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      Depression and self-care in diabetes; adjustment for misclassification bias: application of predictive weighting method

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          Abstract

          Objective

          This study aimed to investigate the association between depression and self-care in diabetic patients potentially influenced by biases in depression measurement using weighting the positive and negative predictive values.

          Methods

          In this cross-sectional study, 1050 patients informedly consented to participate in the study. Using a WHO-5 well-being index, the participants were examined for depressive mood as exposure. The sensitivity and specificity of this index in a systematic review study were 0.86 and 0.81, respectively. Self-care (that is outcome) was assessed using the Summary of Diabetes Self-Care Activities (SDSCA) questionnaire. To correct the misclassification bias of exposure, the predictive weighting method was used in the multivariable logistic regression model adjusted for covariates. Bootstrap sample with replacement and simulation was used to deal with random error.

          Results

          The mean age of patients was 42.8 ± 7.5 years. In this study, 70.1% of diabetic patients ( n = 720) were depressed based on the questionnaire score and only 52.7% ( n = 541) of them had appropriate self-care behaviors. Our study revealed a close relationship between self-care and covariates such as gender, depression, having comorbidities, abdominal obesity, economic status and education. The odds ratio of the association between depressive mood and lack of self-care in primary multivariable logistic regression was 2.21 (95% CI: 1.62-3.00, p < 0.001) and after misclassification bias adjusting, it was equal to 3.4 (95% CI: 1.7–6.6, p < 0.001). The OR percentage of bias was − 0.55.

          Conclusion

          After adjusting for depression misclassification bias and random error, the observed association between depression and self-care was stronger. According to our findings, psychiatric interventions, and counseling and education along with self-care interventions are necessary for these patients. Special attention should be paid to male, low economic classes, less educated and those having a history of comorbidities along with psychological assessment when improving the care and progress of treatment in diabetic patients is expected. Future studies are needed to clarify the role of other psychological disorders on self-care of diabetics.

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          Most cited references47

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          IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045

          Since the year 2000, IDF has been measuring the prevalence of diabetes nationally, regionally and globally.
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            The WHO-5 Well-Being Index: a systematic review of the literature.

            The 5-item World Health Organization Well-Being Index (WHO-5) is among the most widely used questionnaires assessing subjective psychological well-being. Since its first publication in 1998, the WHO-5 has been translated into more than 30 languages and has been used in research studies all over the world. We now provide a systematic review of the literature on the WHO-5.
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              Multicollinearity and misleading statistical results

              Jong Kim (2019)
              Multicollinearity represents a high degree of linear intercorrelation between explanatory variables in a multiple regression model and leads to incorrect results of regression analyses. Diagnostic tools of multicollinearity include the variance inflation factor (VIF), condition index and condition number, and variance decomposition proportion (VDP). The multicollinearity can be expressed by the coefficient of determination (Rh 2) of a multiple regression model with one explanatory variable (Xh ) as the model’s response variable and the others (Xi [i≠h] as its explanatory variables. The variance (σh 2) of the regression coefficients constituting the final regression model are proportional to the VIF ( 1 1 - R h 2 ) . Hence, an increase in Rh 2 (strong multicollinearity) increases σh 2. The larger σh 2 produces unreliable probability values and confidence intervals of the regression coefficients. The square root of the ratio of the maximum eigenvalue to each eigenvalue from the correlation matrix of standardized explanatory variables is referred to as the condition index. The condition number is the maximum condition index. Multicollinearity is present when the VIF is higher than 5 to 10 or the condition indices are higher than 10 to 30. However, they cannot indicate multicollinear explanatory variables. VDPs obtained from the eigenvectors can identify the multicollinear variables by showing the extent of the inflation of σh 2 according to each condition index. When two or more VDPs, which correspond to a common condition index higher than 10 to 30, are higher than 0.8 to 0.9, their associated explanatory variables are multicollinear. Excluding multicollinear explanatory variables leads to statistically stable multiple regression models.
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                Author and article information

                Contributors
                khosravi2000us@yahoo.com
                Journal
                BMC Public Health
                BMC Public Health
                BMC Public Health
                BioMed Central (London )
                1471-2458
                19 December 2023
                19 December 2023
                2023
                : 23
                : 2540
                Affiliations
                [1 ]Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, ( https://ror.org/01c4pz451) Tehran, Iran
                [2 ]Vice-chancellery for Research, Shahroud University of Medical Sciences, ( https://ror.org/023crty50) Shahroud, Iran
                [3 ]Department of Epidemiology, School of Public Health, Shahroud University of Medical Sciences, ( https://ror.org/023crty50) Shahroud, Iran
                Article
                17412
                10.1186/s12889-023-17412-x
                10729342
                38114954
                08ce053f-3504-4d4f-8eb8-3f4d2ef698f9
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 3 September 2023
                : 5 December 2023
                Funding
                Funded by: This work was partially supported by Tehran University of Medical Sciences as thesis in master of epidemiology (NO: 1397.153).
                Award ID: 1397.153
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2023

                Public health
                diabetes mellitus,predictive value,sensitivity and specificity,depression
                Public health
                diabetes mellitus, predictive value, sensitivity and specificity, depression

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