BackgroundThe safety and efficacy of racecadotril to treat acute watery diarrhea (AWD) in children is well established, however its cost effectiveness for infants and children in Europe has not yet been determined.ObjectiveTo evaluate the cost utility of racecadotril adjuvant with oral rehydration solution (ORS) compared to ORS alone for the treatment of AWD in children younger than 5 years old. The analysis is performed from a United Kingdom National Health Service (NHS) perspective.MethodsA decision tree model has been developed in Microsoft® Excel. The model is populated with the best available evidence. Deterministic and probabilistic sensitivity analyses (PSA) have been performed. Health effects are measured as quality-adjusted life years (QALYs) and the model output is cost (2011 GBP) per QALY. The uncertainty in the primary outcome is explored by probabilistic analysis using 1000 iterations of a Monte Carlo simulation.ResultsDeterministic analysis results in a total incremental cost of −£379 in favor of racecadotril and a total incremental QALY gain in favor of racecadotril of +0.0008. The observed cost savings with racecadotril arise from the reduction in primary care reconsultation and secondary referral. The difference in QALYs is largely attributable to the timely resolution of symptoms in the racecadotril arm. Racecadotril remains dominant when base case parameters are varied. Monte Carlo simulation and PSA confirm that racecadotril is the dominant treatment strategy and is almost certainly cost effective, under the central assumptions of the model, at a commonly used willingness to pay proxy threshold range of £20,000–£30,000 per QALY.ConclusionRacecadotril as adjuvant therapy is more effective and less costly compared to ORS alone, from a UK payer perspective, for the treatment of children with acute diarrhea.

Background In sub-Saharan Africa, rates of sustained HIV virologic suppression remain below international goals. HIV resistance testing, while common in resource-rich settings, has not gained traction due to concerns about cost and sustainability. Objective We designed a randomized clinical trial (REVAMP) to determine the feasibility, effectiveness, and cost-effectiveness of routine HIV resistance testing in sub-Saharan Africa. Approach We describe challenges common to intervention studies in resource-limited settings, and strategies used to address them, including: 1) optimizing generalizability and cost-effectiveness estimates to promote transition from study results to policy; 2) minimizing bias due to patient attrition; and 3) addressing ethical issues related to enrollment of pregnant women. Methods The REVAMP study randomizes people in Uganda and South Africa with virologic failure on first-line therapy to standard of care virologic monitoring or immediate resistance testing. To strengthen external validity study procedures are conducted within publicly-supported laboratory and clinical facilities using local staff. To optimize cost estimates, we collect primary data on quality of life and medical resource utilization. To minimize losses from observation, we collect locally-relevant contact information, including Whatsapp account details, for field-based tracking of missing participants. Finally, pregnant women are followed with increase visit frequency to minimize risk to them and their fetuses. Conclusions REVAMP is a pragammatic randomized clinical trial designed to test the effectiveness and cost-effectiveness of HIV resistance testing versus standard of care in sub-Saharan Africa. We anticipate the results will directly inform HIV polity in sub-Saharan to optimize care for HIV-infected patients.

Clinical sequences consisting of 1L and 2L line bevacizumab followed by 3L anti-EGFR potentially yield the greatest health outcomes associated with a reasonable trade-off in additional cost when replacing 1L anti-EGFRs and are potentially cost-saving if replacing 2L anti-EGFRs, per patient per lifetime. To maximize health outcomes, optimal sequences include anti-EGFRs as 3L regimen, with an approximately equivalent trade-off in costs between the most costly (anti-EGFR 2L) and least costly (anti-EGFR 1L) sequences.

ObjectiveTo evaluate the cost utility and the budget impact of adjuvant racecadotril for the treatment of acute diarrhea in children in Thailand.MethodsA cost utility model has been adapted to the context of Thailand to evaluate racecadotril plus oral rehydration solution (R+ORS) versus oral rehydration solution (ORS) alone for acute diarrhea in children <5 years old. The decision tree Excel model evaluates the costs and effects (quality-adjusted life years) over a 6-day time horizon from a public health care payer’s perspective in Thailand. Deterministic sensitivity analysis and budget impact analysis have been undertaken.ResultsAccording to the cost utility model, the intervention (R+ORS) is less costly and more effective than the comparator (ORS) for the base case with a dominant incremental cost-effectiveness ratio of −2,481,390฿ for the intervention. According to the budget impact analysis (assuming an increase of 5% market share for R+ORS over 5 years), the year-on-year reduction for diarrhea as a percentage of the total health care expenditure is −0.0027%, resulting in potential net cost savings of −35,632,482฿ over 5 years.ConclusionSubject to the assumptions and limitations of the models, adjuvant racecadotril versus ORS alone is potentially cost-effective for children in Thailand and uptake could translate into savings for the Thailand public health care system.

Health economics is a discipline of economics applied to health care. One method used in health economics is decision tree modelling, which extrapolates the cost and effectiveness of competing interventions over time. Such decision tree models are the basis of reimbursement decisions in countries using health technology assessment for decision making. In many instances, these competing interventions are diagnostic technologies. Despite a wealth of excellent resources describing the decision analysis of diagnostics, two critical errors persist: not including diagnostic test accuracy in the structure of decision trees and treating sequential diagnostics as independent. These errors have consequences for the accuracy of model results, and thereby impact on decision making. This paper sets out to overcome these errors using color to link fundamental epidemiological calculations to decision tree models in a visually and intuitively appealing pictorial format. The paper is a must-read for modelers developing decision trees in the area of diagnostics for the first time and decision makers reviewing diagnostic reimbursement models.