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Epidemiology of diabetes in Iran: A scoping review of studies published during 2015-2019
Fatemeh Gharishvandi1, Hamed Moheimani2, Shahnaz Esmaeili1
1Diabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, PO Box: 1411413137, Tehran, Iran.
Background:
Proper synthesis of existing epidemiologic studies on diabetes in Iran can guide future research efforts. We aimed to conduct a comprehensive scoping review on all research articles that investigated any aspect of diabetes epidemiology in Iran during 2015-2019.
Methods:
This work was conducted as a part of the Iran Diabetes Research Roadmap and completed under Arksey and O'Malley's framework for scoping reviews. The Scopus and PubMed databases were searched on Feb 15th, 2020. Eligible document types on diabetes epidemiology in the Iranian population, in Persian or English, that published during the 2015-2019 period underwent eligibility assessment. A total of 315 relevant articles were included and further analysis was performed on the original studies (n = 268). Through classifying them into six domains: Diabetes incidence; the prevalence of diabetes and associated factors; the incidence/prevalence of complications/comorbid conditions; mortality/survival; burden; and prediction modeling.
Results:
In total, 64 (20.3%) papers were published in Q1 journals, and 40 (12.6%) were international collaborations. No clear annual trend was present in the number of published primary or secondary articles, the portion of papers published in Q1 journals, international collaborations or relative domain proportions. Few review articles were found on prediction modeling, mortality or burden (excluding global studies).
Conclusions:
Our findings show a minor portion of works on diabetic epidemiology in Iran meets the quality standards of Q1 journals. Researchers have neglected some critical subjects and have occasionally fallen for common pitfalls of epidemiologic research. In particular, adhering to established guidelines can help authors implement rigorous methods to develop, validate, and deploy practical clinical prediction models. Researchers should prioritize investigating longitudinally collected data that aid in measuring disease incidence and enable casual inference.
Supplementary Information:
The online version contains supplementary material available at 10.1007/s40200-022-01094-0.
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