Screening and Management Strategies for Chronic Diseases in High-Risk Populations: A Systematic Review
| Author | Ehsan Moradi-Joo | en |
| Author | Seyed Mohammad Salehi Behbahani | en |
| Author | Maryam Ebrahimi Lagha | en |
| Author | Esmail Mousavi Asl | en |
| Author | Siamak Baghaei | en |
| Author | Yousef Shaabani Mahmoudabad | en |
| Author | Azita Hasanian | en |
| Author | Mohsen Davarpanah | en |
| Author | Ahmad Fakhri | en |
| Orcid | Ehsan Moradi-Joo [0000-0001-6375-1475] | en |
| Orcid | Seyed Mohammad Salehi Behbahani [0009-0006-9413-9629] | en |
| Orcid | Maryam Ebrahimi Lagha [0009-0009-5486-5580] | en |
| Orcid | Esmail Mousavi Asl [0000-0002-1322-5423] | en |
| Orcid | Siamak Baghaei [0000-0003-0338-0611] | en |
| Orcid | Ahmad Fakhri [0000-0002-3648-6757] | en |
| Accessioned Date | 2026-07-14T18:30:25Z | |
| Issued Date | 2026-04-30 | en |
| Abstract | Context: Chronic diseases such as diabetes, cardiovascular conditions, cancers, and respiratory illnesses remain leading causes of global mortality, especially among high-risk groups including the elderly, patients with underlying conditions, and socially disadvantaged populations. Early screening and tailored management strategies are essential to reduce disease burden and improve outcomes. Methods: This systematic review and meta-analysis was conducted in line with PRISMA 2020 guidelines and registered in PROSPERO (CRD42025234789). Comprehensive searches were performed across seven databases (PubMed, Scopus, Web of Science, Embase, Cochrane Library, Google Scholar, and medRxiv) up to October 25, 2025. Of 18 eligible studies, 10 contributed complete data for meta-analysis. Statistical analyses were performed using the DerSimonian and Laird random-effects model in R (version 4.3.1). Pooled sensitivity, specificity, AUC, diagnostic odds ratio (DOR), and likelihood ratios (LR+ and LR-) were calculated. Subgroup analyses were conducted by biomarker type, platform, sample type, and disease stage. Study quality and risk of bias were assessed using QUADAS-2, the recommended tool for diagnostic accuracy studies. Results: A total of 18 studies met inclusion criteria, of which 10 contributed complete data for meta-analysis. The pooled sensitivity was 0.86 (95% CI: 0.82 - 0.89; n = 10 studies, 9,840 participants), specificity was 0.88 (95% CI: 0.84 - 0.91; n = 10 studies, 9,840 participants), and AUC was 0.90 (95% CI: 0.87 - 0.93; n = 9 studies, 8,950 participants). DOR and likelihood ratios confirmed strong diagnostic performance. Subgroup analyses showed that miRNA biomarkers outperformed lncRNA and circRNA, while electrochemical platforms demonstrated higher accuracy compared to optical and nanotechnology-based systems. Serum samples and early-stage screening yielded higher diagnostic validity. Moderate heterogeneity was observed (I² = 47 - 59%). Publication bias was detected for specificity but had limited impact after adjustment. Conclusions: Emerging screening technologies — particularly RNA biomarkers and artificial intelligence algorithms — demonstrate high diagnostic accuracy for chronic diseases in high-risk populations. These findings support the integration of advanced screening tools into health programs, resource allocation, and evidence-based policymaking. | en |
| DOI | https://doi.org/10.5812/jjcdc-167826 | en |
| URI | https://brieflands.com/journals/jjcdc/articles/167826 | en |
| URI | https://repository.brieflands.com/handle/123456789/68010 | |
| Keyword | Screening | en |
| Keyword | Chronic Disease | en |
| Keyword | High-Risk Populations | en |
| Keyword | Biomarkers | en |
| Keyword | Artificial Intelligence | en |
| Keyword | Meta-Analysis | en |
| Publisher | Brieflands | en |
| Title | Screening and Management Strategies for Chronic Diseases in High-Risk Populations: A Systematic Review | en |
| Type | Systematic Review | en |
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