BackgroundDuring each winter the hospital quality of care (QoC) in pediatric wards decreases due to a surge in pediatric infectious diseases leading to overcrowded units. Bed occupancy rates often surpass the good hospital bed management threshold of 85%, which can result in poor conditions in the workplace. This study explores how QoC-scores could be improved by investing in additional beds and/or better vaccination programs against vaccine-preventable infectious diseases.MethodsThe Cobb–Douglas model was selected to define the improvement in QoC (%) as a function of two strategies (rotavirus vaccination coverage [%] and addition of extra hospital beds [% of existing beds]), allowing improvement-isocurves to be produced. Subsequently, budget minimization was applied to determine the combination of the two strategies needed to reach a given QoC improvement at the lowest cost. Data from Jessa Hospital (Hasselt, Belgium) were chosen as an example. The annual population in the catchment area to be vaccinated was 7000 children; the winter period was 90 days with 34 pediatric beds available. Rotavirus vaccination cost per course was €118.26 and the daily cost of a pediatric bed was €436.53. The target QoC increase was fixed at 50%. The model was first built with baseline parameter values.ResultsThe model predicted that a combination of 64% vaccine coverage and 39% extra hospital beds (≈ 13 extra beds) in winter would improve QoC-scores by 50% for the minimum budget allocation.ConclusionThe model allows determination of the most efficient allocation of the healthcare budget between rotavirus vaccination and bed expansion for improving QoC-scores during the annual epidemic winter seasons.

Background This study aims to describe the short-term reactogenicity of the AS03-adjuvanted H5N1 vaccine expressed through adverse events (AEs) and quality-adjusted life-day (QALD) scores. The AEs are likely to be short-term and therefore the quality of life (QoL) questionnaire, SF-36v2, was administered daily to record changes over seven days. A more sensitive application of this instrument should allow for a better understanding of short-term tolerability of adjuvanted vaccines. Methods Participants ( N = 50) received a 2-dose vaccination schedule. Solicited (collected daily: days 0 to 7 [post dose 1] and 21 to 28 [post dose 2]) and unsolicited (collected weekly until day 21) AEs were collected via diary cards. The QoL questionnaires were completed daily (days 0–6) and weekly (days 0, 6, 21, 27) after dose one. Questionnaire data were transformed into SF-6D scores to report QALDs. It was hypothesized post-hoc that the QALD and daily AEs scores should correlate if discrete QoL-changes were captured. Results Pain (92%) and muscle ache (66%) were the most commonly reported solicited local and general AEs respectively, neither increased in intensity nor in frequency after dose 2. No safety concerns were identified during the study. A correlation between the daily AEs and QALD scores existed (correlation coefficient, − 0.97 ( p < 0.001)). The impact of the AEs scores on the QALD was marginal (− 0.02 max for one day). Conclusion Similarly with other H5N1 studies, no safety concern was identified throughout the study. Some time-limited variations in QALD-scores were reported. Our results imply that daily administration of the SF-36v2 captures changes in QALD-scores. Trial registration ClinicalTrials.gov . NCT01788228. Registered 11 February 2013. Electronic supplementary material The online version of this article (10.1186/s12955-019-1147-4) contains supplementary material, which is available to authorized users.

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model with incremental cost-effectiveness ratios or quality-adjusted life-years as outcome measures. Specific information on the decision-analytic models, including the modeling approach, perspective, population studied, interventions and data sources were extracted from each article. RESULTS: 1834 citations were screened and 68 full-text articles retrieved. Overall, 31 model-based studies were identified for extraction: 29 cohort-based state-transition models, one decision tree model, and one other cost-effectiveness model. Comparators included drugs such as dopamine agonists, entacapone, and rasagaline, as well as surgically implanted devices such as deep brain stimulation and levodopa-carbidopa intestinal gel. Overall, 10 modeled only early PD, 7 focused on advanced PD, 10 included early and late stage PD, and 4 did not specify stage. The most commonly modeled health outcomes were Hoehn and Yahr stage progression and % off-time. Other outcomes include motor complications, fluctuations, dyskinesia, falls, and dementia. CONCLUSIONS: The models identified typically had simple frameworks. Many projected disease progression from short-term clinical trial data alone and did not leverage real world observational data now available with longer longitudinal follow-up. Clinical efficacy was often applied by affecting only the initial distribution across health states, which likely does not fully capture the benefits of a treatment. Future studies should explore developing individual patient simulations to be able to more realistically represent the heterogeneity observed in the clinical manifestations and progression rates of the disease, as well as capture the potential benefits and risks of symptomatic or disease modifying treatments.

Background Patients with highly active relapsing-remitting multiple sclerosis inadequately responding to first-line therapies (interferon-based therapies, glatiramer acetate, dimethyl fumarate, and teriflunomide, known collectively as "BRACETD") often switch to natalizumab or fingolimod. Objective The aim was to estimate the comparative effectiveness of switching to natalizumab or fingolimod or within BRACETD using real-world data and to evaluate the cost-effectiveness of switching to natalizumab versus fingolimod using a United Kingdom (UK) third-party payer perspective. Methods Real-world data were obtained from MSBase for patients relapsing on BRACETD in the year before switching to natalizumab or fingolimod or within BRACETD. Three-way-multinomial-propensity-score-matched cohorts were identified, and comparisons between treatment groups were conducted for annualised relapse rate (ARR) and 6-month-confirmed disability worsening (CDW6M) and improvement (CDI6M). Results were applied in a cost-effectiveness model over a lifetime horizon using a published Markov structure with health states based on the Expanded Disability Status Scale. Other model parameters were obtained from the UK MS Survey 2015, published literature, and publicly available UK sources. ResultsThe MSBase analysis found a significant reduction in ARR (rate ratio [RR] = 0.64; 95% confidence interval [CI] 0.57-0.72; p < 0.001) and an increase in CDI6M (hazard ratio [HR] = 1.67; 95% CI 1.30-2.15; p < 0.001) for switching to natalizumab compared with BRACETD. For switching to fingolimod, the reduction in ARR (RR = 0.91; 95% CI 0.81-1.03; p = 0.133) and increase in CDI6M (HR = 1.30; 95% CI 0.99-1.72; p = 0.058) compared with BRACETD were not significant. Switching to natalizumab was associated with a significant reduction in ARR (RR = 0.70; 95% CI 0.62-0.79; p < 0.001) and an increase in CDI6M (HR = 1.28; 95% CI 1.01-1.62; p = 0.040) compared to switching to fingolimod. No evidence of difference in CDW6M was found between treatment groups. Natalizumab dominated (higher quality-adjusted life-years [QALYs] and lower costs) fingolimod in the base-case cost-effectiveness analysis (0.453 higher QALYs and £20,843 lower costs per patient). Results were consistent across sensitivity analyses.

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model with incremental cost-effectiveness ratios or quality-adjusted life-years as outcome measures. Specific information on the decision-analytic models, including the modeling approach, perspective, population studied, interventions and data sources were extracted from each article. RESULTS: 1834 citations were screened and 68 full-text articles retrieved. Overall, 31 model-based studies were identified for extraction: 29 cohort-based state-transition models, one decision tree model, and one other cost-effectiveness model. Comparators included drugs such as dopamine agonists, entacapone, and rasagaline, as well as surgically implanted devices such as deep brain stimulation and levodopa-carbidopa intestinal gel. Overall, 10 modeled only early PD, 7 focused on advanced PD, 10 included early and late stage PD, and 4 did not specify stage. The most commonly modeled health outcomes were Hoehn and Yahr stage progression and % off-time. Other outcomes include motor complications, fluctuations, dyskinesia, falls, and dementia. CONCLUSIONS: The models identified typically had simple frameworks. Many projected disease progression from short-term clinical trial data alone and did not leverage real world observational data now available with longer longitudinal follow-up. Clinical efficacy was often applied by affecting only the initial distribution across health states, which likely does not fully capture the benefits of a treatment. Future studies should explore developing individual patient simulations to be able to more realistically represent the heterogeneity observed in the clinical manifestations and progression rates of the disease, as well as capture the potential benefits and risks of symptomatic or disease modifying treatments.