Scholarlink

Background <p>Structural equation modeling (SEM) and causal modeling (CM) are powerful statistical approaches for identifying complex interrelationships among variables. However, their application in epidemiology remains limited and under-documented, especially in infectious disease research, which requires integrated analytical frameworks for effective control.</p> Methods <p>To examine how SEM and CM have been applied, their methodological characteristics, and reporting practices, a systematic and critical review was conducted following PRISMA guidelines. The search covered studies published between 1987 and 2025 across PubMed, Scopus, Web of Science, ScienceDirect, SpringerLink, Google Scholar, and the Directory of Open Access Journals. After rigorous screening, 458 articles were thoroughly evaluated.</p> Results <p>Most studies focused on neuropsychiatric (32.1%) and chronic (30.1%) conditions, with few addressing infectious diseases (24.0%), primarily malaria, tuberculosis, and HIV, particularly in low-income countries where context-specific evidence is urgently needed to inform targeted interventions. SEM studies predominantly used maximum likelihood estimation (57.7%) and large samples (<InlineEquation ID="IEq1"><EquationSource Format="TEX">\(\ge 200\)</EquationSource></InlineEquation> observations in 85%) with CB-SEM remaining the dominant approach across all sample size categories. In contrast, CM studies showed substantial variability in sample sizes across approaches, ranging from fewer than 100 to over 200 observations (coefficient of variation <InlineEquation ID="IEq2"><EquationSource Format="TEX">\(\approx 45\%\)</EquationSource></InlineEquation>), with no consistent sample size threshold across methods. Methodological reporting was often incomplete, notably regarding study design (17.4%), measurement validity (15.8%), and model fit criteria (5.2%), reducing transparency and reproducibility.</p> Conclusion <p>Overall, broader application of SEM and CM to infectious diseases, combined with improved methodological transparency, could substantially strengthen causal inference and guide evidence-based disease control strategies. Moreover, integrating longitudinal study designs would further enhance the robustness and interpretability of causal findings.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Applications of causal and structural equation modeling in epidemiology: a systematic and critical review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月21日

查看全文
  • Scholastique Midokpè Merveille Essetcheou,
  • Houétchénou Gislain Fortuné Dovonou,
  • Souand Peace Gloria Tahi,
  • Sèton Calmette Ariane Houetohossou,
  • Valère Kolawolé Salako,
  • Marcel Tadogbè Donou Hounsode,
  • Romain Glèlè Kakaï

摘要

Background

Structural equation modeling (SEM) and causal modeling (CM) are powerful statistical approaches for identifying complex interrelationships among variables. However, their application in epidemiology remains limited and under-documented, especially in infectious disease research, which requires integrated analytical frameworks for effective control.

Methods

To examine how SEM and CM have been applied, their methodological characteristics, and reporting practices, a systematic and critical review was conducted following PRISMA guidelines. The search covered studies published between 1987 and 2025 across PubMed, Scopus, Web of Science, ScienceDirect, SpringerLink, Google Scholar, and the Directory of Open Access Journals. After rigorous screening, 458 articles were thoroughly evaluated.

Results

Most studies focused on neuropsychiatric (32.1%) and chronic (30.1%) conditions, with few addressing infectious diseases (24.0%), primarily malaria, tuberculosis, and HIV, particularly in low-income countries where context-specific evidence is urgently needed to inform targeted interventions. SEM studies predominantly used maximum likelihood estimation (57.7%) and large samples (\(\ge 200\) observations in 85%) with CB-SEM remaining the dominant approach across all sample size categories. In contrast, CM studies showed substantial variability in sample sizes across approaches, ranging from fewer than 100 to over 200 observations (coefficient of variation \(\approx 45\%\)), with no consistent sample size threshold across methods. Methodological reporting was often incomplete, notably regarding study design (17.4%), measurement validity (15.8%), and model fit criteria (5.2%), reducing transparency and reproducibility.

Conclusion

Overall, broader application of SEM and CM to infectious diseases, combined with improved methodological transparency, could substantially strengthen causal inference and guide evidence-based disease control strategies. Moreover, integrating longitudinal study designs would further enhance the robustness and interpretability of causal findings.