Evidence map›Paper›PMID 40413266›Full record

ArticleScientific reports2025

Statistical and reliability analysis of communication disability severity in Saudi Arabia using novel probability distribution.

Laila A Al-Essa, Mohammed M Ali Al-Shamiri, Mohammed M A Almazah, Fathi M Hamdoon

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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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1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Laila A Al-EssaDepartment of Mathematical Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.
Mohammed M Ali Al-ShamiriDepartment of Mathematics, College of Sciences and Arts (Muhyil), King Khalid University, Muhyil, 61421, Saudi Arabia.
Mohammed M A AlmazahDepartment of Mathematics, College of Sciences and Arts (Muhyil), King Khalid University, Muhyil, 61421, Saudi Arabia. mmalmazah@kku.edu.sa.
Fathi M HamdoonFaculty of Science, Mathematics Department, Al Azhar University, Assuit Branch, Assiut, 71524, Egypt.

Funding

King Salman Center for Disability Research KSRG-2024-097
6 · The paper itself

Abstract

Understanding the distribution of communication disabilities is crucial for effective policy planning and resource allocation. This study introduces the Extended Generalized Inverted Kumaraswamy Standard Exponential (EGIKwS-Exp) distribution for modeling the percentage of Saudi individuals with severe or total communication disabilities aged two years and above across 13 administrative regions. The dataset, sourced from Disability Statistics 2023, exhibits significant variability, requiring a flexible probabilistic framework. The EGIKwS-Exp distribution, an extension of the exponential model, enhances adaptability for complex datasets like disability statistics. Key distributional and reliability properties, including hazard rate, reversed hazard rate, c umulative hazard, and survival functions, are derived. Parameter estimation is conducted using Maximum Likelihood Estimation, with Bayesian inference via MCMC Metropolis-Hastings, Asymptotic, Boot-P, Boot-T, and Highest Posterior Density confidence intervals ensuring robust analysis. Graphical reliability measures confirm the model's efficiency in capturing trends in communication disability data, offering a comprehensive framework for analyzing regional disparities and informing policymakers. By providing a robust statistical tool, this research supports more informed decision-making in disability studies and public health planning.

Indexed as

Communication DisordersPersons with DisabilitiesAdolescentAdultBayes TheoremChildChild, PreschoolFemaleHumansMaleModels, StatisticalProbabilityReproducibility of ResultsSaudi ArabiaSeverity of Illness IndexAsymptotic confidence intervalsBayesian estimationBoot-P CIBoot-T CICommunication disability statisticsEGIKwS-Exp distributionHazard rate functionHighest posterior density CIMaximum likelihood estimationMetropolis-Hastings algorithm

Identifiers

PMID40413266
PMCPMC12103557

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