Sazgar IoT: A Device-Centric IoT Framework and Approximation Technique for Efficient and Scalable IoT Data Processing.

The Internet of Things (IoT) plays a fundamental role in monitoring applications; however, existing approaches relying on cloud and edge-based IoT data analysis encounter issues such as network delays and high costs, which can adversely impact time-sensitive applications. To address these challenges, this paper proposes an IoT framework called Sazgar IoT. Unlike existing solutions, Sazgar IoT leverages only IoT devices and IoT data analysis approximation techniques to meet the time-bounds of time-sensitive IoT applications. In this framework, the computing resources onboard the IoT devices are utilised to process the data analysis tasks of each time-sensitive IoT application. This eliminates the network delays associated with transferring large volumes of high-velocity IoT data to cloud or edge computers. To ensure that each task meets its application-specific time-bound and accuracy requirements, we employ approximation techniques for the data analysis tasks of time-sensitive IoT applications. These techniques take into account the available computing resources and optimise the processing accordingly. To evaluate the effectiveness of Sazgar IoT, experimental validation has been conducted. The results demonstrate that the framework successfully meets the time-bound and accuracy requirements of the COVID-19 citizen compliance monitoring application by effectively utilising the available IoT devices. The experimental validation further confirms that Sazgar IoT is an efficient and scalable solution for IoT data processing, addressing existing network delay issues for time-sensitive applications and significantly reducing the cost related to cloud and edge computing devices procurement, deployment, and maintenance.

Serological evidence of SARS-CoV-2 infection in dromedary camels and domestic bovids in Oman.

SARS-CoV-2 has demonstrated the ability to infect a wide range of animal species. Here, we investigated SARS-CoV-2 infection in livestock species in Oman and provided serological evidence of SARS-CoV-2 infection in cattle, sheep, goats, and dromedary camel using the surrogate virus neutralization and plaque reduction neutralization tests. To better understand the extent of SARS-CoV-2 infection in animals and associated risks, "One Health" epidemiological investigations targeting animals exposed to COVID-19 human cases should be implemented with integrated data analysis of the epidemiologically linked human and animal cases.

Three-year evaluation of a program teaching social determinants of health in community-based medical education: a general inductive approach for qualitative data analysis.

BACKGROUND: Social determinants of health (SDH) are intricately intertwined with various social and economic factors. Reflection is essential for learning about SDH. However, only a few reports have focused on reflection in SDH programs; most were cross-sectional studies. We aimed to longitudinally evaluate a SDH program in a community-based medical education (CBME) curriculum that we introduced in 2018 based on the level of reflection and content on SDH in students' reports. METHODS: Study design: General inductive approach for qualitative data analysis. Education program: A 4-week mandatory clinical clerkship in general medicine and primary care at the University of Tsukuba School of Medicine in Japan was provided to all fifth- and sixth-year medical students. Students underwent a 3-week rotation in community clinics and hospitals in suburban and rural areas of Ibaraki Prefecture. After a lecture on SDH on the first day, students were instructed to prepare a structural case description based on encounters during the curriculum. On the final day, students shared their experiences in a small group session and submitted a report on SDH. The program was continuously improved and faculty development was provided. STUDY PARTICIPANTS: Students who completed the program during October 2018-June 2021. ANALYSIS: Levels of reflection were categorized as reflective, analytical, or descriptive. The content was analyzed based on the Solid Facts framework. RESULTS: We analyzed 118 reports from 2018-19, 101 reports from 2019-20, and 142 reports from 2020-21. There were 2 (1.7%), 6 (5.9%), and 7 (4.8%) reflective reports; 9 (7.6%), 24 (23.8%), and 52 (35.9%) analytical reports; and 36 (30.5%), 48 (47.5%), and 79 (54.5%) descriptive reports, respectively. The others were not evaluable. The number of Solid Facts framework items in reports were 2.0 ± 1.2, 2.6 ± 1.3, and 3.3 ± 1.4, respectively. CONCLUSIONS: Students' understanding of SDH deepened as the SDH program in the CBME curriculum improved. Faculty development might have contributed to the results. Reflective understanding of SDH might require more faculty development and integrated education of social science and medicine.

Data analysis for COVID-19 deaths using a novel statistical model: Simulation and fuzzy application.

This paper provides a novel model that is more relevant than the well-known conventional distributions, which stand for the two-parameter distribution of the lifetime modified Kies Topp-Leone (MKTL) model. Compared to the current distributions, the most recent one gives an unusually varied collection of probability functions. The density and hazard rate functions exhibit features, demonstrating that the model is flexible to several kinds of data. Multiple statistical characteristics have been obtained. To estimate the parameters of the MKTL model, we employed various estimation techniques, including maximum likelihood estimators (MLEs) and the Bayesian estimation approach. We compared the traditional reliability function model to the fuzzy reliability function model within the reliability analysis framework. A complete Monte Carlo simulation analysis is conducted to determine the precision of these estimators. The suggested model outperforms competing models in real-world applications and may be chosen as an enhanced model for building a statistical model for the COVID-19 data and other data sets with similar features.

Depressive and anxiety symptoms in adults during the COVID-19 pandemic in England: A panel data analysis over 2 years.

BACKGROUND: There has been much research into the mental health impact of the Coronavirus Disease 2019 (COVID-19) pandemic and how it is related to time-invariant individual characteristics. However, there is still a lack of research showing long-term trajectories of mental health across different stages of the pandemic. And little is known regarding the longitudinal association of time-varying factors with mental health outcomes. This study aimed to provide a longitudinal profile of how mental health in adults changed across different stages of the COVID-19 pandemic and to examine their longitudinal associations with time-varying contextual (e.g., COVID-19 policy response and pandemic intensity) and individual level factors. METHODS AND FINDINGS: This study used data from a large panel study of over 57,000 adults living in England, who were followed up regularly for 2 years between March 2020 and April 2022. Mental health outcomes were depressive and anxiety symptoms. Depressive symptoms were assessed by the Patient Health Questionnaire (PHQ-9) and anxiety symptoms by the Generalized Anxiety Disorder assessment (GAD-7). Entropy balancing weights were applied to restore sample representativeness. After weighting, approximately 50% of participants were female, 14% from ethnic minority backgrounds, with a mean age of 48 years. Descriptive analyses showed that mental health changes were largely in line with changes in COVID-19 policy response and pandemic intensity. Further, data were analysed using fixed-effects (FE) models, which controlled for all time-invariant confounders (observed or not). FE models were fitted separately across 3 stages of the COVID-19 pandemic, including the first national lockdown (21/03/2020-23/08/2020), second and third national lockdowns (21/09/2020-11/04/2021), and "freedom" period (12/04/2021-14/11/2021). We found that more stringent policy response (measured by stringency index) was associated with increased depressive symptoms, in particular, during lockdown periods (ß = 0.23, 95% confidence interval (CI) = [0.18 to 0.28], p < 0.001; ß = 0.30, 95% CI = [0.21 to 0.39], p < 0.001; ß = 0.04, 95% CI = [-0.03 to 0.12], p = 0.262). Higher COVID-19 deaths were also associated with increased depressive symptoms, but this association weakened over time (ß = 0.29, 95% CI = [0.25 to 0.32], p < 0.001; ß = 0.09, 95% CI = [0.05 to 0.13], p < 0.001; ß = -0.06, 95% CI = [-0.30 to 0.19], p = 0.655). Similar results were also found for anxiety symptoms, for example, stringency index (ß = 0.17, 95% CI = [0.12 to 0.21], p < 0.001; ß = 0.13, 95% CI = [0.06 to 0.21], p = 0.001; ß = 0.10, 95% CI = [0.03 to 0.17], p = 0.005), COVID-19 deaths (ß = 0.07, 95% CI = [0.04 to 0.10], p < 0.001; ß = 0.04, 95% CI = [0.00 to 0.07], p = 0.03; ß = 0.16, 95% CI = [-0.08 to 0.39], p = 0.192). Finally, there was also evidence for the longitudinal association of mental health with individual level factors, including confidence in government/healthcare/essentials, COVID-19 knowledge, COVID-19 stress, COVID-19 infection, and social support. However, it is worth noting that the magnitudes of these longitudinal associations were generally small. The main limitation of the study was its non-probability sample design. CONCLUSIONS: Our results provided empirical evidence on how changes in contextual and individual level factors were related to changes in depressive and anxiety symptoms. While some factors (e.g., confidence in healthcare, social support) clearly acted as consistent predictors of depressive and/or anxiety symptoms, other factors (e.g., stringency index, COVID-19 knowledge) were dependent on the specific situations occurring within society. This could provide important implications for policy making and for a better understanding of mental health of the general public during a national or global health crisis.

Investigating time-dependent COVID-19 pandemic mental health data: Challenges and opportunities of using panel data analysis.

Mariana Pinto da Costa and Robert Stewart provide commentary on a large prospective panel survey of mental health during the pandemic and consider the implications of such data science initiatives.

Equitable psychiatry, telehealth, and the COVID-19 pandemic: Analysis of national data.

Background: At the beginning of the COVID-19 pandemic, it was foreseen that the number of face-to-face psychiatry consultations would suffer a reduction. In order to compensate, the Australian Government introduced new Medicare-subsidized telephone and video-linked consultations. This study investigates how these developments affected the pre-existing inequity of psychiatry service delivery in Australia. Methods: The study analyses five and a half years of national Medicare data listing all subsidized psychiatry consultation consumption aggregated to areas defined as Statistical Area level 3 (SA3s; which have population sizes of 30 k-300 k). Face-to-face, video-linked and telephone consultations are considered separately. The analysis consists of presenting rates of consumption, concentration graphs, and concentration indices to quantify inequity, using Socio Economic Indexes for Areas (SEIFA) scores to rank the SA3 areas according to socio-economic disadvantage. Results: There is a 22% drop in the rate of face-to-face psychiatry consultation consumption across Australia in the final study period compared with the last study period predating the COVID-19 pandemic. However, the loss is made up by the introduction of the new subsidized telephone and video-linked consultations. Referring to the same time periods, there is a reduction in the inequity of the distribution of face-to-face consultations, where the concentration index reduces from 0.166 to 0.129. The new subsidized video-linked consultations are distributed with severe inequity in the great majority of subpopulations studied. Australia-wide, video-linked consultations are also distributed with gross inequity, with a concentration index of 0.356 in the final study period. The effect of this upon overall inequity was to cancel out the reduction of inequity resulting from the reduction of face-to face appointments. Conclusion: Australian subsidized video-linked psychiatry consultations have been distributed with gross inequity and have been a significant exacerbator of the overall inequity of psychiatric service provision. Future policy decisions wishing to reduce this inequity should take care to reduce the risk posed by expanding telepsychiatry.

Incidence, Prevalence, Hospitalization Rates, and Treatment Patterns in Myasthenia Gravis: A 10-Year Real-World Data Analysis of German Claims Data.

BACKGROUND: Myasthenia gravis (MG) is a rare chronic autoimmune disease caused by autoantibodies directed against postsynaptic antigens of the neuromuscular junction. Over the last decades, increasing incidence and prevalence rates have been reported. Epidemiological data on prevalence and incidence in Germany are lacking. Furthermore, the MG treatment landscape is rapidly changing due to the continued approval of novel monoclonal antibodies. METHOD: This is a retrospective study assessing incidence, prevalence, and hospitalization rates of MG as well as treatment patterns in Germany over 10 years based on medical claims data covering 6.1 million insured persons. RESULTS: Between 2011 and 2020, the prevalence rate of MG increased from 15.7 to 28.2 per 100,000 person-years. The age-adjusted incidence rate was 2.8 per 100,000 person-years within the study period (95% confidence interval, 2.43-3.22) and decreased dramatically in 2020, the year of the COVID-19 pandemic. Similarly, the hospitalization rate fluctuated within the study period but reached an overall low of 8.3% in 2020 (mean hospitalization rate 11.5%). Treatment patterns showed that most MG patients are treated with base therapy. However, crisis intervention is necessary for 2-5% of MG patients, and therapeutic monoclonal antibodies, including rituximab and eculizumab, are increasingly used. CONCLUSION: This is the first study on MG prevalence and incidence rates in Germany. Data show an increase in prevalence by 1.8-fold over 10 years. Decreasing incidence and hospitalization rates in 2020 hint at the impact of the COVID-19 pandemic. Treatment patterns in MG are changing with the advent of therapeutic monoclonal antibodies in this indication.

Galaxy Training: A powerful framework for teaching!

There is an ongoing explosion of scientific datasets being generated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analysis, and stewardship are still rarely taught in life science educational programs, resulting in a skills gap in many of the researchers tasked with analysing these big datasets. In order to address this skills gap and empower researchers to perform their own data analyses, the Galaxy Training Network (GTN) has previously developed the Galaxy Training Platform (https://training.galaxyproject.org), an open access, community-driven framework for the collection of FAIR (Findable, Accessible, Interoperable, Reusable) training materials for data analysis utilizing the user-friendly Galaxy framework as its primary data analysis platform. Since its inception, this training platform has thrived, with the number of tutorials and contributors growing rapidly, and the range of topics extending beyond life sciences to include topics such as climatology, cheminformatics, and machine learning. While initially aimed at supporting researchers directly, the GTN framework has proven to be an invaluable resource for educators as well. We have focused our efforts in recent years on adding increased support for this growing community of instructors. New features have been added to facilitate the use of the materials in a classroom setting, simplifying the contribution flow for new materials, and have added a set of train-the-trainer lessons. Here, we present the latest developments in the GTN project, aimed at facilitating the use of the Galaxy Training materials by educators, and its usage in different learning environments.

Biological risk in Italian prisons: data analysis from the second to the fourth wave of COVID-19 pandemic.

BACKGROUND: The management of COVID-19 in Italian prisons triggered considerable concern at the beginning of the pandemic due to numerous riots which resulted in inmate deaths, damages and prison breaks. The aim of this study is to shed some light, through analysis of the infection and relevant disease parameters, on the period spanning from the second to the fourth wave of the outbreak in Italy's prisons. METHODS: Reproductive number (Rt) and Hospitalisation were calculated through a Eulerian approach applied to differential equations derived from compartmental models. Comparison between trends was performed through paired t-test and linear regression analyses. RESULTS: The infection trends (prevalence and Rt) show a high correlation between the prison population and the external community. Both the indices appear to be lagging 1 week in prison. The prisoners' Rt values are not statistically different from those of the general population. The hospitalisation trend of inmates strongly correlates with the external population's, with a delay of 2 weeks. The magnitude of hospitalisations in prison is less than in the external community for the period analysed. CONCLUSIONS: The comparison with the external community revealed that in prison the infection prevalence was greater, although Rt values showed no significant difference, and the hospitalisation rate was lower. These results suggest that the consistent monitoring of inmates results in a higher infection prevalence while a wide vaccination campaign leads to a lower hospitalisation rate. All three indices demonstrate a lag of 1 or 2 weeks in prison. This delay could represent a useful time-window to strengthen planned countermeasures.

Using Location Intelligence to Evaluate the COVID-19 Vaccination Campaign in the United States: Spatiotemporal Big Data Analysis.

BACKGROUND: Highly effective COVID-19 vaccines are available and free of charge in the United States. With adequate coverage, their use may help return life back to normal and reduce COVID-19-related hospitalization and death. Many barriers to widespread inoculation have prevented herd immunity, including vaccine hesitancy, lack of vaccine knowledge, and misinformation. The Ad Council and COVID Collaborative have been conducting one of the largest nationwide targeted campaigns ("It's Up to You") to communicate vaccine information and encourage timely vaccination across the United States. More than 300 major brands, digital and print media companies, and community-based organizations support the campaigns to reach distinct audiences. OBJECTIVE: The goal of this study was to use aggregated mobility data to assess the effectiveness of the campaign on COVID-19 vaccine uptake. METHODS: Campaign exposure data were collected from the Cuebiq advertising impact measurement platform consisting of about 17 million opted-in and deidentified mobile devices across the country. A Bayesian spatiotemporal hierarchical model was developed to assess campaign effectiveness through estimating the association between county-level campaign exposure and vaccination rates reported by the Centers for Disease Control and Prevention. To minimize potential bias in exposure to the campaign, the model included several control variables (eg, age, race or ethnicity, income, and political affiliation). We also incorporated conditional autoregressive residual models to account for apparent spatiotemporal autocorrelation. RESULTS: The data set covers a panel of 3104 counties from 48 states and the District of Columbia during a period of 22 weeks (March 29 to August 29, 2021). Officially launched in February 2021, the campaign reached about 3% of the anonymous devices on the Cuebiq platform by the end of March, which was the start of the study period. That exposure rate gradually declined to slightly above 1% in August 2021, effectively ending the study period. Results from the Bayesian hierarchical model indicate a statistically significant positive association between campaign exposure and vaccine uptake at the county level. A campaign that reaches everyone would boost the vaccination rate by 2.2% (95% uncertainty interval: 2.0%-2.4%) on a weekly basis, compared to the baseline case of no campaign. CONCLUSIONS: The "It's Up to You" campaign is effective in promoting COVID-19 vaccine uptake, suggesting that a nationwide targeted mass media campaign with multisectoral collaborations could be an impactful health communication strategy to improve progress against this and future pandemics. Methodologically, the results also show that location intelligence and mobile phone-based monitoring platforms can be effective in measuring impact of large-scale digital campaigns in near real time.

Safety of COVID-19 Vaccines among Patients with Type 2 Diabetes Mellitus: Real-World Data Analysis.

BACKGROUND: Little is known about the adverse events (AEs) associated with coronavirus disease 2019 (COVID-19) vaccination in patients with type 2 diabetes mellitus (T2DM). METHODS: This study used vaccine AE reporting system data to investigate severe AEs among vaccinated patients with T2DM. A natural language processing algorithm was applied to identify people with and without diabetes. After 1:3 matching, we collected data for 6,829 patients with T2DM and 20,487 healthy controls. Multiple logistic regression analysis was used to calculate the odds ratio for severe AEs. RESULTS: After COVID-19 vaccination, patients with T2DM were more likely to experience eight severe AEs than controls: cerebral venous sinus thrombosis, encephalitis myelitis encephalomyelitis, Bell's palsy, lymphadenopathy, ischemic stroke, deep vein thrombosis (DVT), thrombocytopenia (TP), and pulmonary embolism (PE). Moreover, patients with T2DM vaccinated with BNT162b2 and mRNA-1273 were more vulnerable to DVT and TP than those vaccinated with JNJ-78436735. Among patients with T2DM administered mRNA vaccines, mRNA-1273 was safer than BNT162b2 in terms of the risk of DVT and PE. CONCLUSION: Careful monitoring of severe AEs in patients with T2DM may be necessary, especially for those related to thrombotic events and neurological dysfunctions after COVID-19 vaccination.

The role of Fintech firms' sustainability during the COVID-19 period.

This study investigates the moderating role of environmental disclosures on the market performance of 48 Fintech and 140 non-Fintech firms during the pandemic using data from 2011 to 2022. Ordinary least squares and correlations were used for data analysis. The study's first finding revealed that Fintech firms had a better environmental performance (78.4%) than non-Fintech firms during the pandemic. The study's second finding indicated that environmental disclosures are crucial for shareholders and contributed almost 10.2% to the Fintech firms' market performance during the pandemic. This study's contribution is significant in enhancing the understanding of the shareholders' sensitivity towards sustainability disclosures during financial crisis. The findings of this study are essential for policymakers, start-up entrepreneurs, and shareholders.

Measuring concordance of data sources used for infectious disease research in the USA: a retrospective data analysis.

OBJECTIVES: As highlighted by the COVID-19 pandemic, researchers are eager to make use of a wide variety of data sources, both government-sponsored and alternative, to characterise the epidemiology of infectious diseases. The objective of this study is to investigate the strengths and limitations of sources currently being used for research. DESIGN: Retrospective descriptive analysis. PRIMARY AND SECONDARY OUTCOME MEASURES: Yearly number of national-level and state-level disease-specific case counts and disease clusters for three diseases (measles, mumps and varicella) during a 5-year study period (2013-2017) across four different data sources: Optum (health insurance billing claims data), HealthMap (online news surveillance data), Morbidity and Mortality Weekly Reports (official government reports) and National Notifiable Disease Surveillance System (government case surveillance data). RESULTS: Our study demonstrated drastic differences in reported infectious disease incidence across data sources. When compared with the other three sources of interest, Optum data showed substantially higher, implausible standardised case counts for all three diseases. Although there was some concordance in identified state-level case counts and disease clusters, all four sources identified variations in state-level reporting. CONCLUSIONS: Researchers should consider data source limitations when attempting to characterise the epidemiology of infectious diseases. Some data sources, such as billing claims data, may be unsuitable for epidemiological research within the infectious disease context.

Association between children living with obesity and Mental Health problems: a data analysis of the Welsh Health Survey, UK.

BACKGROUND: Obesity and mental health problems in children are both significant and growing public health issues. There is mixed evidence on the relationship between obesity and mental health in children. This study examines the association between obesity and mental health problems in a nationally representative sample of children using the Welsh Health Survey for Children (n = 11,279 aged 4-15 years). METHODS: The Chi-square test assessed the difference in the proportion of children reporting abnormal mental health scores (strengths and difficulties score ≥ 20) in children living with obesity (≥ 95 centile for age and sex). Then, a multivarible logistic regression was used to assess any association after accounting for confounding variables. RESULTS: There were 1,582 children living with obesity in the study (19.6%). The Chi-square test indicated a significant difference in the proportion of children with abnormal mental health scores in children living with obesity (p = 0.001). This study found a very small but significant positive association between mental health and childhood obesity after accounting for confounding variables, Odds Ratio 1.02 (95%CI: 1.01 to 1.02, p = 0.001). However, socio-economic status was more of a driver. CONCLUSION: The findings of this study show a very small but significant association between childhood obesity and mental health problems. The multivariable logistic regression indicates that the focus must remain on reducing health inequalities as this is a more important driver of child health and well-being. However, as a precautionary measure it may be worth considering if children living with obesity who present for weight-management services may benefit from a review of their mental health status to identify if further support is needed, if capacity allows, and this can be done in a supportive way.

Cox regression using a calendar time scale was unbiased in simulations of COVID-19 vaccine effectiveness & safety.

BACKGROUND: Observational studies on corona virus disease 2019 (COVID-19) vaccines compare event rates in vaccinated and unvaccinated person time using Poisson or Cox regression. In Cox regression, the chosen time scale needs to account for the time-varying incidence of severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) infection and COVID-19 vaccination.We aimed to quantify bias in person-time based methods, with and without adjustment for calendar time, using simulations and empirical data analysis. METHODS: We simulated 500,000 individuals who were followed for 365 days, and a point exposure resembling COVID-19 vaccination (cumulative incidence 80%). We generated an effectiveness outcome, emulating the incidence of severe acute respiratory syndrome corona virus 2 infection in Denmark during 2021 (risk 10%), and a safety outcome with seasonal variation (myocarditis, risk 1/5,000). Incidence rate ratios (IRRs) were set to 0.1 for effectiveness and 5.0 for safety outcomes. IRRs and hazard ratios (HRs) were estimated using Poisson and Cox regression with a time under observation scale, and a calendar time scale. Bias was defined as estimated IRR or HR-true IRR. Further, we obtained estimates for both outcomes using data from the Danish health registries. RESULTS: Unadjusted IRRs (biaseffectivenes +0.16; biassafety -2.09) and HRs estimated using a time-under-observation scale (+0.28;-2.15) were biased. Adjustment for calendar time reduced bias in Cox (+0.03; +0.33) and Poisson regression (0.00; -0.28). Cox regression using a calendar time scale was least biased (0.00, +0.12). When analyzing empirical data, adjusted Poisson and Cox regression using a calendar time scale yielded estimates in accordance with existing evidence. CONCLUSION: Lack of adjustment for the time-varying incidence of COVID-19 related outcomes may severely bias estimates.

Explanation of factors forming missed nursing care during the COVID-19 pandemic: A qualitative study.

Background: Providing nursing care to patients with COVID-19 has put additional pressure on nurses, making it challenging to meet several care requirements. This situation has caused parts of nursing care to be missed, potentially reducing the quality of nursing care and threatening patient safety. Therefore, the present study aimed at explaining the factors forming missed nursing care during the COVID-19 pandemic from the perspective of nurses. Methods: This qualitative study was conducted using a conventional content analysis approach in Iran, 2020-2021. Data were collected from in-depth, semi-structured interviews with 14 nurses based on purposive sampling. Data analysis was performed simultaneously with data collection. Graneheim and Lundman's approach was used for data analysis, and MAXQDA software was used for data management. After transcribing the recorded interviews, to achieve the accuracy and validity of the study, the criteria proposed by Lincoln and Guba were considered and used. Results: A total of 14 nurses with a mean age and standard deviation of 31.85 ± 4.95 and working in the COVID-19 wards participated in the study. The acquired data were categorized into four main categories: care-related factors, disease-related factors, patient-related factors, and organization-related factors. The category "care-related factors" comprised uncertainty in care, PPE-related limitations, attrition from care, and futile care. The category "disease-related factors" consisted of the extension of symptoms, unpredictable peaks of the disease, and restriction on the presence of patients' companions. The category "patient-related factors" included comorbidities, elderly patients, and deterioration of infected patients. Ultimately, the category "organization-related factors" consisted of restrictions on equipment supply, lack of human resources, weaknesses in teamwork, and an unsupportive work environment. Conclusion: The results of this study showed that several reasons including factors related to care, patient, disease, and organization cause missed nursing care. By modifying the related affecting factors and considering the effective mechanisms to minimize missed nursing care, it is possible to provide better services.