PubMed · 15127498
To get the most out of data collection, here's how to analyze its purpose, application, and cost-effectiveness.
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B M Billinger. 2000. Should your data collection expand or shrink?. https://doi.org/10.1097/00006247-200007000-00004
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BACKGROUND: Studies published in non-English languages are systematically missing in systematic reviews of growth and quality of economic evaluations of health care. The aims of this study were: to characterize German evaluations, published in English or German-language, in terms of various key parameters; to investigate methods to derive quality-of-life weights in cost-utility studies; and to examine changes in study characteristics over the years. METHODS: We conducted a country-specific systematic review of the German and English-language literature of German economic evaluations (assessment of or application to the German health care system) published 1990-2004. Generic and specialized health economic databases were searched. Two independent reviewers verified fulfillment of inclusion criteria and extracted study characteristics. RESULTS: The fulltexts of 730 articles were reviewed of which 283 fulfilled all entry criteria. 32% of included studies were published in German-language. 51% of studies evaluated pharmaceuticals and 63% were cost-effectiveness analyses. Economic appraisals concentrate on few disease categories and important health areas are strongly underrepresented. Declaration of sponsorship was associated with article language (49% English articles vs. 29% German articles, p < 0.001). The methodology used to obtain quality-of-life weights in published cost-utility studies was very diverse, poorly reported and most studies did not use German patients' or community health state evaluations. CONCLUSION: Many of the German-language evaluations included in our study are likely to be missing in international reviews and may be systematically different from English-language reviews from Germany. Lack of transparency and adherence to recommended reporting practices constitute a serious problem in German economic evaluations.
The purpose of this study is to develop a model for optimal nonpoint source pollution control for the Fei-Tsui Reservoir watershed in Northern Taiwan. Several structural best management practices (BMPs) are selected to treat stormwater runoff. The complete model consists of two interacting components: an optimization model based on discrete differential dynamic programming (DDDP) and a zero-dimensional reservoir water quality model. A predefined procedure is used to locate suitable sites for construction of various selected BMPs in the watershed. In the optimization model, the objective function is to find the best combination of BMP type and placement, which minimizes the total construction and operation, maintenance, and repair (OMR) costs of the BMPs. The constraints are the water quality standards for total phosphorus (TP) and total suspended solids (TSS) concentrations in the reservoir. A zero-dimensional reservoir water quality model of the Vollenweider type is embedded in the optimization framework to simulate pollutant concentrations in Fei-Tsui Reservoir. The resulting optimal cost and benefit of water quality improvement are depicted by the model-derived trade-off curves. The modeling framework developed in the present study could be used as an efficient tool for planning a watershed-wide implementation of BMPs for mitigating stormwater pollution impact on the receiving water bodies.
Cost-effectiveness analysis (CEA) is an analytic tool that provides a framework for comparing the health benefits and resource expenditures associated with competing medical and public health interventions, thereby allowing decision makers to identify interventions that yield the greatest amount of health, given their resource constraints. Models are important components of most, if not all, CEAs, and they play a key role in evaluating the cost-effectiveness of cancer screening programs, in particular. In this article, we describe the basic types of models used to evaluate cancer screening programs and provide examples of the use of models in CEAs and to guide cancer screening policy. Finally, we offer some suggestions for important concepts to consider when interpreting model results.