Clinical Characteristics and Outcomes in Hospitalized Patients with COVID-19 and Cancer History: A Multicenter Cross-Sectional Study in Southwestern Iran.

Background: Cancer patients are more exposed to opportunistic infections, such as COVID-19, due to their poor health status. This study aimed to identify the clinical characteristics of cancer and non-cancer patients with COVID-19 that may lead to death, intubation, and ICU admission. Materials and Methods: A Multicenter Cross-Sectional study was conducted on confirmed COVID-19 adult patients with and without a history of cancer from March 2019 to March 2021. Demographic and clinical features, ICU admission, intubation, and discharge status have been extracted from patients' medical records. Chi-square, odds ratio, Mann-Whitney test, and logistic regression were used to analyze the data. Results: The death rate in 1332 cancer patients was 28% compared to the 91464 noncancer patients which was 9% with an odds ratio of 3.94 and p<0.001. ICU admission rates among the cancer group were 43%, while in the noncancer group, it was 17.9% (p<0.001). Moreover, intubation was done for 20.9% of cancer patients and 7.4% of non-cancer patients (p<0.001). However, no significant difference was observed between the two groups in terms of length of stay in the hospital. Multivariable logistic regression analysis showed that age, level of consciousness, SPO2, and autoimmune disorders were associated with mortality in cancer patients with COVID-19. Conclusion: This study showed that older age, loss of consciousness, low oxygen saturation, and suffering from autoimmune disorders were the predictors of death in cancer patients with COVID-19. These results can have important implications for the management and care of cancer patients with COVID-19.

Designing a minimum data set for the information management system (registry) of spinal canal stenosis: An applied-descriptive study.

Background and Aims: Spinal canal stenosis is one of the most common vertebral column diseases, which can lead to disability. Developing a registry system can help in research on the prevention and effective treatment of it. This study designs a minimum data set (MDS) as the first step in creating a registry system for spinal canal stenosis. Method: The present research is of applied-descriptive type, performed in 2022. First, the applicable data elements about the disease were selected from a vast range of English and Farsi references, including peer reviewed articles, academic books, credible websites, and medical records of hospitalized patients. Through the extracted data, the primary MDS plan was designed as a questionnaire. The validity of the questionnaire was conducted via asking the opinion of experts (neurosurgeons, physiotherapists, epidemiologists, and health information management specialists). Also, its reliability was calculated via Cronbach ⍺ coefficient, which was 86%. Finally, the MDS of the spinal canal stenosis national registry system (for Iran) was confirmed through a two stage Delphi technique. Data analysis was applied through descriptive statistics via SPSS21 software. Results: The proposed MDS is offered in two general sets of data: administrative and clinical. For the administrative data set, 40 data elements had been proposed, as five classes. Twenty-six of them were confirmed. In the clinical section, 95 data elements had been proposed in 14 classes; 94 of which were finally confirmed. Conclusion: Since there is no spinal canal stenosis MDS available, this study can be a turning point in the standardization of the data on this disease. Moreover, these precise, coherent, and standard data elements can be contributed to improving disease management and enhancing the public healthcare quality. Also, the MDS proposed in this study can help researchers and experts, design a spinal canal stenosis registry system in other countries.

Predictive factors of anxiety and depression in COVID-19 survivors: A cross-sectional study.

Background: Depression and anxiety are among the most critically recognized psychological complication of the COVID-19 pandemic. Aim: This study aimed to examine the two predictors among COVID-19 survivors in Ahvaz, Iran, in 2022. Methodology: Using a descriptive-analytical design, 347 COVID-19 survivors referred to hospitals in Ahvaz were meticulously examined. The database of the COVID-19 registration system contained primary information about the samples. For data collection, questionnaires, including the Beck Anxiety and Depression Inventory and a demographic questionnaire, were utilized. Results: The results revealed a significant relationship between anxiety score and family size (p = 0.019), education level (p < 0.001), occupation (p = 0.015), household income status (p = 0.017), disease history (p = 0.017), ethnicity (p < 0.001), disease severity (p < 0.001), and quarantine period (p < 0.001). Furthermore, a significant correlation was observed between the average depression score and gender (p = 0.023), number of family households (p = 0.009), level of education (p < 0.001), occupation (p < 0.001), household income (p < 0.001), disease severity (p < 0.001), disease history (p < 0.001), and quarantine period (p < 0.001). Moreover, linear regression analysis indicated only a significant relationship between depression scores and the quarantine period variable (p < 0.001), among others. The simultaneous examination of all variables on depression disorder highlighted a meaningful relationship between depression score and disease history (p = 0.013), occupation (p = 0.002), household income status (p = 0.002), and family size (p = 0.039). Conclusions: This study revealed a significant relationship between certain demographic variables, such as quarantine period, disease history, employment status, household income status, and the number of family households, and an increase in the average depression and anxiety score.

Comparison of the accuracy of inpatient morbidity coding with ICD-11 and ICD-10.

BACKGROUND: One of the challenges when transitioning from International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) to International Classification of Diseases, 11th Revision (ICD-11) is to ensure clinical coding accuracy. OBJECTIVE: To determine the accuracy of clinical coding with ICD-11 in comparison with ICD-10 and identify causes of coding errors in real clinical coding environments. METHOD: The study was conducted prospectively in two general hospitals. Medical records of discharged inpatients were coded by hospital clinical coders with both ICD-11 and ICD-10 on different days. These medical records were recoded by five mentors. Codes assigned by mentors were used as the gold standard for the evaluation of accuracy. RESULTS: The accuracy of ICD-10 and ICD-11 coding for 1578 and 2168 codes was evaluated. Coding accuracy was 89.1% and 74.2% for ICD-10 and ICD-11. In ICD-11, the lowest accuracy was observed in chapters 22 (injuries), 10 (ear) and 11 (circulatory) (51.1%, 53.8% and 62.7%, respectively). In both ICD-10 and ICD-11, the most important cause of the coding errors was clinical coders' mistakes (79.5% and 81.8% for ICD-10 and ICD-11, respectively). CONCLUSION: Accuracy of clinical coding with ICD-11 was lower relative to ICD-10. Hence, it is essential to carry out initial preparations, particularly the training of clinical coders based on their needs, as well as the necessary interventions to enhance the documentation of medical records according to ICD-11 before or simultaneous with the country-wide implementation. IMPLICATIONS: Clinical coders need complete training, especially in using extension codes and post-coordination coding. Local ICD-11 guidelines based on the needs of local users and reporting policies should be developed. Furthermore, documentation guidelines based on ICD-11 requirements should be developed.

Perception of coronavirus disease (COVID-19) and its relationship with coping strategies and perceived social support in iranian people: a descriptive correlational study.

BACKGROUND: Perception of the threatening disease leads to coping behaviors that can affect the treatment process. Social support can be one of the factors influencing the perception of the disease and coping strategies. Our study aimed to determine the perception of the disease, its relationship with coping strategies and social support in COVID-19 patients in Iran. METHODS: This cross-sectional study was conducted on 1014 patients who were hospitalized during October 2020 to May 2021 through multi-stage sampling method. The data-gathering instruments consisted a demographic information checklist, and standard questionnaires including disease perception, social support, and coping strategies. Correlation coefficient, multiple linear regression model, and simple linear regression model were used for data analysis. RESULTS: The mean age of the participants was 40.87 ± 12.42 and the majority of them were female (67.2%(, married (60.1%), and had relatives who had COVID-19 (82.6%). There was a significant inverse relationship between variables (identity, outcomes, emotional expressions etc.) and social support (> 0.01). Also there was a significant direct relationship between variables (self-control, therapeutic susceptibility etc.) and the coping behavior (p < 0.05). There was an inverse relationship between the variables (outcomes, self-blaming, sex, etc.) (P = 0.0001) and a direct one between the variables (education, disease phase, etc.) and perceived social support (P = 0.004). CONCLUSIONS: These results show the importance of promoting positive coping strategies and social support in the face of large-scale health crises. The knowledge of nurses about the results of this study, who are responsible for the care and education of the patient, can be effective in the length of hospitalization and reducing costs.

Factors influencing adoption of e-learning in healthcare: integration of UTAUT and TTF model.

BACKGROUND: The importance of successful implementation of e-learning, especially since the emergence of the Covid-19 pandemic, has become increasingly apparent to universities. Thus, identifying the effective factors in adopting e-learning in the Covid-19 pandemic is crucial. This study was conducted to identify determining factors in adopting E-learning in healthcare. METHOD: This was a descriptive-analytical study in which 143 faculty members from Iran were randomly selected. The faculty members' intentions, concerning the adoption of e-learning, were assessed by the conceptual path model of integration of unified theory of acceptance and use of technology (UTAUT) and The Task-Technology Fit (TTF). RESULTS: The results showed that the combination of the two classical theories, UTAUT and TTF, was an appropriate model to explain faculty members' intention in adopting e-learning. Moreover, the findings showed that technology and task characteristics, task- technology fit, social influences, effort expectancy, performance expectancy and facilitating conditions had direct and significant effect on e-learning adoption. CONCLUSION: By presenting a conceptual path model to elucidate users' behavior in adopting e-learning, this study investigated and identified the key determining factors in adopting e-learning. The findings of the present study can contribute to the design and implementation of e-learning by practitioners, policy makers, and curriculum designers.

COVID-19 Death Rates in Iran and Iraq: Possible Relations Between Iraq's Pre-COVID-19 Mass Gatherings and Its Low Death Rate.

In recent years, COVD-19 has made millions of death worldwide. When reviewing the death rate, we encountered a very notable difference in death rate of Iran and Iraq, which are two neighboring countries. Investigating the COVID-19 risk factors, parameters, such as ethnicity and vaccination, do not appear not to be affecting our observation. We also could not find important differences in mortality rate being under-reported in the two countries. In this letter, we tried to discuss the possible effect of Iraq pre-COVID-19 mass gatherings on the death rate. The authors would like to highlight the effect of immune system on COVID-19.

Machine Learning Models to Predict In-Hospital Mortality among Inpatients with COVID-19: Underestimation and Overestimation Bias Analysis in Subgroup Populations.

Prediction of the death among COVID-19 patients can help healthcare providers manage the patients better. We aimed to develop machine learning models to predict in-hospital death among these patients. We developed different models using different feature sets and datasets developed using the data balancing method. We used demographic and clinical data from a multicenter COVID-19 registry. We extracted 10,657 records for confirmed patients with PCR or CT scans, who were hospitalized at least for 24 hours at the end of March 2021. The death rate was 16.06%. Generally, models with 60 and 40 features performed better. Among the 240 models, the C5 models with 60 and 40 features performed well. The C5 model with 60 features outperformed the rest based on all evaluation metrics; however, in external validation, C5 with 32 features performed better. This model had high accuracy (91.18%), F-score (0.916), Area under the Curve (0.96), sensitivity (94.2%), and specificity (88%). The model suggested in this study uses simple and available data and can be applied to predict death among COVID-19 patients. Furthermore, we concluded that machine learning models may perform differently in different subpopulations in terms of gender and age groups.

A study to design minimum data set of COVID-19 registry system.

BACKGROUND: From the beginning of the COVID-19 pandemic, the development of infrastructures to record, collect and report COVID-19 data has become a fundamental necessity in the world. The disease registry system can help build an infrastructure to collect data systematically. The study aimed to design a minimum data set for the COVID-19 registry system. METHODS: A qualitative study to design an MDS for the COVID-19 registry system was performed in five phases at Ahvaz University of Medical Sciences in Khuzestan Province in southwestern Iran, 2020-2021. In the first phase, assessing the information requirements was performed for the COVID-19 registry system. Data elements were identified in the second phase. In the third phase, the MDS was selected, and in the four phases, the COVID-19 registry system was implemented as a pilot study to test the MDS. Finally, based on the experiences gained from the COVID-19 registry system implementation, the MDS were evaluated, and corrections were made. RESULTS: MDS of the COVID-19 registry system contains eight top groups including administrative (34 data elements), disease exposure (61 data elements), medical history and physical examination (138 data elements), findings of clinical diagnostic tests (101 data elements), disease progress and outcome of treatment (55 data elements), medical diagnosis and cause of death (12 data elements), follow-up (14 data elements), and COVID-19 vaccination (19 data elements) data, respectively. CONCLUSION: Creating a standard and comprehensive MDS can help to design any national data dictionary for COVID-19 and improve the quality of COVID-19 data.

Comprehensive study of antimicrobial susceptibility pattern and extended spectrum beta-lactamase (ESBL) prevalence in bacteria isolated from urine samples.

Nowadays, increasing extended-spectrum ß-lactamase (ESBL)-producing bacteria have become a global concern because of inducing resistance toward most of the antimicrobial classes and making the treatment difficult. In order to achieve an appropriate treatment option, identification of the prevalent species which generate ESBL as well as their antibiotic susceptibility pattern is essential worldwide. Hence, this study aimed to investigate the prevalence of ESBL-producing bacteria and assess their drug susceptibility in Fardis Town, Iran. A total of 21,604 urine samples collected from patients suspected to have urinary tract infection (UTI) were processed in the current study. The antimicrobial susceptibility of the isolates was tested by the disk diffusion method. The ESBL producing bacteria were determined by Double Disc Synergy Test (DDST) procedure. Bacterial growth was detected in 1408 (6.52%) cases. The most common bacterial strains causing UTI were found E. coli (72.16%), followed by K. pneumoniae (10.3%) and S. agalactiae (5.7%). Overall, 398 (28.26%) were ESBL producer. The highest ESBL production was observed in E. coli, followed by Klebsiella species. ESBL producers revealed a higher level of antibiotic resistance compared with non-ESBLs. In conclusion, ESBL production in uropathogens was relatively high. Carbapenems and Aminoglycosides were confirmed as the most effective treatment options for these bacteria.

Regional COVID-19 registry in Khuzestan, Iran: A study protocol and lessons learned from a pilot implementation.

Disease registry systems provide a strong information infrastructure for decision-making and research. The purpose of this study is to describe the implementation method and protocol of the COVID-19 registry in Khuzestan province, Iran. We established a steering committee and formulated the purposes of the registry. Then, based on reviewing the literature, and expert panels, the minimum data set, the data collection forms and the web-based software were developed. Data collection is done retrospectively through Hospital Information Systems, Medical Care Monitoring Center system (MCMC), Management of Communicable Disease Prevention and Control system (MCDPC) as well as, patients' records. For prospective data collection, the data collection forms are compiled with patients' medical records by the medical staff and are then entered into the registry system. We collect patients' administrative and demographic data, history and physical examinations, test and imaging results, disease progression, treatment, outcomes, and follow-ups of the confirmed and suspected inpatients and outpatients. From April 20 to December 5, 2020, the data of 4,812 confirmed cases and 7,113 suspected cases were collected from two COVID-19 referral hospitals. Based on our experience, recording information along with providing care for patients and putting patients' data registration in the medical staff's routine, structuring data, having a flexible technical team and rapid software development for multiple and continuous updates, automating data collection by connecting the registry to existing information systems and having different incentives, the registration process can be strengthened.

Internet addiction, mental health, and sleep quality in students of medical sciences, Iran: A cross-sectional study.

BACKGROUND: Internet addiction (IA) is a severe problem, especially in academic communities. The association between IA and unfavorable emotional conditions (e.g., anxiety, stress, and depression) can harm students' relationships and academic and career performance. The present study evaluated (a) the prevalence of IA in a sample of medical sciences university students; (b) the association between IA and depression, anxiety, stress, and sleep disorders; and (c) the association between demographic characteristics and IA, depression, anxiety, and stress. MATERIALS AND METHODS: In this descriptive cross-sectional study, 268 students from Abadan University of Medical Sciences were selected using Morgan Table. The participants responded to the Young Internet Addiction Test, the Depression Anxiety Stress Scales-42, and the Pittsburgh Sleep Quality Index. Data were analyzed using descriptive statistics, Pearson correlation coefficient, independent sample t-test, one-way ANOVA, and least significant difference. RESULTS: The mean score of IA was 45.65 ± 35.40. IA was found to be significantly related to depression, anxiety, stress, and sleep disorders (except for sleep disturbance) (P < 0.001). Males were found to be more likely than females to suffer from IA, depression, anxiety, stress, and sleep disorders. Furthermore, age was inversely related to IA, depression, and stress. Finally, medical sciences students suffered from depression, anxiety, and stress more than students majoring in other fields. CONCLUSIONS: The simultaneous occurrence of IA and depression, anxiety, stress, and sleep disorders in students indicates that IA is associated with other psychological problems. Therefore, researchers and policymakers need to identify preventive measures to help internet-addicted students.

Developing a national minimum data set for hospital information systems in the Islamic Republic of Iran.

BACKGROUND: Standardized data collection supports disease information management and leads to better quality of care. The Islamic Republic of Iran lacks a standard data set for data collection in hospitals. AIMS: The aim of this study was to design a minimum data set for hospital information systems in the Islamic Republic of Iran. METHODS: This study was conducted in 2015. Data sets of other countries, hospital records, hospital information systems and electronic health record systems in the Islamic Republic of Iran were reviewed for data elements for the minimum data set. Data elements were collected using a data extraction form and were categorized into similar classes, which were divided into administrative and clinical sections. The list of data elements was reviewed by experts in technical offices of the Iranian Ministry of Health and Medical Education, and a minimum data set was drawn up. RESULTS: There were nine and 18 data classes in the administrative and clinical sections with a total of 166 and 684 data elements respectively. After review by the expert panel, 159 administrative and 621 clinical data elements were retained as the minimum data set for the Iranian hospital information system. CONCLUSION: Our dataset can be used by the Iranian health ministry, hospital information system companies and health surveillance centres for more efficient management of health data.

Determinant factors in applying electronic medical records in healthcare.

BACKGROUND: Electronic Medical Record (EMR) offers remarkable facilities such as reducing medical errors, decreasing healthcare costs and promoting quality of healthcare services by collecting, storing and displaying information at the point of care. AIMS: This study was carried out to identify the determinants of electronic medical record (EMR) adoption by presenting a comprehensive model. METHODS: This was a cross-sectional study in which 330 healthcare personnel working in hospitals affiliated to Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran, were selected as the study sample. A proposed conceptual path model of technology, organization and environment (TOE), and technology acceptance model (TAM) was developed to identify the determinants of EMR adoption. The model was authorized by structural equation modeling (SEM) and represented by Analysis of Moment Structures (AMOS). RESULTS: The results of the study showed that the integrated model of TOE-TAM explained 68 percent (R2 = 0.68) of the variance of EMR adoption. The findings also evidenced that perceived ease of use, perceived usefulness, technological context, organization context and environmental context have significant effect on EMR adoption. CONCLUSIONS: The study findings suggest that the proposed conceptual integrated model of TOE-TAM is a favourable model for identifying the relevant factors of EMR adoption. The present study clearly recognized nine significant determinants that affect end-users' intention when comprehensive implementation of ERMs is considered.

Relationship Among Antibiotic Resistance, Biofilm Formation and lasB Gene in Pseudomonas Aeruginosa Isolated from Burn Patients.

BACKGROUND: Pseudomonas aeruginosa is a major cause of hospital-acquired infection due to its high antibiotic resistance and biofilm formation ability. P. aeruginosa produces elastase lasB during biofilm formation, which can influence properties of biofilm. This study was carried out to evaluate the antibiotic resistance and distribution of the lasB gene among biofilm-producing P. aeruginosa strains isolated from burn patients. METHODS: A total of 128 clinical samples were collected from burn patients. The P. aeruginosa isolates were identified using standard bacteriological procedures. Antibiotic susceptibility test was performed by the disk diffusion method. Biofilm formation was measured by microtiter plate assay. The presence of lasB gene was detected by PCR. RESULTS: A total of 75 samples were positive for P. aeruginosa. A high rate of resistance was seen against ceftriaxone, cefotaxime, and piperacillin/tazobactam. Biofilm formation was seen in 57.3% of the isolates and the prevalence of the lasB gene was 85.3%. Biofilm formation in isolates without lasB was lower and these isolates were more sensitive to imipenem and piperacillin/tazobactam. CONCLUSIONS: In the present study, we did not find a statistically significant relationship among elastase gene (lasB) presence, antibiotic resistance, and biofilm formation in P. aeruginosa strains isolated from burn patients.

A conceptual model of the impacts of quality components of a health department website on students' health literacy at Semnan University of Medical Sciences.

BACKGROUND: People with poor health literacy are those who have less knowledge about disease management and health promotion behaviors, and generally have poorer health status. OBJECTIVE: The aim of this study was to investigate the impact of qualitative components of the health website of Semnan University of Medical Sciences on the students' health literacy. METHODS: This study was performed on 529 medical and allied health students in schools affiliated to Semnan University of Medical Sciences, Iran in 2016-2017. A researcher-made questionnaire and standard questionnaire were used. Descriptive statistics and regression test were used by SPSS 19 to analyze data in significance level of 0.01. RESULTS: The findings showed that there were poor relationships between communications in the health website and health literacy (R2=0.265, B=0.50), between understanding of concepts in the health website and health literacy (R2=0.259, B=0.38) and patient centeredness in the health website and health literacy (R2=0.241, B=0.30). CONCLUSION: It is concluded that the empowerment of students' health literacy through the University Health Department website can be realized when the health department website is used as a tool for learning and the website is much more active in social marketing in the field of health.

Improving the Distribution of Rural Health Houses Using Elicitation and GIS in Khuzestan Province (the Southwest of Iran).

BACKGROUND: Rural health houses constitute a major provider of some primary health services in the villages of Iran. Given the challenges of providing health services in rural areas, health houses should be established based on the criteria of health network systems (HNSs). The value of these criteria and their precedence over others have not yet been thoroughly investigated. The present study was conducted to propose a model for improving the distribution of rural health houses in HNSs. METHODS: The present applied study was conducted in Khuzestan province in the southwest of Iran in 2014-2016. First, the descriptive and spatial data required were collected and entered into ArcGIS after modifications, and the Geodatabase was then created. Based on the criteria of the HNS and according to experts' opinions, the main criteria and the sub-criteria for an optimal site selection were determined. To determine the criteria's coefficient of importance (ie, their weight), the main criteria and the sub-criteria were compared in pairs according to experts' opinions. The results of the pairwise comparisons were entered into Expert Choice and the weight of the main criteria and the sub-criteria were determined using the analytic hierarchy process (AHP). The application layers were then formed in geographic information system (GIS). A model was ultimately proposed in the GIS for the optimal distribution of rural health houses by overlaying the weighting layers and the other layers related to villages and rural health houses. RESULTS: Based on the experts' opinions, six criteria were determined as the main criteria for an optimal site selection for rural health houses, including welfare infrastructures, population, dispersion, accessibility, corresponding routes, distance to the rural health center and the absence of natural barriers to accessibility. Of the main criteria proposed, the highest weight was given to "population" (0.506). The priorities suggested in the proposed model for establishing rural health houses are presented within five zoning levels -from excellent to very poor. CONCLUSION: The results of the study showed that the proposed model can help provide a better picture of the distribution of rural health houses. The GIS is recommended to be used as a means of making the HNS more efficient.

Information security risk management for computerized health information systems in hospitals: a case study of Iran.

BACKGROUND: In recent years, hospitals in Iran - similar to those in other countries - have experienced growing use of computerized health information systems (CHISs), which play a significant role in the operations of hospitals. But, the major challenge of CHIS use is information security. This study attempts to evaluate CHIS information security risk management at hospitals of Iran. MATERIALS AND METHODS: This applied study is a descriptive and cross-sectional research that has been conducted in 2015. The data were collected from 551 hospitals of Iran. Based on literature review, experts' opinion, and observations at five hospitals, our intensive questionnaire was designed to assess security risk management for CHISs at the concerned hospitals, which was then sent to all hospitals in Iran by the Ministry of Health. RESULTS: Sixty-nine percent of the studied hospitals pursue information security policies and procedures in conformity with Iran Hospitals Accreditation Standards. At some hospitals, risk identification, risk evaluation, and risk estimation, as well as risk treatment, are unstructured without any specified approach or methodology. There is no significant structured approach to risk management at the studied hospitals. CONCLUSION: Information security risk management is not followed by Iran's hospitals and their information security policies. This problem can cause a large number of challenges for their CHIS security in future. Therefore, Iran's Ministry of Health should develop practical policies to improve information security risk management in the hospitals of Iran.