ABSTRACT
Background and Aim: Death certificate (DC) data provides a basis for public health policies and statistics and contributes to the evaluation of a pandemic's evolution. This study aimed to evaluate the quality of the COVID-19-related DC completion. Methods: A descriptive-analytical study was conducted to review a total of 339 medical records and DCs issued for COVID-19 cases from February 20 to September 21, 2020. A univariate analysis (χ 2 as an unadjusted analysis) was performed, and multiple logistic regression models (odd ratio [OR] and 95% confidence interval [CI] as adjusted analyses) were used to evaluate the associations between variables. Results: Errors in DCs were classified as major and minor. All of the 339 examined DCs were erroneous; more than half of DCs (57.8%) had at least one major error; all of them had at least one minor error. Improper sequencing (49.3%), unacceptable underlying causes of death (UCOD) (33.3%), recording more than one cause per line (20.1%), listing general conditions instead of specific terms (11.2%), illegible handwriting (8.3%), competing causes (6.2%), and mechanisms (3.8%) were most common major errors, respectively. Absence of time interval (100%), listing mechanism allying with UCOD (51.6%), using abbreviations (45.4%), missing major comorbidities (16.5%), and listing major comorbidities in part I (16.5%) were most common minor errors, respectively. Conclusion: The rate of both major and minor errors was high. Using automated tools for recording and selecting death cause(s), promoting certifiers' skills on DC completion, and applying quality control mechanisms in DC documentation can improve death data and statistics.
ABSTRACT
Background and Aim Death certificate (DC) data provides a basis for public health policies and statistics and contributes to the evaluation of a pandemic's evolution. This study aimed to evaluate the quality of the COVID‐19‐related DC completion. Methods A descriptive‐analytical study was conducted to review a total of 339 medical records and DCs issued for COVID‐19 cases from February 20 to September 21, 2020. A univariate analysis (χ2 as an unadjusted analysis) was performed, and multiple logistic regression models (odd ratio [OR] and 95% confidence interval [CI] as adjusted analyses) were used to evaluate the associations between variables. Results Errors in DCs were classified as major and minor. All of the 339 examined DCs were erroneous;more than half of DCs (57.8%) had at least one major error;all of them had at least one minor error. Improper sequencing (49.3%), unacceptable underlying causes of death (UCOD) (33.3%), recording more than one cause per line (20.1%), listing general conditions instead of specific terms (11.2%), illegible handwriting (8.3%), competing causes (6.2%), and mechanisms (3.8%) were most common major errors, respectively. Absence of time interval (100%), listing mechanism allying with UCOD (51.6%), using abbreviations (45.4%), missing major comorbidities (16.5%), and listing major comorbidities in part I (16.5%) were most common minor errors, respectively. Conclusion The rate of both major and minor errors was high. Using automated tools for recording and selecting death cause(s), promoting certifiers' skills on DC completion, and applying quality control mechanisms in DC documentation can improve death data and statistics.
ABSTRACT
BACKGROUND: Iran was one of the first countries to be affected by COVID-19. Identifying factors associated with the severity of COVID-19 is effective in disease management. This study investigated the epidemiological and clinical features and factors related to the severity of COVID-19 in one of the less privileged areas in Iran. METHODS: In a multi-center study, all patients admitted to Zahedan University of Medical Sciences hospitals in southeastern Iran were investigated from February 29 to April 31, 2020. Demographic, epidemiological, and clinical data of patients were extracted from medical records. Bivariate and multivariate logistic regression models were used to explore the risk factors associated with the severity of COVID-19. RESULTS: Among the 413 patients, 55.5% were male, and 145 (35.10%) were in a severe condition at admission time. Multivariate analysis showed that the adjusted odds of the disease severity increased in patients with older age (OR 2.27; 95% CI 1.41-3.65), substance abuse (OR 2.49; 95% CI 1.14-5.43), having one underlying disease (OR 1.52; 95% CI 0.90-2.55), having two underlying disease (OR 2.31; 95% CI 1.19-4.50), and having three or more underlying disease (OR 2.60; 95% CI 1.19-5.66). CONCLUSIONS: COVID-19 was more severe in older patients, patients with a history of substance abuse, and patients with the underlying disease. Understanding the factors affecting the disease severity can help the clinical management of COVID-19, especially in less privileged areas where fewer resources are available.
Subject(s)
COVID-19 , Aged , Hospitalization , Humans , Iran/epidemiology , Male , Retrospective Studies , Risk Factors , SARS-CoV-2ABSTRACT
This article reviews the current knowledges of congenital bleeding disorders (CBD) amid the COVID-19 pandemic. It appears that CBD is not associated with higher risk of getting COVID-19 and so the prevalence of COVID-19 among them seems not higher compared to the general population. In absence of specific therapeutic recommendations, it is essential to make a correct assessment of the risk of haemorrhage/thrombosis. Based on expert opinion, strategies for outpatient management include adherence to prescribed regimens, telemedicine, and communication about COVID-19 in patients with CBD. More data should be also collected to better characterize the impact of COVID-19 on patients with CBD. The current findings encourage further studies to determine the prevalence of SARS-CoV2 infection in CBD patients to understand more fully the burden of this novel pathogen and to develop adequate preventive measures against this infection.