|Year : 2021 | Volume
| Issue : 2 | Page : 79-92
Development of Bengali version of a questionnaire assessing impact of hyperuricemia on quality of life
Sangita Saha1, Rajat Chattopadhyay2, Satadal Das3, Paulami Sarkar2, Chintamani Nayak4, Koushik Bhar4, Pankhuri Misra4, Abhijit Chattopadhyay4, Priyanka Ghosh5, Subhasish Ganguly5, Shyamal Mukherjee3, Munmun Koley6, Subhranil Saha7
1 Departments of Organon of Medicine and Homoeopathic Philosophy, The Calcutta Homoeopathic Medical College and Hospital, Kolkata, West Bengal, India
2 Department of Practice of Medicine, The Calcutta Homoeopathic Medical College and Hospital, Kolkata, West Bengal, India
3 Department of Community Medicine, D. N. De Homoeopathic Medical College and Hospital, Kolkata, West Bengal, India
4 Department of Materia Medica, National Institute of Homoeopathy, Under Ministry of AYUSH, Government of India, Kolkata, West Bengal, India
5 Department of Organon of Medicine and Homoeopathic Philosophy, D. N. De Homoeopathic Medical College and Hospital, Kolkata, West Bengal, India
6 Department of Organon of Medicine and Homoeopathic Philosophy, State National Homoeopathic Medical College and Hospital, Lucknow, Uttar Pradesh, India
7 Department of Repertory, D. N. De Homoeopathic Medical College and Hospital, Kolkata, West Bengal, India
|Date of Submission||07-Dec-2020|
|Date of Acceptance||01-May-2021|
|Date of Web Publication||18-Aug-2021|
Department of Organon of Medicine and Homoeopathic Philosophy, The Calcutta Homoeopathic Medical College and Hospital, 265-266, Acharya Prafulla Chandra Road, Kolkata - 700 009, West Bengal
Source of Support: None, Conflict of Interest: None
Context: Hyperuricemia and gout has been found to be associated with reduced health-related quality of life (HRQoL); however, there is no available Bengali questionnaire assessing the same. Aims: We aimed to develop the Bengali version of a questionnaire and examine its cross-cultural adaptability considering linguistic equivalence. Settings and Design: A multicentric, mixed methods, cross-sectional study was conducted through consecutive sampling at the outpatients of three homeopathic hospitals in West Bengal. Subjects and Methods: The Bengali version of the questionnaire was produced by standardized forward–backward translations. Psychometric analysis was run to examine its factor structure, validity, and reliability. Statistical Analysis Used: Reliability was examined using internal consistency (n = 210). Construct validity was examined by exploratory factor analysis (n = 105) using principal component analysis (PCA; varimax rotation). Subsequently, confirmatory factor analysis (CFA; n = 105) was performed to verify the model fit. Results: The internal consistency (Cronbach's α =0.880; 95% confidence interval 0.855–0.902), test–retest reliability and concurrent validity of the questionnaire– all were within acceptable limits. The (Kaiser–Meyer–Olkin = 0.832) and Bartlett's test of sphericity (Chi-square: 1644.344 at df = 210, P < 0.001) both suggested adequacy of the sample. In factor analysis using varimax, all the items loaded above the prespecified value of 0.4 and identified 6 components, explaining 77% of the variation. One item revealed a negative variance; hence the whole component of 2 items was removed from further evaluation. The goodness-of-fit of the 5-components model in CFA was also acceptable (Comparative fit index = 0.702, tucker Lewis index = 0.641, Root Mean Square Error of Approximation = 0.156, and Standardized Root Mean Square Residual = 0.123). Conclusions: The developed Bengali version of the questionnaire consisting of 19 items and framed within 5 components, appeared to be a valid and reliable instrument measuring HRQoL in patients suffering from hyperuricemia.
Keywords: Bengali language, confirmatory factor analysis, hyperuricemia, principal component analysis, quality of life, questionnaire
|How to cite this article:|
Saha S, Chattopadhyay R, Das S, Sarkar P, Nayak C, Bhar K, Misra P, Chattopadhyay A, Ghosh P, Ganguly S, Mukherjee S, Koley M, Saha S. Development of Bengali version of a questionnaire assessing impact of hyperuricemia on quality of life. J Sci Soc 2021;48:79-92
|How to cite this URL:|
Saha S, Chattopadhyay R, Das S, Sarkar P, Nayak C, Bhar K, Misra P, Chattopadhyay A, Ghosh P, Ganguly S, Mukherjee S, Koley M, Saha S. Development of Bengali version of a questionnaire assessing impact of hyperuricemia on quality of life. J Sci Soc [serial online] 2021 [cited 2021 Dec 6];48:79-92. Available from: https://www.jscisociety.com/text.asp?2021/48/2/79/324072
| Introduction|| |
Hyperuricemia is an abnormal condition if the concentration of serum uric acid reaches to 357 μmol/l (6 mg/dL) for women and 416 μmol/l (7.0 mg/dL) for men. Hyperuricemia is due to an imbalance of increased production of uric acid and/or decreased excretion of uric acid. Over the past few decades, there has been an increasing global prevalence of hyperuricemia, both in developed and developing countries.,,,,, It is ascribed to all-cause mortality and different health issues such as metabolic syndrome,, hypertension, cardiovascular and cerebrovascular diseases,,,, insulin resistance, end stage renal disease, obesity, and dyslipidemia., Hyperuricemia also increases the risk of gout; however, only a minority of people with hyperuricemia develop gout.
Health-related quality of life (HRQoL) is increasingly being used as an outcome in clinical trials, effectiveness research, and research on quality of care. In recent studies, hyperuricemia and gout were found to be associated with reduced quality of life., Gout Assessment Questionnaire version 2 is a gout-specific patient-reported outcome measure with 24 items, consisting of 5 different subscales: gout concern overall, gout medication side effects, well-being during attack, unmet gout treatment needs and gout concern during attack. Each item is rated “strongly agree” to “strongly disagree,” “all of the time” to “none of the time” or “not a bit” to “extremely” on a 5-point Likert scale. A higher score denotes a greater impact of disease.
Till date, there is no available questionnaire assessing the impact of hyperuricemia on HRQoL. The Bengali version of the questionnaire was first conceptualized from the available GAQ-2 questionnaire through standardized forward–backward translation, and subsequently was used in three trials – two published, and one unpublished till now; but its validity and reliability remained unaddressed formally so far.
| Subjects and Methods|| |
This multi-centric, noninterventional, cross-sectional, validation study was a mixed method study. It consisted of initial conceptualization of the questionnaire items, standardized translation procedures, face validation by pilot testing, field testing, and psychometric assessment of the questionnaire.
It was conducted at the outpatient departments of the Calcutta Homoeopathic Medical College and Hospital (CHMCH), National Institute of Homoeopathy (NIH), and D. N. De Homoeopathic Medical College and Hospital (DNDHMCH). Institutional Ethical Committees (IEC) of respective institutions approved the protocol prior to initiation (CHMCH Ref. No. CHMCH/IEC/02/19, dated Sept 9, 2019; NIH Ref. No. 5–23/NIH/PG/Ethical Comm. 2009/Vol. 5/2671 [A/S], dated April 10, 2018; and DNDHMCH Ref. No. DHC/Eth-45/2018/64319, dated September 17, 2019).
Draft of initial version
Questionnaire items were derived from the available GAQ-2 questionnaire and the initial version was drafted by incorporating the items those seemed to be appropriate in hyperuricemia.
After little modifications, the items were finalized in the committee meeting.
Questionnaire translation stages
The translation method followed standardized forward–backward processes as follows:
- Forward translation: An expert committee was constructed, consisting of trained psychologist experienced in scale development and psychologists, linguistic experts, and research methodologists. First, two Bengali speakers, one psychologist and one linguistic expert, translated the English items into Bengali (T1 and T2)
- 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
- Back translation: Two English language translators (BT1 and BT2; one psychologist and one linguistic expert), blinded to the original English version, translated T1, 2 back into English independently
- 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
- Face validation: The prefinal version of the questionnaire was tested on randomly chosen 15 patients visiting the out-patient clinics of the three hospitals 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
- Committee appraisal: The final version of the questionnaire was developed by the committee based on the inputs from face validity [Appendice 1] and [Appendice 2]. The different translation stages and the complete study flow are presented in [Figure 1]
- Field testing and validation: During development of the original English version, content validity of the questionnaire was already evaluated, and we refrained from repeating so [Figure 1].
Diagnosed cases of hyperuricemia (serum uric acid level above 7 mg/dl in men and above 6 mg/dl in women) (2020 ICD-10-CM diagnosis code E79.0) with or without gout, age 18–65 years, patients of either sex, literate patients having ability to read Bengali and consenting to participate.
Cases of secondary gout, advanced cases with deformities, psychiatric diseases, uncontrolled systemic illness and/or infections, substance abuse and/or dependence, pregnant and puerperial women, lactating mothers, and self-reported immune-compromised state.
Although recommendations for adequate sample size to conduct factor analysis lack clear scientifically sound recommendations and remain controversial, still a sample size between 50 and 250 is usually preferred with most authors recommending at least 100 subjects. Gorsuch's formula of subject to item ratio (5:1 or 10:1) is also used for estimation of sample size for validation studies, thus indicating a requirement of 95–190 samples for our study. However, out of 243 participants approached, we were able to capture 210 responses in total with a response rate of 86.4%, of which first 105 were subjected to principal component analysis (PCA) and the next 105 to confirmatory factor analysis (CFA); thus, sample size for out study might be considered as adequate.
Patients suffering from hyperuricemia with or without gout who attended the outpatients of the hospitals on the days of data collection were approached by consecutive sampling and were invited to participate in the study subject to fulfilment of the prespecified eligibility criteria.
Prior to obtaining responses on the patients' self-administered questionnaire, 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 questionnaires were put inside envelops and sealed at the study site. The same self-administered questionnaire was filled in again by 30 participants selected randomly after 2 weeks, most likely in patients whose clinical state had not changed (confirmed over phone). A Microsoft Excel spreadsheet was used for extraction of data and finally that was subjected to statistical analysis.
It was conducted using IBM® Statistical Package for 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 with significant Bartlett's test of sphericity (P < 0.05) was considered appropriate for factor analysis. Then, exploratory factor analysis (EFA) using PCA with varimax rotation (Eigenvalue above 1) was conducted to examine the unidimensionality 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 0.30 and above were retained. Weak loadings, that is, failure to load above 0.29 on any component and general loadings of 0.30 on more than one component would lead to exclusion of the items from the matrix. Next, questionnaire 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 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. Intra-class correlation coefficient values above 0.7 indicated that the questionnaire was stable over time, 0.4–0.7 indicated fair reliability, while poor reliability was demonstrated by values <0.4. Paired t-tests were used on randomly chosen 30 patients' responses to evaluate whether change in scores between the test–retest evaluations were statistically significant. Test–retest reliability was checked at 15 days interval in patients whose clinical state had not changed (confirmed over phone). Correlation statistics was used to assess the inter-item correlations between domains (item discriminant validity) and the overall score (internal item convergence). The instrument was considered to be internally consistent if the correlation value was found to be 0.4 or higher. Subsequently, concurrently validity was evaluated by Pearson's r statistics comparing the total questionnaire scores with simultaneously obtained Measure Yourself Medical Outcome Profile version 2 (MYMOP-2) profile scores of randomly selected 60 cases. 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. In CFA, an existence of a relationship is hypothesized between a set of experimental variables and their underlying constructs. A multivariate analysis substantiates this factor structure. 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, 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., Statistical tests were two-tailed and were conducted with α fixed at 0.05.
| Results|| |
Descriptive statistics, namely sociodemographic features and obtained response statistics on individual items on the questionnaire were presented in terms of means, standard deviations, medians, inter-quartile ranges, skewness, and kurtosis [Table 1] and [Table 2].
|Table 2: Descriptive statistics of the questionnaire items, domains, and overall score (n=210)|
Click here to view
Exploratory factor analysis
Sample size was adequate as evidenced by the KMO = 0.832, much greater than the minimum Kaiser criterion of 0.5. A significant Bartlett's test of sphericity (Chi-square: 1644.344 at df = 210, P < 0.001) also signified that the R-matrix was not an identity matrix. Extraction was performed using principal component method to verify the number of factors those bet explained the covariation matrix within the experimental sets of data. The first 6 components disclosed high eigenvalues, and subsequently, the curve dropped gradually before the final plateau was reached [Figure 2]. The correlation matrix was searched for values >0.9 to identify multicollinearity and singularity. Determinant of the correlation matrix was 0.375. Thus, multicollinearity 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 105 was adequate for running PCA as the average communalities after extraction was 0.770, above the preferred cut-off of 0.5. The factor component matrix also supported the scree plot by representing information from initial unrotated solution and extracting 6 components explaining 77% of the total variance [Table 3] [Figure 2]. Each of the components with their respective Eigenvalues and percentage of total variances explained are presented in [Table 3]. The values were loads that related the variable to the particular factor. The exhibit of coefficients was arranged by size. Factor loadings were analogous to regression slopes and symbolized the strength of relationship between the factors and the components. 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 subcomponents of the main construct were identified and named as below [Table 4]:
|Table 4: Rotated component matrix – Factor loadings revealing 6 component structures (n=105)|
Click here to view
- Items Ia, Ib, Ic, Id, and Ih: “Concern”
- Items IIb, IIc, IId, IIIb, IIId, IIIe, IIIf, and IIIg: “Inconvenience in daily activities”
- Items Ij and IIa: “Professional restriction”
- Items If and IIIa: “Mood and temperament”
- Items Ie and Ik: “Treatment hazards”
The Cronbach's alpha value for the overall questionnaire was 0.880 and alpha for the 5 subscales ranged between 0.662 and 0.906, indicating acceptable to good reliability. Estimated Spearman–Brown coefficient and Guttman's lambda split-half coefficient were also in acceptable limits [Table 5].
Individual subscale scores and total scores were largely stable with insignificant mean differences (all P > 0.05), thus indicating acceptable test–retest reliability [Table 6].
|Table 6: Test–retest reliability of the questionnaire domains and overall score (n=30)|
Click here to view
The questionnaire was found to have acceptable concurrently validity (Pearson's r = 0.446, P < 0.001).
Confirmatory factor analysis
The path coefficients of CFA model are not correlation coefficients. Path coefficient θ of 0.93 means that with 1 standard deviation increase of the mean of the domain 'restraint', the domain 'compulsion' would be expected to increase by 0.93 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 = 501.6, degrees of freedom = 142, P < 0.001) were: CFI = 0.702, NFI = 0.636, TLI = 0.641, RMSEA = 0.156, SRMR = 0.123, BIC = 725.017, and Hoelter index (at α 0.05) =36, indicating an acceptable model fit and five distinct components [Figure 3].
| Discussion|| |
The developed questionnaire to assess impact of hyperuricemia on quality of life appeared to be a valid and reliable instrument. EFA using PCA of the questionnaire identified a 6-component model. One item belonging to component 6 revealed a negative variance; hence the whole component was removed from further evaluation. The overall goodness of fit of the 5-component model was confirmed by CFA.
One of the major strengths of this study was to apply EFA and CFA on two different samples, each of size of 105. Our study yielded similar number of 5 individual subscales of the questionnaire as in earlier studies, although the generated subscales were somewhat different than that of GAQ, 2006, and GAQ-2, 2008. The original GAQ has 7 subscales – gout concern, well-being, productivity, gout pain and severity, treatment convenience, treatment satisfaction, and treatment bother. The GAQ-2 is scored in 5 subscales– gout concern overall, gout medication side effects, unmet gout treatment need, well-being during attack, and gout concern during attack. Both GAQ and GAQ-2 were aimed at assessing HRQoL in patients suffering from gout; whereas, ours aimed at evaluating HRQoL in patients with hyperuricemia, with or without gout. Both GAQ and GAQ-2 were provided with a 5-point Likert scales; however, in this newly developed questionnaire, we added another option – “irrelevant.” GAQ and GAQ-2 studies involved sample sizes of 126 and 308, respectively; ours were 210. None of these 3 questionnaires apply any recall period. Concurrent validity of GAQ-2 was evaluated using SF-36 questionnaire, but we used MYMOP-2 in place of SF-36. Scores of all these 3 questionnaires are interpreted in similar ways – higher scores represent higher disease burden. GAQ subscales were developed by factor analysis and GAQ-2 subscales by Rasch modelling and CFA with structural equation modeling (SEM). Test–retest reliability was assessed for the very first time in our study and was found to be satisfactory. Cronbach's alpha for GAQ and GAQ-2 were 0.78–0.97 and 0.60–0.94, respectively, similar to our findings of 0.662–0.906.
In the unrotated component matrix of 21 items, 81% (17/21) had strong factor loadings of 0.60 and above. After rotation and deletion of a 2-items component, the 5-component model of the questionnaire had an acceptable model fit in CFA. Thus, further translation and validation of the questionnaire is warranted into other Indian languages and on larger sample for better and large-scale utilization in a multi-ethnic Indian population.
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 hyperuricemia until now. Our findings revealed that the internal consistency was overall reasonable and comparable to the existing versions. Alpha coefficients that are <0.5 are usually unacceptable, especially for scales purporting to be unidimensional. A low value for alpha may mean that there aren't enough questions on the test. Adding more relevant items to the test can increase alpha. The usual practice is to remove few poorly correlated items (coefficients <0.30) to increase the overall consistency or to add more items that constitute the construct. However, as the number of items was not too many, and the overall score were higher than 0.30, and retaining all the items revealed a fair fit in the CFA model, we decided not to eliminate any of the items. 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 is a relationship between alpha (α), theta (θ), and omega (Ω) coefficients. If the items take parallel values, three coefficients are equal each other and will be 1.0. Otherwise, the relationship of magnitude for the coefficients will be α < θ < Ω. Another important caveat was that consecutive sampling used that might have introduced sampling bias into the study.
Thus, the validated Bengali questionnaire may serve as an important patient-administered outcome questionnaire to measure impact of hyperuricemia on HRQoL. Future research should include utilization of the questionnaire as outcome measure in clinical trials. The responsiveness and sensitivity to change of the questionnaire to measure symptoms and treatment effects need to be determined in future investigations. Finally, to confirm that the developed questionnaire 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. The questionnaire assessed 5 distinct dimensions that may provide evaluation of efficacy or effectiveness of any targeted interventions.
| Conclusions|| |
The developed Bengali questionnaire contains 19 items which are constructed within a 5-component model. It is a reasonably valid and reliable tool, enabled to measure impact of hyperuricemia on HRQoL in Bengali patients. However, to strengthen the validity of the questionnaire further, independent replications are recommended.
The authors appreciate the kind help received from Dr. Malay Mundle, Research Methodologist, Dr. Atanu Dogra and Dr. Kaustabh Manna, Psychologists, and Mr. Kohinoor Chakraborty and Mr. Indrajit Mitra, Linguistic Experts, for their services as expert panelist in the review committee. 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|| |
Ralston SH, Penman ID, Strachan MW, Hobson RP, editors. Davidson's Principles and Practice of Medicine. 23rd
ed. Edinburgh: Elsevier Ltd.; 2018. p. 1360.
Zhu Y, Pandya BJ, Choi HK. Prevalence of gout and hyperuricemia in the US general population: The National Health and Nutrition Examination Survey 2007-2008. Arthritis Rheum 2011;63:3136-41.
Liu R, Han C, Wu D, Xia X, Gu J, Guan H, et al.
Prevalence of hyperuricemia and gout in mainland China from 2000 to 2014: A systematic review and meta-analysis. Biomed Res Int 2015;2015:762820.
Han B, Wang N, Chen Y, Li Q, Zhu C, Chen Y, et al.
Prevalence of hyperuricaemia in an Eastern Chinese population: A cross-sectional study. BMJ Open 2020;10:e035614.
Zhang Q, Gong H, Lin C, Liu Q, Baima Y, Wang Y, et al.
The prevalence of gout and hyperuricemia in middle-aged and elderly people in Tibet Autonomous Region, China: A preliminary study. Medicine (Baltimore) 2020;99:e18542.
Remedios C, Shah M, Bhasker AG, Lakdawala M. Hyperuricemia: A reality in the Indian obese. Obes Surg 2012;22:945-8.
Ali N, Perveen R, Rahman S, Mahmood S, Rahman S, Islam S, et al.
Prevalence of hyperuricemia and the relationship between serum uric acid and obesity: A study on Bangladeshi adults. PLoS One 2018;13:e0206850.
Konta T, Ichikawa K, Kawasaki R, Fujimoto S, Iseki K, Moriyama T, et al.
Association between serum uric acid levels and mortality: A nationwide community-based cohort study. Sci Rep 2020;10:6066.
Zhao Y, Yu Y, Li H, Li M, Zhang D, Guo D, et al.
The association between metabolic syndrome and biochemical markers in Beijing adolescents. Int J Environ Res Public Health 2019;16:1-10.
Borges RL, Ribeiro AB, Zanella MT, Batista MC. Uric acid as a factor in the metabolic syndrome. Curr Hypertens Rep 2010;12:113-9.
Zhao G, Huang L, Song M, Song Y. Baseline serum uric acid level as a predictor of cardiovascular disease related mortality and all-cause mortality: A meta-analysis of prospective studies. Atherosclerosis 2013;231:61-8.
Li M, Hu X, Fan Y, Li K, Zhang X, Hou W, et al.
Hyperuricemia and the risk for coronary heart disease morbidity and mortality a systematic review and dose-response meta-analysis. Sci Rep 2016;6:19520.
Zhang W, Iso H, Murakami Y, Miura K, Nagai M, Sugiyama D, et al.
Serum uric acid and mortality form cardiovascular disease: EPOCH-JAPAN Study. J Atheroscler Thromb 2016;23:692-703.
Kamei K, Konta T, Hirayama A, Ichikawa K, Kubota I, Fujimoto S, et al.
Associations between serum uric acid levels and the incidence of nonfatal stroke: A nationwide community-based cohort study. Clin Exp Nephrol 2017;21:497-503.
Toyoki D, Shibata S, Kuribayashi-Okuma E, Xu N, Ishizawa K, Hosoyamada M, et al.
Insulin stimulates uric acid reabsorption via regulating urate transporter 1 and ATP-binding cassette subfamily G member 2. Am J Physiol Renal Physiol 2017;313:F826-34.
Hsu CY, Iribarren C, McCulloch CE, Darbinian J, Go AS. Risk factors for end-stage renal disease: 25-year follow-up. Arch Intern Med 2009;169:342-50.
Oguri M, Fujimaki T, Horibe H, Kato K, Matsui K, Takeuchi I, et al.
Obesity-related changes in clinical parameters and conditions in a longitudinal population-based epidemiological study. Obes Res Clin Pract 2017;11:299-314.
Liu F, Du GL, Song N, Ma YT, Li XM, Gao XM, et al.
Hyperuricemia and its association with adiposity and dyslipidemia in Northwest China: Results from cardiovascular risk survey in Xinjiang (CRS 2008-2012). Lipids Health Dis 2020;19:58.
Chen JH, Pan WH, Hsu CC, Yeh WT, Chuang SY, Chen PY, et al.
Impact of obesity and hypertriglyceridemia on gout development with or without hyperuricemia: A prospective study. Arthritis Care Res (Hoboken) 2013;65:133-40.
Chhana A, Lee G, Dalbeth N. Factors influencing the crystallization of monosodium urate: A systematic literature review. BMC Musculoskelet Disord 2015;16:296.
Bang ZH, Lee YJ, Choi JH, Kim Y, An SH, Lee MK, et al.
Association between hyperuricemia and health-related quality of life in Korean adults: Based on the seventh Korean National Health and Nutrition Examination Survey (2016-2017). Korean J Fam Pract 2019;9:532-8.
Scire CA, Manara M, Cimmino MA, Govoni M, Salaffi F, Punzi L, et al.
Gout impacts on function and health-related quality of life beyond associated risk factors and medical conditions: Results from the KING observational study of the Italian Society for Rheumatology (SIR). Arthritis Res Ther 2013;15:R101.
Hirsch JD, Lee SJ, Terkeltaub R, Khanna D, Singh J, Sarkin A, et al.
Evaluation of an instrument assessing influence of Gout on health-related quality of life. J Rheumatol 2008;35:2406-14.
Saha S, Sarkar P, Chattopadhyay R, Saha S. An open-label prospective observational trial for assessing the effect of homoeopathic medicines in patients suffering from gout. Indian J Res Homoeopathy 2019;13:236-43. [Full text]
Nayak C, Pattanaik N, Chattopadhyay A, Misra P, Bhar K, Michael J, et al.
Individualized homeopathic medicines and Urtica urens mother tincture in treatment of hyperuricemia: An open, randomized, pragmatic, pilot trial. J Complement Integr Med 2020;Online ahead of print. doi: 10.1515/jcim-2020-0129.
Anthoine E, Moret L, Regnault A, Sébille V, Hardouin JB. Sample size used to validate a scale: A review of publications on newly-developed patient reported outcomes measures. Health Qual Life Outcomes 2014;12:176.
Preacher KJ, MacCallum RC. Exploratory factor analysis in behavior genetics research: Factor recovery with small sample sizes. Behav Genet 2002;32:153-61.
Gorsuch RL. Factor Analysis. 2nd
ed. Hillsdale: Lawrence Erlbaum Associates; 1983.
Comrey AL, Lee HB. A First Course in Factor Analysis. 2nd
ed. Hillsdale, NJ: Lawrence Erlbaum Associates; 1992. p. 488.
Sitzia J. How valid and reliable are patient satisfaction data? An analysis of 195 studies. Int J Qual Health Care 1999;11:319-28.
Streiner DL, Norman GR. Health Measurement Scales: A Practical Guide to their Development and Use. Oxford: Oxford University Press; 2008.
Cicchetti DV. Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychol Assess 994;6:284-90.
Maccallum RC, Browne MW, Sugawara HM. Power analysis and determination of sample size for covariance structure modeling of fit involving a particular measure of model. Psychol Methods 1996;13:130-49.
Marx RG, Menezes A, Horovitz L, Jones EC, Warren RF. A comparison of two time intervals for test-retest reliability of health status instruments. J Clin Epidemiol 2003;56:730-5.
Hu L, Bentler P. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Model 1999;6:1-55.
Brown TA. Confirmatory Factor Analysis for Applied Research. New York, NY: Guilford Press; 2006.
Taylor WJ. Gout measures: Gout Assessment Questionnaire (GAQ, GAQ2.0), and physical measurement of tophi. Arthritis Care Res (Hoboken) 2011;63 Suppl 11:S59-63.
Colwell HH, Hunt BJ, Pasta DJ, Palo WA, Mathias SD, Joseph-Ridge N. Gout Assessment Questionnaire: Initial results of reliability, validity and responsiveness. Int J Clin Pract 2006;60:1210-7.
[Figure 1], [Figure 2], [Figure 3]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]