Coronavirus disease 2019 (COVID-19) surveillance system: Development of COVID-19 minimum data set and interoperable reporting framework

Designing a core data set for benign hysterectomy registration system and its implementation in a referral teaching hospital in Northwest Iran

BACKGROUND AND AIMS

Although minimally invasive hysterectomy offers advantages, abdominal hysterectomy remains the predominant surgical method. Creating a standardized dataset and establishing a hysterectomy registry system present opportunities for early interventions in reducing volume and selecting benign hysterectomy methods. This research aims to develop a dataset for designing benign hysterectomy registration system.

METHODS

Between April and September 2020, a qualitative study was carried out to create a data set for enrolling patients who were candidate for hysterectomy. At this stage, the research team conducted an information needs assessment, relevant data element identification, registry software development, and field testing; Subsequently, a web-based application was designed. In June 2023the registry software was evaluated using data extracted from medical records of patients admitted at Al-Zahra Hospital in Tabriz, Iran.

RESULTS

During two months, 40 patients with benign hysterectomy were successfully registered. The final dataset for the hysterectomy patient registry comprise 11 main groups, 27 subclasses, and a total of 91 Data elements. Mandatory data and essential reports were defined. Furthermore, a web-based registry system designed and evaluated based on data set and various scenarios.

CONCLUSION

Creating a hysterectomy registration system is the initial stride toward identifying and registering hysterectomy candidate patients. this system capture information about the procedure techniques, and associated complications. In Iran, this registry can serve as a valuable resource for assessing the quality of care delivered and the distribution of clinical measures.

The effect of educational intervention based on the self-efficacy theory of high school students in adopting preventive behaviors of COVID-19

BACKGROUND

The COVID-19 pandemic has become a major problem for education systems. This study was conducted to investigate the effect of educational intervention based on the self-efficacy theory of high-school students in adopting preventive behaviors of COVID-19.

MATERIAL AND METHODS

This quasi-experimental study was performed on Hazrat Zahra and Shahed high-school students in Jahrom (southern Iran) in 2021. In total, 160 students (80 each in the intervention group and the control group) were selected by multistage random sampling. Data collection tools included a demographic information questionnaire and self-efficacy in adopting preventive behaviors from COVID-19 researcher-made questionnaire. Questionnaires were completed by all participants before and 3 months after the educational intervention. The educational intervention was performed for 6 weeks by using an educational program based on Bandura self-efficacy theory. The intervention was performed during 12 sessions of face-to-face training in the classroom (two 1-h sessions per week), distributing educational packages and sending educational videos through cyberspace. Data were analyzed using Chi-square test, independent t test, paired t test, and linear regression.

RESULTS

After the educational intervention, a significant difference was observed in the intervention group before and after the educational intervention (P = 0.018) and between the intervention and the control groups (P = 0.001) in the self-efficacy in adopting preventive behaviors from COVID-19 score. Also, the average self-efficacy score of students increased with an increase in parental education level and a history of COVID-19 in family members.

CONCLUSION

Educational intervention based on self-efficacy theory was shown as an effective method to improve students' self-efficacy in adopting preventive behaviors from COVID-19.

Design, development, and evaluation of a surveillance system for suicidal behaviors in Iran

BACKGROUND

Suicide is a serious cause of morbidity and mortality in Iran and worldwide. Although several organizations gather information on suicide and suicide attempts, there is substantial misperception regarding the description of the phenomenon. This study proposes the minimum data set (MDS) for suicidal behaviors surveillance.

METHODS

A literature review was first conducted to achieve a thorough overview of suicide-related items and map the existing evidence supporting the development of the MDS. The data items included in the literature review were then analyzed using a two-round Delphi technique with content validation by an expert panel. The suicidal behaviors surveillance system was then established based on the confirmed MDS, and ultimately, its performance was assessed by involving the end-users.

RESULTS

The panel of experts consisted of 50 experts who participated in the Delphi phase and validity content review. Of these, 46% were men, and their mean age and average work experience were (36.4, SD ± 6.4) and (12.32, SD ± 5.2) years, respectively. The final MDS platform of our study contained 108 items classified into eight main categories. A web-based system with a modular and layered architecture was developed based on the derived MDS.

CONCLUSION

The developed system provides a framework for recording suicidal behaviors' data. The integration of multiple suicide-related information systems at the regional and national levels makes it possible to assess the long-term outcomes and evolutions of suicide prevention interventions.

A clinical case definition of post-COVID-19 condition by a Delphi consensus

People with COVID-19 might have sustained postinfection sequelae. Known by a variety of names, including long COVID or long-haul COVID, and listed in the ICD-10 classification as post-COVID-19 condition since September, 2020, this occurrence is variable in its expression and its impact. The absence of a globally standardised and agreed-upon definition hampers progress in characterisation of its epidemiology and the development of candidate treatments. In a WHO-led Delphi process, we engaged with an international panel of 265 patients, clinicians, researchers, and WHO staff to develop a consensus definition for this condition. 14 domains and 45 items were evaluated in two rounds of the Delphi process to create a final consensus definition for adults: post-COVID-19 condition occurs in individuals with a history of probable or confirmed SARS-CoV-2 infection, usually 3 months from the onset, with symptoms that last for at least 2 months and cannot be explained by an alternative diagnosis. Common symptoms include, but are not limited to, fatigue, shortness of breath, and cognitive dysfunction, and generally have an impact on everyday functioning. Symptoms might be new onset following initial recovery from an acute COVID-19 episode or persist from the initial illness. Symptoms might also fluctuate or relapse over time. A separate definition might be applicable for children. Although the consensus definition is likely to change as knowledge increases, this common framework provides a foundation for ongoing and future studies of epidemiology, risk factors, clinical characteristics, and therapy.

A clinical case definition of post-COVID-19 condition by a Delphi consensus

People with COVID-19 might have sustained postinfection sequelae. Known by a variety of names, including long COVID or long-haul COVID, and listed in the ICD-10 classification as post-COVID-19 condition since September, 2020, this occurrence is variable in its expression and its impact. The absence of a globally standardised and agreed-upon definition hampers progress in characterisation of its epidemiology and the development of candidate treatments. In a WHO-led Delphi process, we engaged with an international panel of 265 patients, clinicians, researchers, and WHO staff to develop a consensus definition for this condition. 14 domains and 45 items were evaluated in two rounds of the Delphi process to create a final consensus definition for adults: post-COVID-19 condition occurs in individuals with a history of probable or confirmed SARS-CoV-2 infection, usually 3 months from the onset, with symptoms that last for at least 2 months and cannot be explained by an alternative diagnosis. Common symptoms include, but are not limited to, fatigue, shortness of breath, and cognitive dysfunction, and generally have an impact on everyday functioning. Symptoms might be new onset following initial recovery from an acute COVID-19 episode or persist from the initial illness. Symptoms might also fluctuate or relapse over time. A separate definition might be applicable for children. Although the consensus definition is likely to change as knowledge increases, this common framework provides a foundation for ongoing and future studies of epidemiology, risk factors, clinical characteristics, and therapy.

Information Flow and Data Gaps in COVID-19 Recording and Reporting at National and Provincial Levels in Indonesia

Epidemiological surveillance is an essential component of public health practice especially during infectious disease outbreaks. It is critical to offer transparent epidemiological information in a rigorous manner at different regional levels in countries for managing the outbreak situations. The objectives of this research are to better understand the information flow of COVID-19 health monitoring systems and to determine the data gaps of COVID-19 incidence at the national and provincial levels in Indonesia. COVID-19 information flow was researched using government websites at the national and various provincial levels. To find the disparities, we assessed the number of cases reported at both levels at the same time and displayed the absolute and relative differences. The findings revealed that out of a total of 34 provinces in Indonesia, data differences were seen in 25 (73.52%) provinces in terms of positive cases, 31 (91.18%) provinces in terms of cured cases, and 28 (82.35%) provinces of the number of deaths. Our results showed a pressing need for high-quality, transparent, and timely information. The integration of COVID-19 data in Indonesia has not been optimal, implying that the reported COVID-19 incidence rate may be biased or delayed. COVID-19 incidents must be better monitored to disrupt the disease’s transmission chain.

Using logistic regression to develop a diagnostic model for COVID-19: A single-center study

BACKGROUND

The main manifestations of coronavirus disease-2019 (COVID-19) are similar to the many other respiratory diseases. In addition, the existence of numerous uncertainties in the prognosis of this condition has multiplied the need to establish a valid and accurate prediction model. This study aimed to develop a diagnostic model based on logistic regression to enhance the diagnostic accuracy of COVID-19.

MATERIALS AND METHODS

A standardized diagnostic model was developed on data of 400 patients who were referred to Ayatollah Talleghani Hospital, Abadan, Iran, for the COVID-19 diagnosis. We used the Chi-square correlation coefficient for feature selection, and logistic regression in SPSS V25 software to model the relationship between each of the clinical features. Potentially diagnostic determinants extracted from the patient's history, physical examination, and laboratory and imaging testing were entered in a logistic regression analysis. The discriminative ability of the model was expressed as sensitivity, specificity, accuracy, and area under the curve, respectively.

RESULTS

After determining the correlation of each diagnostic regressor with COVID-19 using the Chi-square method, the 15 important regressors were obtained at the level of P < 0.05. The experimental results demonstrated that the binary logistic regression model yielded specificity, sensitivity, and accuracy of 97.3%, 98.8%, and 98.2%, respectively.

CONCLUSION

The destructive effects of the COVID-19 outbreak and the shortage of healthcare resources in fighting against this pandemic require increasing attention to using the Clinical Decision Support Systems equipped with supervised learning classification algorithms such as logistic regression.

Understanding Health Care Administrators’ Data and Information Needs for Decision Making during the COVID-19 Pandemic: A Qualitative Study at an Academic Health System

Objective. The COVID-19 pandemic created an unprecedented strain on the health care system, and administrators had to make many critical decisions to respond appropriately. This study sought to understand how health care administrators used data and information for decision making during the first 6 mo of the COVID-19 pandemic. Materials and Methods. We conducted semistructured interviews with administrators across University of Florida (UF) Health. We performed an inductive thematic analysis of the transcripts. Results. Four themes emerged from the interviews: 1) common types of health systems or hospital operations data; 2) public health and other external data sources; 3) data interaction, integration, and exchange; and 4) novelty and evolution in data, information, or tools used over time. Participants illustrated the organizational, public health, and regional information they considered essential (e.g., hospital census, community positivity rate, etc.). Participants named specific challenges they faced due to data quality and timeliness. Participants elaborated on the necessity of data integration, validation, and coordination across different boundaries (e.g., different hospital systems in the same metro areas, public health agencies at the local, state, and federal level, etc.). Participants indicated that even within the first 6 mo of the COVID-19 pandemic, the data and tools used for making critical decisions changed. Discussion. While existing medical informatics infrastructure can facilitate decision making in pandemic response, data may not always be readily available in a usable format. Interoperable infrastructure and data standardization across multiple health systems would help provide more reliable and timely information for decision making. Conclusion. Our findings contribute to future discussions of improving data infrastructure and developing harmonized data standards needed to facilitate critical decisions at multiple health care system levels.

The role of educating health-care personnel in prevention, diagnosis, or treatment of COVID-19: A narrative mini review

Front-line clinicians and health-care workers need to be educated to provide care in critical situations such as large-scale catastrophes and pandemics. This narrative review is focused on investigating educational strategies in confrontation with coronavirus disease 2019 (COVID-19) pandemic. We conducted a literature search in December 2020 through LitCovid, PubMed, ERIC, and Cochrane Library in order to retrieve relevant studies regarding the role of education in prevention, diagnosis, and treatment of COVID-19. There were 12 reviewed studies related to this specific subject. The articles selected for this study demonstrated that education and training had a positive impact on the knowledge and attitude of the participants and also the educational interventions, whether they were simulation-based or other formats of training, would be deemed crucial for enhancing participants' level of perceptions and confidence. Therefore, it is highly recommended that public health policymakers consider this important issue.

Performance evaluation of selected machine learning algorithms for COVID-19 prediction using routine clinical data: With versus Without CT scan features

BACKGROUND

Given coronavirus disease (COVID-19's) unknown nature, diagnosis, and treatment is very complex up to the present time. Thus, it is essential to have a framework for an early prediction of the disease. In this regard, machines learning (ML) could be crucial to extract concealed patterns from mining of huge raw datasets then it establishes high-quality predictive models. At this juncture, we aimed to apply different ML techniques to develop clinical predictive models and select the best performance of them.

MATERIALS AND METHODS

The dataset of Ayatollah Talleghani hospital, COVID-19 focal center affiliated to Abadan University of Medical Sciences have been taken into consideration. The dataset used in this study consists of 501 case records with two classes (COVID-19 and non COVID-19) and 32 columns for the diagnostic features. ML algorithms such as Naïve Bayesian, Bayesian Net, random forest (RF), multilayer perceptron, K-star, C4.5, and support vector machine were developed. Then, the recital of selected ML models was assessed by the comparison of some performance indices such as accuracy, sensitivity, specificity, precision, F-score, and receiver operating characteristic (ROC).

RESULTS

The experimental results indicate that RF algorithm with the accuracy of 92.42%, specificity of 75.70%, precision of 92.30%, sensitivity of 92.40%, F-measure of 92.00%, and ROC of 97.15% has the best capability for COVID-19 diagnosis and screening.

CONCLUSION

The empirical results reveal that RF model yielded higher performance as compared to other six classification models. It is promising to the implementation of RF model in the health-care settings to increase the accuracy and speed of disease diagnosis for primary prevention, screening, surveillance, and early treatment.

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.

An overview of 2019 novel coronavirus COVID-19 pandemic: A review study

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, was first reported in December 2019 in Wuhan, Hubei province, China. It is now known as a pandemic and a global crisis due to rapid human-to-human transmission with the vast expansion that has affected almost all countries. The primary source of the disease is still unknown, but it is possible that the virus was transmitted through bat to an intermediate host and then to humans. The main and early symptoms of COVID-19 infection are fatigue, fever, dry cough, myalgia, and dyspnea. The incubation period of the disease is about 2-14 days, which is one of the important parameters for planning to prevent disease outbreak. PT-polymerase chain reaction test is used to diagnose the disease; chest computed tomography scan, chest X-ray, blood tests, and symptoms are also very helpful in diagnosing the disease. There is a strong emphasis on controlling infections and hand hygiene to prevent the transmission of the disease. There is not enough knowledge about this disease yet, and there are no specific vaccines or medications available to prevent and treat this disease. The current review study uses articles indexed on databases of Embase, Elsevier, PubMed, and World Health Organization and Centers for Disease Control and Prevention, and keywords of coronavirus, COVID-19, acute respiratory distress syndrome and China.

Emergency teleradiological activity is an epidemiological estimator and predictor of the covid-19 pandemic in mainland France

Background

COVID-19 pandemic highlighted the need for real-time monitoring of diseases evolution to rapidly adapt restrictive measures. This prospective multicentric study aimed at investigating radiological markers of COVID-19-related emergency activity as global estimators of pandemic evolution in France. We incorporated two sources of data from March to November 2020: an open-source epidemiological dataset, collecting daily hospitalisations, intensive care unit admissions, hospital deaths and discharges, and a teleradiology dataset corresponding to the weekly number of CT-scans performed in 65 emergency centres and interpreted remotely. CT-scans specifically requested for COVID-19 suspicion were monitored. Teleradiological and epidemiological time series were aligned. Their relationships were estimated through a cross-correlation function, and their extremes and breakpoints were compared. Dynamic linear models were trained to forecast the weekly hospitalisations based on teleradiological activity predictors.

Results

A total of 100,018 CT-scans were included over 36 weeks, and 19,133 (19%) performed within the COVID-19 workflow. Concomitantly, 227,677 hospitalisations were reported. Teleradiological and epidemiological time series were almost perfectly superimposed (cross-correlation coefficients at lag 0: 0.90–0.92). Maximal number of COVID-19 CT-scans was reached the week of 2020-03-23 (1 086 CT-scans), 1 week before the highest hospitalisations (23,542 patients). The best valid forecasting model combined the number of COVID-19 CT-scans and the number of hospitalisations during the prior two weeks and provided the lowest mean absolute percentage (5.09%, testing period: 2020-11-02 to 2020-11-29).

Conclusion

Monitoring COVID-19 CT-scan activity in emergencies accurately and instantly predicts hospitalisations and helps adjust medical resources, paving the way for complementary public health indicators.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13244-021-01040-3.

Delphi methodology in healthcare research: How to decide its appropriateness

The Delphi technique is a systematic process of forecasting using the collective opinion of panel members. The structured method of developing consensus among panel members using Delphi methodology has gained acceptance in diverse fields of medicine. The Delphi methods assumed a pivotal role in the last few decades to develop best practice guidance using collective intelligence where research is limited, ethically/logistically difficult or evidence is conflicting. However, the attempts to assess the quality standard of Delphi studies have reported significant variance, and details of the process followed are usually unclear. We recommend systematic quality tools for evaluation of Delphi methodology; identification of problem area of research, selection of panel, anonymity of panelists, controlled feedback, iterative Delphi rounds, consensus criteria, analysis of consensus, closing criteria, and stability of the results. Based on these nine qualitative evaluation points, we assessed the quality of Delphi studies in the medical field related to coronavirus disease 2019. There was inconsistency in reporting vital elements of Delphi methods such as identification of panel members, defining consensus, closing criteria for rounds, and presenting the results. We propose our evaluation points for researchers, medical journal editorial boards, and reviewers to evaluate the quality of the Delphi methods in healthcare research.

Delphi methodology in healthcare research: How to decide its appropriateness

The Delphi technique is a systematic process of forecasting using the collective opinion of panel members. The structured method of developing consensus among panel members using Delphi methodology has gained acceptance in diverse fields of medicine. The Delphi methods assumed a pivotal role in the last few decades to develop best practice guidance using collective intelligence where research is limited, ethically/logistically difficult or evidence is conflicting. However, the attempts to assess the quality standard of Delphi studies have reported significant variance, and details of the process followed are usually unclear. We recommend systematic quality tools for evaluation of Delphi methodology; identification of problem area of research, selection of panel, anonymity of panelists, controlled feedback, iterative Delphi rounds, consensus criteria, analysis of consensus, closing criteria, and stability of the results. Based on these nine qualitative evaluation points, we assessed the quality of Delphi studies in the medical field related to coronavirus disease 2019. There was inconsistency in reporting vital elements of Delphi methods such as identification of panel members, defining consensus, closing criteria for rounds, and presenting the results. We propose our evaluation points for researchers, medical journal editorial boards, and reviewers to evaluate the quality of the Delphi methods in healthcare research.

The chunampet community health information management system: A health and demographic surveillance system from a rural South India

BACKGROUND

Developed countries have strong health and demographic surveillance system (HDSS), whereas there is a dearth of such system in developing countries like India. India depends on national surveys and individual studies for public health information. At present All India Institute of Medical Sciences - New Delhi HDSS and Vadu HDSS are well established HDSS in India.

MATERIALS AND METHODS

We developed a HDSS in a remote rural area of South India and named as Community Health Information Management System (CHIMS) This covered 20 villages around Rural Health Training Centre - Chunampet. We collected the family and demographic information from March 2018 to October 2018. Pregnancy, birth, under-five and mortality data were collected once in every 3 months with the help of interns, Medical Social Workers. Data collection done using CHIMS Guide and entered in EpiData software. EpiAnalysis, Quantum Geographic Information System, Dropbox were the other freely available software used in this program.

RESULTS

CHIMS HDSS covered 14924 individuals belonging to 4486 households in the surrounding twenty villages. Population density was 213/km². CHIMS consumed very limited resources in terms of workforce, materials, and transport. CHIMS database was used as a baseline database for many other studies. This CHIMS HDSS helped in many publications, postgraduate thesis dissertations and mainly attracted many extramural research funds from leading government Research Institutes from India.

CONCLUSION

CHIMS proved to be a robust surveillance system in providing vital public health information about the community and attracted more extramural funds to the institute.

Notifiable diseases interoperable framework toward improving Iran public health surveillance system: Lessons learned from COVID-19 pandemic

BACKGROUND

Direct transmission of notifiable disease information in a real-time and reliable way to public health decision-makers is imperative for early identification of epidemiological trends as well as proper response to potential pandemic like ongoing coronavirus disease 2019 crisis. Thus, this research aimed to develop of semantic-sharing and collaborative-modeling to meet the information exchange requirements of Iran's notifiable diseases surveillance system.

MATERIALS AND METHODS

First, the Iran's Notifiable diseases Minimum Data Set (INMDS) was determined according to a literature review coupled with agreements of experts. Then the INMDS was mapped to international terminologies and classification systems, and the Health Level seven-Clinical Document Architecture (HL7-CDA) standard was leveraged to define the exchangeable and machine-readable data formats.

RESULTS

A core dataset consisting of 15 classes and 96 data fields was defined. Data elements and response values were mapped to Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) reference terminology. Then HL7-CDA standard for interoperable data exchange were defined.

CONCLUSION

The notifiable disease surveillance requires an integrative participation of multidisciplinary team. In this field, data interoperability is more essential due to the heterogeneous nature of health information systems. Developing of INMDS based on HL7-CDA along with SNOMED-CT codes offers an inclusive and interoperable dataset that can help make notifiable diseases data more comparable and reportable across studies and organizations. The proposed data model will be further modifications in the future according probable changes in Iran's notifiable diseases list.

Knowledge, skill, and preventive behaviors regarding COVID-19 among the public in Shahrekord of Iran

BACKGROUND

COVID-19 is a serious and costly problem for communities. Observing health issues and performing preventive behaviors is influenced by their knowledge and skills of people toward COVID-19. Hence, this study investigated the knowledge, skill, and practice of the public of Shahrekord toward COVID-19.

MATERIALS AND METHOD

The present study was performed as a cross sectional. Data were collected using an online questionnaire from 540 Shahrekord residents who were selected via convenience sampling in 2020. Knowledge, skill, and practice toward COVID-19 were assessed using a prevalidated questionnaire. Collected data were analyzed by SPSS-21 software (one-way analysis of variance, Pearson test, and regression test).

RESULTS

In this study, 39.1% were male, and 60.9% were female and 46.3% were 20-25 years. The mean score of knowledge, skill, and preventive behaviors was 75.69 ± 17.85, 53.87 ± 27.40, and 59.97 ± 23.79, respectively. There was a significant direct relationship between knowledge and skill (P = 0.05 and r = 081) so that public with more knowledge had a better skill as well. There was a significant association between age (P = 0.036), education level (P = 0.019) , and job (P = 0.014) with knowledge. In addition, a significant association between job (P = 0.027) and marital status (P = 0.014) with skill was observed. Knowledge, skill, and demographic variables predict 37.6% preventive behavior.

CONCLUSION

The majority of the participants had good knowledge, moderate skill, and relatively enough practice. Government and policymakers must consider these knowledge levels, skill, and practices to implement productive interventions for preventing the COVID-19.

Exploring the knowledge, attitude, and practice of health-care professionals on coronavirus (COVID-19) pandemic infection

BACKGROUND

To prevent the rapid spread of infectious COVID-19 in India, many steps have been taken. Adherence to the control measures depends on the knowledge, attitude, and practices (KAPs) toward COVID-19 disease in health-care professionals. The present study was conducted among health-care professionals in Jaipur. The objective was to evaluate the KAPs about COVID-19 among health-care professionals.

MATERIALS AND METHODS

A total of 385 participants took part in the study. A self-designed questionnaire was filled by the participants online (Google Form). The knowledge of the participants was assessed using 12 questions. Attitude and practices of the participants were assessed by two questions for each.

RESULTS

Among the study completers, 52.20% were male, 71.42% of the participants were aged above 30 years, and 59.22% were nurse. The knowledge score was approximately 90%. The majority of the participants had a strong conviction that India can overcome this infectious disease in the near future. Multiple regression analysis found that good knowledge score of COVID-19 was associated with lower negative attitudes and less risky practices (P < 0.001).

CONCLUSION

Participants of the study were knowledgeable, held positive attitudes, and had adequate practices for COVID-19.

The effect of COVID-19 epidemic on the mental health of nurses' family members

BACKGROUND

The COVID-19 epidemic is a global health crisis that, in addition to physical health, has affected the mental health of all individuals, especially health-care workers, including nurses and family members. In this regard, the present study aimed to investigate the effect of COVID-19 epidemic on mental health of nurses' family members.

MATERIALS AND METHODS

The present descriptive-analytic and cross-sectional study was conducted in 2020 year on family members of nurses working in Hajar Hospital for admission and hospitalization of patients with COVID-19, after obtaining the necessary permits and coordination. To prevent the spread of COVID-19 through respiratory or contact droplets, a blog was designed to collect data using software. Participation in this study was voluntary. Data collection tools included consent, demographic questionnaire, and mental health questionnaire (Patient Health Questionnaire-9). Data were analyzed by SPSS version 22.

RESULTS

Findings of this study showed that out of 208 family members of nurses participating in this study, 96 were male and 112 were female and their mean age was 35.78 years, and 55.3% of the participants had university education. Their mean mental health score was 12.91 ± 3.75, so that 22.6% of people had mild depression, 71.4% of people had moderate depression, and 1.8% of people had severe depression. Independent t-test showed that there is a statistically significant relationship between mental health and gender and marital status (P < 0.001). Furthermore, one-way analysis of variance test showed that there is a significant relationship between mental health and age (P < 0.001); this test also showed that there is no relationship between education and mental health (P = 0.75).

CONCLUSION

The results indicate that the mental health of nurses' family members is affected by the challenges of COVID-19 epidemic time, so that many of them suffer from some degree of depression. Therefore, supporting this population requires urgent action.

Teaching adults how to prevent COVID-19 infection by health workers: The application of intervention mapping approach

INTRODUCTION

Education is one of the most important approaches to preventing infectious diseases at the time of the pandemic. The purpose of the study was to develop an intervention-training program using an intervention mapping approach (IMA) to prevent COVID-19 infection in adults at the time of the pandemic by health workers.

MATERIALS AND METHODS

The present study was a study protocol where IMA was used as a planning framework for developing an intervention-training program to prevent COVID-19 infection in adults at the time of the pandemic by health workers in Ardabil city. Six intervention mapping (IM) steps have been described in this protocol. As the first step, needs assessment was performed by reviewing the studies, qualitative evaluation, and interviews. In the second step, the matrix of change objectives was designed from the intersection of performance goals and determinants. Later on, after designing the program and planning the program implementation, the program evaluation plan was developed.

RESULTS

IMA guided us in designing and implementing a control-oriented training program with the participation of the participants along with the definition of outcomes, performance goals and determinants, theoretical methods and practical applications, intervention program, implementation, and step by step assessment.

CONCLUSION

IM is a control-oriented, systematic, participation-based approach to design and implement targeted and on-going health promotion programs based on the needs of the target group at the time of the pandemic by health workers.