Cost Effectiveness and Resource Allocation - Latest Articles

The indirect cost due to pulmonary Tuberculosis in patients receiving treatment in Bauchi State-- Nigeria

Nisser Umar — 2012-05-11T00:00:00Z

ObjectiveTo determine the time spent and income lost by patients and their households for seekingtuberculosis diagnosis and treatment in Bauchi State-Nigeria.MethodA cross sectional study where 242 TB patients were sampled from 27 out of 67 facilitiesproviding TB services in a north-eastern state of Nigeria. Sampling was stratified based onfacility type, patients' HIV status and gender. Results: The income lost among the hospitalized group was estimated at $156/patient and about $114in the non-hospitalized patients group. Age, gender, facility of diagnosis, level of educationand occupation were significant (p-values <0.05) associated with total (both patients and theirhouseholds) income lost. However, AFB sputum-smear result and HIV status had nosignificant effects on the income lost. Hospitalised patients spent an average time of 924.98hours for diagnosis and treatment whereas the non-hospitalised spent an average of 141.29hours. The estimated US dollar valued of these hours was US517.98 and US$79.13 forhospitalised and non-hospitalised patient groups respectively. Hospitalisation and the facilityof diagnosis were statistically significant (p-value <0.05) predictors of the time patients andhousehold spent on TB. Conclusion: Tuberculosis poses causes tremendous burden in terms of time and productivity lost to bothpatients and their households in Bauchi State Nigeria

Cost and cost effectiveness of long-lasting insecticide-treated bed nets - A model-based analysis

Anni-Maria Pulkki-Brannstrom — 2012-04-04T00:00:00Z

Background: The World Health Organization recommends that national malaria programmes universally distribute long-lasting insecticide-treated bed nets (LLINs). LLINs provide effective insecticide protection for at least three years while conventional nets must be retreated every 6-12 months. LLINs may also promise longer physical durability (lifespan), but at a higher unit price. No prospective data currently available is sufficient to calculate the comparative cost effectiveness of different net types. We thus constructed a model to explore the cost effectiveness of LLINs, asking how a longer lifespan affects the relative cost effectiveness of nets, and if, when and why LLINs might be preferred to conventional insecticide-treated nets. An innovation of our model is that we also considered the replenishment need i.e. loss of nets over time. Methods: We modelled the choice of net over a 10-year period to facilitate the comparison of nets with different lifespan (and/or price) and replenishment need over time. Our base case represents a large-scale programme which achieves high coverage and usage throughout the population by distributing either LLINs or conventional nets through existing health services, and retreats a large proportion of conventional nets regularly at low cost. We identified the determinants of bed net programme cost effectiveness and parameter values for usage rate, delivery and retreatment cost from the literature. One-way sensitivity analysis was conducted to explicitly compare the differential effect of changing parameters such as price, lifespan, usage and replenishment need. Results: If conventional and long-lasting bed nets have the same physical lifespan (3 years), LLINs are more cost effective unless they are priced at more than USD 1.5 above the price of conventional nets. Because a longer lifespan brings delivery cost savings, each one year increase in lifespan can be accompanied by a USD 1 or more increase in price without the cheaper net (of the same type) becoming more cost effective. Distributing replenishment nets each year in addition to the replacement of all nets every 3-4 years increases the number of under-5 deaths averted by 5-14% at a cost of USD 17-25 per additional person protected per annum or USD 1080-1610 per additional under-5 death averted. Conclusions: Our results support the World Health Organization recommendation to distribute only LLINs, while giving guidance on the price thresholds above which this recommendation will no longer hold. Programme planners should be willing to pay a premium for nets which have a longer physical lifespan, and if planners are willing to pay USD 1600 per under-5 death averted, investing in replenishment is cost effective.

Cost effectiveness of Community-Based and In-Patient Therapeutic Feeding Programs to Treat Severe Acute Malnutrition in Ethiopia

Asayehegn Tekeste — 2012-03-19T00:00:00Z

Background: This study estimated the cost effectiveness of community-based therapeutic care (CTC) for children with severe acute malnutrition (SAM) in Sidama Zone, Ethiopia compared to facility based therapeutic feeding center (TFC). Methods: A cost effectiveness analysis comparing costs and outcomes of two treatment programmes was conducted. The societal perspective, which considers costs to all sectors of the society, was employed. Outcomes and health service costs of CTC and TFC were obtained from Save the Children USA (SC/USA) CTC and TFC programme, government health services and UNICEF(in kind supplies) cost estimates of unit costs. Parental costs were estimated through interviewing 306 caretakers. Cost categories were compared and a single cost effectiveness ratio of costs to treat a child with SAM in each program (regardless of outcome) was computed and compared. Results: A total of 328 patient cards/records of children treated in the programs were reviewed; out of which 306 (157 CTC and 149 TFC) were traced back to their households to interview their caretakers. The cure rate in TFC was 95.36% compared to 94.30% in CTC. The death rate in TFC was 0% and in CTC 1.2%. The mean cost per child treated was $284.56 in TFC and $134.88 in CTC. The institutional cost per child treated was $262.62 in TFC and $128.58 in CTC. Out of these institutional costs in TFC 46.6% was personnel cost. In contrast, majority (43.2%) of the institutional costs in CTC went to ready to use therapeutic food (RUTF). The opportunity cost per caretaker in the TFC was $21.01 whereas it was $5.87 in CTC. The result of this study shows that community based CTC was two times more cost effective than TFC. Conclusion: CTC was found to be relatively more cost effective than TFC in this setting. This indicates that CTC is a viable approach on just economic grounds in addition to other benefits such improved access, sustainability and appropriateness documented elsewhere. If costs of RUTF can be reduced such as through local production the CTC costs per child can be further reduced as RUTF constitutes the highest cost in these study settings.

The role of cognition in cost-effectiveness analyses of behavioral interventions

Rilana Prenger — 2012-03-01T00:00:00Z

Background: Behavioral interventions typically focus on objective behavioral endpoints like weight loss and smoking cessation. In reality, though, achieving full behavior change is a complex process in which several steps towards success are taken. Any progress in this process may also be considered as a beneficial outcome of the intervention, assuming that this increases the likelihood to achieve successful behavior change eventually. Until recently, there has been little consideration about whether partial behavior change at follow-up should be incorporated in cost-effectiveness analyses (CEAs). The aim of this explorative review is to identify CEAs of behavioral interventions in which cognitive outcome measures of behavior change are analyzed. Methods: Data sources were searched for publications before May 2011. Results: Twelve studies were found eligible for inclusion. Two different approaches were found: three studies calculated separate incremental cost-effectiveness ratios for cognitive outcome measures, and one study modeled partial behavior change into the final outcome. Both approaches rely on the assumption, be it implicitly or explicitly, that changes in cognitive outcome measures are predictive of future behavior change and may affect CEA outcomes. Conclusion: Potential value of cognitive states in CEA, as a way to account for partial behavior change, is to some extent recognized but not (yet) integrated in the field. In conclusion, CEAs should consider, and where appropriate incorporate measures of partial behavior change when reporting effectiveness and hence cost-effectiveness.

Field testing of a multicriteria decision analysis (MCDA) framework for coverage of a screening test for cervical cancer in South Africa

Jacqui Miot — 2012-02-29T00:00:00Z

Background: Systematic and transparent approaches to priority setting are needed, particularly in low-resource settings, to produce decisions that are sound and acceptable to stakeholders. The EVIDEM framework brings together Health Technology Assessment (HTA) and multi-criteria decision analysis (MCDA) by proposing a comprehensive set of decision criteria together with standardized processes to support decisionmaking. The objective of the study was to field test the framework for decisionmaking on a screening test by a private health plan in South Africa. Methods: Liquid-based cytology (LBC) for cervical cancer screening was selected by the health plan for this field test. An HTA report structured by decision criterion (14 criteria organized in the MCDA matrix and 4 contextual criteria) was produced based on a literature review and input from the health plan. During workshop sessions, committee members 1) weighted each MCDA decision criterion to express their individual perspectives, and 2) to appraise LBC, assigned scores to each MCDA criterion on the basis of the by-criterion HTA report.Committee members then considered the potential impacts of four contextual criteria on the use of LBC in the context of their health plan. Feedback on the framework and process was collected through discussion and from a questionnaire. Results: For 9 of the MCDA matrix decision criteria, 89% or more of committee members thought they should always be considered in decisionmaking. Greatest weights were given to the criteria "Budget impact", "Cost-effectiveness" and "Completeness and consistency of reporting evidence". When appraising LBC for cervical cancer screening, the committee assigned the highest scores to "Relevance and validity of evidence" and "Disease severity". Combination of weights and scores yielded a mean MCDA value estimate of 46% (SD 7%) of the potential maximum value. Overall, the committee felt the framework brought greater clarity to the decisionmaking process and was easily adaptable to different types of health interventions. Conclusions: The EVIDEM framework was easily adapted to evaluating a screening technology in South Africa, thereby broadening its applicability in healthcare decision making.

Methodologies used in cost-effectiveness models for evaluating treatments in major depressive disorder: a systematic review

Evelina Zimovetz — 2012-02-01T00:00:00Z

Background: Decision makers in many jurisdictions use cost-effectiveness estimates as an aid for selecting interventions with an appropriate balance between health benefits and costs. This systematic literature review aims to provide an overview of published cost-effectiveness models in major depressive disorder (MDD) with a focus on the methods employed. Key components of the identified models are discussed and any challenges in developing models are highlighted. Methods: A systematic literature search was performed to identify all primary model-based economic evaluations of MDD interventions indexed in MEDLINE, the Cochrane Library, EMBASE, EconLit, and PsycINFO between January 2000 and May 2010. Results: A total of 37 studies were included in the review. These studies predominantly evaluated antidepressant medications. The analyses were performed across a broad set of countries. The majority of models were decision-trees; eight were Markov models. Most models had a time horizon of less than 1 year. The majority of analyses took a payer perspective. Clinical input data were obtained from pooled placebo-controlled comparative trials, single head-to-head trials, or meta-analyses. The majority of studies (24 of 37) used treatment success or symptom-free days as main outcomes, 14 studies incorporated health state utilities, and 2 used disability-adjusted life-years. A few models (14 of 37) incorporated probabilities and costs associated with suicide and/or suicide attempts. Two models examined the cost-effectiveness of second-line treatment in patients who had failed to respond to initial therapy. Resource use data used in the models were obtained mostly from expert opinion. All studies, with the exception of one, explored parameter uncertainty. Conclusions: The review identified several model input data gaps, including utility values in partial responders, efficacy of second-line treatments, and resource utilisation estimates obtained from relevant, high-quality studies. It highlighted the differences in outcome measures among the trials of MDD interventions, which can lead to difficulty in performing indirect comparisons, and the inconsistencies in definitions of health states used in the clinical trials and those used in utility studies. Clinical outcomes contributed to the uncertainty in cost-effectiveness estimates to a greater degree than costs or utility weights.

Case management to improve adherence for HIV-infected patients receiving antiretroviral therapy in Ethiopia: A micro-costing study

Elliot Marseille — 2011-12-20T00:00:00Z

Background: Adherence to antiretroviral medication regimens is essential to good clinical outcomes for HIV-infected patients. Little is known about the costs of case management (CM) designed to improve adherence for patients identified as being at risk for poor adherence in resource-constrained settings. This study analyzed the costs, outputs, unit costs and correlates of unit cost variation for CM services in 14 ART sites in Ethiopia from October 2008 through September 2009. Methods: This study applied standard micro-costing methods to identify the incremental costs of the CM program. We divided total CM-attributable costs by three output measures (patient-quarters of CM services delivered, number of patients served and successful patient exits) to derive three separate indices of unit costs. The relationships between unit costs and two operational factors (scale and service-volume to staff ratios) were quantified through bivariate analyses. Results: The CM program delivered 4,598 patient-quarters of services, serving 5,056 patients and 1,995 successful exits at a cost of $167,457 over 12 months, or $36 per patient-quarter, $33 per patient served and $84 per successful exit from the CM program. Among the 14 sites, mean costs were $11,961 (sd, $3,965) for the 12-month study period, and $51 (sd, $36) per patient-quarter; $48 (sd, $32) per patient served; and $183 (sd, $157) per successful exit. Unit costs varied inversely with scale (r, -0.70 for cost per patient-quarter versus patient-quarters of service) and with the service-volume to staff ratio (r, -0.68 for cost per patient-quarter versus staff per patient-quarter). Conclusions: For those receiving CM, the program adds 0.52% to the lifetime cost of ART. These data reflect wide variation in unit costs among the study sites and suggest that high patient volume may be a major determinant of CM program efficiency. The observed variations in unit costs also indicate that there may be opportunities to identify staffing patterns that increase overall program efficiency.

Estimating the cost-effectiveness of lifestyle intervention programmes to prevent diabetes based on an example from Germany: Markov modelling

Anne Neumann — 2011-11-18T00:00:00Z

Background: Type 2 diabetes mellitus (T2D) poses a large worldwide burden for health care systems. One possible tool to decrease this burden is primary prevention. As it is unethical to wait until perfect data are available to conclude whether T2D primary prevention intervention programmes are cost-effective, we need a model that simulates the effect of prevention initiatives. Thus, the aim of this study is to investigate the long-term cost-effectiveness of lifestyle intervention programmes for the prevention of T2D using a Markov model. As decision makers often face difficulties in applying health economic results, we visualise our results with health economic tools. Methods: We use four-state Markov modelling with a probabilistic cohort analysis to calculate the cost per quality-adjusted life year (QALY) gained. A one-year cycle length and a lifetime time horizon are applied. Best available evidence supplies the model with data on transition probabilities between glycaemic states, mortality risks, utility weights, and disease costs. The costs are calculated from a societal perspective. A 3% discount rate is used for costs and QALYs. Cost-effectiveness acceptability curves are presented to assist decision makers. Results: The model indicates that diabetes prevention interventions have the potential to be cost-effective, but the outcome reveals a high level of uncertainty. Incremental cost-effectiveness ratios (ICERs) were negative for the intervention, ie, the intervention leads to a cost reduction for men and women aged 30 or 50 years at initiation of the intervention. For men and women aged 70 at initiation of the intervention, the ICER was EUR27,546/QALY gained and EUR19,433/QALY gained, respectively. In all cases, the QALYs gained were low. Cost-effectiveness acceptability curves show that the higher the willingness-to-pay threshold value, the higher the probability that the intervention is cost-effective. Nonetheless, all curves are flat. The threshold value of EUR50,000/QALY gained has a 30-55% probability that the intervention is cost-effective. Conclusions: Lifestyle interventions for primary prevention of type 2 diabetes are cost-saving for men and women aged 30 or 50 years at the start of the intervention, and cost-effective for men and women aged 70 years. However, there is a high degree of uncertainty around the ICERs. With the conservative approach adopted for this model, the long-term effectiveness of the intervention could be underestimated.

Correction: The EVIDEM framework and its usefulness for priority setting across a broad range of health interventions.

Sitaporn Youngkong — 2011-10-26T00:00:00Z

After the publication of this article [Youngkong,Tromp, and Chitama; Cost Effectiveness and Resource Allocation, 2011. 9:8], we became aware that two last sentences in the paragraph relied on original ideas following personal communication with a researcher, and should not have been presented here. Consequently, the reference number 9 which was cited for the removed issue should be taken from the article. The correct paragraph is provided below:

Targeted versus universal prevention. a resource allocation model to prioritize cardiovascular prevention

Talitha Feenstra — 2011-10-06T00:00:00Z

Background: Diabetes mellitus brings an increased risk for cardiovascular complications and patients profit from prevention. This prevention also suits the general population. The question arises what is a better strategy: target the general population or diabetes patients. Methods: A mathematical programming model was developed to calculate optimal allocations for the Dutch population of the following interventions: smoking cessation support, diet and exercise to reduce overweight, statins, and medication to reduce blood pressure. Outcomes were total lifetime health care costs and QALYs. Budget sizes were varied and the division of resources between the general population and diabetes patients was assessed. Results: Full implementation of all interventions resulted in a gain of 560,000 QALY at a cost of €640 per capita, about €12,900 per QALY on average. The large majority of these QALY gains could be obtained at incremental costs below €20,000 per QALY. Low or high budgets (below €9 or above €100 per capita) were predominantly spent in the general population. Moderate budgets were mostly spent in diabetes patients. Conclusions: Major health gains can be realized efficiently by offering prevention to both the general and the diabetic population. However, a priori setting a specific distribution of resources is suboptimal. Resource allocation models allow accounting for capacity constraints and program size in addition to efficiency.