Hierarchical forecasts of Diabetes mortality in Mexico by marginalization and sex to establish resource allocation

Authors

  • Eliud Silva Universidad Anáhuac México
  • Corey Sparks The University of Texas at San Antonio

DOI:

https://doi.org/10.18381/eq.v18i2.7225

Keywords:

Diabetes, mortality, hierarchical forecasts, marginalization, resource allocation

Abstract

Objective: The mexican population has experimented an astounding rise in type II Diabetes mortality as well as a growing trend for the economic burden in the recent years. The paper’s purpose is to propose an approach to establish a distribution of resource allocation objectively to face the future economic burden. Methodology: Hierarchical forecasts of Diabetes mortality to 2030 by sub-domains of the population are estimated based on marginalization and sex. Results: The forecasts confirm that differences related to sub-domains will be significant. In fact, the rates will increase most notably both in low and high marginalized. Limitations: The hierarchical method just provide point forecast without prediction intervals. Originality: There is not a similar application for Mexican data to do that objectively. Conclusions: The most recommendable budget distribution should be mainly addressed among the low and high levels. Implications of these estimates should support unpostponable health policy in general and for the mentioned sub-domains in particular.                                                                                                                             Recepción: 25/10/2020 Aceptación: 10/03/2021

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References

Agudelo-Botero, M. & Dávila-Cervantes, C. (2015). Carga de la mortalidad por Diabetes mellitus en América Latina 2000-2011: los casos de Argentina, Chile, Colombia y México. Gaceta Sanitaria, 29 (3), 172-177. Retrieved from https://dx.doi.org/10.1016/j.gaceta.2015.01.015

Arredondo, A. (2020). Recent trends for the management of Diabetes for older adults in the context of universal coverage and COVID-19:Evidence from Mexico. International Health, 0, 1-4. https://doi.org/10.1093/inthealth/ihaa098

Arredondo, A., Orozco, E., Alcalde-Rabanal, J., Navarro, J., & Azar, A. (2018). Challenges on the epidemiological and economic burden of Diabetes and hypertension in Mexico. Revista de Saúde Pública, 52 (23). DOI: 10.11606/s1518-8787.2018052000293

Arredondo, A., Orozco, E., Duarte, M., Cuadra, M., Recaman, A., & Azar, A. (2019) Trends and challenges in Diabetes for middle-income countries: Evidence from Mexico. Global Public Health, 14 (2), 227-240. https://doi.org/10.1080/17441692.2018.1498115

Arredondo, A. & Reyes, G. (2013). Health disparities from economic burden of Diabetes in middle-income countries: Evidence from México. PLoS ONE 8 (7), e68443. https://doi.org/10.1371/journal.pone.0068443

Barbu, C. (2013). zoom: A spatial data visualization tool (Version 2.0.4). Retrieved from https://github.com/cbarbu/R-package-zoom

Barquera, S., Campos-Nonato, I., & Hernández-Barrera, L. (2013). Prevalencia de obesidad en adultos mexicanos, 2000-2012. Salud Pública de México, 55 (1), 151-160.

Barquera, S., Tovar-Guzmán, V., Campos-Nonato, I., González-Villalpando, C., & Rivera-Dommarco, J. (2003). Geography of Diabetes mellitus mortality in Mexico: An epidemiologic transition analysis. Archives of medical research, 34 (5), 407-414.

Barraza-Lloréns M., Guajardo-Barrón, V., Picó, J., García, R., Hernández, C., Mora, F., Athié, J., Crable, E., & Urtiz, A. (2015). Carga económica de la Diabetes mellitus en México 2013. México: Funsalud. Retrieved from https://funsalud.org.mx/wp-content/uploads/2019/11/Carga-Economica-Diabetes-en-Mexico-2013.pdf

Bustamante-Montes, P., Lezama-Fernández, M., Fernández-De Hoyos, R., Villa-Romero, A., & Borja-Aburto, V. (1990). El análisis de la mortalidad por causa múltiple: un nuevo enfoque. Salud Pública de México, 32 (3), 309-319.

Consejo Nacional de Población-Conapo. (2016). Índice de marginación por entidad federativa y municipio 2015. México: Autor.

Consejo Nacional de Población-Conapo. (2019). Población a mitad de año, para la República Mexicana, 1950-2050. México: Autor.

Dávila-Cervantes C. & Pardo A. (2014). Diabetes mellitus: Contribution to changes in the life expectancy in Mexico 1990, 2000, & 2010. Revista de Salud Pública (Bogotá, Colombia), 16 (6), 910-923. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/26120860

Dowle, M. & Srinivasan, A. (2019). data.table: Extension of `data.frame`. R package version 1.12.2. Retrieved from https://CRAN.R-project.org/package=data.table

Flores, M., Sparks, C., & Silva, E. (2016). Spatio-temporal analysis of Diabetes mortality in Mexico, 1995- 2013: A Bayesian analysis. Paper presented at the Population Association of America annual meeting.

Frenk, J., Bobadilla, J., Sepúlveda, J., & López, M. (1989). Health transition in middle- income countries: New challenges for health care.

Frenk, J. & Chacón, F. (1991a). Bases conceptuales de la nueva salud internacional. Salud Pública de México, 33 (4), 307-313.

Frenk, J. & Chacón, F. (1991b). International health in transition. Asia-Pacific journal of public health / Asia-Pacific Academic Consortium for Public Health, 5 (2), 170-175. DOI:10.1177/101053959100500211

Hill, J., Galloway, J., Goley, A., Marrero, D., Minners, R., Montgomery, B., Peterson, G., Ratner, R., Sanchez, E., & Aroda, V. (2013). Scientific statement: Socioecological determinants of prediabetes and type 2 Diabetes. Diabetes Care, 36 (8), 2430-2439. https://doi.org/10.2337/dc13-1161

Hyndman, R., Ahmed, A., Athanasopoulos, G., & Shang, H. (2011). Optimal combination forecasts for hierarchical time series. Computational Statistics and Data Analysis, 55 (9), 2579-2589. Retrieved from http://robjhyndman.com/papers/hierarchical/

Hyndman, R. & Athanasopoulos, G. (2018). Forecasting: Principles and practice (2nd edition, OTexts.com/fpp2). Melbourne: OTexts.

Hyndman, R., Athanasopoulos, G., Bergmeir, C., Caceres, G., Chhay, L., O’Hara-Wild, M., Petropoulos, F., Razbash, S., Wang, E., & Yasmeen, F. (2019). forecast: Forecasting functions for time series and linear models. R package version 8.9. Retrieved from http://pkg.robjhyndman.com/forecast

Hyndman, R. & Koehler, A. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22, 679-688.

Hyndman, R. & Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 26 (3), 1-22.

Hyndman, R., Lee, A., Wang, E., & Wickramasuriya, S. (2018). hts: Hierarchical and Grouped Time Series. R package version 5.1.5. Retrieved from https://CRAN.R-project.org/package=hts

Instituto Nacional de Estadística y Geografía-INEGI. (2015). Encuesta intercensal 2015. Tabulados del cuestionario básico. México: Autor.

Instituto Nacional de Estadística y Geografía-INEGI, Instituto Nacional de Salud Pública- INSP, & Secretaría de Salud-SS. (2019) Encuesta Nacional de Salud y Nutrición 2018: Presentación de Resultados. [Diapositiva de PowerPoint]. Retrieved from https://ensanut.insp.mx/encuestas/ensanut2018/doctos/informes/ensanut_2018_presentacion_resultados.pdf

Instituto Nacional de Estadística y Geografía-INEGI & Secretaría de Salud-SS. (1985-2017). Estadísticas vitales de defunciones. Base de datos, 1985-2017. México: Autores.

Palloni, A., Beltrán-Sánchez, H., Novak, B., Pinto, G., & Wong, R. (2015). Adult obesity, disease and longevity in Mexico. Salud Pública de México, 57 (suppl. 1), S22-S30. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/26172231

R Core Team. (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Retrieved from https://www.R-project.org/

Sparks, P. & Sparks, C. (2012). Socioeconomic position, rural residence, and marginality influences on obesity status in the adult Mexican population. International Journal of Population Research, ID 757538. https://doi.org/10.1155/2012/757538

Villalpando, S., Shamah-Levy, T., Rojas, R., & Aguilar-Salinas, C. (2010). Trends for type 2 Diabetes and other cardiovascular risk factors in Mexico from 1993-2006. Salud Pública de México, 52 (suppl. 1), S72-S79. DOI: 10.1590/s0036-36342010000700011

Published

2021-06-25 — Updated on 2021-06-25

How to Cite

Silva, E., & Sparks, C. (2021). Hierarchical forecasts of Diabetes mortality in Mexico by marginalization and sex to establish resource allocation. EconoQuantum, 18(2), 82–98. https://doi.org/10.18381/eq.v18i2.7225