhanced healthcare accessibility and affordability, as the integration of technology can optimize resource allocation and improve the overall efficiency of healthcare services. It also may help alleviate the burden on overburdened healthcare facilities by streamlining patient care processes and reducing wait times, ensuring that medical attention reaches those in need more swiftly.
Collapse
Affiliation(s)
- Zahra Mohammadzadeh
- Health Information Management Research Center, Kashan University of Medical Sciences, Kashan, Iran
- Department of Health Information Management and Technology, School of Allied Medical Sciences, Kashan University of Medical Sciences, Kashan, Iran
| | - Hamid Reza Saeidnia
- Department of Knowledge and Information Science, Tarbiat Modares University, Tehran, Iran
| | - Aynaz Lotfata
- School Of Veterinary Medicine, Department of Veterinary Pathology, University of California, Davis, USA
| | - Mohammad Hassanzadeh
- Department of Knowledge and Information Science, Tarbiat Modares University, Tehran, Iran
| | - Nasrin Ghiasi
- Department of Public Health, School of Health, Ilam University of Medical Sciences, Ilam, Iran.
| |
Collapse
|
6
|
Nazari Ashani M, Alesheikh AA, Neisani Samani Z, Lotfata A, Bayat S, Alipour S, Hoseini B. Socioeconomic and environmental determinants of foot and mouth disease incidence: an ecological, cross-sectional study across Iran using spatial modeling. Sci Rep 2023; 13:13526. [PMID: 37598281 PMCID: PMC10439931 DOI: 10.1038/s41598-023-40865-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 08/17/2023] [Indexed: 08/21/2023] Open
Abstract
Foot-and-mouth disease (FMD) is a highly contagious animal disease caused by a ribonucleic acid (RNA) virus, with significant economic costs and uneven distribution across Asia, Africa, and South America. While spatial analysis and modeling of FMD are still in their early stages, this research aimed to identify socio-environmental determinants of FMD incidence in Iran at the provincial level by studying 135 outbreaks reported between March 21, 2017, and March 21, 2018. We obtained 46 potential socio-environmental determinants and selected four variables, including percentage of population, precipitation in January, percentage of sheep, and percentage of goats, to be used in spatial regression models to estimate variation in spatial heterogeneity. In our analysis, we employed global models, namely ordinary least squares (OLS), spatial error model (SEM), and spatial lag model (SLM), as well as local models, including geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR). The MGWR model yielded the highest adjusted [Formula: see text] of 90%, outperforming the other local and global models. Using local models to map the effects of environmental determinants (such as the percentage of sheep and precipitation) on the spatial variability of FMD incidence provides decision-makers with helpful information for targeted interventions. Our findings advocate for multiscale and multidisciplinary policies to reduce FMD incidence.
Collapse
Affiliation(s)
- Mahdi Nazari Ashani
- Department of Geospatial Information Systems, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Ali Asghar Alesheikh
- Department of Geospatial Information Systems, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Zeinab Neisani Samani
- Department of Geospatial Information Systems, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Aynaz Lotfata
- Department of Pathology, Microbiology, and Immunology, School of Veterinary Medicine, University of California, Davis, USA
| | - Sayeh Bayat
- Department of Biomedical Engineering, University of Calgary, Calgary, AB, Canada
- Department of Geomatics Engineering, University of Calgary, Calgary, AB, Canada
| | - Siamak Alipour
- Department of Geospatial Information Systems, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Benyamin Hoseini
- Pharmaceutical Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
- Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
| |
Collapse
|
7
|
Lotfata A, Moosazadeh M, Helbich M, Hoseini B. Socioeconomic and environmental determinants of asthma prevalence: a cross-sectional study at the U.S. County level using geographically weighted random forests. Int J Health Geogr 2023; 22:18. [PMID: 37563691 PMCID: PMC10413687 DOI: 10.1186/s12942-023-00343-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 08/04/2023] [Indexed: 08/12/2023] Open
Abstract
BACKGROUND Some studies have established associations between the prevalence of new-onset asthma and asthma exacerbation and socioeconomic and environmental determinants. However, research remains limited concerning the shape of these associations, the importance of the risk factors, and how these factors vary geographically. OBJECTIVE We aimed (1) to examine ecological associations between asthma prevalence and multiple socio-physical determinants in the United States; and (2) to assess geographic variations in their relative importance. METHODS Our study design is cross sectional based on county-level data for 2020 across the United States. We obtained self-reported asthma prevalence data of adults aged 18 years or older for each county. We applied conventional and geographically weighted random forest (GWRF) to investigate the associations between asthma prevalence and socioeconomic (e.g., poverty) and environmental determinants (e.g., air pollution and green space). To enhance the interpretability of the GWRF, we (1) assessed the shape of the associations through partial dependence plots, (2) ranked the determinants according to their global importance scores, and (3) mapped the local variable importance spatially. RESULTS Of the 3059 counties, the average asthma prevalence was 9.9 (standard deviation ± 0.99). The GWRF outperformed the conventional random forest. We found an indication, for example, that temperature was inversely associated with asthma prevalence, while poverty showed positive associations. The partial dependence plots showed that these associations had a non-linear shape. Ranking the socio-physical environmental factors concerning their global importance showed that smoking prevalence and depression prevalence were most relevant, while green space and limited language were of minor relevance. The local variable importance measures showed striking geographical differences. CONCLUSION Our findings strengthen the evidence that socio-physical environments play a role in explaining asthma prevalence, but their relevance seems to vary geographically. The results are vital for implementing future asthma prevention programs that should be tailor-made for specific areas.
Collapse
Affiliation(s)
- Aynaz Lotfata
- Department of Pathology, Microbiology, and Immunology, School of Veterinary Medicine, University of California, Davis, CA, USA
| | - Mohammad Moosazadeh
- Integrated Engineering, Department of Environmental Science and Engineering, College of Engineering, KyungHee University, Yongin, 446-701, Republic of Korea
| | - Marco Helbich
- Department of Human Geography and Spatial Planning, Faculty of Geosciences, University Utrecht, Utrecht, The Netherlands
| | - Benyamin Hoseini
- Pharmaceutical Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
- Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
| |
Collapse
|
8
|
Barati Jozan MM, Ghorbani BD, Khalid MS, Lotfata A, Tabesh H. Impact assessment of e-trainings in occupational safety and health: a literature review. BMC Public Health 2023; 23:1187. [PMID: 37340453 DOI: 10.1186/s12889-023-16114-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 06/13/2023] [Indexed: 06/22/2023] Open
Abstract
BACKGROUND Implementing workplace preventive interventions reduces occupational accidents and injuries, as well as the negative consequences of those accidents and injuries. Online occupational safety and health training is one of the most effective preventive interventions. This study aims to present current knowledge on e-training interventions, make recommendations on the flexibility, accessibility, and cost-effectiveness of online training, and identify research gaps and obstacles. METHOD All studies that addressed occupational safety and health e-training interventions designed to address worker injuries, accidents, and diseases were chosen from PubMed and Scopus until 2021. Two independent reviewers conducted the screening process for titles, abstracts, and full texts, and disagreements on the inclusion or exclusion of an article were resolved by consensus and, if necessary, by a third reviewer. The included articles were analyzed and synthesized using the constant comparative analysis method. RESULT The search identified 7,497 articles and 7,325 unique records. Following the title, abstract, and full-text screening, 25 studies met the review criteria. Of the 25 studies, 23 were conducted in developed and two in developing countries. The interventions were carried out on either the mobile platform, the website platform, or both. The study designs and the number of outcomes of the interventions varied significantly (multi-outcomes vs. single-outcome). Obesity, hypertension, neck/shoulder pain, office ergonomics issues, sedentary behaviors, heart disease, physical inactivity, dairy farm injuries, nutrition, respiratory problems, and diabetes were all addressed in the articles. CONCLUSION According to the findings of this literature study, e-trainings can significantly improve occupational safety and health. E-training is adaptable, affordable, and can increase workers' knowledge and abilities, resulting in fewer workplace injuries and accidents. Furthermore, e-training platforms can assist businesses in tracking employee development and ensuring that training needs are completed. Overall, this analysis reveals that e-training has enormous promise in the field of occupational safety and health for both businesses and employees.
Collapse
Affiliation(s)
- Mohammad Mahdi Barati Jozan
- Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | | | - Md Saifuddin Khalid
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Aynaz Lotfata
- School Of Veterinary Medicine, Department Of Veterinary Pathology, University of California, Davis, USA
| | - Hamed Tabesh
- Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
| |
Collapse
|
9
|
Lotfata A, Georganos S. Spatial machine learning for predicting physical inactivity prevalence from socioecological determinants in Chicago, Illinois, USA. J Geogr Syst 2023:1-21. [PMID: 37358962 PMCID: PMC10241140 DOI: 10.1007/s10109-023-00415-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 05/04/2023] [Indexed: 06/28/2023]
Abstract
The increase in physical inactivity prevalence in the USA has been associated with neighborhood characteristics. While several studies have found an association between neighborhood and health, the relative importance of each component related to physical inactivity or how this value varies geographically (i.e., across different neighborhoods) remains unexplored. This study ranks the contribution of seven socioecological neighborhood factors to physical inactivity prevalence in Chicago, Illinois, using machine learning models at the census tract level, and evaluates their predictive capabilities. First, we use geographical random forest (GRF), a recently proposed nonlinear machine learning regression method that assesses each predictive factor's spatial variation and contribution to physical inactivity prevalence. Then, we compare the predictive performance of GRF to geographically weighted artificial neural networks, another recently proposed spatial machine learning algorithm. Our results suggest that poverty is the most important determinant in the Chicago tracts, while on the other hand, green space is the least important determinant in the rise of physical inactivity prevalence. As a result, interventions can be designed and implemented based on specific local circumstances rather than broad concepts that apply to Chicago and other large cities. Supplementary Information The online version contains supplementary material available at 10.1007/s10109-023-00415-y.
Collapse
Affiliation(s)
- Aynaz Lotfata
- School of Veterinary Medicine, Department of Veterinary Pathology, University of California, Davis, USA
| | - Stefanos Georganos
- Geomatics, Department of Environmental and Life Sciences, Faculty of Health, Science and Technology, Karlstad University, Karlstad, Sweden
| |
Collapse
|
10
|
Jafari Ramiani A, Sarvari H, Chan DWM, Nassereddine H, Lotfata A. Critical success factors for private sector participation in accomplishing abandoned public sports facilities projects in Iran. International Journal of Construction Management 2022. [DOI: 10.1080/15623599.2022.2147647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Affiliation(s)
- Amineh Jafari Ramiani
- Department of Civil Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
| | - Hadi Sarvari
- Department of Civil Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
- Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Daniel W. M. Chan
- Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Hala Nassereddine
- Department of Civil Engineering, University of Kentucky, Lexington, KY, USA
| | - Aynaz Lotfata
- Geography Department, Chicago State University, Chicago, IL, USA
| |
Collapse
|
11
|
Guhlincozzi AR, Lotfata A. Travel distance to flu and COVID-19 vaccination sites for people with disabilities and age 65 and older, Chicago metropolitan area. JHR 2021. [DOI: 10.1108/jhr-03-2021-0196] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
PurposeHaving easy access to the flu and COVID-19 vaccination sites may be important for controlling the spread of the infection. Chicago implemented a broad strategy of opening COVID-19 vaccination locations across the city in a variety of locations.Design/methodology/approachWe defined access as having vaccinations within one mile. Data came from the American Community Survey (ACS), Centers for Disease Control and Prevention (CDC), Social Vulnerability Index (SVI), Illinois Department of Public Health (IDPH) and the Chicago Data Portal. We calculated the street-network distance from the population-weighted centroid of census tracts to the nearest vaccination sites before, during and post COVID-19 pandemic. We compared the demographics of census tracts within one mile to those greater than one mile during each period.FindingsPeople age 65 and above and with disabilities saw significant decreases in flu vaccination site access to locations within one mile of their census tract in 2020–2021 compared to 2018–2019. The COVID-19 vaccination sites mimic these flu vaccination site patterns, suggesting a severe lack of geographic access for a group likely to experience limited mobility. Results combining instances of both flu and COVID-19 vaccination sites suggest that making COVID-19 vaccination sites available at flu shot site locations would significantly reduce the number of people with limited mobility lacking geographic access.Originality/valuePolicymakers should explore how this expanded network of vaccination locations could facilitate permanent improvements to access to vaccination sites for people with disabilities.
Collapse
|