Item request has been placed! ×
Item request cannot be made. ×
loading  Processing Request

Prevalence of Congenital Hypothyroidism in Iranian Neonates: A Systematic Review and Meta-Analysis.

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
  • معلومة اضافية
    • الموضوع:
    • نبذة مختصرة :
      Background Congenital hypothyroidism, characterized by insufficient thyroid hormone production at birth, significantly impacts neonatal growth and development. This deficiency can impair neonatal growth and development. This study aimed to estimate the prevalence of congenital hypothyroidism in Iranian neonates through a systematic review and meta-analysis. Methods A systematic search was conducted in PubMed, Scopus, Web of Science, Science Direct, SID, and Magiran up to January 2025 to identify relevant studies. Manual searches of key review articles and primary studies were also performed. Only studies published in Persian or English were included. The Newcastle-Ottawa Scale checklist was used to assess the risk of bias in the selected studies. Data were analyzed using Comprehensive Meta-Analysis software (version 3). Results Thirty-nine studies, comprising 3,124,702 neonates, were included in the analysis. The meta-analysis showed a congenital hypothyroidism prevalence of 2 per 1000 live births (95% CI: 0.002-0.003; p < 0.05). The prevalence was 3 per 1000 live births in both males (95% CI: 0.002-0.004; p < 0.05) and females (95% CI: 0.002-0.004; p < 0.05). No significant publication bias was observed (p > 0.05). Conclusion The elevated prevalence of congenital hypothyroidism in Iran highlights the necessity for enhanced screening programs, early diagnostic protocols, intervention, and allocation of necessary resources are essential for the effective management of congenital hypothyroidism prevalence. [ABSTRACT FROM AUTHOR]
    • نبذة مختصرة :
      Copyright of Studies in Medical Sciences is the property of Urmia University of Medical Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)