Holistic Evaluation of the Morbidity Due to Diabetes Mellitus Type 2 and Its Main Risk Factors in the State of San Luis Potosi, Mexico


Objective: To evaluate the morbidity due to diabetes mellitus type 2 within the State of San Luis Potosí, México, through a strong methodology, through which the multivariate relations were identified of the main social and environmental determiners in the disease, thus managing to quantify their respective levels of responsibility. Material and Methods: This evaluation began as a hypothesis of a multicasual theoretical model on diabetes mellitus and its main determining factors, which was analyzed through the application of multivariate exploratory statistical methodologies and confirmed as it is the case of the principal components analysis and the structural equation models. Results: Three components were extracted that explain the 96% of the total variance of the indicators; the main risk factors which were identified in the first component were, the use of the car, age, homes with TV use, urban life and feminine population; the indicators from the second and third component have little influence in the impact of the disease. Conclusions: the study shows the usefulness of the model for the analysis and prioritization of the environmental and social determiners of the disease, information that could sustain the design of public guidelines for the prevention and control of the analyzed disease.

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Darío, G. , Gabriela, D. , Jesús, M. and Ernesto, M. (2015) Holistic Evaluation of the Morbidity Due to Diabetes Mellitus Type 2 and Its Main Risk Factors in the State of San Luis Potosi, Mexico. Journal of Diabetes Mellitus, 5, 36-47. doi: 10.4236/jdm.2015.51005.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] World Health Organization (2012) Diabetes. Data and Numbers. Descriptive Note 312.
[2] International Diabetes Federation (2014) IDF Diabetes Atlas. 6th Edition.
[3] Hernández, M., Gutiérrez, J.P. and Reynoso-Noverón, N. (2013) Diabetes Mellitus in México. The State of the Outbreak. Public Health in Mexico, 55, 120-136.
[4] Pan American Health Organization (2007) Regional Strategy and Action Plan for an Integrated Approach on the Prevention and Control of Chronicle Diseases.
[5] International Diabetes Federation (2013) Risk Factors.
[6] Kuhmbou, W. (2013) The Escalating Diabetes Epidemic: Determinants of Prevalence Disparity between Country Income Groups. Master Thesis, University of Tromso, Noruega.
[7] Dinca-Panaitescu, S., Dinca-Panaitescu, M., Bryant, T., Daiski, I., Pilkington, B. and Raphael, D. (2011) Diabetes Prevalence and Income: Results of the Canadian Community Health Survey. Health Policy, 99, 116-123. http://www.ncbi.nlm.nih.gov/pubmed/20724018
[8] Bener, A., Zirie, M., Ibrahim, M., Janahi, I.M., Al-Hamaq, A., Musallam, M. and Wareham, N.J. (2009) Prevalence of Diagnosed and Undiagnosed Diabetes Mellitus and Its Risk Factors in a Population-Based Study of Qatar. Diabetes Research and Clinical Practice, 84, 99-106.
[9] Hu, D., Sun, L., Fu, P., Xie, J., Lu, J., Zhou, J., Yu, D., Whelton, P., He, J. and Gu, D. (2009) Prevalence and Risk Factors for Type 2 Diabetes Mellitus in the Chinese Adult Population: The InterASIA Study. Diabetes Research and Clinical Practice, 84, 288-295.
[10] Deo, S., Zantye, A., Mokal, R., Mithbawkar, S., Rane, S. and Takur, K. (2006) To Identify the Risk Factors for High Prevalence of Diabetes and Impaired Glucose Tolerance in Indian Rural Population. International Journal of Diabetes in Developing Countries, 26, 19-23.
[11] Escolar, A. (2009) Social Determiners Facing Life Styles in Diabetes Mellitus Type 2 in Andalucía: The Difficulty to Make Ends Meet or Obesity? Gaceta Sanitaria, 23, 427-432.
[12] Hu, F.B., Li, T.Y., Colditz, G.A., Willett, W.C. and Manson, J.E. (2003) Television Watching and Other Sedentary Behaviors in Relation to Risk of Obesity and Type 2 Diabetes Mellitus in Women. Journal of the American Medical Association, 289, 1785-1791. http://dx.doi.org/10.1001/jama.289.14.1785
[13] González, M.T. and Landero, R. (2008) Confirmation of an Explicative Model of Stress and of the Psychosomatic Symptoms through Structural Equations. Revista Panamericana de Salud Pública, 23, 7-18.http://www.scielosp.org/pdf/rpsp/v23n1/a02v23n1
[14] Oliver, A., Navarro, E., Meléndez, J.C., Molina, C. and Tomás, J.M. (2009) Structural Equations Model to Predict the Wellbeing and Functional Dependence on Elderly People in the Dominican Republic. Revista Panamericana de Salud Pública, 26, 189-196.
[15] San Luis Potosí State Government. México (2013) Foreign Affairs Bureau.
[16] Health Services in the State of San Luis Potosí. México (2012) Unique Information System for Epidemiological Surveillance (SUIVE-2007).
[17] National Institute for Statistics and Geography. México (2013) Time Series. Dataset: Total Population and 5 Years and Over by Demographic and Social Characteristics.
http://www.inegi.org.mx/sistemas/olap/Proyectos/bd/censos/comparativo/PDS.asp?s=est&c=17161 &proy=sh_pty5ds
[18] National Institute for Statistics and Geography. México (2011) Data Base on Population and Homes. Population and House Count. Population and Homes Census 2010.
[19] National Institute for Statistics and Geography. México (2011) Data Base for Registered Automobiles on the Road.http://www3.inegi.org.mx/sistemas/biinegi/default.aspx
[20] National Health Information System. México (2012) Population Estimates CONAPO-COLMEX. Coverage Data Base on Health Services. http://www.sinais.salud.gob.mx/basesdedatos/index.html
[21] National Population Council. México (2012) Data Base on Marginalization Indexes by Counties, 2005.
[22] National Population Council. México (2012) Data Base on Marginalization Indexes by Counties, 2010. http://www.conapo.gob.mx/es/CONAPO/Indices_de_Marginacion_2010_por_entidad_federativa_y _municipio
[23] National Council for Evaluation of Social Development Policies. México (2012) Data Base on Marginalization Indexes for Federal Entities and Counties 2005 and 2010. Excel for States and Municipalities.http://www.coneval.gob.mx/Medicion/Paginas/%c3%8dndice-de-Rezago-social-2010. aspx
[24] Meyers, L.S., Gamst, G. and Guarino, A.J. (2006) Applied Multivariate Research. Design and Interpretation. SAGE Publications, Thousand Oaks.
[25] Hair, J., Anderson, R., Tatham, R. and Black, W. (2007) Multivariate Analysis. 5th Edition, Prentice-Hall, Madrid.
[26] Carver, R. and Nash, J. (2011) Doing Data Analysis. With SPSS Version 18. Cengage Learning, E.U.A.
[27] Bentler, P.M. and Chou, C. (1987) Practical Issues in Structural Modeling. Sociological Methods and Research, 16, 78- 117. http://dx.doi.org/10.1177/0049124187016001004
[28] Dillon, W., Kumar, A. and Mulani, N. (1987) Offending Estimates in Covariance Structure Analysis—Comments on the Causes and Solutions to Heywood Cases. Psychological Bulletin, 101, 126-135.
[29] Green, S.B., Akey, T.M., Fleming, K.K., Hershberger, S.C. and Marquis, J.G. (1997) Effect of the Number of Scale Points of Chi-Square Fit Indices in Confirmatory Factor Analysis. Structural Equation Modeling, 4, 108-120.http://dx.doi.org/10.1080/10705519709540064
[30] Widaman, K.F. and Thompson J.S. (2003) On Specifying the Null Model for Incremental Fit Indices in Structural Equation Modeling. Psychological Methods, 8, 16-37.
[31] Bollen, K.A. (1989) A New Incremental Fit Index for General Structural Equation Models. Sociological Methods and Research, 17, 303-316. http://dx.doi.org/10.1177/0049124189017003004
[32] Bentler, P.M. (1990) Comparative Fit Indexes in Structural Models. Psychological Bulletin, 107, 238-246.http://www.uri.edu/research/cprc/Publications/PDFs/ByTitle/Comparative%20Fit%20Indexes%20 in%20Structural%20Models.pdf
[33] Rodríguez, J. (2006) Validation for the Consumer′s Psycoeconomic model. Causative Analysis with Structural Equations. Thought and Management, 20, 1-54.
[34] Muñoz, J.M. (2011) Overweight, Obesity and Diabetes: Several Approaches for Its Study. Julián Manzur Ocaña Collection. Autonomous Juarez University of Tabasco, Villahermosa.
[35] Jacobson, S.H., King, D.M. and Yuan, R. (2011) A Note on the Relationship between Obesity and Driving. Transport Policy, 18, 772-776. http://dx.doi.org/10.1016/j.tranpol.2011.03.008
[36] Schaller, N., Seiler, H., Himmerich, S., Karg, G., Gedrich, K., Wolfram, G. and Linseisen, J. (2005) Estimated Physical Activity in Bavaria, Germany, and Its Implications for Obesity Risk: Results from the BVS-II Study. International Journal of Behavioral Nutrition and Physical Activity, 2, 6.
[37] Public Health National Institute, México (2013) National Survey on Health and Nutrition 2012. Results by Federal Entity. http://ensanut.insp.mx/informes/SanLuisPotosi-OCT.pdf
[38] State Government of San Luis Potosí. México (2013) Health Services in the State of San Luis Potosí.
[39] Government of the Mexican Republic. México (2013) National Strategy for the Control and Prevention of Overweight, Obesity and Diabetes.

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