Dataset Information

Comparing cost-of-illness estimates from alternative approaches: an application to diabetes.

ABSTRACT:

Objective

To compare disease cost estimates from two commonly used approaches.

Data source

Pooled Medical Expenditure Panel Survey (MEPS) data for 1998-2003.

Study design

We compared regression-based (RB) and attributable fraction (AF) approaches for estimating disease-attributable costs with an application to diabetes. The RB approach used results from econometric models of disease costs, while the AF approach used epidemiologic formulas for diabetes-attributable fractions combined with the total costs for seven conditions that result from diabetes.

Data extraction

We used SAS version 9.1 to create a dataset that combined data from six consecutive years of MEPS.

Principal findings

The RB approach produced higher estimates of diabetes-attributable medical spending ($52.9 billion in 2004 dollars) than the AF approach ($37.1 billion in 2004 dollars). RB model estimates may in part be higher because of the challenges of implementing the two approaches in a similar manner, but may also be higher because they capture the costs of increased treatment intensity for those with the disease.

Conclusions

We recommend using the RB approach for estimating disease costs whenever individual-level data on health care spending are available and when the presence of the disease affects treatment costs for other conditions, as in the case of diabetes.

SUBMITTER: Honeycutt AA

PROVIDER: S-EPMC2669634 | biostudies-literature | 2009 Feb

REPOSITORIES: biostudies-literature

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Publications

Comparing cost-of-illness estimates from alternative approaches: an application to diabetes.

Honeycutt Amanda A AA Segel Joel E JE Hoerger Thomas J TJ Finkelstein Eric A EA

Health services research 20090201 1

<h4>Objective</h4>To compare disease cost estimates from two commonly used approaches.<h4>Data source</h4>Pooled Medical Expenditure Panel Survey (MEPS) data for 1998-2003.<h4>Study design</h4>We compared regression-based (RB) and attributable fraction (AF) approaches for estimating disease-attributable costs with an application to diabetes. The RB approach used results from econometric models of disease costs, while the AF approach used epidemiologic formulas for diabetes-attributable fractions ...[more]

PMID: 19146569

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Project description:As crossbreeding is extensively used in some livestock species, we aimed to evaluate the performance of single-step GBLUP (ssGBLUP) and weighted ssGBLUP (WssGBLUP) methods to predict Genomic Estimated Breeding Values (GEBVs) of crossbred animals. Different training population scenarios were evaluated: (SC1) ssGBLUP based on a single-trait model considering purebred and crossbred animals in a joint training population; (SC2) ssGBLUP based on a multiple-trait model to enable considering phenotypes recorded in purebred and crossbred training animals as different traits; (SC3) WssGBLUP based on a single-trait model considering purebred and crossbred animals jointly in the training population (both populations were used for SNP weights' estimation); (SC4) WssGBLUP based on a single-trait model considering only purebred animals in the training population (crossbred population only used for SNP weights' estimation); (SC5) WssGBLUP based on a single-trait model and the training population characterized by purebred animals (purebred population used for SNP weights' estimation). A complex trait was simulated assuming alternative genetic architectures. Different scaling factors to blend the inverse of the genomic (G -1) and pedigree ( A 22 - 1 ) relationship matrices were also tested. The predictive performance of each scenario was evaluated based on the validation accuracy and regression coefficient. The genetic correlations across simulated populations in the different scenarios ranged from moderate to high (0.71-0.99). The scenario mimicking a completely polygenic trait ( h Q T L 2 = 0) yielded the lowest validation accuracy (0.12; for SC3 and SC4). The simulated scenarios assuming 4,500 QTLs affecting the trait and h Q T L 2 = h 2 resulted in the greatest GEBV accuracies (0.47; for SC1 and SC2). The regression coefficients ranged from 0.28 (for SC3 assuming polygenic effect) to 1.27 (for SC2 considering 4,500 QTLs). In general, SC3 and SC5 resulted in inflated GEBVs, whereas other scenarios yielded deflated GEBVs. The scaling factors used to combine G -1 and A 22 - 1 had a small influence on the validation accuracies, but a greater effect on the regression coefficients. Due to the complexity of multiple-trait models and WssGBLUP analyses, and a similar predictive performance across the methods evaluated, SC1 is recommended for genomic evaluation in crossbred populations with similar genetic structures [moderate-to-high (0.71-0.99) genetic correlations between purebred and crossbred populations].

Dataset Information

Comparing cost-of-illness estimates from alternative approaches: an application to diabetes.

Objective

Data source

Study design

Data extraction

Principal findings

Conclusions

Publications

Comparing cost-of-illness estimates from alternative approaches: an application to diabetes.

Similar Datasets

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