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The Global Burden of Disease fertility forecasts: Summary of the approach used and associated statistical concerns

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  • Alkema, Leontine

Abstract

BACKGROUND The Global Burden of Disease (GBD) project’s forecasts up to 2100 suggest fertility drops will be even greater in sub-Saharan Africa than the UN Population Division (UNPD) has predicted. OBJECTIVE This reflection summarizes the main assumptions used in the GBD fertility forecasts. I assess the methods used, focusing on high fertility countries and the use of met need for contraceptives as a predictor. RESULTS Based on GBD’s forecasting method, I draw two conclusions. Firstly, GBD fertility forecasts are based on unvalidated assumptions about increasing met need for contraception and may overestimate decreases in fertility in countries with low levels of modern contraceptive use. Secondly, the GBD forecast model for fertility is not a causal model for predicting changes. Claims GBD researchers make about the effect of changing access to family planning on fertility are not informative for guiding policy. Based on the GBD validation exercise, I conclude that the GBD study did not check the performance of the method for predicting left-out fertility data. Also the approach used to compare the predictive performance of UNPD and GBD forecasting methods may give the GBD method an inherent advantage. CONCLUSIONS Communication regarding the GBD method and its findings must avoid causal language and acknowledge the method’s limitations. Future research should examine the performance of the method, especially for countries with low modern contraceptive use. CONTRIBUTION This paper summarizes the GBD fertility forecasting method and indicates three areas of concern about it and its use.

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  • Alkema, Leontine, 2020. "The Global Burden of Disease fertility forecasts: Summary of the approach used and associated statistical concerns," OSF Preprints 3m6va, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:3m6va
    DOI: 10.31219/osf.io/3m6va
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    1. Ševčíková, Hana & Alkema, Leontine & Raftery, Adrian, 2011. "bayesTFR: An R package for Probabilistic Projections of the Total Fertility Rate," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 43(i01).
    2. Leontine Alkema & Adrian Raftery & Patrick Gerland & Samuel Clark & François Pelletier & Thomas Buettner & Gerhard Heilig, 2011. "Probabilistic Projections of the Total Fertility Rate for All Countries," Demography, Springer;Population Association of America (PAA), vol. 48(3), pages 815-839, August.
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    1. Raftery, Adrian E. & Ševčíková, Hana, 2023. "Probabilistic population forecasting: Short to very long-term," International Journal of Forecasting, Elsevier, vol. 39(1), pages 73-97.

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