Assessing Optimal Target Populations for Influenza Vaccination Programmes: An Evidence Synthesis and Modelling Study
Background:
Influenza vaccine policies that maximise health benefit through efficient use of limited resources are needed. Generally, influenza vaccination programmes have targeted individuals 65 y and over and those at risk, according to World Health Organization recommendations. We developed methods to synthesise the multiplicity of surveillance datasets in order to evaluate how changing target populations in the seasonal vaccination programme would affect infection rate and mortality.
Methods and Findings:
Using a contemporary evidence-synthesis approach, we use virological, clinical, epidemiological, and behavioural data to develop an age- and risk-stratified transmission model that reproduces the strain-specific behaviour of influenza over 14 seasons in England and Wales, having accounted for the vaccination uptake over this period. We estimate the reduction in infections and deaths achieved by the historical programme compared with no vaccination, and the reduction had different policies been in place over the period. We find that the current programme has averted 0.39 (95% credible interval 0.34–0.45) infections per dose of vaccine and 1.74 (1.16–3.02) deaths per 1,000 doses. Targeting transmitters by extending the current programme to 5–16-y-old children would increase the efficiency of the total programme, resulting in an overall reduction of 0.70 (0.52–0.81) infections per dose and 1.95 (1.28–3.39) deaths per 1,000 doses. In comparison, choosing the next group most at risk (50–64-y-olds) would prevent only 0.43 (0.35–0.52) infections per dose and 1.77 (1.15–3.14) deaths per 1,000 doses.
Conclusions:
This study proposes a framework to integrate influenza surveillance data into transmission models. Application to data from England and Wales confirms the role of children as key infection spreaders. The most efficient use of vaccine to reduce overall influenza morbidity and mortality is thus to target children in addition to older adults.
Please see later in the article for the Editors' Summary
Vyšlo v časopise:
Assessing Optimal Target Populations for Influenza Vaccination Programmes: An Evidence Synthesis and Modelling Study. PLoS Med 10(10): e32767. doi:10.1371/journal.pmed.1001527
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pmed.1001527
Souhrn
Background:
Influenza vaccine policies that maximise health benefit through efficient use of limited resources are needed. Generally, influenza vaccination programmes have targeted individuals 65 y and over and those at risk, according to World Health Organization recommendations. We developed methods to synthesise the multiplicity of surveillance datasets in order to evaluate how changing target populations in the seasonal vaccination programme would affect infection rate and mortality.
Methods and Findings:
Using a contemporary evidence-synthesis approach, we use virological, clinical, epidemiological, and behavioural data to develop an age- and risk-stratified transmission model that reproduces the strain-specific behaviour of influenza over 14 seasons in England and Wales, having accounted for the vaccination uptake over this period. We estimate the reduction in infections and deaths achieved by the historical programme compared with no vaccination, and the reduction had different policies been in place over the period. We find that the current programme has averted 0.39 (95% credible interval 0.34–0.45) infections per dose of vaccine and 1.74 (1.16–3.02) deaths per 1,000 doses. Targeting transmitters by extending the current programme to 5–16-y-old children would increase the efficiency of the total programme, resulting in an overall reduction of 0.70 (0.52–0.81) infections per dose and 1.95 (1.28–3.39) deaths per 1,000 doses. In comparison, choosing the next group most at risk (50–64-y-olds) would prevent only 0.43 (0.35–0.52) infections per dose and 1.77 (1.15–3.14) deaths per 1,000 doses.
Conclusions:
This study proposes a framework to integrate influenza surveillance data into transmission models. Application to data from England and Wales confirms the role of children as key infection spreaders. The most efficient use of vaccine to reduce overall influenza morbidity and mortality is thus to target children in addition to older adults.
Please see later in the article for the Editors' Summary
Zdroje
1. HardelidP, PebodyR, AndrewsN (2012) Mortality caused by influenza and respiratory syncytial virus by age group in England and Wales 1999–2010. Influenza Other Respi Viruses 7: 35–45.
2. MauskopfJ, KlesseM, LeeS, Herrera-TaracenaG (2012) The burden of influenza complications in different high-risk groups: a targeted literature review. J Med Econ 16: 264–277.
3. World Health Organization (2005) Influenza vaccines—WHO position paper. Wkly Epidemiol Rec 33: 279–287 Available: http://www.who.int/immunization/wer8033influenza_August2005_position_paper.pdf. Accessed 5 September 2013.
4. World Health Organization (2012) Vaccines against influenza—WHO position paper. Wkly Epidemiol Rec 87: 461–476.
5. ViboudC, BoëlleP-Y, CauchemezS, LavenuA, ValleronA-J, et al. (2004) Risk factors of influenza transmission in households. Br J Gen Pract 54: 684–689 Available: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1326070/. Accessed 5 September 2013.
6. CauchemezS, ValleronA-J, BoëlleP-Y, FlahaultA, FergusonNM (2008) Estimating the impact of school closure on influenza transmission from Sentinel data. Nature 452: 750–754.
7. LonginiIM, HalloranME (2005) Strategy for distribution of influenza vaccine to high-risk groups and children. Am J Epidemiol 161: 303–306 Available: http://aje.oxfordjournals.org/content/161/4/303.full. Accessed 19 November 2012.
8. VynnyckyE, PitmanR, SiddiquiR, GayN, EdmundsWJ (2008) Estimating the impact of childhood influenza vaccination programmes in England and Wales. Vaccine 26: 5321–5330.
9. BastaNE, ChaoDL, HalloranME, MatrajtL, LonginiIM (2009) Strategies for pandemic and seasonal influenza vaccination of schoolchildren in the United States. Am J Epidemiol 170: 679–686 Available: http://aje.oxfordjournals.org/content/170/6/679.long. Accessed 30 May 2013.
10. PitmanRJ, WhiteLJ, SculpherM (2012) Estimating the clinical impact of introducing paediatric influenza vaccination in England and Wales. Vaccine 30: 1208–1224 Available: http://dx.doi.org/10.1016/j.vaccine.2011.11.106. Accessed 19 November 2012
11. PresanisAM, De AngelisD, HagyA, ReedC, RileyS, et al. (2009) The severity of pandemic H1N1 influenza in the United States, from April to July 2009: a Bayesian analysis. PLoS Med 6: e1000207 doi:10.1371/journal.pmed.1000207
12. BaguelinM, HoschlerK, StanfordE, WaightP, HardelidP, et al. (2011) Age-specific incidence of A/H1N1 2009 influenza infection in England from sequential antibody prevalence data using likelihood-based estimation. PLoS ONE 6: e17074 doi:10.1371/journal.pone.0017074
13. LeeVJ, ChenMI, YapJ, OngJ, LimW-Y, et al. (2011) Comparability of different methods for estimating influenza infection rates over a single epidemic wave. Am J Epidemiol 174: 468–478 Available: http://aje.oxfordjournals.org/content/174/4/468.full. Accessed 5 September 2013.
14. HensN, AyeleGM, GoeyvaertsN, AertsM, MossongJ, et al. (2009) Estimating the impact of school closure on social mixing behaviour and the transmission of close contact infections in eight European countries. BMC Infect Dis 9: 187 Available: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2799408/. Accessed 5 September 2013.
15. MossongJ, HensN, JitM, BeutelsP, AuranenK, et al. (2008) Social contacts and mixing patterns relevant to the spread of infectious diseases. PLoS Med 5: e74 doi:10.1371/journal.pmed.0050074
16. FlemingDM, MilesJ (2010) The representativeness of sentinel practice networks. J Public Health (Oxf) 32: 90–96.
17. JosephC, GoddardN, GelbD (2005) Influenza vaccine uptake and distribution in England and Wales using data from the General Practice Research Database, 1989/90–2003/04. J Public Health (Oxf) 27: 371–377.
18. Health Protection Agency (2013) Influenza vaccination uptake monitoring on behalf of the Department of Health. Available: http://www.hpa.org.uk/web/HPAweb%26HPAwebStandard/HPAweb_C/1195733756886. Accessed 5 September 2013.
19. JohnsonBF, WilsonLE, EllisJ, ElliotAJ, BarclayWS, et al. (2009) Fatal cases of influenza a in childhood. PLoS ONE 4: e7671 doi:10.1371/journal.pone.0007671
20. Bernardo JM, Smith AFM (2000) Bayesian theory. Hoboken (New Jersey): Wiley.
21. RobertsGO, RosenthalJS (2001) Optimal scaling for various Metropolis-Hastings algorithms. Stat Sci 16: 351–367 Available: http://projecteuclid.org/euclid.ss/1015346320. Accessed 15 August 2011.
22. LunnDJ, ThomasA, BestN, SpiegelhalterD (2000) WinBUGS—a Bayesian modelling framework: concepts, structure, and extensibility. Stat Comput 10: 325–337.
23. WallingaJ, TeunisP, KretzschmarM (2006) Using data on social contacts to estimate age-specific transmission parameters for respiratory-spread infectious agents. Am J Epidemiol 164: 936–944.
24. KanaanMN, FarringtonCP (2005) Matrix models for childhood infections: a Bayesian approach with applications to rubella and mumps. Epidemiol Infect 133: 1009–1021.
25. BaguelinM, HoekAJV, JitM, FlascheS, WhitePJ, et al. (2010) Vaccination against pandemic influenza A/H1N1v in England: a real-time economic evaluation. Vaccine 28: 2370–2384.
26. MillerE, HoschlerK, HardelidP, StanfordE, AndrewsN, et al. (2010) Incidence of 2009 pandemic influenza A H1N1 infection in England: a cross-sectional serological study. Lancet 375: 1100–1108.
27. JeffersonT, Di PietrantonjC, RivettiA, Bawazeer Ga, Al-Ansary La, et al. (2010) Vaccines for preventing influenza in healthy adults. Cochrane Database Syst Rev 2010: CD001269.
28. FlemingDM, AndrewsNJ, EllisJS, BerminghamA, SebastianpillaiP, et al. (2010) Estimating influenza vaccine effectiveness using routinely collected laboratory data. J Epidemiol Community Health 64: 1062–1067.
29. Department of Health (2008) Vaccination uptake among the 65 years and over and under 65 years at risk in England 2007–08. Available: http://webarchive.nationalarchives.gov.uk/20130107105354/http://www.dh.gov.uk/prod_consum_dh/groups/dh_digitalassets/documents/digitalasset/dh_104094.pdf. Accessed 5 September 2013.
30. FergusonNM, CummingsDA, CauchemezS, FraserC, RileyS, et al. (2005) Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature 437: 209–214.
31. CarratF, VerguE, FergusonNM, LemaitreM, CauchemezS, et al. (2008) Time lines of infection and disease in human influenza: a review of volunteer challenge studies. Am J Epidemiol 167: 775–785.
32. RussellCA, JonesTC, BarrIG, CoxNJ, GartenRJ, et al. (2008) The global circulation of seasonal influenza A (H3N2) viruses. Science 320: 340–346.
33. FlascheS, HensN, BoëlleP-Y, MossongJ, van BallegooijenWM, et al. (2011) Different transmission patterns in the early stages of the influenza A(H1N1)v pandemic: a comparative analysis of 12 European countries. Epidemics 3: 125–133.
34. Anderson RM, May RM (1992) Infectious diseases of humans: dynamics and control. Oxford: Oxford University Press.
35. R Development Core Team (2011) R: a language and environment for statistical computing [computer program]. Available: http://www.r-project.org/. Accessed 9 September 2013.
36. TannerMA, WongWH (1987) The calculation of posterior distributions by data augmentation. J Am Stat Assoc 82: 528–540.
37. LoebM, RussellML, MossL, FonsecaK, FoxJ, et al. (2010) Effect of influenza vaccination of children on infection rates in Hutterite communities: a randomized trial. JAMA 303: 943–950 Available: http://jama.jamanetwork.com/article.aspx?articleid=185509. Accessed 25 June 2013.
38. US Centers for Disease Control and Prevention (2013) Flu vaccination coverage, United States, 2011–12 influenza season. Available: http://www.cdc.gov/flu/pdf/professionals/vaccination/vax-coverage-1112estimates.pdf. Accessed 28 June 2013.
39. Brooks-PollockE, TilstonN, EdmundsWJ, EamesKTD (2011) Using an online survey of healthcare-seeking behaviour to estimate the magnitude and severity of the 2009 H1N1v influenza epidemic in England. BMC Infect Dis 11: 68 Available: http://www.biomedcentral.com/1471-2334/11/68. Accessed 3 September 2013.
Štítky
Interné lekárstvoČlánok vyšiel v časopise
PLOS Medicine
2013 Číslo 10
- Statinová intolerance
- Očkování proti virové hemoragické horečce Ebola experimentální vakcínou rVSVDG-ZEBOV-GP
- Co dělat při intoleranci statinů?
- Pleiotropní účinky statinů na kardiovaskulární systém
- DESATORO PRE PRAX: Aktuálne odporúčanie ESPEN pre nutričný manažment u pacientov s COVID-19
Najčítanejšie v tomto čísle
- Utility of the Xpert MTB/RIF Assay for Diagnosis of Tuberculous Meningitis
- Modelling the Strategic Use of Antiretroviral Therapy for the Treatment and Prevention of HIV
- Diagnostic Accuracy of Quantitative PCR (Xpert MTB/RIF) for Tuberculous Meningitis in a High Burden Setting: A Prospective Study
- Assessing Optimal Target Populations for Influenza Vaccination Programmes: An Evidence Synthesis and Modelling Study