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. 2022 Nov 10;17(11):e0276966.
doi: 10.1371/journal.pone.0276966. eCollection 2022.

Stochastic mortality forecasts for Bangladesh

Ahbab Mohammad Fazle Rabbi  1 , Hafiz T A Khan  2   3
Affiliations

Stochastic mortality forecasts for Bangladesh

Ahbab Mohammad Fazle Rabbi et al. PLoS One. .

Abstract

Mortality forecasts are essential part for policymaking in any aging society. In recent years, methods to model and forecast mortality have improved considerably. Among them, Lee-Carter method is one of the most influential method. In this paper, Lee-Carter method is applied to forecast mortality and life expectancy of Bangladesh. A functional data analysis approach is used to decompose the smoothed log-mortality rates in Lee-Carter framework for higher goodness-of-fit of the models and for longer forecast horizons. Bangladesh has been experiencing a mortality transition and has gained life expectancy in last few decades. The fitted model here showed higher pace of mortality decline for women in Bangladesh than that of men. The forecasts showed continuation of mortality improvement in long run and by 2060 life expectancy at birth is expected to reach over 80 years for both sexes in Bangladesh. The study also predicts the effect of reduction in infant mortality on the life expectancy in Bangladesh.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Trend of life expectancies in Bangladesh (Data: Matlab HDSS; 1974–2016).
Fig 2
Fig 2. Smoothed log-mortality rates for Matlab HDSS (1974–2016).
Years are plotted using a rainbow palette so the earlier years are shown in red, followed by orange, yellow, green, blue and indigo with the most recent years plotted in violet.
Fig 3
Fig 3. Fitted parameters of the FDA model for men of Matlab HDSS (1974–2016).
Fig 4
Fig 4. Fitted parameters of the FDA model for women of Matlab HDSS (1974–2016).
Fig 5
Fig 5. Fitted log-mortality rates from FDA model for Matlab HDSS (1974–2016).
Years are plotted using a rainbow palette as before.
Fig 6
Fig 6. Forecast of log-mortality rates from FDA models for Matlab HDSS (2017–2060).
Years are plotted using a rainbow palette as before. Observed mortality rates are showed in gray lines for reference.
Fig 7
Fig 7. Difference between forecast of e0 and e1 for Matlab HDSS (2017–2060).
The green line represents null difference after which e0 will be larger than e1. The vertical blue line is drawn to identify the possible timing for crossover.
Fig 8
Fig 8. Prediction interval of e0 by HU, until 2060 for Matlab HDSS.
The blue area represents the 80% prediction interval and the red lines indicate the 95% prediction interval.
Fig 9
Fig 9. Prediction interval of e60 by HU, until 2060 for Matlab HDSS.
The blue area represents the 80% prediction interval and the red lines indicate the 95% prediction interval.
See this image and copyright information in PMC

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