Quality of in-hospital care in traumatic spinal column and cord injuries (TSC/SCI) in I.R Iran.

PURPOSE: This study aimed to implement the Quality of Care (QoC) Assessment Tool from the National Spinal Cord/Column Injury Registry of Iran (NSCIR-IR) to map the current state of in-hospital QoC of individuals with Traumatic Spinal Column and Cord Injuries (TSCCI). METHODS: The QoC Assessment Tool, developed from a scoping review of the literature, was implemented in NSCIR-IR. We collected the required data from two primary sources. Questions regarding health system structures and care processes were completed by the registrar nurse reviewing the hospital records. Questions regarding patient outcomes were gathered through patient interviews. RESULTS: We registered 2812 patients with TSCCI over six years from eight referral hospitals in NSCIR-IR. The median length of stay in the general hospital and intensive care unit was four and five days, respectively. During hospitalization 4.2% of patients developed pressure ulcers, 83.5% of patients reported satisfactory pain control and none had symptomatic urinary tract infections. 100%, 80%, and 90% of SCI registration centers had 24/7 access to CT scans, MRI scans, and operating rooms, respectively. Only 18.8% of patients who needed surgery underwent a surgical operation in the first 24 h after admission. In-hospital mortality rate for patients with SCI was 19.3%. CONCLUSION: Our study showed that the current in-hospital care of our patients with TSCCI is acceptable in terms of pain control, structure and length of stay and poor regarding in-hospital mortality rate and timeliness. We must continue to work on lowering rates of pressure sores, as well as delays in decompression surgery and fatalities.

Monkeypox detection using deep neural networks.

BACKGROUND: In May 2022, the World Health Organization (WHO) European Region announced an atypical Monkeypox epidemic in response to reports of numerous cases in some member countries unrelated to those where the illness is endemic. This issue has raised concerns about the widespread nature of this disease around the world. The experience with Coronavirus Disease 2019 (COVID-19) has increased awareness about pandemics among researchers and health authorities. METHODS: Deep Neural Networks (DNNs) have shown promising performance in detecting COVID-19 and predicting its outcomes. As a result, researchers have begun applying similar methods to detect Monkeypox disease. In this study, we utilize a dataset comprising skin images of three diseases: Monkeypox, Chickenpox, Measles, and Normal cases. We develop seven DNN models to identify Monkeypox from these images. Two scenarios of including two classes and four classes are implemented. RESULTS: The results show that our proposed DenseNet201-based architecture has the best performance, with Accuracy = 97.63%, F1-Score = 90.51%, and Area Under Curve (AUC) = 94.27% in two-class scenario; and Accuracy = 95.18%, F1-Score = 89.61%, AUC = 92.06% for four-class scenario. Comparing our study with previous studies with similar scenarios, shows that our proposed model demonstrates superior performance, particularly in terms of the F1-Score metric. For the sake of transparency and explainability, Local Interpretable Model-Agnostic Explanations (LIME) and Gradient-weighted Class Activation Mapping (Grad-Cam) were developed to interpret the results. These techniques aim to provide insights into the decision-making process, thereby increasing the trust of clinicians. CONCLUSION: The DenseNet201 model outperforms the other models in terms of the confusion metrics, regardless of the scenario. One significant accomplishment of this study is the utilization of LIME and Grad-Cam to identify the affected areas and assess their significance in diagnosing diseases based on skin images. By incorporating these techniques, we enhance our understanding of the infected regions and their relevance in distinguishing Monkeypox from other similar diseases. Our proposed model can serve as a valuable auxiliary tool for diagnosing Monkeypox and distinguishing it from other related conditions.

Potential drug-drug interactions in a multi-center study of death cases of COVID-19: The significance of appropriate drug choice.

OBJECTIVES: The prevalence, types, severity, risk ratings, and common pairs of involved drugs, and the most important potential drug-drug interactions (pDDIs) in coronavirus disease 2019 (-COVID-19) deceased cases were evaluated. MATERIALS AND METHODS: We reviewed the medical records of 157 confirmed COVID-19 deceased cases hospitalized in 27 province-wide hospitals. Patients' demographics and clinical data (including comorbidities, vital signs, length of in-hospital survival, electrocardiograms (ECGs), medications, and lab test results) were extracted. The online Lexi-interact database and Stockley's drug interactions reference were used to detect pDDIs retrospectively. The QTc interval and total Tisdale risk score were also calculated. Descriptive analysis, analysis of variance, Fisher exact test, and multivariate analysis were conducted for data analysis. RESULTS: Of 157 study cases, 63% were male, had a mean age of 68 years, and 55.7% had one or more underlying diseases. All patients had polypharmacy, with 69.2% having ≥ 15 drugs/day. We detected 2,416 pDDIs in patients' records, of which 658 (27.2%) were interactions with COVID drugs. Lopinavir/ritonavir among -COVID drugs and fentanyl among non-COVID drugs were commonly involved in the interactions. pDDIs was significantly higher in the polypharmacy group of ≥ 15 medications (p < 0.001). A majority (83%) had received drug(s) with the QTc prolongation effect, of whom 67% had actual QTc prolongations in their ECGs. The regression analysis showed that by increasing 6.7% in polypharmacy, one day increase in-hospital survival can be expected. Moreover, an increase of 2.3% in white blood cells or 10.5% in serum potassium level decreased in-hospital survival by 1%. CONCLUSION: The findings underscored the importance of careful drug choice, especially in the hectic search for early treatments in pandemics of novel diseases. Close monitoring of patients' drug choice is warranted for reducing pDDIs and their adverse effects in any new disease outbreak.

Post-discharge follow-up of patients with spine trauma in the National Spinal Cord Injury Registry of Iran during the COVID-19 pandemic: Challenges and lessons learned.

PURPOSE: The purpose of the National Spinal Cord Injury Registry of Iran (NSCIR-IR) is to create an infrastructure to assess the quality of care for spine trauma and in this study, we aim to investigate whether the NSCIR-IR successfully provides necessary post-discharge follow-up data for these patients. METHODS: An observational prospective study was conducted from April 11, 2021 to April 22, 2022 in 8 centers enrolled in NSCIR-IR, respectively Arak, Rasht, Urmia, Shahroud, Yazd, Kashan, Tabriz, and Tehran. Patients were classified into three groups based on their need for care resources, respectively: (1) non-spinal cord injury (SCI) patients without surgery (group 1), (2) non-SCI patients with surgery (group 2), and (3) SCI patients (group 3). The assessment tool was a self-designed questionnaire to evaluate the care quality in 3 phases: pre-hospital, in-hospital, and post-hospital. The data from the first 2 phases were collected through the registry. The post-hospital data were collected by conducting follow-up assessments. Telephone follow-ups were conducted for groups 1 and 2 (non-SCI patients), while group 3 (SCI patients) had a face-to-face visit. This study took place during the COVID-19 pandemic. Data on age and time interval from injury to follow-up were expressed as mean ± standard deviation (SD) and response rate and follow-up loss as a percentage. RESULTS: Altogether 1538 telephone follow-up records related to 1292 patients were registered in the NSCIR-IR. Of the total calls, 918 (71.05%) were related to successful follow-ups, but 38 cases died and thus were excluded from data analysis. In the end, post-hospital data from 880 patients alive were gathered. The success rate of follow-ups by telephone for groups 1 and 2 was 73.38% and 67.05% respectively, compared to 66.67% by face-to-face visits for group 3, which was very hard during the COVID-19 pandemic. The data completion rate after discharge ranged from 48% to 100%, 22%-100% and 29%-100% for groups 1 - 3. CONCLUSIONS: To improve patient accessibility, NSCIR-IR should take measures during data gathering to increase the accuracy of registered contact information. Regarding the loss to follow-ups of SCI patients, NSCIR-IR should find strategies for remote assessment or motivate them to participate in follow-ups through, for example, providing transportation facilities or financial support.

Application of machine learning techniques for predicting survival in ovarian cancer.

BACKGROUND: Ovarian cancer is the fifth leading cause of mortality among women in the United States. Ovarian cancer is also known as forgotten cancer or silent disease. The survival of ovarian cancer patients depends on several factors, including the treatment process and the prognosis. METHODS: The ovarian cancer patients' dataset is compiled from the Surveillance, Epidemiology, and End Results (SEER) database. With the help of a clinician, the dataset is curated, and the most relevant features are selected. Pearson's second coefficient of skewness test is used to evaluate the skewness of the dataset. Pearson correlation coefficient is also used to investigate the associations between features. Statistical test is utilized to evaluate the significance of the features. Six Machine Learning (ML) models, including K-Nearest Neighbors , Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost), are implemented for survival prediction in both classification and regression approaches. An interpretable method, Shapley Additive Explanations (SHAP), is applied to clarify the decision-making process and determine the importance of each feature in prediction. Additionally, DTs of the RF model are displayed to show how the model predicts the survival intervals. RESULTS: Our results show that RF (Accuracy = 88.72%, AUC = 82.38%) and XGBoost (Root Mean Squad Error (RMSE)) = 20.61%, R2 = 0.4667) have the best performance for classification and regression approaches, respectively. Furthermore, using the SHAP method along with extracted DTs of the RF model, the most important features in the dataset are identified. Histologic type ICD-O-3, chemotherapy recode, year of diagnosis, age at diagnosis, tumor stage, and grade are the most important determinant factors in survival prediction. CONCLUSION: To the best of our knowledge, our study is the first study that develops various ML models to predict ovarian cancer patients' survival on the SEER database in both classification and regression approaches. These ML algorithms also achieve more accurate results and outperform statistical methods. Furthermore, our study is the first study to use the SHAP method to increase confidence and transparency of the proposed models' prediction for clinicians. Moreover, our developed models, as an automated auxiliary tool, can help clinicians to have a better understanding of the estimated survival as well as important features that affect survival.

Patient empowerment through a user-centered design of an electronic personal health record: a qualitative study of user requirements in chronic kidney disease.

BACKGROUND: To improve chronic disease outcomes, self-management is an effective strategy. An electronic personal health record (ePHR) is a promising tool with the potential to support chronic patient's education, counseling, and self-management. Fitting ePHRs within the daily practices of chronic care providers and chronic patients requires user-centered design approaches. We aimed to understand users' needs and requirements in chronic kidney disease (CKD) care to consider in the design of an ePHR to facilitate its implementation, adoption, and use. METHODS: A qualitative study was conducted in a major Iranian nephrology center including inpatient and outpatient settings in 2019. We conducted 28 semi-structured interviews with CKD patients, nurses, and adult nephrologists. To confirm or modify the requirements extracted from the interviews, a focus group was also held. Data were analyzed to extract especially those requirements that can facilitate implementation, adoption, and sustained use based on the PHR adoption model and the unified theory of acceptance and use of technology. RESULTS: Participants requested an ePHR that provides access to up to date patient information, facilitates patient-provider communication, and increases awareness about patient individualized conditions. Participants expected a system that is able to cater to low patient e-health literacy and high provider workload. They requested the ePHR to include purposeful documentation of medical history, diagnostic and therapeutic procedures, tailored educational content, and scheduled care reminders. Messaging function, tailored educational content to individual patients' conditions, and controlled access to information were highly valued in order to facilitate its implementation, adoption, and use. CONCLUSIONS: We focused on the ePHR's content and functionalities in the face of facilitators and/or barriers envisioned for its adoption in nephrology care. Designers and implementers should value CKD patients' needs and requirements for self-management such as providing personalized education and counseling (on the basis of their condition and risk factors), health literacy, and disease progression levels. The socio-technical aspects of care also need further attention to facilitate ePHR's adoption.

Evaluating mechanism and severity of injuries among trauma patients admitted to Sina Hospital, the National Trauma Registry of Iran.

PURPOSE: Injuries are one of the leading causes of death and lead to a high social and financial burden. Injury patterns can vary significantly among different age groups and body regions. This study aimed to evaluate the relationship between mechanism of injury, patient comorbidities and severity of injuries. METHODS: The study included trauma patients from July 2016 to June 2018, who were admitted to Sina Hospital, Tehran, Iran. The inclusion criteria were all injured patients who had at least one of the following: hospital length of stay more than 24 h, death in hospital, and transfer from the intensive care unit of another hospital. Data collection was performed using the National Trauma Registry of Iran minimum dataset. RESULTS: The most common injury mechanism was road traffic injuries (49.0%), followed by falls (25.5%). The mean age of those who fell was significantly higher in comparison with other mechanisms (p < 0.001). Severe extremity injuries occurred more often in the fall group than in the vehicle collision group (69.0% vs. 43.5%, p < 0.001). Moreover, cases of severe multiple trauma were higher amongst vehicle collisions than injuries caused by falls (27.8% vs. 12.9%, p = 0.003). CONCLUSION: Comparing falls with motor vehicle collisions, patients who fell were older and sustained more extremity injuries. Patients injured by motor vehicle collision were more likely to have sustained multiple trauma than those presenting with falls. Recognition of the relationship between mechanisms and consequences of injuries may lead to more effective interventions.

Barriers to patient, provider, and caregiver adoption and use of electronic personal health records in chronic care: a systematic review.

BACKGROUND: Electronic personal health records (ePHRs) are defined as electronic applications through which individuals can access, manage, and share health information in a private, secure, and confidential environment. Existing evidence shows their benefits in improving outcomes, especially for chronic disease patients. However, their use has not been as widespread as expected partly due to barriers faced in their adoption and use. We aimed to identify the types of barriers to a patient, provider, and caregiver adoption/use of ePHRs and to analyze their extent in chronic disease care. METHODS: A systematic search in Medline, PubMed, Science Direct, Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Central Register of Controlled Trials, and the Institute of Electrical and Electronics Engineers (IEEE) database was performed to find original studies assessing barriers to ePHR adoption/use in chronic care until the end of 2018. Two researchers independently screened and extracted data. We used the PHR adoption model and the Unified Theory of Acceptance and Use of Technology to analyze the results. The Mixed Methods Appraisal Tool (MMAT) version 2018 was used to assess the quality of evidence in the included studies. RESULTS: Sixty publications met our inclusion criteria. Issues found hindering ePHR adoption/use in chronic disease care were associated with demographic factors (e.g., patient age and gender) along with key variables related to health status, computer literacy, preferences for direct communication, and patient's strategy for coping with a chronic condition; as well as factors related to medical practice/environment (e.g., providers' lack of interest or resistance to adopting ePHRs due to workload, lack of reimbursement, and lack of user training); technological (e.g., concerns over privacy and security, interoperability with electronic health record systems, and lack of customized features for chronic conditions); and chronic disease characteristics (e.g., multiplicities of co-morbid conditions, settings, and providers involved in chronic care). CONCLUSIONS: ePHRs can be meaningfully used in chronic disease care if they are implemented as a component of comprehensive care models specifically developed for this care. Our results provide insight into hurdles and barriers mitigating ePHR adoption/use in chronic disease care. A deeper understating of the interplay between these barriers will provide opportunities that can lead to an enhanced ePHR adoption/use.

Translation of evidence into kidney transplant clinical practice: managing drug-lab interactions by a context-aware clinical decision support system.

BACKGROUND: Drug-laboratory (lab) interactions (DLIs) are a common source of preventable medication errors. Clinical decision support systems (CDSSs) are promising tools to decrease such errors by improving prescription quality in terms of lab values. However, alert fatigue counteracts their impact. We aimed to develop a novel user-friendly, evidence-based, clinical context-aware CDSS to alert nephrologists about DLIs clinically important lab values in prescriptions of kidney recipients. METHODS: For the most frequently prescribed medications identified by a prospective cross-sectional study in a kidney transplant clinic, DLI-rules were extracted using main pharmacology references and clinical inputs from clinicians. A CDSS was then developed linking a computerized prescription system and lab records. The system performance was tested using data of both fictitious and real patients. The "Questionnaire for User Interface Satisfaction" was used to measure user satisfaction of the human-computer interface. RESULTS: Among 27 study medications, 17 needed adjustments regarding renal function, 15 required considerations based on hepatic function, 8 had drug-pregnancy interactions, and 13 required baselines or follow-up lab monitoring. Using IF & THEN rules and the contents of associated alert, a DLI-alerting CDSS was designed. To avoid alert fatigue, the alert appearance was considered as interruptive only when medications with serious risks were contraindicated or needed to be discontinued or adjusted. Other alerts appeared in a non-interruptive mode with visual clues on the prescription window for easy, intuitive notice. When the system was used for real 100 patients, it correctly detected 260 DLIs and displayed 249 monitoring, seven hepatic, four pregnancy, and none renal alerts. The system delivered patient-specific recommendations based on individual lab values in real-time. Clinicians were highly satisfied with the usability of the system. CONCLUSIONS: To our knowledge, this is the first study of a comprehensive DLI-CDSS for kidney transplant care. By alerting on considerations in renal and hepatic dysfunctions, maternal and fetal toxicity, or required lab monitoring, this system can potentially improve medication safety in kidney recipients. Our experience provides a strong foundation for designing specialized systems to promote individualized transplant follow-up care.

Evaluation of blended medical education from lecturers' and students' viewpoint: a qualitative study in a developing country.

BACKGROUND: To improve the quality of education, many academic medical institutions are investing in the application of blended education to support new teaching and learning methods. To take necessary measures to implement the blended learning smoothly, and to achieve its goals, we aimed to identify its strengths, weaknesses, opportunities, and threats (SWOT) from its key users' viewpoints. METHODS: A qualitative study consisting of 24 interviews with lecturers and students and document analysis was conducted at Urmia University of Medical Sciences, in Iran, in 2018. The SWOT framework was used to analyze the data. RESULTS: The most important strengths were the promotion of lecturer-student interactions, the focus on students' learning needs and self-learning, and problem-solving skills. The supports of university executives, alignment with the national health education transformation plan, and access to the shared infrastructures of the national virtual medical science university were opportunities to facilitate its implementation. However, this endeavor had weaknesses such as bottlenecks in technical, organizational, and human resource infrastructures and lack of culture readiness. The threats envisioned for its maintenance were its dependency on the education transformation plan and the lack of an independent e-learning center for better planning and support services, lack of proper evaluation and supervision of virtual activities, and insufficiency of the privileges considered for users. CONCLUSIONS: One of the important implications of this study is that different aspects surrounding blended learning might work as a double-edge sword from time to time, which requires a thorough overview. While retaining the strengths and enjoying the opportunities in such interventions, the weaknesses should be recognized and threats are faced and addressed. Therefore, if the SWOT items are considered mindfully, they can help to adopt the right implementation strategies to reap full benefits.

Predictors of coronavirus disease 19 (COVID-19) pneumonitis outcome based on computed tomography (CT) imaging obtained prior to hospitalization: a retrospective study.

PURPOSE: Computed tomography (CT) has been utilized as a diagnostic modality in the coronavirus disease 19 (COVID-19), while some studies have also suggested a prognostic role for it. This study aimed to assess the diagnostic and prognostic value of computed tomography (CT) imaging in COVID-19 patients. METHODS: This was a retrospective study of fifty patients with COVID-19 pneumonia. Twenty-seven patients survived, while 23 passed away. CT imaging was performed in all of the patients on the day of admission. Imaging findings were interpreted based on current guidelines by two expert radiologists. Imaging findings were compared between surviving and deceased patients. Lung scores were assigned to patients based on CT chest findings. Then, the receiver operating characteristic curve was used to determine cutoff values for lung scores. RESULTS: The common radiologic findings were ground-glass opacities (82%) and airspace consolidation (42%), respectively. Air bronchogram was more commonly seen in deceased patients (p = 0.04). Bilateral and multilobar involvement was more frequently found in deceased patients (p = 0.049 and 0.014, respectively). The mean number of involved lobes was 3.46 ± 1.80 lobes in surviving patients and 4.57 ± 0.60 lobes in the deceased patients (p = 0.009). The difference was statistically significant. The area under the curve for a lung score cutoff of 12 was 0.790. CONCLUSION: Air bronchogram and bilateral and multilobar involvement were more frequently seen in deceased patients and may suggest a poor outcome for COVID-19 pneumonia.

Predicting the function of transplanted kidney in long-term care processes: Application of a hybrid model.

BACKGROUND: A tool that can predict the estimated glomerular filtration rate (eGFR) in routine daily care can help clinicians to make better decisions for kidney transplant patients and to improve transplantation outcome. In this paper, we proposed a hybrid prediction model for predicting a future value for eGFR during long-term care processes. METHODS: Longitudinal, historical data of 942 transplant patients who received a kidney between 2001 and 2016 at Urmia kidney transplant center was used to develop a hybrid model. The model was based on three primary models: multi-layer perceptron (MLP), linear regression (LR), and a model that predicted a smoothed value of eGFR. The hybrid model used at-hand, longitudinal data of physical examinations and laboratory test values available at each visit. Two different datasets, a generalized dataset (GData) and a personalized dataset (PData), were created. Then, in both datasets, two data subsets of development and validation were created. For prediction, all records related to the fourth to tenth previous visits of patients in time order from the target date, i.e., window size (WS) = 4-10, were used. The performance of the models was evaluated using Mean Square Error (MSE) and Mean Absolute Error (MAE). The differences between the models were evaluated with the F-test and the Akaike Information Criterion (AIC). RESULTS: The datasets contained 35,066 records, totally. The GData contained 26,210 and 8856 records and the PData had 24,079 and 9103 records in the development and validation datasets, respectively. In the hybrid model, the MSE and MAE were 153 and 8.9 in the GData, and 113 and 7.5 in the PData, respectively. The model performance improved using a wider WS of historical records (from 4 to 10). When the WS of ten was used the MSE and MAE declined to 141 and 8.5 in the GData and to 91 and 6.9 in the PData, respectively. In both datasets, the F-test showed that the hybrid model was significantly different from other models. The AIC showed that the hybrid model had a better performance than that of others. CONCLUSIONS: The hybrid model can predict a reliable future value for eGFR. Our results showed that longitudinal covariates help the models to produce better results. Smoothing eGFR values and using a personalized dataset to develop the models also improved the models' performances. They can be considered as a step forward towards personalized medicine.

National Spinal Cord Injury Registry of Iran (NSCIR-IR) - a critical appraisal of its strengths and weaknesses.

The National Spinal Cord Injury Registry of Iran (NSCIR-IR) is a not-for-profit, hospital-based, and prospective observational registry that appraises the quality of care, long-term outcomes and the personal and psychological burden of traumatic spinal cord injury in Iran. Benchmarking validity in every registry includes rigorous attention to data quality. Data quality assurance is essential for any registry to make sure that correct patients are being enrolled and that the data being collected are valid. We reviewed strengths and weaknesses of the NSCIR-IR while considering the methodological guidelines and recommendations for efficient and rational governance of patient registries. In summary, the steering committee, funded and maintained by the Ministry of Health and Medical Education of Iran, the international collaborations, continued staff training, suitable data quality, and the ethical approval are considered to be the strengths of the registry, while limited human and financial resources, poor interoperability with other health systems, and time-consuming processes are among its main weaknesses.

Survival prediction of glioblastoma patients using modern deep learning and machine learning techniques.

In this study, we utilized data from the Surveillance, Epidemiology, and End Results (SEER) database to predict the glioblastoma patients' survival outcomes. To assess dataset skewness and detect feature importance, we applied Pearson's second coefficient test of skewness and the Ordinary Least Squares method, respectively. Using two sampling strategies, holdout and five-fold cross-validation, we developed five machine learning (ML) models alongside a feed-forward deep neural network (DNN) for the multiclass classification and regression prediction of glioblastoma patient survival. After balancing the classification and regression datasets, we obtained 46,340 and 28,573 samples, respectively. Shapley additive explanations (SHAP) were then used to explain the decision-making process of the best model. In both classification and regression tasks, as well as across holdout and cross-validation sampling strategies, the DNN consistently outperformed the ML models. Notably, the accuracy were 90.25% and 90.22% for holdout and five-fold cross-validation, respectively, while the corresponding R2 values were 0.6565 and 0.6622. SHAP analysis revealed the importance of age at diagnosis as the most influential feature in the DNN's survival predictions. These findings suggest that the DNN holds promise as a practical auxiliary tool for clinicians, aiding them in optimal decision-making concerning the treatment and care trajectories for glioblastoma patients.

Quality of Life and Socioeconomic Status in Northwest of Iran: First Wave of the Persian Traffic Cohort Study.

This study aimed to evaluate the association between Quality of Life (QOL) and independent factors, emphasizing Socio Economic Status (SES) in northwestern Iran. A population-based cross-sectional study was performed within the Persian Traffic safety and health Cohort in 2020. Participants were chosen using stratified random sampling method. The majority of participants (69%) were aged between 30 and 65. Around half of the participants were males (54.44%). Most of the female respondents were categorized as very low and medium levels of SES Based on multiple linear regression analysis, the QOL among females was lower compared to males (ß: - 0.92, 95% CI - 1.82 to - 0.22). There was a negative association between SES and QOL; individuals with low and very low levels of SES had a lower QOL than those with a medium level of SES (ß: - 4.38, 95% CI - 5.9 to - 2.86) (ß: - 2.65, 95% CI - 4.08 to - 1.22). The current study highlights that higher SES and educational levels are positively associated with higher QOL. Conversely, older age, females, and widowed individuals are linked with lower QOL.

Association between Socioeconomic Factors and the Risk of Gastric Cancer Incidence: Results from an Ecological Study.

Background: Gastric cancer (GC), one of the most common cancer worldwide, remains the third leading cause of cancer-related mortality. The etiology of GC may arise from genetic and environmental factors. This study aimed to determine the association between GC incidence and socioeconomic status in Iran. Methods: An ecological study was designed to investigate the relationship between socioeconomic factors and the risk of GC incidence. The data of socioeconomic variables such as income changes, unemployment rate, urbanization ratio, inflation rate, and air pollution changes in 31 provinces were collected from the Statistical Center of Iran, and the data of GC of 31 provinces were provided from the Iranian National Population-based Cancer Registry (INPCR). Data from 2014 to 2017 was analyzed using panel data analysis, the fixed effects model by EViews software. Results: Panel data model was suitable for the present study. Results showed that there was a positive and significant relationship between GC incidence and socioeconomic factors including income changes (P≤ 0.001), unemployment rate (P≤0.01), inflation rate (P≤ 0.05), and air pollution changes (P≤ 0.001). The urbanization ratio showed a negative relationship and was not statistically associated with GC incidence (P> 0.05). Conclusion: Our findings suggest a positive and significant association between socioeconomic status and GC incidence, proposing a GC risk factor. The key public health policies and welfare policies' priority should therefore be to schedule for the GC management.

A qualitative study of factors influencing ePHR adoption by caregivers and care providers of Alzheimer's patients: An extension of the unified theory of acceptance and use of technology model.

Background and Aims: As the nowadays provision of many healthcare services relies on technology, a better understanding of the factors contributing to the acceptance and use of technology in health care is essential. For Alzheimer's patients, an electronic personal health record (ePHR) is one such technology. Stakeholders should understand the factors affecting the adoption of this technology for its smooth implementation, adoption, and sustainable use. So far, these factors have not fully been understood for Alzheimer's disease (AD)-specific ePHR. Therefore, the present study aimed to understand these factors in ePHR adoption based on the perceptions and views of care providers and caregivers involved in AD care. Methods: This qualitative study was conducted from February 2020 to August 2021 in Kerman, Iran. Seven neurologists and 13 caregivers involved in AD care were interviewed using semi-structured and in-depth interviews. All interviews were conducted through phone contacts amid Covid-19 imposed restrictions, recorded, and transcribed verbatim. The transcripts were coded using thematic analysis based on the unified theory of acceptance and use of technology (UTAUT) model. ATLAS.ti8 was used for data analysis. Results: The factors affecting ePHR adoption in our study comprised subthemes under the five main themes of performance expectancy, effort expectancy, social influence, facilitating conditions of the UTAUT model, and the participants' sociodemographic factors. From the 37 facilitating factors and 13 barriers identified for ePHR adoption, in general, the participants had positive attitudes toward the ease of use of this system. The stated obstacles were dependent on the participants' sociodemographic factors (such as age and level of education) and social influence (including concern about confidentiality and privacy). In general, the participants considered ePHRs efficient and useful in increasing neurologists' information about their patients and managing their symptoms in order to provide better and timely treatment. Conclusion: The present study gives a comprehensive insight into the acceptance of ePHR for AD in a developing setting. The results of this study can be utilized for similar healthcare settings with regard to technical, legal, or cultural characteristics. To develop a useful and user-friendly system, ePHR developers should involve users in the design process to take into account the functions and features that match their skills, requirements, and preferences.

Pre-Hospital and Post-Hospital Quality of Care in Traumatic Spinal Column and Cord Injuries in Iran.

STUDY DESIGN: A retrospective study. OBJECTIVES: The quality of care (QoC) for spinal column/cord injury patients is a major health care concern. This study aimed to implement the QoC assessment tool (QoCAT) in the National Spinal Cord/Column Injury Registry of Iran (NSCIR-IR) to define the current state of pre- and post-hospital QoC of individuals with Traumatic Spinal Column and Spinal Cord Injuries (TSC/SCIs). METHODS: The QoCAT, previously developed by our team to measure the QoC in patients with TSC/SCIs, was implemented in the NSCIR-IR. The pre-hospital QoC was evaluated through a retrospective analysis of NSCIR-IR registry data. Telephone interviews and follow-ups of patients with SCI evaluated the QoC in the post-hospital phase. RESULTS: In the pre-hospital phase, cervical collars and immobilization were implemented in 46.4% and 48.5% of the cases, respectively. Transport time from the scene to the hospital was documented as <1 hour and <8 hours in 33.4% and 93.9% of the patients, respectively. Post-hospital indicators in patients with SCI revealed a first-year mortality rate of 12.5% (20/160), a high incidence of secondary complications, reduced access to electrical wheelchairs (4.2%) and modified cars (7.7%), and low employment rate (21.4%). CONCLUSION: These findings revealed a significant delay in transport time to the first care facilities, low use of immobilization equipment indicating low pre-hospital QoC. Further, the high incidence of secondary complications, low employment rate, and low access to electrical wheelchairs and modified cars indicate lower post-hospital QoC in patients with SCI. These findings imply the need for further planning to improve the QoC for patients with TSC/SCIs.

Clinical Breast Cancer Registry of IR. Iran (CBCR-IR): Study Protocol and First Results.

BACKGROUND: Breast cancer (BC), as a significant global health problem, is the most common cancer in women. Despite the importance of clinical cancer registries in improving the quality of cancer care and cancer research, there are few reports on them from low- and middle-income countries. We established a multicenter clinical breast cancer registry in Iran (CBCR-IR) to collect data on BC cases, the pattern of care, and the quality-of-care indicators in different hospitals across the country. METHODS: We established a clinical cancer registry in 12 provinces of Iran. We defined the organizational structure, developed minimal data sets and data dictionaries, verified data sources and registration processes, and developed the necessary registry software. During this registry, we studied the clinical characteristics and outcomes of patients with cancer who were admitted from 2014 onwards. RESULTS: We registered 13086 BC cases (7874 eligible cases) between 1.1.2014 and 1.1.2022. Core needle biopsy from the tumor (61.25%) and diagnostic mammography (68.78%) were the two most commonly used diagnostic methods. Stage distribution was 2.03% carcinoma in situ, 12% stage I, 44.65% stage II, 21.32% stage III, and 4.61% stage IV; stage information was missing in 1532 patients (19.46%). Surgery (95.01%) and chemotherapy (79.65%) were the most common treatments for all patients. CONCLUSION: The information provided by this registry can be used to evaluate and improve the quality of care for BC patients. It will be scaled up to the national level as an important resource for measuring quality of care and conducting clinical cancer research in Iran.

Objectives, Outcomes, Facilitators, and Barriers of Telemedicine Systems for Patients with Alzheimer's Disease and their Caregivers and Care Providers: A Systematic Review.

BACKGROUND: Alzheimer's disease is an extremely expensive chronic disease, which is rapidly becoming a major cause of mortality in adults. For over two decades, telemedicine has been used to assist patients and their caregivers to manage this disease. The present study aimed to evaluate the objectives, outcomes, facilitators, and barriers influencing the use of telemedicine systems for patients with Alzheimer's disease and their caregivers and care providers. METHODS: In this systematic review, we searched for the original articles published in databases such as PubMed, Web of Science, and Scopus until November 2021 using relevant keywords. A qualitative content analysis was performed the based on the theory of planned behavior and the health belief model using the ATLAS.ti software. RESULTS: In total, 1191 articles were identified, and 60 articles were included in this study. While having different objectives, most of the studies compared telemedicine systems to in-person visits (21.43%) and assessed the feasibility of the implemented method (16.07%). The overall outcomes of telemedicine in the articles were classified as cost-effectiveness (e.g., reduced commute, fuel, and time to access care), clinical outcomes (e.g., lower anxiety, stress, and depression), and patient, caregiver, and healthcare provider satisfaction. In total, 19 facilitators and 12 barriers influencing the use of telemedicine for patients with Alzheimer's disease and their caregivers were identified. CONCLUSION: According to the results, telemedicine systems could be implemented for various reasons. Developing a clear framework of the drivers and barriers before the implementation of these systems could improve decision-making prior to the design and implementation of telemedicine systems.