Community-Based Values for 2009 Pandemic Influenza A H1N1 Illnesses and Vaccination-Related Adverse Events

OBJECTIVE: To evaluate community-based values for avoiding pandemic influenza (A) H1N1 (pH1N1) illness and vaccination-related adverse events in adults and children. METHODS: Adult community members were randomly selected from a nationally representative research panel to complete an internet survey (response rate = 65%; n = 718). Respondents answered a series of time trade-off questions to value four hypothetical health state scenarios for varying ages (1, 8, 35, or 70 years): uncomplicated pH1N1 illness, pH1N1 illness-related hospitalization, severe allergic reaction to the pH1N1 vaccine, and Guillain-Barré syndrome. We calculated descriptive statistics for time trade-off amounts and derived quality adjusted life year losses for these events. Multivariate regression analyses evaluated the effect of scenario age, as well as respondent socio-demographic and health characteristics on time trade-off amounts. RESULTS: Respondents were willing to trade more time to avoid the more severe outcomes, hospitalization and Guillain-Barré syndrome. In our adjusted and unadjusted analyses, age of the patient in the scenario was significantly associated with time trade-off amounts (p-value<0.05), with respondents willing to trade more time to prevent outcomes in children versus adults. Persons who had received the pH1N1 vaccination were willing to trade significantly more time to avoid hospitalization, severe allergic reaction, and Guillain-Barré syndrome, controlling for other variables in adjusted analyses.(p-value<0.05) CONCLUSIONS: Community members placed the highest value on preventing outcomes in children, compared with adults, and the time trade-off values reported were consistent with the severity of the outcomes presented. Considering these public values along with other decision-making factors may help policy makers improve the allocation of pandemic vaccine resources

Time trade off question with sample health scenarios.

<p>Time trade off question with sample health scenarios.</p

Multivariate regression results: Time trade-off amounts by scenario age, sociodemographics, illness experience, and vaccination status, predicted number of days traded.

<p>*p-value<0.05; indicates statistical difference of value compared to the reference group.</p>#<p>Reference group.</p>1<p>Model goodness-of-fit concordance coefficients and confidence intervals- uncomplicated pH1N1 illness: 0.129 (95% CI:0.090, 0.168), pH1N1 illness-related hospitalization: 0.071 (95% CI: 0.051,0.092), Severe Allergic Reaction: 0.095 (95% CI: 0.071,0.118), Guillain-Barré syndrome: 0.112 (95% CI: 0.089,0.135).</p

Loss in quality adjusted life years (QALYs) for 2009 pandemic influenza (A) H1N1 illness and vaccination-related adverse events¹.

1<p>Using unweighted data.</p>2<p>To generate confidence intervals around our mean values, we used bootstrap re-sampling of size equal to the sample size (approximately 1300, depending on the health state) with 3000 iterations. From each of the 3000 bootstrap samples generated, we calculate the overall means and means by scenario age to create a sampling distribution around the original mean values.</p>3<p>Kruskal-Wallis test evaluated whether median values differed by scenario age.</p

Respondent influenza-related characteristics (n = 659).

1<p>Post stratification weights were provided by Knowledge Networks to account for sampling and non-response bias.</p

Respondent demographic characteristics (n = 659).

1<p>Post stratification weights were provided by Knowledge Networks to account for sampling and non-response bias.</p

Time-tradeoff amounts for 2009 pandemic influenza (A) H1N1 illness and vaccination-related adverse events¹.

1<p>Using unweighted data.</p>2<p>To generate confidence intervals around our mean values, we used bootstrap re-sampling of size equal to the sample size (approximately 1300, depending on the health state) with 3000 iterations. From each of the 3000 bootstrap samples generated, we calculate the overall means and means by scenario age to create a sampling distribution around the original mean values.</p>3<p>Kruskal-Wallis test evaluated whether median values differed by scenario age.</p