Risk of disease and willingness to vaccinate in the United States: A population-based survey
Autoři:
Bert Baumgaertner aff001; Benjamin J. Ridenhour aff002; Florian Justwan aff001; Juliet E. Carlisle aff003; Craig R. Miller aff004
Působiště autorů:
Department of Politics and Philosophy, University of Idaho, Moscow, Idaho, United States of America
aff001; Department of Mathematics, University of Idaho, Moscow, Idaho, United States of America
aff002; Department of Political Science, The University of Utah, Salt Lake City, Utah, United States of America
aff003; Department of Biology, University of Idaho, Moscow, Idaho, United States of America
aff004
Vyšlo v časopise:
Risk of disease and willingness to vaccinate in the United States: A population-based survey. PLoS Med 17(10): e32767. doi:10.1371/journal.pmed.1003354
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pmed.1003354
Souhrn
Background
Vaccination complacency occurs when perceived risks of vaccine-preventable diseases are sufficiently low so that vaccination is no longer perceived as a necessary precaution. Disease outbreaks can once again increase perceptions of risk, thereby decrease vaccine complacency, and in turn decrease vaccine hesitancy. It is not well understood, however, how change in perceived risk translates into change in vaccine hesitancy.
We advance the concept of vaccine propensity, which relates a change in willingness to vaccinate with a change in perceived risk of infection—holding fixed other considerations such as vaccine confidence and convenience.
Methods and findings
We used an original survey instrument that presents 7 vaccine-preventable “new” diseases to gather demographically diverse sample data from the United States in 2018 (N = 2,411). Our survey was conducted online between January 25, 2018, and February 2, 2018, and was structured in 3 parts. First, we collected information concerning the places participants live and visit in a typical week. Second, participants were presented with one of 7 hypothetical disease outbreaks and asked how they would respond. Third, we collected sociodemographic information. The survey was designed to match population parameters in the US on 5 major dimensions: age, sex, income, race, and census region. We also were able to closely match education. The aggregate demographic details for study participants were a mean age of 43.80 years, 47% male and 53% female, 38.5% with a college degree, and 24% nonwhite. We found an overall change of at least 30% in proportion willing to vaccinate as risk of infection increases. When considering morbidity information, the proportion willing to vaccinate went from 0.476 (0.449–0.503) at 0 local cases of disease to 0.871 (0.852–0.888) at 100 local cases (upper and lower 95% confidence intervals). When considering mortality information, the proportion went from 0.526 (0.494–0.557) at 0 local cases of disease to 0.916 (0.897–0.931) at 100 local cases. In addition, we ffound that the risk of mortality invokes a larger proportion willing to vaccinate than mere morbidity (P = 0.0002), that older populations are more willing than younger (P<0.0001), that the highest income bracket (>$90,000) is more willing than all others (P = 0.0001), that men are more willing than women (P = 0.0011), and that the proportion willing to vaccinate is related to both ideology and the level of risk (P = 0.004). Limitations of this study include that it does not consider how other factors (such as social influence) interact with local case counts in people’s vaccine decision-making, it cannot determine whether different degrees of severity in morbidity or mortality failed to be statistically significant because of survey design or because participants use heuristically driven decision-making that glosses over degrees, and the study does not capture the part of the US that is not online.
Conclusions
In this study, we found that different degrees of risk (in terms of local cases of disease) correspond with different proportions of populations willing to vaccinate. We also identified several sociodemographic aspects of vaccine propensity.
Understanding how vaccine propensity is affected by sociodemographic factors is invaluable for predicting where outbreaks are more likely to occur and their expected size, even with the resulting cascade of changing vaccination rates and the respective feedback on potential outbreaks.
Klíčová slova:
Epidemiology – Medical risk factors – Morbidity – Religion – Schools – Surveys – Vaccination and immunization – Vaccines
Zdroje
1. Larson HJ, Jarrett C, Eckersberger E, Smith DM, Paterson P. Understanding vaccine hesitancy around vaccines and vaccination from a global perspective: a systematic review of published literature, 2007–2012. Vaccine. 2014;32(19):2150–2159. doi: 10.1016/j.vaccine.2014.01.081 24598724
2. MacDonald N, the SAGE Working Group on Vaccine Hesitancy. Vaccine hesitancy: Definition, scope and determinants. Vaccine. 2015;33(34):4161–4164. doi: 10.1016/j.vaccine.2015.04.036 25896383
3. Wadman M, You J. The vaccine wars. Science. 2017;356(6336):364–365. doi: 10.1126/science.356.6336.364 28450592
4. Olive JK, Hotez PJ, Damania A, Nolan MS. The state of the antivaccine movement in the United States: A focused examination of nonmedical exemptions in states and counties. PLoS Med. 2018;15(6):1–10. doi: 10.1371/journal.pmed.1002578 29894470
5. Dales L, Kizer KW, Rutherford G, Pertowski C, Waterman S, Woodford G. Measles epidemic from failure to immunize. Western Journal of Medicine. 1993;159(4):455–464. 8273330
6. Wu S, Yang P, Li H, Ma C, Zhang Y, Wang Q. Influenza vaccination coverage rates among adults before and after the 2009 influenza pandemic and the reasons for non-vaccination in Beijing, China: A cross-sectional study. BMC Public Health. 2013;13(1):636.
7. Poland GA. The 2009–2010 influenza pandemic: effects on pandemic and seasonal vaccine uptake and lessons learned for seasonal vaccination campaigns. Vaccine. 2010;28:D3–D13. doi: 10.1016/j.vaccine.2010.08.024 20713258
8. Borse RH, Shrestha SS, Fiore AE, Atkins CY, Singleton JA, Furlow C, et al. Effects of vaccine program against pandemic influenza A(H1N1) virus, United States, 2009–2010. Emerging Infectious Diseases. 2013;19(3):1–10.
9. Jena AB, Khullar D. To Increase Vaccination Rates, Share Information on Disease Outbreaks; 2017. Available from: https://hbr.org/2017/02/to-increase-vaccination-rates-share-information-on-disease-outbreaks. [cited 2017 May 5].
10. SteelFisher GK, Blendon RJ, Bekheit MM, Lubell K. The Public's Response to the 2009 H1N1 Influenza Pandemic. New England Journal of Medicine. 2010;362(22):e65. doi: 10.1056/NEJMp1005102 20484390
11. Brewer NT, Chapman GB, Gibbons FX, Gerrard M, McCaul KD, Weinstein ND. Meta-analysis of the relationship between risk perception and health behavior: The example of vaccination. Health Psychology. 2007;Mar;26(2):136–45 doi: 10.1037/0278-6133.26.2.136 17385964
12. Justwan F, Baumgaertner B, Carlisle JE, Carson E, Kizer J. The effect of trust and proximity on vaccine propensity PLoS ONE. 2019; 14(8): e0220658 doi: 10.1371/journal.pone.0220658 31461443
13. Vandermeulen C, Roelants M, Theeten H, Van Damme P, Hoppenbrouwers K. Vaccination coverage and sociodemographic determinants of measles–mumps–rubella vaccination in three different age groups. European Journal of Pediatrics. 2008;167(10):1161. doi: 10.1007/s00431-007-0652-3 18204860
14. Ru-Chien CHI, Neuzil KM. The Association of Sociodemographic Factors and Patient Attitudes on Influenza Vaccination Rates in Older Persons. The American Journal of the Medical Sciences. 2004;327(3):113–117. doi: 10.1097/00000441-200403000-00001 15090748
15. Waldhoer T, Haidinger G, Vutuc C, Haschke F, Plank R. The impact of sociodemographic variables on immunization coverage of children. European Journal of Epidemiology. 1997;13(2):145–149. doi: 10.1023/a:1007359632218 9084996
16. Yang YT, Delamater PL, Leslie TF, Mello MM. Sociodemographic predictors of vaccination exemptions on the basis of personal belief in California. American Journal of Public Health. 2016;106(1):172–177. doi: 10.2105/AJPH.2015.302926 26562114
17. Lieu TA, Ray GT, Klein NP, Chung C, Kulldorff M. Geographic clusters in underimmunization and vaccine refusal. Pediatrics. 2015;135(2):280–289. doi: 10.1542/peds.2014-2715 25601971
18. Hotez PJ. Texas and Its Measles Epidemics. PLoS Med. 2016;13(10):e1002153. doi: 10.1371/journal.pmed.1002153 27780206
19. THHS. Texas Health and Human Resources; 05/2017. http://www.dshs.texas.gov/immunize/coverage/conscientious-exemptions-data.shtm. [cited 2017 May 5].
20. Omer SB, Salmon DA, Orenstein WA, Dehart MP, Halsey N. Vaccine refusal, mandatory immunization, and the risks of vaccine-preventable diseases. New England Journal of Medicine. 2009;360(19):1981–1988. doi: 10.1056/NEJMsa0806477 19420367
21. Omer SB, Enger KS, Moulton LH, Halsey NA, Stokley S, Salmon DA. Geographic clustering of nonmedical exemptions to school immunization requirements and associations with geographic clustering of pertussis. American Journal of Epidemiology. 2008;168(12):1389–1396. doi: 10.1093/aje/kwn263 18922998
22. Liu F, Enanoria WT, Zipprich J, Blumberg S, Harriman K, Ackley SF, et al. The role of vaccination coverage, individual behaviors, and the public health response in the control of measles epidemics: an agent-based simulation for California. BMC public health. 2015;15(1):447.
23. Glasser JW, Feng Z, Omer SB, Smith PJ, Rodewald LE. The effect of heterogeneity in uptake of the measles, mumps, and rubella vaccine on the potential for outbreaks of measles: a modelling study. The Lancet Infectious Diseases. 2016;16(5):599–605. doi: 10.1016/S1473-3099(16)00004-9 26852723
24. Salathé M, Bonhoeffer S. The effect of opinion clustering on disease outbreaks. Journal of The Royal Society Interface. 2008;5(29):1505–1508.
25. McBryde ES. Network structure can play a role in vaccination thresholds and herd immunity: a simulation using a network mathematical model. Clinical Infectious Diseases. 2009;48(5):685–686. doi: 10.1086/597012 19191659
26. Goldstein S, MacDonald NE, Guirguis S, et al. Health communication and vaccine hesitancy. Vaccine. 2015;33(34):4212–4214. doi: 10.1016/j.vaccine.2015.04.042 25896382
27. R Core Team. R: A Language and Environment for Statistical Computing; 2018. https://www.R-project.org/.
28. Albert SM, Duffy J. Differences in risk aversion between young and older adults. Neuroscience and neuroeconomics. 2012;2012(1).
29. Mata R, Josef AK, Samanez-Larkin GR, Hertwig R. Age differences in risky choice: A meta-analysis. Annals of the New York Academy of Sciences. 2011;1235(1):18–29.
30. Borghans L, Heckman JJ, Golsteyn BH, Meijers H. Gender differences in risk aversion and ambiguity aversion. Journal of the European Economic Association. 2009;7(2–3):649–658.
31. Flanagan KL, Fink AL, Plebanski M, Klein SL. Sex and Gender Differences in the Outcomes of Vaccination over the Life Course. Annual review of cell and developmental biology. 2017;33:577–599. doi: 10.1146/annurev-cellbio-100616-060718 28992436
32. Sakai Y. The Vaccination Kuznets Curve: Do vaccination rates rise and fall with income? Journal of Health Economics. 2018;57:195–205. doi: 10.1016/j.jhealeco.2017.12.002 29277000
33. Reich JA. Neoliberal Mothering and Vaccine Refusal: Imagined Gated Communities and the Privilege of Choice. Gender & Society. 2014;28(5):679–704. doi: 10.1177/0891243214532711
34. Baumgaertner B, Carlisle JE, Justwan F. The influence of political ideology and trust on willingness to vaccinate. PLoS ONE. 2018;13(1):1–13. doi: 10.1371/journal.pone.0191728 29370265
35. Taber CS, Lodge M. Motivated skepticism in the evaluation of political beliefs. American Journal of Political Science. 2006;50(3):755–769.
36. Ferguson N. Capturing human behaviour. Nature. 2007;446:733 EP–.
37. Funk S, Salathé M, Jansen VAA. Modelling the influence of human behaviour on the spread of infectious diseases: a review. Journal of The Royal Society Interface. 2010;7(50):1247–1256. doi: 10.1098/rsif.2010.0142 20504800
38. Manfredi P, D'Onofrio A. Modeling the interplay between human behavior and the spread of infectious diseases. Springer Science & Business Media; 2013.
39. Funk S, Bansal S, Bauch CT, Eames KT, Edmunds WJ, Galvani AP, et al. Nine challenges in incorporating the dynamics of behaviour in infectious diseases models. Epidemics. 2015;10:21–25. doi: 10.1016/j.epidem.2014.09.005 25843377
40. Nardin LG, Miller CR, Ridenhour BJ, Krone SM, Joyce P, Baumgaertner BO. Planning horizon affects prophylactic decision-making and epidemic dynamics. PeerJ. 2016;4:e2678. doi: 10.7717/peerj.2678 27843714
41. Efron B Regression and ANOVA with zero-one data: Measures of residual variation. Journal of the American Statistical Association. 1978;73:113–121.
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