#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Forecasting the impact of population ageing on tuberculosis incidence


Autoři: Chu-Chang Ku aff001;  Peter J. Dodd aff001
Působiště autorů: School of Health and Related Research, University of Sheffield, Sheffield, England, United Kingdom aff001
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0222937

Souhrn

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.

Klíčová slova:

Age groups – Aging – Death rates – Taiwan – Tuberculosis – Epidemiology of aging – Epidemiological methods and statistics


Zdroje

1. World Health Organization. Global tuberculosis report 2018. World Health Organization; 2018;

2. World Health Organization. Global strategy and targets for tuberculosis prevention, care and control after 2015. Geneva: World Health Organization. 2014;

3. Houben RMGJ, Dodd PJ. The Global Burden of Latent Tuberculosis Infection: A Re-estimation Using Mathematical Modelling. PLoS Med. 2016;13: e1002152. doi: 10.1371/journal.pmed.1002152 27780211

4. Schaaf HS, Collins A, Bekker A, Davies PDO. Tuberculosis at extremes of age. Respirology. 2010;15: 747–763. doi: 10.1111/j.1440-1843.2010.01784.x 20546192

5. Vynnycky E, Borgdorff MW, Leung CC, Tam CM, Fine PEM. Limited impact of tuberculosis control in Hong Kong: attributable to high risks of reactivation disease. Epidemiol Infect. 2008;136: 943–952. doi: 10.1017/S0950268807008552 17678555

6. United Nations Publications. World Population Ageing, 2015. UN; 2017.

7. National Development Council, Taiwan. Population Projections for the R.O.C. (Taiwan): 2018–2065. National Development Council, Taiwan; 2018.

8. Dye C, Williams BG. The population dynamics and control of tuberculosis. Science. 2010;328: 856–861. doi: 10.1126/science.1185449 20466923

9. Pratt RH, Winston CA, Steve Kammerer J, Armstrong LR. Tuberculosis in Older Adults in the United States, 1993–2008 [Internet]. Journal of the American Geriatrics Society. 2011. pp. 851–857. doi: 10.1111/j.1532-5415.2011.03369.x 21517786

10. Hagiya H, Koyama T, Zamami Y, Minato Y, Tatebe Y, Mikami N, et al. Trends in incidence and mortality of tuberculosis in Japan: a population-based study, 1997–2016. Epidemiol Infect. 2018; 1–10.

11. Negin J, Abimbola S, Marais BJ. Tuberculosis among older adults–time to take notice [Internet]. International Journal of Infectious Diseases. 2015. pp. 135–137. doi: 10.1016/j.ijid.2014.11.018 25809769

12. Bras AL, Gomes D, Filipe PA, de Sousa B, Nunes C. Trends, seasonality and forecasts of pulmonary tuberculosis in Portugal. Int J Tuberc Lung Dis. 2014;18: 1202–1210. doi: 10.5588/ijtld.14.0158 25216834

13. van Aart C, Boshuizen H, Dekkers A, Altes HK. Time Lag Between Immigration and Tuberculosis Rates in Immigrants in the Netherlands: A Time-Series Analysis. International Journal of Tuberculosis and Lung Disease. 2017;21: 486–492. doi: 10.5588/ijtld.16.0548 28399962

14. Iqbal SA, Winston CA, Bardenheier BH, Armstrong LR, Navin TR. Age-Period-Cohort Analyses of Tuberculosis Incidence Rates by Nativity, United States, 1996–2016. Am J Public Health. 2018;108: S315–S320. doi: 10.2105/AJPH.2018.304687 30383432

15. Wu P, Cowling BJ, Schooling CM, Wong IOL, Johnston JM, Leung C-C, et al. Age-period-cohort analysis of tuberculosis notifications in Hong Kong from 1961 to 2005. Thorax. 2008;63: 312–316. doi: 10.1136/thx.2007.082354 18024541

16. Harris RC, Sumner T, Knight GM, Evans T, Cardenas V, Chen C, et al. Age-targeted tuberculosis vaccination in China and implications for vaccine development: a modelling study. Lancet Glob Health. Elsevier; 2019;7: e209–e218. doi: 10.1016/S2214-109X(18)30452-2 30630775

17. Brooks-Pollock E, Cohen T, Murray M. The impact of realistic age structure in simple models of tuberculosis transmission. PLoS One. 2010;5: e8479. doi: 10.1371/journal.pone.0008479 20062531

18. Arregui S, Iglesias MJ, Samper S, Marinova D, Martin C, Sanz J, et al. Data-driven model for the assessment of Mycobacterium tuberculosis transmission in evolving demographic structures. Proc Natl Acad Sci U S A. 2018;115: E3238–E3245. doi: 10.1073/pnas.1720606115 29563223

19. Lee RD, Carter LR. Modeling and Forecasting U. S. Mortality. J Am Stat Assoc. [American Statistical Association, Taylor & Francis, Ltd.]; 1992;87: 659–671.

20. Brouhns N, Denuit M, Vermunt JK. A Poisson log-bilinear regression approach to the construction of projected lifetables [Internet]. Insurance: Mathematics and Economics. 2002. pp. 373–393. doi: 10.1016/s0167-6687(02)00185-3

21. Lee R. The Lee-Carter Method for Forecasting Mortality, with Various Extensions and Applications. N Am Actuar J. Routledge; 2000;4: 80–91.

22. Lee R. The Lee-Carter Method for Forecasting Mortality, with Various Extensions and Applications. N Am Actuar J. Routledge; 2000;4: 80–91.

23. Renshaw AE, Haberman S. On simulation-based approaches to risk measurement in mortality with specific reference to Poisson Lee–Carter modelling [Internet]. Insurance: Mathematics and Economics. 2008. pp. 797–816. doi: 10.1016/j.insmatheco.2007.08.009

24. Coale AJ, Kisker EE. Defects in data on old-age mortality in the United States: new procedures for calculating mortality schedules and life tables at the highest ages. Coale Kisker 1990 Asian and Pacifc Population Forum. Honolulu Hawaii Coale Ansley J. 1990 Spring.; 1990;

25. Hyndman RJ, Booth H. Stochastic population forecasts using functional data models for mortality, fertility and migration. Int J Forecast. 2008;24: 323–342.

26. Steenland K, Armstrong B. An overview of methods for calculating the burden of disease due to specific risk factors. Epidemiology. JSTOR; 2006;17: 512–519.

27. R Core Team. R: A Language and Environment for Statistical Computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2018. Available: https://www.R-project.org/

28. Wickham H. ggplot2: Elegant Graphics for Data Analysis [Internet]. Springer-Verlag New York; 2016. Available: http://ggplot2.org

29. Andres V, Millossovich P, Vladimir K. StMoMo: Stochastic Mortality Modeling in R. J Stat Softw. arts.units.it; 2018;84: 1–38.

30. Chan K-S, Ripley B. TSA: Time Series Analysis [Internet]. 2018. Available: https://CRAN.R-project.org/package=TSA

31. Onozaki I, Law I, Sismanidis C, Zignol M, Glaziou P, Floyd K. National tuberculosis prevalence surveys in Asia, 1990–2012: an overview of results and lessons learned. Trop Med Int Health. 2015;20: 1128–1145. doi: 10.1111/tmi.12534 25943163

32. Suarez PG, Watt CJ, Alarcon E, Portocarrero J, Zavala D, Canales R, et al. The Dynamics of Tuberculosis in Response to 10 Years of Intensive Control Effort in Peru. J Infect Dis. 2001;184: 473–478. doi: 10.1086/322777 11471105

33. Rueda-Sabater C, Alvarez-Esteban PC. The analysis of age-specific fertility patterns via logistic models. J Appl Stat. Taylor & Francis; 2008;35: 1053–1070.

34. Cowan SK. Cohort Abortion Measures for the United States. Popul Dev Rev. 2013;39: 289–307. doi: 10.1111/j.1728-4457.2013.00592.x 26052166

35. Kainz A, Hronsky M, Stel VS, Jager KJ, Geroldinger A, Dunkler D, et al. Prediction of prevalence of chronic kidney disease in diabetic patients in countries of the European Union up to 2025. Nephrol Dial Transplant. 2015;30 Suppl 4: iv113–8.

36. Yue JC, Wang H-C, Leong Y-Y, Su W-P. Using Taiwan National Health Insurance Database to model cancer incidence and mortality rates. Insur Math Econ. 2018;78: 316–324.

37. Marais BJ, Lönnroth K, Lawn SD, Migliori GB, Mwaba P, Glaziou P, et al. Tuberculosis comorbidity with communicable and non-communicable diseases: integrating health services and control efforts. Lancet Infect Dis. 2013;13: 436–448. doi: 10.1016/S1473-3099(13)70015-X 23531392


Článek vyšel v časopise

PLOS One


2019 Číslo 9
Nejčtenější tento týden
Nejčtenější v tomto čísle
Kurzy

Zvyšte si kvalifikaci online z pohodlí domova

Svět praktické medicíny 1/2024 (znalostní test z časopisu)
nový kurz

Koncepce osteologické péče pro gynekology a praktické lékaře
Autoři: MUDr. František Šenk

Sekvenční léčba schizofrenie
Autoři: MUDr. Jana Hořínková

Hypertenze a hypercholesterolémie – synergický efekt léčby
Autoři: prof. MUDr. Hana Rosolová, DrSc.

Význam metforminu pro „udržitelnou“ terapii diabetu
Autoři: prof. MUDr. Milan Kvapil, CSc., MBA

Všechny kurzy
Kurzy Podcasty Doporučená témata Časopisy
Přihlášení
Zapomenuté heslo

Zadejte e-mailovou adresu, se kterou jste vytvářel(a) účet, budou Vám na ni zaslány informace k nastavení nového hesla.

Přihlášení

Nemáte účet?  Registrujte se

#ADS_BOTTOM_SCRIPTS#