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Using Structural Equation Modelling in Routine Clinical Data on Diabetes and Depression: Observational Cohort Study.

Amy Ronaldson1, Mark Freestone1, Haoyuan Zhang2

  • 1Wolfson Institute of Population Health, Queen Mary University of London, London, United Kingdom.

Jmirx Med
|September 19, 2023
PubMed
Summary
This summary is machine-generated.

Structural Equation Modelling (SEM) shows promise for analyzing routine clinical data in type 2 diabetes patients. While SEM partially supported known associations between depression and poorer outcomes, data quality issues were noted, impacting its full utility.

Keywords:
PLS-SEMaccidentacute careclinical datadepressiondiabeteselectronic health recordsemergencyemergency careequation modellingpath analysisstructural equation modelling

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Area of Science:

  • Health Services Research
  • Biostatistics
  • Clinical Informatics

Background:

  • Routine clinical data is increasingly available for health research.
  • Structural Equation Modelling (SEM) can create research-friendly constructs from complex clinical variables.
  • SEM offers a potential analytic method for large-scale routine clinical data.

Purpose of the Study:

  • To apply SEM to routine clinical data in East London to model established associations in type 2 diabetes.
  • To investigate the relationship between depression, diabetic control, and healthcare utilization.
  • To test the utility of SEM in routine clinical data by modeling known clinical associations.

Main Methods:

  • SEM was employed on a large dataset of routine clinical data from East London.
  • The analysis investigated associations between depression, diabetic control, diabetes care, mental health treatment, and Accident & Emergency (A&E) use.
  • Latent variables and their associations were defined based on existing clinical knowledge.

Main Results:

  • Depression was associated with worse diabetic control and increased A&E use.
  • Worse diabetic control was unexpectedly linked to lower A&E use.
  • Mental health treatment did not significantly impact diabetic control, but diabetes care receipt was linked to better control and other positive outcomes.

Conclusions:

  • SEM partially supported the modeling of established clinical associations in type 2 diabetes patients using routine data.
  • The study highlighted data quality issues that may have affected SEM's utility.
  • Further data improvement could enhance the application of SEM in routine clinical datasets.