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      Comparison of Side Effects of COVID-19 Vaccines: Sinopharm, AstraZeneca, Sputnik V, and Covaxin in Women in Terms of Menstruation Disturbances, Hirsutism, and Metrorrhagia: A Descriptive-Analytical Cross-Sectional Study

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          Abstract

          Background:

          Present study assessed whether Sinopharm, AstraZeneca, Sputnik V, and Covaxin’s vaccinated women reveal a distinct incidence of menstruation disturbances, hirsutism, and metrorrhagia.

          Materials and Methods:

          Data collection was performed from June to August 2021, and 427 women working in seven selected hospitals in Tehran were studied in this descriptive-analytical cross-sectional study. All of these women had received one or both doses of the vaccines with one of the assessed vaccines. Required data was collected via questionnaire and imported to SPSS 16 for further assessment and analysis. Fisher’s Exact Test and Chi-Squared test were main statistical tests used to understand whether any significant relation exists or not.

          Results:

          The participant’s mean age and body mass index (BMI) were 29.78 ± 10.55 and 23.27 ± 3.82, respectively. Three hundred ninety-five cases (92.4%) had received both doses of the vaccines. Also, 154 cases (36.1%) had a history of COVID-19. A total of 38 cases (8.8%) of menstruation disturbances, 20 cases (4.6%) of metrorrhagia, and 7 cases (1.6%) of hirsutism were reported after receiving the vaccines. There was a significant difference among the vaccinated groups with the vaccines as mentioned earlier in terms of menstruation disturbances (hypermenorrhea, dysmenorrhea, Amenorrhea) (P=0.01). The highest and the lowest incidence of menstruation disturbances were recorded in the group vaccinated with Covaxin (17.6%) and Sputnik V (5%), respectively. There was also no significant difference amongst the vaccinated groups with the four vaccines regarding the incidence of metrorrhagia and hirsutism (P=0.10 and P=0.12, respectively). There was no significant relationship between all three complications incidence with the previous infection concerning all vaccines (coefficient=0.46, 1.27, -0.15 respectively for menstruation disturbances, metrorrhagia, and, hirsutism).

          Conclusion:

          Seemingly, Covaxin revealed the most side effects in terms of menstruation disturbances. As a result, professionals must carry out several studies with reasonable samples to recommend the vaccine to those women confidently

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          Making sense of Cronbach's alpha

          Medical educators attempt to create reliable and valid tests and questionnaires in order to enhance the accuracy of their assessment and evaluations. Validity and reliability are two fundamental elements in the evaluation of a measurement instrument. Instruments can be conventional knowledge, skill or attitude tests, clinical simulations or survey questionnaires. Instruments can measure concepts, psychomotor skills or affective values. Validity is concerned with the extent to which an instrument measures what it is intended to measure. Reliability is concerned with the ability of an instrument to measure consistently. 1 It should be noted that the reliability of an instrument is closely associated with its validity. An instrument cannot be valid unless it is reliable. However, the reliability of an instrument does not depend on its validity. 2 It is possible to objectively measure the reliability of an instrument and in this paper we explain the meaning of Cronbach’s alpha, the most widely used objective measure of reliability. Calculating alpha has become common practice in medical education research when multiple-item measures of a concept or construct are employed. This is because it is easier to use in comparison to other estimates (e.g. test-retest reliability estimates) 3 as it only requires one test administration. However, in spite of the widespread use of alpha in the literature the meaning, proper use and interpretation of alpha is not clearly understood. 2 , 4 , 5 We feel it is important, therefore, to further explain the underlying assumptions behind alpha in order to promote its more effective use. It should be emphasised that the purpose of this brief overview is just to focus on Cronbach’s alpha as an index of reliability. Alternative methods of measuring reliability based on other psychometric methods, such as generalisability theory or item-response theory, can be used for monitoring and improving the quality of OSCE examinations 6 - 10 , but will not be discussed here. What is Cronbach alpha? Alpha was developed by Lee Cronbach in 1951 11 to provide a measure of the internal consistency of a test or scale; it is expressed as a number between 0 and 1. Internal consistency describes the extent to which all the items in a test measure the same concept or construct and hence it is connected to the inter-relatedness of the items within the test. Internal consistency should be determined before a test can be employed for research or examination purposes to ensure validity. In addition, reliability estimates show the amount of measurement error in a test. Put simply, this interpretation of reliability is the correlation of test with itself. Squaring this correlation and subtracting from 1.00 produces the index of measurement error. For example, if a test has a reliability of 0.80, there is 0.36 error variance (random error) in the scores (0.80×0.80 = 0.64; 1.00 – 0.64 = 0.36). 12 As the estimate of reliability increases, the fraction of a test score that is attributable to error will decrease. 2 It is of note that the reliability of a test reveals the effect of measurement error on the observed score of a student cohort rather than on an individual student. To calculate the effect of measurement error on the observed score of an individual student, the standard error of measurement must be calculated (SEM). 13 If the items in a test are correlated to each other, the value of alpha is increased. However, a high coefficient alpha does not always mean a high degree of internal consistency. This is because alpha is also affected by the length of the test. If the test length is too short, the value of alpha is reduced. 2 , 14 Thus, to increase alpha, more related items testing the same concept should be added to the test. It is also important to note that alpha is a property of the scores on a test from a specific sample of testees. Therefore investigators should not rely on published alpha estimates and should measure alpha each time the test is administered. 14 Use of Cronbach’s alpha Improper use of alpha can lead to situations in which either a test or scale is wrongly discarded or the test is criticised for not generating trustworthy results. To avoid this situation an understanding of the associated concepts of internal consistency, homogeneity or unidimensionality can help to improve the use of alpha. Internal consistency is concerned with the interrelatedness of a sample of test items, whereas homogeneity refers to unidimensionality. A measure is said to be unidimensional if its items measure a single latent trait or construct. Internal consistency is a necessary but not sufficient condition for measuring homogeneity or unidimensionality in a sample of test items. 5 , 15 Fundamentally, the concept of reliability assumes that unidimensionality exists in a sample of test items 16 and if this assumption is violated it does cause a major underestimate of reliability. It has been well documented that a multidimensional test does not necessary have a lower alpha than a unidimensional test. Thus a more rigorous view of alpha is that it cannot simply be interpreted as an index for the internal consistency of a test. 5 , 15 , 17 Factor Analysis can be used to identify the dimensions of a test. 18 Other reliable techniques have been used and we encourage the reader to consult the paper “Applied Dimensionality and Test Structure Assessment with the START-M Mathematics Test” and to compare methods for assessing the dimensionality and underlying structure of a test. 19 Alpha, therefore, does not simply measure the unidimensionality of a set of items, but can be used to confirm whether or not a sample of items is actually unidimensional. 5 On the other hand if a test has more than one concept or construct, it may not make sense to report alpha for the test as a whole as the larger number of questions will inevitable inflate the value of alpha. In principle therefore, alpha should be calculated for each of the concepts rather than for the entire test or scale. 2 , 3 The implication for a summative examination containing heterogeneous, case-based questions is that alpha should be calculated for each case. More importantly, alpha is grounded in the ‘tau equivalent model’ which assumes that each test item measures the same latent trait on the same scale. Therefore, if multiple factors/traits underlie the items on a scale, as revealed by Factor Analysis, this assumption is violated and alpha underestimates the reliability of the test. 17 If the number of test items is too small it will also violate the assumption of tau-equivalence and will underestimate reliability. 20 When test items meet the assumptions of the tau-equivalent model, alpha approaches a better estimate of reliability. In practice, Cronbach’s alpha is a lower-bound estimate of reliability because heterogeneous test items would violate the assumptions of the tau-equivalent model. 5 If the calculation of “standardised item alpha” in SPSS is higher than “Cronbach’s alpha”, a further examination of the tau-equivalent measurement in the data may be essential. Numerical values of alpha As pointed out earlier, the number of test items, item inter-relatedness and dimensionality affect the value of alpha. 5 There are different reports about the acceptable values of alpha, ranging from 0.70 to 0.95. 2 , 21 , 22 A low value of alpha could be due to a low number of questions, poor inter-relatedness between items or heterogeneous constructs. For example if a low alpha is due to poor correlation between items then some should be revised or discarded. The easiest method to find them is to compute the correlation of each test item with the total score test; items with low correlations (approaching zero) are deleted. If alpha is too high it may suggest that some items are redundant as they are testing the same question but in a different guise. A maximum alpha value of 0.90 has been recommended. 14 Summary High quality tests are important to evaluate the reliability of data supplied in an examination or a research study. Alpha is a commonly employed index of test reliability. Alpha is affected by the test length and dimensionality. Alpha as an index of reliability should follow the assumptions of the essentially tau-equivalent approach. A low alpha appears if these assumptions are not meet. Alpha does not simply measure test homogeneity or unidimensionality as test reliability is a function of test length. A longer test increases the reliability of a test regardless of whether the test is homogenous or not. A high value of alpha (> 0.90) may suggest redundancies and show that the test length should be shortened. Conclusions Alpha is an important concept in the evaluation of assessments and questionnaires. It is mandatory that assessors and researchers should estimate this quantity to add validity and accuracy to the interpretation of their data. Nevertheless alpha has frequently been reported in an uncritical way and without adequate understanding and interpretation. In this editorial we have attempted to explain the assumptions underlying the calculation of alpha, the factors influencing its magnitude and the ways in which its value can be interpreted. We hope that investigators in future will be more critical when reporting values of alpha in their studies.
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            Coagulopathy and Antiphospholipid Antibodies in Patients with Covid-19

            Coagulopathy in Critical Illness with Covid-19 The authors describe a 69-year-old man with Covid-19 diagnosed in January 2020 in Wuhan, China, along with two other critically ill patients with Covid-19 who were also seen in the same intensive care unit. Coagulopathy and antiphospholipid antibodies were seen in all three patients.
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              Protection of BNT162b2 Vaccine Booster against Covid-19 in Israel

              Background On July 30, 2021, the administration of a third (booster) dose of the BNT162b2 messenger RNA vaccine (Pfizer–BioNTech) was approved in Israel for persons who were 60 years of age or older and who had received a second dose of vaccine at least 5 months earlier. Data are needed regarding the effect of the booster dose on the rate of confirmed coronavirus 2019 disease (Covid-19) and the rate of severe illness. Methods We extracted data for the period from July 30 through August 31, 2021, from the Israeli Ministry of Health database regarding 1,137,804 persons who were 60 years of age or older and had been fully vaccinated (i.e., had received two doses of BNT162b2) at least 5 months earlier. In the primary analysis, we compared the rate of confirmed Covid-19 and the rate of severe illness between those who had received a booster injection at least 12 days earlier (booster group) and those who had not received a booster injection (nonbooster group). In a secondary analysis, we evaluated the rate of infection 4 to 6 days after the booster dose as compared with the rate at least 12 days after the booster. In all the analyses, we used Poisson regression after adjusting for possible confounding factors. Results At least 12 days after the booster dose, the rate of confirmed infection was lower in the booster group than in the nonbooster group by a factor of 11.3 (95% confidence interval [CI], 10.4 to 12.3); the rate of severe illness was lower by a factor of 19.5 (95% CI, 12.9 to 29.5). In a secondary analysis, the rate of confirmed infection at least 12 days after vaccination was lower than the rate after 4 to 6 days by a factor of 5.4 (95% CI, 4.8 to 6.1). Conclusions In this study involving participants who were 60 years of age or older and had received two doses of the BNT162b2 vaccine at least 5 months earlier, we found that the rates of confirmed Covid-19 and severe illness were substantially lower among those who received a booster (third) dose of the BNT162b2 vaccine.
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                Author and article information

                Journal
                Int J Fertil Steril
                Int J Fertil Steril
                Royan Institute
                International Journal of Fertility & Sterility
                Royan Institute
                2008-076X
                2008-0778
                Jul-Sep 2022
                21 August 2022
                : 16
                : 3
                : 237-243
                Affiliations
                [1. ]Mazandaran University of Medical Sciences, Sari, Iran
                [2. ]Department of Infectious Diseases, Faculty of Medicin, Aja University of Medical Sciences, Tehran, Iran
                [3. ]Department of Pediatric Nursing, Faculty of Nursing, Aja University of Medical Sciences, Tehran, Iran
                [4. ]Department of Social Medicine, Faculty of Medicine, Aja University of Medical Sciences, Tehran, Iran
                [5. ]Department of critical Nursing, Faculty of Nursing, Aja University of Medical Sciences, Tehran, Iran
                [6. ]Department of Trauma, Faculty of Medicin, Aja University of Medical Sciences, Tehran, Iran
                Author notes
                [*Corresponding Address: ] P.O.Box: 1411718541 Department of Infectious Diseases Faculty of Medicin Aja University of Medical Sciences Tehran Iran Email: imna3030@ 123456gmail.com
                Article
                Int-J-Fertil-Steril-16-237
                10.22074/IJFS.2022.544706.1236
                9396006
                36029063
                7c1d778e-e0b7-4682-8a06-6b4bd06ff40c
                Any use, distribution, reproduction or abstract of this publication in any medium, with the exception of commercial purposes, is permitted provided the original work is properly cited.

                This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial 3.0 (CC BY-NC 3.0) License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 11 December 2021
                : 21 May 2022
                Categories
                Original Article
                Virology
                Gynecology and Female Infertility
                Custom metadata
                Abdollahi A, Naseh I, Kalroozi F, Kazemi-Galougahi MH, Nezamzadeh M, Sabeti Billandi Sh, Yousefi Zoshk M. Comparison of side effects of COVID-19 vaccines: sinopharm, astraZeneca, sputnik V, and covaxin in women in terms of menstruation disturbances, hirsutism, and metrorrhagia: a descriptive-analytical cross-sectional study. Int J Fertil Steril. 2022; 16(3): 237-243. doi: 10.22074/IJFS.2022.544706.1236. This open-access article has been published under the terms of the Creative Commons Attribution Non-Commercial 3.0 (CC BY-NC 3.0).

                side effects,menstrual cycle,hirsutism,metrorrhagia,covid-19 vaccines

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