Prevalence and reasons for self‐medication for prevention of <scp>COVID</scp>‐19 among the adult population in <scp>Kermanshah‐Iran</scp>

Janatolmakan, Maryam; Naghipour, Armin; Khatony, Alireza

doi:10.1002/nop2.1653

Cited by 4 publications

(6 citation statements)

References 35 publications

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“…This outcome is in line with what we discovered regarding the website that served as the information's primary source. Since young college students are the biggest Internet users across all platforms, including websites, social networks, etc., numerous investigations, including Sadio et al's study in Togo, Janatolmakan et al's study in Iran, and Zhang et al's study in China, found the level of education to be a risk factor (25,45,46). Several publications also recognized gender, old age, health sector worker, insurance coverage, and rating anxiety scale as risk factors (1,25,32,45,46)…”

Section: Discussionmentioning

confidence: 99%

Self-medication During the Coronavirus Disease 2019 Pandemic in North Central of Morocco: Prevalence, Causes, and Associated Factors

Bouazzi

Jaouhar

Talbi

et al. 2023

Jundishapur J Nat Pharm Prod

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Background: Self-medication is a significant public health problem that negatively affects human health. Many Moroccans have turned to this practice as a form of prevention or treatment for the newly emerged coronavirus disease 2019 (COVID-19) worldwide. Objectives: This study is the first research conducted in Morocco in the context of this pandemic, aiming to explore the prevalence, sources of information, causes, and risk factors of self-medication during the pandemic period. Methods: A cross-sectional survey using a self-administered questionnaire was conducted in the city of Meknes (North Central of Morocco) from March 2022 to June 2022 to investigate the level of self-medication practices before and during the COVID-19 pandemic. Results: The survey included 400 respondents from the general population (mean age = 34.97 ± 15.8 years). The self-medication rate was obtained as 291 (72.75%). There was a statistically significant decrease in the number of respondents practicing self-medication during COVID-19 compared to the pre-pandemic period. Most medications used for self-medication to prevent or treat COVID-19 were supplements such as vitamin C (81.10%) and zinc (74.91%), followed by acetaminophen (72.51%) and herbal products (69.41%). In the bivariate logistic regression model, age, gender, education level, self-reported health, sector of activity, and medical insurance were analyzed. Multivariate analyses showed that the youngest age group (≤ 30 years) (odds ratio (OR) = 1.59; 95% confidence interval (CI): 0.96 - 2.78), university education (OR = 1.47; 95% CI: 0.87 - 2.99), and better self-reported health (OR = 2.83; 95% CI: 1.43 - 3.71) were independently associated with self-medication. Conclusions: Self-medication was a serious health concern in Morocco during the COVID-19 pandemic. This study will be of great interest to the authorities in order to develop new pandemic management strategies, particularly self-medication awareness programs.

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Section: Discussionmentioning

confidence: 99%

Self-medication During the Coronavirus Disease 2019 Pandemic in North Central of Morocco: Prevalence, Causes, and Associated Factors

Bouazzi

Jaouhar

Talbi

et al. 2023

Jundishapur J Nat Pharm Prod

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“…11 Also, a study has determined that the social, personal, economic and health effects of this disease may remain in societies for many years. 12,13 Studies have shown that data mining models, including DT, can be useful for modeling and predicting people at risk of COVID-19. 5 Many studies have been conducted to identify the most important variables related to various diseases, in which data mining models have been used and the correctness of these models has been confirmed.…”

Section: Introductionmentioning

confidence: 99%

“…In December 2019, a pneumonia prevalence of unknown origin was reported in Wuhan city, Hubei province, China. 1 Pneumonia cases were epidemiologically linked to the Huanan seafood wholesale market. Inoculation of respiratory samples into human airway epithelial cells, the Vero E6 and Huh7 cell lines, led to the isolation of a novel respiratory virus, which genome analysis showed to be a novel SARS‐Cov related coronavirus and therefore, it was named as acute respiratory syndrome of the coronavirus (SARS‐CoV‐2) which is a beta coronavirus belonging to the Sarbeco virus subgenus.…”

Section: Introductionmentioning

confidence: 99%

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The most important variables associated with death due to COVID‐19 disease, based on three data mining models Decision Tree, AdaBoost, and Support Vector Machine: A cross‐sectional study

gharehhasani,

Rezaei,

Naghipour

et al. 2024

Health Science Reports

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IntroductionDeath due to covid‐19 is one of the biggest health challenges in the world. There are many models that can predict death due to COVID‐19. This study aimed to fit and compare Decision Tree (DT), Support Vector Machine (SVM), and AdaBoost models to predict death due to COVID‐19.MethodsTo describe the variables, mean (SD) and frequency (%) were reported. To determine the relationship between the variables and the death caused by COVID‐19, chi‐square test was performed with a significance level of 0.05. To compare DT, SVM and AdaBoost models for predicting death due to COVID‐19 from sensitivity, specificity, accuracy and the area under the rock curve under R software using psych, caTools, random over‐sampling examples, rpart, rpartplot packages was done.ResultsOut of the total of 23,054 patients studied, 10,935 cases (46.5%) were women, and 12,569 cases (53.5%) were men. Additionally, the mean age of the patients was 54.9 ± 21.0 years. There is a statistically significant relationship between gender, fever, cough, muscle pain, smell and taste, abdominal pain, nausea and vomiting, diarrhea, anorexia, dizziness, chest pain, intubation, cancer, diabetes, chronic blood disease, Violation of immunity, pregnancy, Dialysis, chronic lung disease with the death of covid‐19 patients showed (p < 0.05). The results showed that the sensitivity, specificity, accuracy and the area under the receiver operating characteristic curve were respectively 0.60, 0.68, 0.71, and 0.75 in the DT model, 0.54, 0.62, 0.63, and 0.71 in the SVM model, and 0.59, 0.65, 0.69 and 0.74 in the AdaBoost model.ConclusionThe results showed that DT had a high predictive power compared to other data mining models. Therefore, it is suggested to researchers in different fields to use DT to predict the studied variables. Also, it is suggested to use other approaches such as random forest or XGBoost to improve the accuracy in future studies.

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“…Coronavirus (COVID- 19) is an acute respiratory illness caused by the new RNA virus, acute respiratory syndrome, and coronavirus SARS-CoV-2 (1,2). The number of people with COVID-19 worldwide has increased significantly since the first report of SARS-CoV-2 patients in Wuhan, China, in December 2019 (3)(4)(5). A large wave of COVID-19 generated by SARS-CoV-2 variants has been experienced in 2022 (Delta, Lambda, Mo, and Omicron).…”

Section: Introductionmentioning

confidence: 99%

The Effects of Blood Group Types on the Mortality and Survival of Patients with COVID-19 Infection

Naghibifar,

Janbakhsh,

Shirvani

et al. 2023

J Kermanshah Univ Med Sci

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Background: Identifying patient characteristics that may contribute to significant complications and mortality is essential. Objectives: This study was conducted to evaluate the mortality and survival of patients with COVID-19 by blood type. Methods: This study was performed on 644 patients with COVID-19 by a retrospective cohort method using recorded data of patients admitted to Farabi and Golestan-Kermanshah hospitals. The data were extracted from patients' records through a researcher-made checklist. Logistic regression and Cox regression models were utilized in STATA software version 14 to analyze the data. Results: About 74.53% of patients were male, and the average age was 52.55 ± 16.9 years. The prevalence of blood groups was 22.67% (n = 146) O, 11.49% (n = 74) AB, 24.38% (n = 157) B, and 41.46% (n = 267) A, respectively. Approximately 69.05% of patients were in the intensive care unit, 37.27% experienced intubations, and 18.94% died. The results showed that the chance of death in blood group AB, B, and A was 1.05 (P = 0.910) times, 2.09 (P = 0.022) times, and 2.29 (P = 0.005) times higher than in blood group O. In addition, the survival of patients with blood group A was lower than other blood groups (P = 0.001). Conclusions: Based on the results, blood groups O and B had a protective role against the death of COVID-19. Blood type O also required less intubation but was hospitalized longer.

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Prevalence and reasons for self‐medication for prevention of COVID‐19 among the adult population in Kermanshah‐Iran

Cited by 4 publications

References 35 publications

Self-medication During the Coronavirus Disease 2019 Pandemic in North Central of Morocco: Prevalence, Causes, and Associated Factors

Self-medication During the Coronavirus Disease 2019 Pandemic in North Central of Morocco: Prevalence, Causes, and Associated Factors

The most important variables associated with death due to COVID‐19 disease, based on three data mining models Decision Tree, AdaBoost, and Support Vector Machine: A cross‐sectional study

The Effects of Blood Group Types on the Mortality and Survival of Patients with COVID-19 Infection

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