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Optimal allocation of resources in a biomarker setting.

ABSTRACT: Nutrient intake is often measured with substantial error both in commonly used surrogate instruments such as a food frequency questionnaire (FFQ) and in gold standard-type instruments such as a diet record (DR). If there is a correlated error between the FFQ and DR, then standard measurement error correction methods based on regression calibration can produce biased estimates of the regression coefficient (?) of true intake on surrogate intake. However, if a biomarker exists and the error in the biomarker is independent of the error in the FFQ and DR, then the method of triads can be used to obtain unbiased estimates of ?, provided that there are replicate biomarker data on at least a subsample of validation study subjects. Because biomarker measurements are expensive, for a fixed budget, one can use a either design where a large number of subjects have one biomarker measure and only a small subsample is replicated or a design that has a smaller number of subjects and has most or all subjects validated. The purpose of this paper is to optimize the proportion of subjects with replicated biomarker measures, where optimization is with respect to minimizing the variance of ln(??). The methodology is illustrated using vitamin C intake data from the European Prospective Investigation into Cancer and Nutrition study where plasma vitamin C is the biomarker. In this example, the optimal validation study design is to have 21% of subjects with replicated biomarker measures.

SUBMITTER: Rosner B

PROVIDER: S-EPMC4268307 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature

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Optimal allocation of resources in a biomarker setting.

Rosner Bernard B Hendrickson Sara S Willett Walter W

Statistics in medicine 20141024 2

Nutrient intake is often measured with substantial error both in commonly used surrogate instruments such as a food frequency questionnaire (FFQ) and in gold standard-type instruments such as a diet record (DR). If there is a correlated error between the FFQ and DR, then standard measurement error correction methods based on regression calibration can produce biased estimates of the regression coefficient (λ) of true intake on surrogate intake. However, if a biomarker exists and the error in the ...[more]

PMID: 25346516

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Project description:BACKGROUND:Health resources are limited, which means spending should be focused on the people, places and programs that matter most. Choosing the mix of programs to maximize a health outcome is termed allocative efficiency. Here, we extend the methodology of allocative efficiency to answer the question of how resources should be distributed among different geographic regions. METHODS:We describe a novel geographical optimization algorithm, which has been implemented as an extension to the Optima HIV model. This algorithm identifies an optimal funding of services and programs across regions, such as multiple countries or multiple districts within a country. The algorithm consists of three steps: (1) calibrating the model to each region, (2) determining the optimal allocation for each region across a range of different budget levels, and (3) finding the budget level in each region that minimizes the outcome (such as reducing new HIV infections and/or HIV-related deaths), subject to the constraint of fixed total budget across all regions. As a case study, we applied this method to determine an illustrative allocation of HIV program funding across three representative oblasts (regions) in Ukraine (Mykolayiv, Poltava, and Zhytomyr) to minimize the number of new HIV infections. RESULTS:Geographical optimization was found to identify solutions with better outcomes than would be possible by considering region-specific allocations alone. In the case of Ukraine, prior to optimization (i.e. with status quo spending), a total of 244,000 HIV-related disability-adjusted life years (DALYs) were estimated to occur from 2016 to 2030 across the three oblasts. With optimization within (but not between) oblasts, this was estimated to be reduced to 181,000. With geographical optimization (i.e., allowing reallocation of funds between oblasts), this was estimated to be further reduced to 173,000. CONCLUSIONS:With the increasing availability of region- and even facility-level data, geographical optimization is likely to play an increasingly important role in health economic decision making. Although the largest gains are typically due to reallocating resources to the most effective interventions, especially treatment, further gains can be achieved by optimally reallocating resources between regions. Finally, the methods described here are not restricted to geographical optimization, and can be applied to other problems where competing resources need to be allocated with constraints, such as between diseases.

Project description:IntroductionGreat strides in responding to the HIV epidemic have led to improved access to and uptake of HIV services in Guyana, a lower-middle-income country with a generalized HIV epidemic. Despite efforts to scale up HIV treatment and adopt the test and start strategy, little is known about costs of HIV services across the care cascade.MethodsWe collected cost data from the national laboratory and nine selected treatment facilities in five of the country's ten Regions, and estimated the costs associated with HIV testing and services (HTS) and antiretroviral therapy (ART) from a provider perspective from January 1, 2016 to December 31, 2016. We then used the unit costs to construct four resource allocation scenarios. In the first two scenarios, we calculated how close Guyana would currently be to its 2020 targets if the allocation of funding across programs and regions over 2017-2020 had (a) remained unchanged from latest-reported levels, or (b) been optimally distributed to minimize incidence and deaths. In the next two, we estimated the resources that would have been required to meet the 2020 targets if those resources had been distributed (a) according to latest-reported patterns, or (b) optimally to minimize incidence and deaths.ResultsThe mean cost per test was US$15 and the mean cost per person tested positive was US$796. The mean annual cost per of maintaining established adult and pediatric patients on ART were US$428 and US$410, respectively. The mean annual cost of maintaining virally suppressed patients was US$648. Cost variation across sites may suggest opportunities for improvements in efficiency, or may reflect variation in facility type and patient volume. There may also be scope for improvements in allocative efficiency; we estimated a 28% reduction in the total resources required to meet Guyana's 2020 targets if funds had been optimally distributed to minimize infections and deaths.ConclusionsWe provide the first estimates of costs along the HIV cascade in the Caribbean and assessed efficiencies using novel context-specific data on the costs associated with diagnostic, treatment, and viral suppression. The findings call for better targeting of services, and efficient service delivery models and resource allocation, while scaling up HIV services to maximize investment impact.

Project description:Background and aimsHeteroblasty is an encompassing term referring to ontogenetic changes in the plant shoot. A shaded environment is known to affect the process of heteroblastic development; however, it is not known whether crowded or high density growing conditions can also alter heteroblasty. Compound leaves of the shade-intolerant Acacia implexa allocate less biomass per unit photosynthetic area than transitional leaves or phyllodes and it is hypothesized that this trait will convey an advantage in a crowded environment. Compound leaves also have larger photosynthetic capture area - a trait known to be advantageous in shade. This studied tested the hypothesis that more compound leaves will be developed under shade and crowded environments. Furthermore, this species should undergo optimal allocation of biomass to shoots and roots given shaded and crowded environments.MethodsA full factorial design of irradiance (high and low) and density levels (high, medium and low) on three populations sourced from varying rainfall regions (high, medium and low) was established under controlled glasshouse conditions. Traits measured include the number of nodes expressing a compound leaf, biomass allocation to shoots and roots, and growth traits. Key Results A higher number of nodes expressed a compound leaf under low irradiance and in high density treatments; however, there were no significant interactions across treatments. Phenotypes strongly associated with the shade avoidance syndrome were developed under low irradiance; however, this was not observed under high density. There was no significant difference in relative growth rates across light treatments, but growth was significantly slower in a crowded environment. Conclusions Heteroblastic development in Acacia can be altered by shade and crowded environments. In this experiment, light was clearly the most limiting factor to growth in a shaded environment; however, in a crowded environment there were additional limiting resources to growth.

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Optimal allocation of resources in a biomarker setting.

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Optimal allocation of resources in a biomarker setting.

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