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Forecasting the impact of population ageing on tuberculosis incidence

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  • Chu-Chang Ku
  • Peter J Dodd

Abstract

Background: Tuberculosis (TB) disease reactivates from distant latent infection or recent (re)infection. Progression risks increase with age. Across the World Health Organisation Western Pacific region, many populations are ageing and have the highest per capita TB incidence rates in older age groups. However, methods for analysing age-specific TB incidence and forecasting epidemic trends while accounting for demographic change remain limited. Methods: We applied the Lee-Carter models, which were originally developed for mortality modelling, to model the temporal trends in age-specific TB incidence data from 2005 to 2018 in Taiwan. Females and males were modelled separately. We combined our demographic forecasts, and age-specific TB incidence forecasts to project TB incidence until 2035. We compared TB incidence projections with demography fixed in 2018 to projections accounting for demographic change. Results: Our models quantified increasing incidence rates with age and declining temporal trends. By 2035, the forecast suggests that the TB incidence rate in Taiwan will decrease by 54% (95% Prediction Interval (PI): 45%-59%) compared to 2015, while most age-specific incidence rates will reduce by more than 60%. In 2035, adults aged 65 and above will make up 78% of incident TB cases. Forecast TB incidence in 2035 accounting for demographic change will be 39% (95% PI: 36%-42%) higher than without population ageing. Conclusions: Age-specific incidence forecasts coupled with demographic forecasts can inform the impact of population ageing on TB epidemics. The TB control programme in Taiwan should develop plans specific to older age groups and their care needs.

Suggested Citation

  • Chu-Chang Ku & Peter J Dodd, 2019. "Forecasting the impact of population ageing on tuberculosis incidence," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-13, September.
    • Handle: RePEc:plo:pone00:0222937
    DOI: 10.1371/journal.pone.0222937
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    1. Sarah K. Cowan, 2013. "Cohort Abortion Measures for the United States," Population and Development Review, The Population Council, Inc., vol. 39(2), pages 289-307, June.
    2. Cristina Rueda-Sabater & Pedro Alvarez-Esteban, 2008. "The analysis of age-specific fertility patterns via logistic models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(9), pages 1053-1070.
    3. Ellen Brooks-Pollock & Ted Cohen & Megan Murray, 2010. "The Impact of Realistic Age Structure in Simple Models of Tuberculosis Transmission," PLOS ONE, Public Library of Science, vol. 5(1), pages 1-6, January.
    4. Yue, Jack C. & Wang, Hsin-Chung & Leong, Yin-Yee & Su, Wei-Ping, 2018. "Using Taiwan National Health Insurance Database to model cancer incidence and mortality rates," Insurance: Mathematics and Economics, Elsevier, vol. 78(C), pages 316-324.
    5. Ronald Lee, 2000. "The Lee-Carter Method for Forecasting Mortality, with Various Extensions and Applications," North American Actuarial Journal, Taylor & Francis Journals, vol. 4(1), pages 80-91.
    6. Hyndman, Rob J. & Booth, Heather, 2008. "Stochastic population forecasts using functional data models for mortality, fertility and migration," International Journal of Forecasting, Elsevier, vol. 24(3), pages 323-342.
    7. Brouhns, Natacha & Denuit, Michel & Vermunt, Jeroen K., 2002. "A Poisson log-bilinear regression approach to the construction of projected lifetables," Insurance: Mathematics and Economics, Elsevier, vol. 31(3), pages 373-393, December.
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