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 Table of Contents  
Year : 2021  |  Volume : 5  |  Issue : 2  |  Page : 57-66

Psychometric analysis of the Bengali Version of the Sino-Nasal outcome Test-20 questionnaire in patients suffering from chronic rhino-sinusitis: A cross-sectional study

1 Department of Materia Medica, National Institute of Homoeopathy, Under Ministry of AYUSH, Government of India, Howrah, West Bengal, India
2 Department of Repertory, National Institute of Homoeopathy, Under Ministry of AYUSH, Government of India, Howrah, West Bengal, India
3 Department of Homoeopathic Pharmacy, National Institute of Homoeopathy, Under Ministry of AYUSH, Government of India, Howrah, West Bengal, India
4 Department of Practice of Medicine, Mahesh Bhattacharyya Homoeopathic Medical College and Hospital, Government of West Bengal, Howrah, West Bengal, India
5 Department of Organon of Medicine and Homoeopathic Philosophy, State National Homoeopathic Medical College and Hospital, Government of Uttar Pradesh, Lucknow, Uttar Pradesh, India
6 Department of Repertory, D. N. De Homoeopathic Medical College and Hospital, Govt. of West Bengal, Kolkata, West Bengal, India

Date of Submission10-Jan-2021
Date of Acceptance13-Jun-2021
Date of Web Publication09-Dec-2021

Correspondence Address:
Dr. Pankhuri Misra
Department of Materia Medica, National Institute of Homoeopathy, Under Ministry of AYUSH, Government of India, Block GE, Sector III, Salt Lake, Kolkata - 700 106, West Bengal
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/aiao.aiao_2_21

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Introduction: The Sino-nasal outcome test-20 (SNOT-20) questionnaire contains 20 items provided with a 6-point Likert scale and are aimed at measuring rhino-sinusitis specific and general symptoms; however, no Bengali version is available. Aims and Objectives: We aimed to develop SNOT-20 Bengali version (SNOT-20-B) and examine its cross-cultural adaptability considering linguistic equivalence in patients suffering from chronic rhino-sinusitis. Materials and Methods: A cross-sectional study was conducted on 270 patients by consecutive sampling at the ear-nose-throat outpatient of National Institute of Homoeopathy, Kolkata. The SNOT-20-B was produced by standardized forward-backward translations. Construct validity was examined by the exploratory factor analysis (n = 150) using the principal component analysis (varimax rotation). Subsequently, confirmatory factor analysis (CFA; n = 120) was performed to verify model fit. Concurrent validity was checked by correlating SNOT-20-B score with simultaneously measured EQ-5D-5 L scores. Observations and Results: Internal consistency (Cronbach's α =0.860; 95% confidence interval 0.834, 0.883) and test-retest reliability (P > 0.05) both were satisfactory. Concurrent validity was agreeable (P = 0.007). In factor analysis, all the items loaded above pre-specified value of 0.4 and identified 6 components, explaining 67.8% of variation. The goodness-of-fit in the CFA model was acceptable (χ2 = 517.396, P < 0.001; Comparative Fit Index = 0.634, Tucker Lewis Index = 0.551, root mean square error of approximation = 0.140, standardized root mean square residual = 0.298). Conclusions: SNOT-20-B, consisting of 20 items and framed within six components, appeared to be a valid and reliable questionnaire but measured different dimensions from the English version.

Keywords: Bengali language, confirmatory factor analysis, factor analysis, principal component analysis, Sino-Nasal outcome test-20

How to cite this article:
Misra P, Banerjee A, Goenka R, Nayak C, Ali SS, Koley M, Saha S. Psychometric analysis of the Bengali Version of the Sino-Nasal outcome Test-20 questionnaire in patients suffering from chronic rhino-sinusitis: A cross-sectional study. Ann Indian Acad Otorhinolaryngol Head Neck Surg 2021;5:57-66

How to cite this URL:
Misra P, Banerjee A, Goenka R, Nayak C, Ali SS, Koley M, Saha S. Psychometric analysis of the Bengali Version of the Sino-Nasal outcome Test-20 questionnaire in patients suffering from chronic rhino-sinusitis: A cross-sectional study. Ann Indian Acad Otorhinolaryngol Head Neck Surg [serial online] 2021 [cited 2022 Sep 28];5:57-66. Available from: https://www.aiaohns.in/text.asp?2021/5/2/57/332064

  Introduction Top

Chronic rhinosinusitis (CRS) is a common and debilitating condition affecting 134 million Indians.[1] CRS has been shown to impair quality of life (QOL), reduce productivity, increased risk of depression and sleep disorders,[2],[3] significant morbidity and health-care expenditure.[4] The patients suffer from discoloured nasal drainage, facial pain, difficulty breathing, and decreased sense of smell.[5]

The sino-nasal outcome test-20 (SNOT-20) is a user-friendly, patient-reported outcome measure aimed at measuring specific and general sino-nasal symptoms and is intended for the populations of people with rhinosinusitis, rather than simply rhinitis. Developed and validated in 2002 by Piccirillo et al.,[6] and later developing further insights into the domains and constructs,[7] it has showed a higher patient compliance because of the lower time and effort for the patients. It queries 20 symptoms of rhinosinusitis which can be assigned to five subgroups (nasal symptoms, paranasal symptoms, sleep-related symptoms, social impairment, and emotional impairment). The patients rate the severity of the symptoms on a 6-point Likert scale. The score of the SNOT-20 is calculated by summation of all the symptom scores. Therefore, scale values of the SNOT-20 range from 0 to 100. In addition, patients can circle those five symptoms which have the highest impact on the impairment.[8] During the last years, the SNOT-20 was increasingly used for QOL measurements in patients with CRS.[9],[10] It was validated in nonsurgical patients as well.[11]

Till date, there is no available validated Bengali version of SNOT-20 (SNOT-20-B). We intended to develop the Bengali version of the questionnaire through standardized forward-backwards translation, and subsequently evaluated whether the SNOT-20-B was a psychometrically sound tool to measure the construct and examined its cross-cultural adaptation considering linguistic equivalence.

  Subjects and Methods Top

Study design

This noninterventional, cross-sectional, validation study was a mixed method study; it consisted of standardized translation procedures, face validation by pilot testing, and field testing and psychometric assessment of the SNOT-20-B. The study was conducted during April–September 2017.

Study setting

It was conducted at the ear-nose-throat (ENT) outpatient of National Institute of Homoeopathy (NIH), Kolkata, under Ministry of AYUSH, Govt. of India. Institutional Ethics Committee approved the protocol prior to initiation (Ref. No. 5-023/NIH/PG/Ethical Comm. 2009/Vol. III/1973 (A/S), dated March 27, 2017).

Questionnaire translation stages

  1. Forward translation: An expert committee was constructed, consisting of trained psychologist experienced in the scale development and conventionally trained oto-rhino-laryngologists, linguistic experts, and research methodologists. First, two Bengali speakers, one oto-rhino-laryngologist and one linguistic expert, translated the English version of SNOT-20 into Bengali (T1 and T2)
  2. Synthesis of T1, 2: The two translators then agreed upon a consensus version of the translation (T1, 2). Then, the expert committee verified the version
  3. Back translation: Two English language translators (BT1 and BT2; one oto-rhino-laryngologist and one linguistic expert), blinded to the original English version, translated T1, 2 back into English independently
  4. Committee review: All the translations (T1 and T2, T1, 2, B1, and B2) were reviewed by the committee and a written report was prepared comparing the back-translations with the forward translations. Based on these, the prefinal version was developed
  5. Face validation: The prefinal version of the questionnaire was tested on randomly chosen 10 patients suffering from CRS and visiting the ENT outpatient clinic of the hospital for the purpose of testing contextual clarity, layout, language transparency, ease of understanding the content and use, comprehensibility of the instructions and response scales. Difficulties, if any, were noted. A written report was prepared by the interviewers, including detected insufficiencies and recommended changes and was then submitted back to the committee
  6. Committee appraisal: The final version of the SNOT-20-B was developed by the committee based on the inputs from face validity (supplementary file). The different translation stages and the complete study flow are presented in [Figure 1]
  7. Field testing and validation: During the development of the original English version, content validity of the SNOT-20-B questionnaire was already evaluated, and we refrained from repeating so [Figure 1].
Figure 1: Study flow diagram

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Inclusion criteria

The inclusion criteria included the cases suffering from CRS for 12 weeks or more (2020 ICD-10-CM diagnosis code J32.9), had not taken any treatment since last 2 weeks, and fulfilled the diagnostic criteria of American Academy of Otolaryngology – Head and Neck Surgery (AAO-HNS), age between 18 and 65 years, both sexes, literate patients who could read Bengali, and given written consent to participate in the study.

Exclusion criteria

These included cases suffering from uncontrolled systemic illness or life-threatening infections, suffering with the complications of CRS, cases insisting for or in need of surgical interventions, cases already undergoing treatment for CRS, substance abuse and/or dependence, pregnant or lactating women, patients with psychiatric diseases and self-reported immune-compromised states.

Sample size

Although recommendations for adequate sample size to conduct factor analysis lack clear scientifically sound recommendations and remain controversial,[12] a sample size between 50 and 250 is usually preferred with most authors recommending at least 100 participants.[13] Subject to item ratio (5:1 or 10:1) is also used to calculate the sample size based on Gorsuch's formula, thus necessitating 100–200 participants in this study.[14] However, out of 295 participants approached, we were able to capture 270 responses in total with a response rate of 91.5%, of which first 150 were subjected to principal component analysis (PCA) and the next 120 to confirmatory factor analysis (CFA).


Patients suffering from CRS who attended the ENT outpatient of the hospital on the days of data collection were approached by consecutive sampling and were invited to participate in the study subject to fulfilment of the pre-specified eligibility criteria.

Data collection

Prior to obtaining responses on the SNOT-20-B, all the participants were provided with patient information sheets in local vernacular Bengali and written informed consents were obtained. Patients' privacy was maintained by concealing all the identifiable information. Another section in the questionnaire sought information regarding patients' sociodemographic features. The filled-in SNOT-20-B questionnaires were put inside envelops and sealed at the study site. Thirty randomly chosen participants were selected for retest visits at approximately 2–3 weeks interval to fill the same questionnaire again. All the data were extracted in a specially designed Microsoft Excel spreadsheet and that was analyzed statistically.

Statistical analysis

It was conducted by using IBM® Statistical Package for the Social Sciences (SPSS)® software, version 20.0 and SPSS Amos ® version 20.0 (IBM Corp., Armonk, NY, USA). First, adequacy of sample was checked using Kaiser-Meyer-Olkin (KMO) value and data appropriateness for PCA using Bartlett's test of sphericity. The KMO value 0.50 and above[15] with significant Bartlett's test of sphericity (P < 0.05) was considered appropriate for the factor analysis. Then, exploratory factor analysis (EFA) using PCA with varimax rotation (Eigenvalue above 1) was conducted to examine the uni-dimensionality of the construct. The purpose was to test how much the groups of items represent a common underlying (latent) variable. In this, a dataset is simplified by reducing data dimensionality by eliminating the components with small eigenvalues (explained variance per variable) and therefore of lesser significance. Only factors with loadings of 0.40 and above were retained. Weak loadings, that is, failure to load above 0.39 on any component and general loadings of 0.40 on more than one component would lead to exclusion of the items from the matrix. Next, SNOT-20-B reliability was evaluated by analyses of internal inconsistency and test-retest reliability. High internal consistencies were denoted by Cronbach's alpha of 0.5–0.7[16] and average item-total correlation in a moderate range of 0.3–0.9. Alpha value of 0.9 and above was considered as excellent, while no meaningful construct was indicated by a correlation near 0.[17] Intra-class correlation coefficient (ICC) values above 0.7 indicated that SNOT-20-B was stable over time, 0.4–0.7 indicated fair reliability, while poor reliability was demonstrated by values <0.4.[18] Paired t-tests were used on randomly chosen 30 patients' responses to evaluate whether change in scores on the SNOT-20-B between the test-retest evaluations were statistically significant. Correlation statistics was used to assess the inter-item correlations between domains (item discriminant validity) and the overall SNOT-20-B (internal item convergence). The instrument was considered to be internally consistent if the correlation value was found to be 0.4 or higher. Concurrent validity was examined using Pearson's r statistics comparing the total SNOT-20-B scores with simultaneously measured EQ-5D-5 L scores (n = 270). Correlation coefficients of 0.10 were considered being small, 0.30 as moderate, and 0.50 as large. Finally a CFA model was developed to verify the goodness-of-fit of the a priori detected scales as suggested by EFA. Actually, the objective of CFA is to explain as much of the variation as possible with the model specified and to test whether the data fit a hypothesized measurement model. CFA allows verifying the factor structure of a set of observed variable by testing the hypothesis that a relationship exists between the observed variables and their underlying constructs. Thus, it is a multivariate analysis technique to analyze structural relationship. Causal modelling or path analysis hypothesizes causal relationships among both the manifest (observed directly and endogenous/dependent; presented in rectangular boxes) and latent variables (factors or hypothetical exogenous constructs that are presumed to exist, but not measured or observed directly and are invoked to explain observed covariations; presented in oval shapes) and tests the causal models with a linear equation system. In CFA, specific hypotheses are framed about the structure of factor loadings and then the inter-correlations are tested. The goodness of fit of the CFA models were evaluated utilizing the following multiple fit indices: Comparative Fit Index (CFI), Normed Fit Index (NFI), Tucker Lewis Index (TLI), root mean square error of approximation (RMSEA), standardized root mean square residual (SRMR), Bayesian Information Criterion (BIC), and Hoelter index. The recommendations for cut-off values indicating a good model fit are CFI or TLI ≥0.95, RMSEA ≤0.6 and SRMR ≤0.8.[19],[20] Statistical tests were two-tailed and were conducted with α fixed at 0.05.

  Results Top

Descriptive statistics

Descriptive statistics, namely socio-demographic features, and obtained response percentages on individual items on SNOT-20-B questionnaires have been presented in terms of means, standard deviations, medians, inter-quartile ranges, skewness, and kurtosis of each individual item [Table 1], [Table 2], [Table 3].
Table 1: Baseline sociodemographic features (n =270)

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Table 2: Descriptive statistics of the sino-nasal outcome test-20-B responses (n=270)

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Table 3: Response percentages on sino-nasal outcome test-20-B questionnaire (n=270)

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Exploratory factor analysis

Sample size was adequate as evidenced by the KMO = 0.712 (χ2190: 1232.850, P < 0.001), much greater than the minimum Kaiser criterion of 0.5. A significant Bartlett's test of sphericity (P < 0.001) also signified that the R-matrix was not an identity matrix. We performed extraction using principal component method for determining how many factors best explained the observed covariation matrix within the data set. The screeplot revealed high eigenvalue for the first 6 components and thereafter the curve began to tail off gradually before the final plateau was reached [Figure 2]. The correlation matrix was searched for values more than 0.9 to identify multi-co-linearity and singularity. Determinant of the correlation matrix was <0.001. Thus, multi-colinearity was not a problem for the dataset. All the items correlated well and none of the correlation coefficients were predominantly large; thus contradicting elimination of any item at this stage. Sample size of 150 was adequate for running PCA as the average communalities after extraction was 0.678, much above the preferred cut-off of 0.5 [Table 4]. The factor component matrix also supported the screeplot by representing information from initial unrotated solution and extracting 6 components explaining 67.8% of the total variance [Table 5] and [Table 6]. Each of the components with their respective Eigenvalues and percentage of total variances explained are presented in [Table 6]. The values were weights that related the item (or variable) to the respective factor. Display of coefficients was sorted by size. Factor loadings were similar to regression weights (or slopes) and represented the strength of the association between the variables and the factors. The rotated (varimax) component matrix was a matrix of factor loadings for each variable onto each factor. The absolute values <0.4 were suppressed, ensuring that factor loadings within ± 0.4 were not displayed in the output. After conducting factor rotation, those items were eliminated that loaded onto the same factor. Six sub-components of the main construct were identified and named as below: [Table 6].
Figure 2: Scree plot

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Table 4: Communalities-initial and after extraction (n=150; principal component analysis)

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Table 5: Total variances explained (n=150; principal component analysis)

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Table 6: Rotated component matrix-factor loadings revealing six component structures (n=150)

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  1. Psychological impact (Q14–20)
  2. Sleep disturbance (Q11, 12 and 13)
  3. Nasal discharge and earache (Q5, 6 and 9)
  4. Nasal distress (Q1 and 3)
  5. Ear and facial discomfort and dizziness (Q7, 8 and 10), and
  6. Flu-like symptoms (Q2 and 4).

Internal consistency

The Cronbach's alpha value for the overall SNOT-20-B was 0.860 (95% confidence interval 0.834–0.883) and alpha for the 6 subscales were 0.847, 0.820, 0.647, 0.693, 0.605 and 0.525 respectively, indicating acceptable to good reliability. The ICC, inter-item correlation matrix and item-total statistics also substantiated SNOT-20-B questionnaire as internally consistent or reliable. Estimated Spearman-Brown coefficient, Guttman's split-half coefficient, and average Guttman's lambda for the overall SNOT-20-B questionnaire were 0.715, 0.691 and 0.833 respectively.[Table 7], [Table 8], [Table 9]
Table 7: Internal consistency of the sino-nasal outcome test-20-B questionnaire (n=270)

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Table 8: Inter-item correlation matrix (n =270)

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Table 9: Item-total statistics (n=270)

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Test-retest reliability

SNOT-20-B subscale scores and total scores were largely stable with insignificant mean differences, thus indicating acceptable test-retest reliability [Table 10].
Table 10: Test-retest reliability of the sino-nasal outcome test-20-B questionnaire domains and total score (n =30)

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Concurrent validity

SNOT-20-B total score had a small but significant correlation with EQ-5D-5 L score (SNOT-20-B: 41.5 ± 17.1 vs. EQ-5D-5 L: 59.1 ± 13.5, Pearson's r = 0.165, P = 0.007) scores, thus ensuring acceptable concurrent validity.

Confirmatory factor analysis

The path coefficients of CFA model are not correlation coefficients. The meaning of the path coefficient theta 0.47 is that if the domain “restraint” increases by one standard deviation from its mean, the domain 'compulsion' would be expected to increase by 0.47 of its own standard deviations from its own mean while holding all other relevant regional connections constant. The indices of CFA that confirmed model fit (Chi-square = 517.396, degrees of freedom = 155, P < 0.001) were: CFI = 0.634, NFI: 0.561, TLI = 0.551, RMSEA = 0.140, SRMR = 0.298, BIC = 780.708, and Hoelter index (at α 0.05) = 43, indicating a mediocre model fit and six distinct components [Figure 3].
Figure 3: The confirmatory factor analysis model. PSI: Psychological impact, SLD: Sleep disturbance, NDE: Nasal discharge and earache, ND: Nasal distress, EFDD: Ear and facial discomfort and dizziness, FLS: Flu like symptoms

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  Discussion Top

SNOT-20 is a validated questionnaire comprised of 20 questions and aimed at assessing; sino-nasal symptoms in people with rhinosinusitis; but, until now, no validated Bengali version of the questionnaire was available. The English questionnaire underwent standardized forward-backward translation to produce the SNOT-20-B version. EFA using PCA of the SNOT-20-B identified 6 components– psychological impact, sleep disturbance, nasal discharge and earache, nasal distress, ear and facial discomfort and dizziness, and flu-like symptoms. The overall goodness of fit of the six-component model was further confirmed by CFA. SNOT-20-B appeared to be valid and reliable with Cronbach's α, ICC coefficients, test-retest reliability, and concurrent validity within acceptable limits.

Earlier, this SNOT-20-B was used in two homeopathy studies – initially in an open-label, single arm study,[21] and subsequently, in a double-blind, randomized, placebo-controlled trial (CTRI/2018/03/012557) as well; however, psychometric validity remained unevaluated. One of the major strengths of this study was to apply EFA and CFA on two different samples. Our study shows that the overall and individual subscales of SNOT-20-B were similar to other studies. Unlike other validation studies, there was no control (normal/healthy) group; hence, assessment of item discriminant validity was not possible. Besides, responsiveness of the questionnaire was not assessed because the treatment offered by the study site was homeopathy exclusively and that was not an accepted standard treatment for CRS until now. Our findings revealed that the internal consistency, either overall or six individual components, all were reasonable and comparable to the existing versions. Alpha measure depends on the number of items and covariances between items. A score below 0.70 suggests that the items within the tool may not be measuring the same underlying construct and poorly correlated items need to be deleted. It should also be kept in mind that alpha has very strict assumptions including unidimensionality, uncorrelated errors, and identical covariances between the items (tau equivalence). In most of the cases, these assumptions are violated and thus over-or underestimates the true reliability. Thus, alpha may not be the best choice for measuring reliability. The probable alternative may be Guttman's lambda or McDonald's omega which are not based on tau-equivalence. There were satisfactorily high inter-item correlations among the subscales. While running PCA, sample size achieved by us was much higher than the original SNOT-20 development and validation study;[6] even we achieved more 120 samples to perform CFA. Seventy-five percent (15/20) of the items had strong factor loadings of 0.60 and above. Second, the SNOT-20-B was administered to the patients who were competent in reading and understanding the Bengali language. Therefore, the study findings are generalized to Bengalee population only. Finally, the 6-component model had a mediocre, but acceptable model fit in CFA. Test-retest scores, similar to the study by Piccirillo et al. were highly correlated (r = 0.9). Thus, further translation and validation of the questionnaire are warranted into other Indian languages and on larger sample for better and large-scale utilization in a multi-ethnic Indian population. Another drawback was the consecutive sampling used that might have introduced sampling bias into the study.

Thus, the validated SNOT-20-B served as an important patient-administered outcome questionnaire to measure sino-nasal symptoms in CRS. Future research should include utilization of the SNOT-20-B as outcome measure in clinical trials. Hence, the responsiveness and sensitivity to change of the SNOT-20-B to measure symptoms and treatment effects need to be determined in future investigations. Finally, in order to confirm that SNOT-20-B can measure the impact of clinical treatment, the final step in this development will be to define a minimally important difference of change reflecting a clinically meaningful difference.

  Conclusions Top

The developed SNOT-20-B contains 20 items which are constructed within a six components model. It is a reasonably valid and reliable tool, enabled to measure symptom severity and QOL measure in Bengalee patients suffering from CRS. However, in order to strengthen the validity of the questionnaire, further analyses are recommended.


The authors appreciate the kind help received from Dr. Malay Mundle, Research Methodologist, Dr. Atanu Dogra, Psychologist, Mr. Kohinoor Chakraborty and Mr. Indrajit Mitra, Linguistic Experts for their services as expert panelist in the review committee. The authors are grateful to institutional heads, both academic and hospital section for allowing us to conduct the trial. We are also grateful to the patients for their sincere participation.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

  References Top

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Alt JA, Smith TL, Schlosser RJ, Mace JC, Soler ZM. Sleep and quality of life improvements after endoscopic sinus surgery in patients with chronic rhinosinusitis. Int Forum Allergy Rhinol 2014;4:693-701.  Back to cited text no. 3
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  [Figure 1], [Figure 2], [Figure 3]

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7], [Table 8], [Table 9], [Table 10]


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