Vaccine

School-located vaccination against influenza (SLV-I) has been suggested to help meet the need for annual vaccination of large numbers of school-aged children with seasonal influenza vaccine. However, little is known about the cost and cost-effectiveness of SLV-I. We conducted a cost-analysis and a cost-effectiveness analysis based on a randomized controlled trial (RCT) of an SLV-I program implemented in Monroe County, New York during the 2009-2010 vaccination season. We hypothesized that SLV-I is more cost effective, or less-costly, compared to a conventional, office-located influenza vaccination delivery. First and second SLV-I clinics were offered in 21 intervention elementary schools (n=9027 children) with standard of care (no SLV-I) in 11 control schools (n=4534 children). The direct costs, to purchase and administer vaccines, were estimated from our RCT. The effectiveness measure, receipt of \ue2\u2030\ua51 dose of influenza vaccine, was 13.2 percentage points higher in SLV-I schools than control schools. The school costs ($9.16/dose in 2009 dollars) plus project costs ($23.00/dose) plus vendor costs excluding vaccine purchase ($19.89/dose) was higher in direct costs ($52.05/dose) than the previously reported mean/median cost [$38.23/$21.44 per dose] for providing influenza vaccination in pediatric practices. However SLV-I averted parent costs to visit medical practices ($35.08 per vaccine). Combining direct and averted costs through Monte Carlo Simulation, SLV-I costs were$19.26/dose in net costs, which is below practice-based influenza vaccination costs. The incremental cost-effectiveness ratio (ICER) was estimated to be $92.50 or$38.59 (also including averted parent costs). When additionally accounting for the costs averted by disease prevention (i.e., both reduced disease transmission to household members and reduced loss of productivity from caring for a sick child), the SLV-I model appears to be cost-saving to society, compared to "no vaccination". Our findings support the expanded implementation of SLV-I, but also the need to focus on efficient delivery to reduce direct costs.1K25AI073915/AI/NIAID NIH HHS/United StatesK25 AI073915/AI/NIAID NIH HHS/United StatesU01IP000195/IP/NCIRD CDC HHS/United States2015-09-25T00:00:00Z23499607PMC458306

J Pediatr

ObjectivesTo estimate the cost-effectiveness of Haemophilus influenzae type b (Hib) conjugate vaccine in low-and middle-income countries and identify the model variables, which are most important for the result.Study designA static decision tree model was developed to predict incremental costs and health impacts. Estimates were generated for 4 country groups: countries eligible for funding by the GAVI Alliance in Africa and Asia, lower middle-income countries, and upper middle-income countries. Values, including disease incidence, case fatality rates, and treatment costs, were based on international country estimates and the scientific literature.ResultsFrom the societal perspective, it is estimated that the probability of Hib conjugate vaccine cost saving is 34%\u201353% in Global Alliance for Vaccines and Immunization eligible African and Asian countries, respectively. In middle-income countries, costs per discounted disability adjusted life year averted are between US$37 and US$733. Variation in vaccine prices and risks of meningitis sequelae and mortality explain most of the difference in results. For all country groups, disease incidence cause the largest part of the uncertainty in the result.ConclusionsHib conjugate vaccine is cost saving or highly cost-effective in low- and middle-income settings. This conclusion is especially influenced by the recent decline in Hib conjugate vaccine prices and new data revealing the high costs of lost productivity associated with meningitis sequelae.CC999999/Intramural CDC HHS/United States2018-01-02T00:00:00Z23773595PMC5749634vault:2575

Can Interactions between Timing of Vaccine-Altered Influenza Pandemic Waves and Seasonality in Influenza Complications Lead to More Severe Outcomes?

Vaccination can delay the peak of a pandemic influenza wave by reducing the number of individuals initially susceptible to influenza infection. Emerging evidence indicates that susceptibility to severe secondary bacterial infections following a primary influenza infection may vary seasonally, with peak susceptibility occurring in winter. Taken together, these two observations suggest that vaccinating to prevent a fall pandemic wave might delay it long enough to inadvertently increase influenza infections in winter, when primary influenza infection is more likely to cause severe outcomes. This could potentially cause a net increase in severe outcomes. Most pandemic models implicitly assume that the probability of severe outcomes does not vary seasonally and hence cannot capture this effect. Here we show that the probability of intensive care unit (ICU) admission per influenza infection in the 2009 H1N1 pandemic followed a seasonal pattern. We combine this with an influenza transmission model to investigate conditions under which a vaccination program could inadvertently shift influenza susceptibility to months where the risk of ICU admission due to influenza is higher. We find that vaccination in advance of a fall pandemic wave can actually increase the number of ICU admissions in situations where antigenic drift is sufficiently rapid or where importation of a cross-reactive strain is possible. Moreover, this effect is stronger for vaccination programs that prevent more primary influenza infections. Sensitivity analysis indicates several mechanisms that may cause this effect. We also find that the predicted number of ICU admissions changes dramatically depending on whether the probability of ICU admission varies seasonally, or whether it is held constant. These results suggest that pandemic planning should explore the potential interactions between seasonally varying susceptibility to severe influenza outcomes and the timing of vaccine-altered pandemic influenza waves

The development testing and implementation of the 4 Pillars™ practice transformation program for immunization: achieving public health outcomes through primary care quality improvement

The 4 Pillars™ Practice Transformation Program for Immunization is an evidence-based quality improvement program to improve immunization outcomes in primary care. The intervention strategies, implementation methods and the 4 Pillars™ of Convenience and Access, Patient Communication, Enhanced Vaccination Systems, and Motivation were informed by theoretical frameworks from the sciences of medicine, public health, systems and implementation and the social ecological model. The program was most-recently deployed in three different settings; a multi-center cluster-randomized clinical trial, a continuing medical education performance-in-practice module, and a quality improvement initiative in a regional community medicine health care organization. In clinical trials, the program demonstrated efficacy to improve increased uptake of seasonal influenza vaccine in children; meningococcal and Tdap vaccines and HPV initiation and completion in adolescents; seasonal influenza, pneumococcal, and pertussis vaccines in adults; and pneumococcal vaccines in older adults. Population-level cost-effectiveness models of the data report that the program was a good value with incremental cost-effectiveness ratios of $4937 within American Board of Family Medicine physicians seeking continuing education credit and$31,700 as a clinical trial per quality adjusted life year gained. An analysis of the efficacy of the program as conducted in 63 practices of a primary care division of a large regional health organization was inconclusive failing to replicate the results observed in clinical trials. Secular trends, data availability, and methodological limitations interfered with the fidelity of the intervention which led to sub-optimal results. Public Health Significance - System limitations in practice of health care were observed. Nearly all domains of medical quality improvement would benefit from substantial changes to the user experience at the point of care, health data systems interoperability, and the availability of consistent patient-level data for epidemiologic research and the development of simulation models of health systems and health behavior dynamics. Translating a complex intervention from a laboratory controlled clinical trial to an organization-directed quality improvement program is a significant challenge in public health. This process of scaling mirrors the barriers of influencing behavior in nested social ecological levels. Consequently, the 4 Pillars™ can provide guidance to improve efficacy of future public health programs

Economic evaluation and access strategy development for the 20-valent pneumococcal conjugate vaccine (PCV20) in Spanish adults

Máster en Evaluación Sanitaria y Acceso al Mercad

Stochastic Optimization Models for Perishable Products

For many years, researchers have focused on developing optimization models to design and manage supply chains. These models have helped companies in different industries to minimize costs, maximize performance while balancing their social and environmental impacts. There is an increasing interest in developing models which optimize supply chain decisions of perishable products. This is mainly because many of the products we use today are perishable, managing their inventory is challenging due to their short shelf life, and out-dated products become waste. Therefore, these supply chain decisions impact profitability and sustainability of companies and the quality of the environment. Perishable products wastage is inevitable when demand is not known beforehand. A number of models in the literature use simulation and probabilistic models to capture supply chain uncertainties. However, when demand distribution cannot be described using standard distributions, probabilistic models are not effective. In this case, using stochastic optimization methods is preferred over obtaining approximate inventory management policies through simulation. This dissertation proposes models to help businesses and non-prot organizations make inventory replenishment, pricing and transportation decisions that improve the performance of their system. These models focus on perishable products which either deteriorate over time or have a fixed shelf life. The demand and/or supply for these products and/or, the remaining shelf life are stochastic. Stochastic optimization models, including a two-stage stochastic mixed integer linear program, a two-stage stochastic mixed integer non linear program, and a chance constraint program are proposed to capture uncertainties. The objective is to minimize the total replenishment costs which impact prots and service rate. These models are motivated by applications in the vaccine distribution supply chain, and other supply chains used to distribute perishable products. This dissertation also focuses on developing solution algorithms to solve the proposed optimization models. The computational complexity of these models motivated the development of extensions to standard models used to solve stochastic optimization problems. These algorithms use sample average approximation (SAA) to represent uncertainty. The algorithms proposed are extensions of the stochastic Benders decomposition algorithm, the L-shaped method (LS). These extensions use Gomory mixed integer cuts, mixed-integer rounding cuts, and piecewise linear relaxation of bilinear terms. These extensions lead to the development of linear approximations of the models developed. Computational results reveal that the solution approach presented here outperforms the standard LS method. Finally, this dissertation develops case studies using real-life data from the Demographic Health Surveys in Niger and Bangladesh to build predictive models to meet requirements for various childhood immunization vaccines. The results of this study provide support tools for policymakers to design vaccine distribution networks

Lives saved with vaccination for 10 pathogens across 112 countries in a pre-COVID-19 world.

BackgroundVaccination is one of the most effective public health interventions. We investigate the impact of vaccination activities for Haemophilus influenzae type b, hepatitis B, human papillomavirus, Japanese encephalitis, measles, Neisseria meningitidis serogroup A, rotavirus, rubella, Streptococcus pneumoniae, and yellow fever over the years 2000-2030 across 112 countries.MethodsTwenty-one mathematical models estimated disease burden using standardised demographic and immunisation data. Impact was attributed to the year of vaccination through vaccine-activity-stratified impact ratios.ResultsWe estimate 97 (95%CrI[80, 120]) million deaths would be averted due to vaccination activities over 2000-2030, with 50 (95%CrI[41, 62]) million deaths averted by activities between 2000 and 2019. For children under-5 born between 2000 and 2030, we estimate 52 (95%CrI[41, 69]) million more deaths would occur over their lifetimes without vaccination against these diseases.ConclusionsThis study represents the largest assessment of vaccine impact before COVID-19-related disruptions and provides motivation for sustaining and improving global vaccination coverage in the future.FundingVIMC is jointly funded by Gavi, the Vaccine Alliance, and the Bill and Melinda Gates Foundation (BMGF) (BMGF grant number: OPP1157270 / INV-009125). Funding from Gavi is channelled via VIMC to the Consortium's modelling groups (VIMC-funded institutions represented in this paper: Imperial College London, London School of Hygiene and Tropical Medicine, Oxford University Clinical Research Unit, Public Health England, Johns Hopkins University, The Pennsylvania State University, Center for Disease Analysis Foundation, Kaiser Permanente Washington, University of Cambridge, University of Notre Dame, Harvard University, Conservatoire National des Arts et Métiers, Emory University, National University of Singapore). Funding from BMGF was used for salaries of the Consortium secretariat (authors represented here: TBH, MJ, XL, SE-L, JT, KW, NMF, KAMG); and channelled via VIMC for travel and subsistence costs of all Consortium members (all authors). We also acknowledge funding from the UK Medical Research Council and Department for International Development, which supported aspects of VIMC's work (MRC grant number: MR/R015600/1).JHH acknowledges funding from National Science Foundation Graduate Research Fellowship; Richard and Peggy Notebaert Premier Fellowship from the University of Notre Dame. BAL acknowledges funding from NIH/NIGMS (grant number R01 GM124280) and NIH/NIAID (grant number R01 AI112970). The Lives Saved Tool (LiST) receives funding support from the Bill and Melinda Gates Foundation.This paper was compiled by all coauthors, including two coauthors from Gavi. Other funders had no role in study design, data collection, data analysis, data interpretation, or writing of the report. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication

Pneumonia and poverty: a prospective population-based study among children in Brazil

<p>Abstract</p> <p>Background</p> <p>Children in developing country suffer the highest burden of pneumonia. However, few studies have evaluated associations between poverty and pneumonia.</p> <p>Methods</p> <p>A prospective population-based study on pneumonia was carried out as part of the Latin America Epidemiological Assessment of Pneumococcus (LEAP study). Chest x-rays were obtained for children one to 35 months old with suspected pneumonia presenting to emergency care centers and hospital emergency rooms in Goiania, Brazil. Chest radiographs were evaluated according to WHO guidelines. Clustering of radiologically-confirmed pneumonia were evaluated using a Poisson-based spatial scan statistic. Associations between census socioeconomic indicators and pneumonia incidence rates were analyzed using generalized linear models.</p> <p>Results</p> <p>From May, 2007 to May, 2009, chest radiographs were obtained from 11 521 children with clinical pneumonia; 3955 episodes were classified as radiologically-confirmed. Incidence rates were significantly higher in very low income areas (4825.2 per 10⁵) compared to high income areas (1637.3 per 10⁵). Spatial analysis identified clustering of confirmed pneumonia in Western (RR 1.78; p = 0.001) and Southeast (RR 1.46; p = 0.001) regions of the city, and clustering of hospitalized pneumonia in the Western region (RR 1.69; p = 0.001). Lower income households and illiteracy were associated with pneumonia incidence.</p> <p>Conclusions</p> <p>In infants the risk of developing pneumonia is inversely associated with the head of household income and with the woman educational level. Areas with deprived socioeconomic conditions had higher incidence of pneumonia and should be targeted for high vaccination coverage.</p