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      Public Awareness of Early and Late Complications of Type 2 Diabetes - Application of Latent Profile Analysis in Determining Questionnaire Cut-Off Points

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          Abstract

          Objectives

          A questionnaire was designed to determine public understanding of early and late complications of Type 2 diabetes mellitus (T2DM).

          Methods

          A cross-sectional study was performed in participants who were selected using a multi-stage sampling method and a standard questionnaire of 67 questions was proposed. An expert panel selected 53 closed-ended questions for content validity to be included in the questionnaire. The reliability of the questionnaire was tested using Cronbach’s alpha coefficient giving a score of 0.84.

          Results

          Of the 825 participants, 443 (57.6%) were male, and 322 (41.87%) were 40 years or more. The proportion of low-, moderate- and high- awareness about T2DM and its complications was 29.26%, 62.68%, and 8.06%, respectively. Friends (56.31%) and internet and social networks (20.55%) were the 2 major sources of awareness, respectively. The medical staff (e.g., physicians) had the lowest share in the level of public awareness (3.64%) compared to other sources.

          Conclusion

          These results present data that shows the general population awareness of T2DM is low. Healthcare policymakers need to be effective at raising awarenes of diabetes and it should be through improved education.

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          Most cited references18

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          Statistical Power to Detect the Correct Number of Classes in Latent Profile Analysis.

          Stefany Coxe, P M H Cham, Jenn-Yun Tein (2013)
          Little research has examined factors influencing statistical power to detect the correct number of latent classes using latent profile analysis (LPA). This simulation study examined power related to inter-class distance between latent classes given true number of classes, sample size, and number of indicators. Seven model selection methods were evaluated. None had adequate power to select the correct number of classes with a small (Cohen's d = .2) or medium (d = .5) degree of separation. With a very large degree of separation (d = 1.5), the Lo-Mendell-Rubin test (LMR), adjusted LMR, bootstrap likelihood-ratio test, BIC, and sample-size adjusted BIC were good at selecting the correct number of classes. However, with a large degree of separation (d = .8), power depended on number of indicators and sample size. The AIC and entropy poorly selected the correct number of classes, regardless of degree of separation, number of indicators, or sample size.
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            Diabetic kidney disease: a report from an ADA Consensus Conference.

            Katherine Tuttle, George L. Bakris, Rudolf W. Bilous … (2014)
            The incidence and prevalence of diabetes mellitus have grown significantly throughout the world, due primarily to the increase in type 2 diabetes. This overall increase in the number of people with diabetes has had a major impact on development of diabetic kidney disease (DKD), one of the most frequent complications of both types of diabetes. DKD is the leading cause of end-stage renal disease (ESRD), accounting for approximately 50% of cases in the developed world. Although incidence rates for ESRD attributable to DKD have recently stabilized, these rates continue to rise in high-risk groups such as middle-aged African Americans, Native Americans, and Hispanics. The costs of care for people with DKD are extraordinarily high. In the Medicare population alone, DKD-related expenditures among this mostly older group were nearly $25 billion in 2011. Due to the high human and societal costs, the Consensus Conference on Chronic Kidney Disease and Diabetes was convened by the American Diabetes Association in collaboration with the American Society of Nephrology and the National Kidney Foundation to appraise issues regarding patient management, highlighting current practices and new directions. Major topic areas in DKD included (1) identification and monitoring, (2) cardiovascular disease and management of dyslipidemia, (3) hypertension and use of renin-angiotensin-aldosterone system blockade and mineralocorticoid receptor blockade, (4) glycemia measurement, hypoglycemia, and drug therapies, (5) nutrition and general care in advanced-stage chronic kidney disease, (6) children and adolescents, and (7) multidisciplinary approaches and medical home models for health care delivery. This current state summary and research recommendations are designed to guide advances in care and the generation of new knowledge that will meaningfully improve life for people with DKD.
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              An introduction to latent variable mixture modeling (part 1): overview and cross-sectional latent class and latent profile analyses.

              Kristoffer Berlin, Natalie Williams, Gilbert R. Parra (2014)
              Pediatric psychologists are often interested in finding patterns in heterogeneous cross-sectional data. Latent variable mixture modeling is an emerging person-centered statistical approach that models heterogeneity by classifying individuals into unobserved groupings (latent classes) with similar (more homogenous) patterns. The purpose of this article is to offer a nontechnical introduction to cross-sectional mixture modeling.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Osong Public Health Res Perspect
                Journal ID (iso-abbrev): Osong Public Health Res Perspect
                Journal ID (publisher-id): kphrp1
                Title: Osong Public Health and Research Perspectives
                Publisher: Korea Centers for Disease Control and Prevention
                ISSN (Print): 2210-9099
                ISSN (Electronic): 2233-6052
                Publication date (Print): October 2018
                Volume: 9
                Issue: 5
                Pages: 261-268
                Affiliations
                [a ]Modeling of noncommunicable diseases Research Center, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
                [b ]Department of Biostatistics, Modeling of noncommunicable diseases Research Center, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
                [c ]Department of Internal Medicine, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
                Author notes
                [* ]Corresponding author: Ali Reza Soltanian, Department of Biostatistics, Modeling of noncommunicable diseases Research Center, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran, E-mail: soltanian@ 123456umsha.ac.ir
                Article
                Publisher ID: ophrp-09-0261
                DOI: 10.24171/j.phrp.2018.9.5.08
                PMC ID: 6202015
                PubMed ID: 30402382
                SO-VID: b2342c03-0a4d-4322-b4dc-effb69bb073f
                Copyright © Copyright ©2018, Korea Centers for Disease Control and Prevention
                License:

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/)

                History
                Date received : 10 July 2018
                Date revision received : 17 September 2018
                Date accepted : 27 September 2018
                Categories
                Subject: Original Article

                Keywords: awareness,questionnaire,statistical model,type 2 diabetes
                Data availability:
                Keywords: awareness, questionnaire, statistical model, type 2 diabetes

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