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Copyright (c) 2023 Karol Cervantes De La Torre, Adel Mendoza Mendoza, Enrique De La Hoz Domínguez
This work is licensed under a Creative Commons Attribution 4.0 International License.
Forecasting infant survival rates and life expectancy in Latin American countries: 2016-2019
Corresponding Author(s) : Adel Mendoza Mendoza
Ciencia e Innovación en Salud,
2023
Abstract
Background: In recent years, the survival rate of infants (neonatal and under-five) as well as life expectancy has increased globally, these levels have been increasing almost in a linear fashion with respect to countries' spending on health systems.
Methods: Retrospective descriptive research on health indicators. A prediction of infant survival rates and life expectancy at birth is made in 19 Latin American countries using classic polynomial regression models for the period 2016-2019. We used data from 2000 to 2015 from the World Health Organization and World Bank data repositories.
Results: The research results show that the models developed have an average R2 value of 0.9963 for Neonatal Survival Rate, 0.9965 for Survival Rate of children up to 5 years and 0.9599 for Life Expectancy, as well as very low values of the mean absolute percentage error (MAPE) indicating a good fit of the models.
Conclusion: The proposed models can be used by the different countries of the region since the adjustment of the models is very good and the results obtained can serve as a basis for the different social and economic planning processes of a country.
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- López AD Adair T. Slower increase in life expectancy in Australia than in other high-income countries: the contributions of age and cause of death. Med J Aust. 2019; 210(9): 403-409. DOI: 10.5694/mja2.50144
- González RM, Gilleskie D. Infant mortality rate as a measure of a country’s health: a robust method to improve reliability and comparability. Demography. 2017; 54(2): 701-720. DOI: 10.1007/s13524-017-0553-7
- Alho JM. Forecasting life expectancy: A statistical look at model choice and use of auxiliary series. In: Old and new perspectives on mortality forecasting. Cham: Springer; 2019. p.185-195
- Linden M, Ray D. Life expectancy effects of public and private health expenditures in OECD countries 1970–2012: Panel time series approach. Economic Analysis and Policy. 2017; 56. 101-113. DOI: 10.1016/j.eap.2017.06.005
- Kostis V, Bennett JE, Mathers CD, Li G, Foreman K, Ezzati M. Future life expectancy in 35 industrialized countries: projections with a Bayesian model ensemble. The Lancet, 2017; 389(10076), 1323-1335. DOI: 10.1016/S0140-6736(16)32381-9
- Wong CH, Tsui AK. Forecasting life expectancy: Evidence from a new survival function. Insurance: Mathematics and Economics, 2015; 65. 208-226. DOI: 10.1016/j.insmatheco.2015.08.006
- Arvan M, Fahimnia B, Reisi M, Siemsen E. Integrating human judgement into quantitative forecasting methods: A review. Omega, 2019; 86. 237-252. DOI: 10.1016/j.omega.2018.07.012
- Deb C, Zhang F, Yang J, Lee SE, Shah KW. A review on time series forecasting techniques for building energy consumption. Renewable and Sustainable Energy Reviews. 2017; 74. 902-924. DOI: 10.1016/j.rser.2017.02.085
- Rosén M. Forecasting life expectancy and mortality in Sweden–some comments on methodological problems and potential approaches. In Old and New Perspectives on Mortality Forecasting. Cham: Springer; 2019. p. 261-269.
- Zafeiris KN. A Method for the Forecasting of Mortality. In Demography and Health Issues. Springer, Cham; 2018. p. 71-82
- Rabbi AMF, Mazzuco S. Mortality and life expectancy forecast for (comparatively) high mortality countries. Genus. 2018; 74(1), 18. DOI: 10.1186/s41118-018-0042-x
- Shang HL. Mortality and life expectancy forecasting for a group of populations in developed countries: A robust multilevel functional data method. In Recent Advances in Robust Statistics: Theory and Applications. New Delhi: Springer; 2016. p. 169-184.
- Torri T, Vaupel JW. Forecasting life expectancy in an international context. Int J Forecast. 2012; 28(2), 519-531. DOI: 10.1016/j.ijforecast.2011.01.009
- Akinlo AE, Sulola AO. Health care expenditure and infant mortality in sub-Saharan Africa. J Policy Model. 2019; 41(1), 168-178.
- Luo L, Luo L, Zhang X, He X. Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models. BMC Health Serv Res. 2017; 17(1), 469. DOI: 10.1186%2Fs12913-017-2407-9
- Villani M, Earnest A, Nanayakkara N, Smith K, De Courten B, Zoungas S. Time series modelling to forecast prehospital EMS demand for diabetic emergencies. BMC Health Serv Res. 2017; 17(1), 332. DOI: 10.1186/s12913-017-2280-6
- Kim S, Kim H. A new metric of absolute percentage error for intermittent demand forecasts. Int J Forecast. 2016; 32(3), 669-679. DOI: 10.1016/j.ijforecast.2015.12.003
- Lee R. Mortality forecasts and linear life expectancy trends. In: Old and New Perspectives on Mortality Forecasting. Cham: Springer; 2019. p. 167-183
- Ketenci N, Murthy VN. Some determinants of life expectancy in the United States: Results from cointegration tests under structural breaks. J Econ Finan. 2018; 42(3), 508-525. DOI: 10.1007/s12197-017-9401-2
References
López AD Adair T. Slower increase in life expectancy in Australia than in other high-income countries: the contributions of age and cause of death. Med J Aust. 2019; 210(9): 403-409. DOI: 10.5694/mja2.50144
González RM, Gilleskie D. Infant mortality rate as a measure of a country’s health: a robust method to improve reliability and comparability. Demography. 2017; 54(2): 701-720. DOI: 10.1007/s13524-017-0553-7
Alho JM. Forecasting life expectancy: A statistical look at model choice and use of auxiliary series. In: Old and new perspectives on mortality forecasting. Cham: Springer; 2019. p.185-195
Linden M, Ray D. Life expectancy effects of public and private health expenditures in OECD countries 1970–2012: Panel time series approach. Economic Analysis and Policy. 2017; 56. 101-113. DOI: 10.1016/j.eap.2017.06.005
Kostis V, Bennett JE, Mathers CD, Li G, Foreman K, Ezzati M. Future life expectancy in 35 industrialized countries: projections with a Bayesian model ensemble. The Lancet, 2017; 389(10076), 1323-1335. DOI: 10.1016/S0140-6736(16)32381-9
Wong CH, Tsui AK. Forecasting life expectancy: Evidence from a new survival function. Insurance: Mathematics and Economics, 2015; 65. 208-226. DOI: 10.1016/j.insmatheco.2015.08.006
Arvan M, Fahimnia B, Reisi M, Siemsen E. Integrating human judgement into quantitative forecasting methods: A review. Omega, 2019; 86. 237-252. DOI: 10.1016/j.omega.2018.07.012
Deb C, Zhang F, Yang J, Lee SE, Shah KW. A review on time series forecasting techniques for building energy consumption. Renewable and Sustainable Energy Reviews. 2017; 74. 902-924. DOI: 10.1016/j.rser.2017.02.085
Rosén M. Forecasting life expectancy and mortality in Sweden–some comments on methodological problems and potential approaches. In Old and New Perspectives on Mortality Forecasting. Cham: Springer; 2019. p. 261-269.
Zafeiris KN. A Method for the Forecasting of Mortality. In Demography and Health Issues. Springer, Cham; 2018. p. 71-82
Rabbi AMF, Mazzuco S. Mortality and life expectancy forecast for (comparatively) high mortality countries. Genus. 2018; 74(1), 18. DOI: 10.1186/s41118-018-0042-x
Shang HL. Mortality and life expectancy forecasting for a group of populations in developed countries: A robust multilevel functional data method. In Recent Advances in Robust Statistics: Theory and Applications. New Delhi: Springer; 2016. p. 169-184.
Torri T, Vaupel JW. Forecasting life expectancy in an international context. Int J Forecast. 2012; 28(2), 519-531. DOI: 10.1016/j.ijforecast.2011.01.009
Akinlo AE, Sulola AO. Health care expenditure and infant mortality in sub-Saharan Africa. J Policy Model. 2019; 41(1), 168-178.
Luo L, Luo L, Zhang X, He X. Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models. BMC Health Serv Res. 2017; 17(1), 469. DOI: 10.1186%2Fs12913-017-2407-9
Villani M, Earnest A, Nanayakkara N, Smith K, De Courten B, Zoungas S. Time series modelling to forecast prehospital EMS demand for diabetic emergencies. BMC Health Serv Res. 2017; 17(1), 332. DOI: 10.1186/s12913-017-2280-6
Kim S, Kim H. A new metric of absolute percentage error for intermittent demand forecasts. Int J Forecast. 2016; 32(3), 669-679. DOI: 10.1016/j.ijforecast.2015.12.003
Lee R. Mortality forecasts and linear life expectancy trends. In: Old and New Perspectives on Mortality Forecasting. Cham: Springer; 2019. p. 167-183
Ketenci N, Murthy VN. Some determinants of life expectancy in the United States: Results from cointegration tests under structural breaks. J Econ Finan. 2018; 42(3), 508-525. DOI: 10.1007/s12197-017-9401-2