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Projecting diabetes prevalence among Mexicans aged 50 years and older: the Future Elderly Model-Mexico (FEM-Mexico)
  1. Cesar Gonzalez-Gonzalez1,
  2. Bryan Tysinger2,
  3. Dana P Goldman3,
  4. Rebeca Wong4
  1. 1 School of Social Work, University of Colima, Colima, Mexico
  2. 2 Roybal Center for Health Policy Simulation, University of Southern California, Los Angeles, California, USA
  3. 3 Leonard D Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles, California, USA
  4. 4 Department of Preventive Medicine and Community Health, Sealy Center on Aging, University of Texas Medical Branch, Galveston, Texas, USA
  1. Correspondence to Dr Cesar Gonzalez-Gonzalez; cgonzalez31{at}ucol.mx

Abstract

Objective Diabetes has been growing as a major health problem and a significant burden on the population and on health systems of developing countries like Mexico that are also ageing fast. The goal of the study was to estimate the future prevalence of diabetes among Mexico’s older adults to assess the current and future health and economic burden of diabetes.

Design A simulation study using longitudinal data from three waves (2001, 2003 and 2012) of the Mexican Health and Aging Study and adapting the Future Elderly Model to simulate four scenarios of hypothetical interventions that would reduce diabetes incidence and to project the future diabetes prevalence rates among populations 50 years and older.

Participants Data from 14 662 participants with information on self-reported diabetes, demographic characteristics, health and mortality.

Outcome measures We obtained, for each scenario of diabetes incidence reduction, the following summary measures for the population aged 50 and older from 2012 to 2050: prevalence of diabetes, total population with diabetes, number of medical visits.

Results In 2012, there were approximately 20.7 million persons aged 50 and older in Mexico; 19.3% had been diagnosed with diabetes and the 2001–2003 diabetes incidence was 4.3%. The no-intervention scenario shows that the prevalence of diabetes is projected to increase from 19.3% in 2012 to 34.0% in 2050. Under the 30% incidence reduction scenario, the prevalence of diabetes will be 28.6% in 2050. Comparing the no-intervention scenario with the 30% and 60% diabetes incidence reduction scenarios, we estimate a total of 816 320 and 1.6 million annual averted cases of diabetes, respectively, for the year 2020.

Discussion Our study underscores the importance of diabetes as a disease by itself and also the potential healthcare demands and social burden of this disease and the need for policy interventions to reduce diabetes prevalence.

  • general diabetes
  • diabetes & endocrinology
  • health economics
  • public health

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Footnotes

  • Contributors CG-G, BT, RW and DPG made substantial contributions to the conception and design of the paper. BT assisted with analysis and interpretation of data for this manuscript. CG-G analysed the data and drafted and revised the paper. RW, BT and DPG revised the draft paper critically for important intellectual content. All authors approved of the final version to be published. CG-G is the guarantor.

  • Funding Research reported in this publication was supported by the National Institute On Aging of the National Institutes of Health under Award Numbers P30AG024968 and R01AG018016. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

  • Competing interests All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: support from the National Institute of Aging (Grants P30AG024968 and R01AG018016); Dr. Goldman serves as a consultant to Precision Health Economics, a health care consultancy that provides services to the life sciences industry, and holds equity in its parent company, Precision Medicine Group; and no other relationships or activities that could appear to have influenced the submitted work.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data sharing statement The MHAS is a public use data set that can be obtained from the MHAS webpage http://www.mhasweb.org/

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