Evidence map›Paper›PMID 35211343›Full record

ArticleOman medical journal2022

Development and Validation of a Risk Score for Diabetes Screening in Oman.

Najla A Al-Lawati, Helman Alfonso, Jawad Al-Lawati

Abstract read
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Article in Oman medical journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Predictive voting model for early diagnosis of diabetes dataset.Frontiers in cardiovascular medicine · 2026
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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Najla A Al-LawatiDepartment of Non-Communicable Diseases, Directorate General of Primary Health Care, Ministry of Health, Muscat, Oman.
Helman AlfonsoFaculty of Health Sciences, School of Public Health, Curtin University, Perth, Australia.
Jawad Al-LawatiDirectorate General of Primary Health Care, Ministry of Health, Muscat, Oman.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesWe sought to develop and validate a diabetic risk score model as a non-invasive and self-administered screening tool to be used in the general Omani population.

methodsThe 2008 World Health Survey (WHS) data from Oman (n = 2720) was used to develop the risk score model. Multivariable logistic regression with the backward stepwise method was implemented to obtain risk factors regression coefficients for sex, age, educational attainment, marital status, place of residence, hypertension, body mass index (BMI), waist circumference, tobacco use, daily fruit and vegetable intake, and weekly physical activity. The model coefficients were multiplied by a factor of five to allocate each variable category a risk score. The total score was calculated as the sum of these individual scores. The score was validated using another Omani cohort (Sur Survey 2006 dataset, n = 1355) by calculating the area under the receiver-operating characteristic (ROC) curve (AUC), and optimal score sensitivity and specificity were determined.

resultsA robust diabetes risk score model was produced composed of eight variables (age, sex, education level, marital status, place of residence, hypertension, smoking status, and BMI) with an optimal cutoff point of ≥ 15 to classify persons with possible prevalent type 2 diabetes mellitus (T2DM). At this cutoff point, the model had a sensitivity of 71.1%, specificity of 74.4%, and AUC of 0.80 (95% confidence interval (CI): 0.78-0.82), when internally validated (in the WHS 2008 cohort). When the model was externally validated (using the Sur 2006 cohort), the optimal cutoff point for the score was ≥ 13, with a lower sensitivity (54.0%), higher specificity (79.0%), and an AUC of 0.74 (95% CI: 0.70-0.78). In contrast, the test of the old Omani, Kuwaiti, Saudi, and Finnish diabetes risk scores in our study populations showed poor performance of these models among Omanis with poor sensitivity (29% to 63.5%) and reasonable specificity (70% to 80%).

conclusionsThe developed diabetes risk score for screening prevalent T2DM, provides an easy-to-use self-administered tool to identify most individuals at risk of this condition in Oman. The score incorporates eight diabetes-associated risk factors that can also act as a tool to increase people's awareness about the importance of diabetes-related risk factors and provide information for policymakers to establish diabetes prevention programs.

Indexed as

Diabetes Mellitus, Type 2OmanRisk Factors

Identifiers

PMID35211343
PMCPMC8844580

What Socratic holds

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LicenceCC BY-NC
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.