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[Future type 2 diabetes mellitus scenario estimated with a predictive dynamic simulation model].

Darío Gaytán-Hernández1, Sandra Olimpia Gutiérrez-Enríquez1, Aracely Díaz-Oviedo1

  • 1Facultad Enfermería, Universidad Autónoma de San Luis Potosí-Posgrado, San Luis Potosí, San Luis Potosí, Mexico. La correspondencia se debe dirigir a Sandra Olimpia Gutiérrez. Correo electrónico: sgutierr@uaslp.mx.

Revista Panamericana De Salud Publica = Pan American Journal of Public Health
|February 22, 2018
PubMed
Summary

Type 2 diabetes mellitus (T2DM) incidence is projected to grow exponentially. Key risk factors include the population aged 45-49, increased urban living, and television access in homes.

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Area of Science:

  • Epidemiology
  • Public Health Modeling
  • Biostatistics

Context:

  • Type 2 Diabetes Mellitus (T2DM) poses a significant public health challenge globally.
  • Accurate forecasting of T2DM incidence is crucial for resource allocation and intervention planning.
  • Mexico, specifically San Luis Potosí, faces increasing T2DM prevalence, necessitating predictive modeling.

Purpose:

  • To develop and validate a predictive dynamic model for estimating future Type 2 Diabetes Mellitus (T2DM) incidence rates.
  • To identify and quantify the influence of key socio-demographic factors on T2DM incidence.
  • To project T2DM scenarios up to 2030.

Summary:

  • A retrospective ecological study analyzed data from 58 municipalities in San Luis Potosí, Mexico (2013-2015).
  • Predictive dynamic submodels for T2DM, urban population, television access in dwellings, and the 45-49 age group were developed using linear correlation, multiple linear regression, and structural equations.
  • The holistic model explained 27.2% of T2DM variance, with population aged 45-49 years (156.69 units), television access (4.46 units), and urban population (2.84 units) being significant predictors.

Impact:

  • The model forecasts exponential T2DM growth, with incidence rates per 100,000 population projected to rise from 1,052.4 in 2015 to 2,351.1 in 2030.
  • Identified risk factors provide targeted insights for public health interventions.
  • This dynamic model offers a framework for understanding and predicting T2DM trends in similar demographic contexts.