DS_10.1177_0272989X18776636 – Supplemental material for Sponsorship Bias in Base-Case Values and Uncertainty Bounds of Health Economic Evaluations?

<p>Supplemental material, DS_10.1177_0272989X18776636 for Sponsorship Bias in Base-Case Values and Uncertainty Bounds of Health Economic Evaluations? by Joke Bilcke, Frederik Verelst and Philippe Beutels in Medical Decision Making</p

Quality-Adjusted Life-Years lost for ambulatory ILI patients.

<p>Estimated average Quality-Adjusted Life-Years lost as a function of having an underlying illness and age, separately for ambulatory ILI respondents categorized as ‘likely flu’ or ‘unlikely flu’.</p

Costs for ambulatory ILI patients.

<p>Estimated average direct medical cost (€, using lowest unit cost for medication) as a function of having an underlying illness and age, separately for ambulatory ILI respondents categorized as ‘likely flu’ or ‘unlikely flu’.</p

Influenza-Like-Illness and Clinically Diagnosed Flu: Disease Burden, Costs and Quality of Life for Patients Seeking Ambulatory Care or No Professional Care at All

<div><p>This is one of the first studies to (1) describe the out-of-hospital burden of influenza-like-illness (ILI) and clinically diagnosed flu, also for patients not seeking professional medical care, (2) assess influential background characteristics, and (3) formally compare the burden of ILI in patients with and without a clinical diagnosis of flu. A general population sample with recent ILI experience was recruited during the 2011–2012 influenza season in Belgium. Half of the 2250 respondents sought professional medical care, reported more symptoms (especially more often fever), a longer duration of illness, more use of medication (especially antibiotics) and a higher direct medical cost than patients not seeking medical care. The disease and economic burden were similar for ambulatory ILI patients, irrespective of whether they received a clinical diagnosis of flu. On average, they experienced 5–6 symptoms over a 6-day period; required 1.6 physician visits and 86–91% took medication. An average episode amounted to €51–€53 in direct medical costs, 4 days of absence from work or school and the loss of 0.005 quality-adjusted life-years. Underlying illness led to greater costs and lower quality-of-life. The costs of ILI patients with clinically diagnosed flu tended to increase, while those of ILI patients without clinically diagnosed flu tended to decrease with age. Recently vaccinated persons experienced lower costs and a higher quality-of-life, but this was only the case for patients not seeking professional medical care. This information can be used directly to evaluate the implementation of cost-effective prevention and control measures for influenza. In particular to inform the evaluation of more widespread seasonal influenza vaccination, including in children, which is currently considered by many countries.</p></div

Medication use of ILI patients.

<p>Number of respondents using different types of medication, separately for medication bought in the pharmacy and medication that respondents had in storage at home. Each respondent could specify several types of medication bought or at home. ‘Don’t know’ refers to medication taken without being sure about which type.</p

Estimated direct medical cost and quality-of-life associated with ILI and clinically diagnosed flu patients in Belgium, as a function of significant predictor variables (gender, underlying condition ‘cond’, age and/or vaccination status (‘vac’ = vaccinated just before or during the last flu season)).

<p>95% uncertainty intervals are obtained by bootstrapping (1000 samples). #Age was included as continuous predictor variable in the regression models but for clarity this table presents estimates for only 3 ages (in years).</p

Additional file 2 of Lessons from a decade of individual-based models for infectious disease transmission: a systematic review (2006-2015)

Reference list. List of the included studies using an individual-based model to study infectious disease transmission. References are sorted by topic and disease and listed together with their terminology, modeling purpose, (non)inclusion of an economic analysis, intervention strategy and methodology classification. (CSV 183 kb

Fig 3 -

RSV cases, hospitalizations, and deaths per 1,000 person-years according to OM I and II. Number and 95% confidence intervals of hospitalizations and deaths per 1,000 person-years according to OM I and II, detailed in Fig 1 and Section S1.4 in S1 Text. The green bands arise from OM I, calculated by taking the splines of community-based incidence (Spline I), probability of hospitalization (Spline III), and probability of death among hospitalized cases (Spline IV). The orange bands arise from OM II, calculated by taking the splines of hospital-based incidence (Spline II), probability of hospitalization (Spline III) to back-calculate cases in the community, and probability of death among hospitalized cases (Spline IV). LIC, low-income countries; LMIC, lower-middle-income countries; OM, outcomes model; RSV, respiratory syncytial virus; UMIC, upper middle-income countries.</p

Mean age, median age, and peak age of infection, hospitalization, and deaths due to RSV.

The segments correspond to 95% confidence intervals of each of the summaries and the dots correspond to the median estimate of each of the summaries; these are shown in green when calculated using OM I and in orange when calculated using OM II. LIC, low-income countries; LMIC, lower-middle-income countries; OM, outcomes model; RSV, respiratory syncytial virus; UMIC, upper-middle-income countries.</p

Supplementary Results: Additional Projections.

Fig A. Splines of the probability of severe and very severe disease among community-based and hospital-based cases. Fig B. Splines of the probability of severe and very severe disease among community-based and hospital-based cases. Fig C. RSV cases, hospitalizations of severe and very severe disease per 1,000 person-years according to Spline Models (SM) I and II. Table A. Model selection via the generalized likelihood ratio test for severe and very severe disease. Fig D. Mean, median, and peak age of each severe and very severe disease among community-based and hospital-based cases. Fig E. Proportions of each outcome that fall under key age brackets for severe and very severe case burden. Fig F. Sensitivity analysis of the burden of RSV cases, hospitalizations, and deaths by age in low-income countries (LICs). Fig G. Sensitivity analysis of the burden of RSV cases, hospitalizations, and deaths by age in low-income countries (LMICs). Fig H. Sensitivity analysis of the burden of RSV cases, hospitalizations, and deaths by age in low-income countries (UMICs). (PDF)</p