Routine Measurement of Health-Related Quality of Life in Assessing Cost-Effectiveness in Secondary Health Care

Aims: The aims of this study were 1) to identify and describe health economic studies that have used quality-adjusted life years (QALYs) based on actual measurements of patients' health-related quality of life (HRQoL); 2) to test the feasibility of routine collection of health-related quality of life (HRQoL) data as an indicator of effectiveness of secondary health care; and 3) to establish and compare the cost-utility of three large-volume surgical procedures in a real-world setting in the Helsinki University Central Hospital, a large referral hospital providing secondary and tertiary health-care services for a population of approximately 1.4 million. Patients and methods: So as to identify studies that have used QALYs as an outcome measure, a systematic search of the literature was performed using the Medline, Embase, CINAHL, SCI and Cochrane Library electronic databases. Initial screening of the identified articles involved two reviewers independently reading the abstracts; the full-text articles were also evaluated independently by two reviewers, with a third reviewer used in cases where the two reviewers could not agree a consensus on which articles should be included. The feasibility of routinely evaluating the cost-effectiveness of secondary health care was tested by setting up a system for collecting HRQoL data on approximately 4 900 patients' HRQoL before and after operative treatments performed in the hospital. The HRQoL data used as an indicator of treatment effectiveness was combined with diagnostic and financial indicators routinely collected in the hospital. To compare the cost-effectiveness of three surgical interventions, 712 patients admitted for routine operative treatment completed the 15D HRQoL questionnaire before and also 3-12 months after the operation. QALYs were calculated using the obtained utility data and expected remaining life years of the patients. Direct hospital costs were obtained from the clinical patient administration database of the hospital and a cost-utility analysis was performed from the perspective of the provider of secondary health care services. Main results: The systematic review (Study I) showed that although QALYs gained are considered an important measure of the effectiveness of health care, the number of studies in which QALYs are based on actual measurements of patients' HRQoL is still fairly limited. Of the reviewed full-text articles, only 70 reported QALYs based on actual before after measurements using a valid HRQoL instrument. Collection of simple cost-effectiveness data in secondary health care is feasible and could easily be expanded and performed on a routine basis (Study II). It allows meaningful comparisons between various treatments and provides a means for allocating limited health care resources. The cost per QALY gained was 2 770 for cervical operations and 1 740 for lumbar operations. In cases where surgery was delayed the cost per QALY was doubled (Study III). The cost per QALY ranges between subgroups in cataract surgery (Study IV). The cost per QALY gained was 5 130 for patients having both eyes operated on and 8 210 for patients with only one eye operated on during the 6-month follow-up. In patients whose first eye had been operated on previous to the study period, the mean HRQoL deteriorated after surgery, thus precluding the establishment of the cost per QALY. In arthroplasty patients (Study V) the mean cost per QALY gained in a one-year period was 6 710 for primary hip replacement, 52 270 for revision hip replacement, and 14 000 for primary knee replacement. Conclusions: Although the importance of cost-utility analyses has during recent years been stressed, there are only a limited number of studies in which the evaluation is based on patients own assessment of the treatment effectiveness. Most of the cost-effectiveness and cost-utility analyses are based on modeling that employs expert opinion regarding the outcome of treatment, not on patient-derived assessments. Routine collection of effectiveness information from patients entering treatment in secondary health care turned out to be easy enough and did not, for instance, require additional personnel on the wards in which the study was executed. The mean patient response rate was more than 70 %, suggesting that patients were happy to participate and appreciated the fact that the hospital showed an interest in their well-being even after the actual treatment episode had ended. Spinal surgery leads to a statistically significant and clinically important improvement in HRQoL. The cost per QALY gained was reasonable, at less than half of that observed for instance for hip replacement surgery. However, prolonged waiting for an operation approximately doubled the cost per QALY gained from the surgical intervention. The mean utility gain following routine cataract surgery in a real world setting was relatively small and confined mostly to patients who had had both eyes operated on. The cost of cataract surgery per QALY gained was higher than previously reported and was associated with considerable degree of uncertainty. Hip and knee replacement both improve HRQoL. The cost per QALY gained from knee replacement is two-fold compared to hip replacement. Cost-utility results from the three studied specialties showed that there is great variation in the cost-utility of surgical interventions performed in a real-world setting even when only common, widely accepted interventions are considered. However, the cost per QALY of all the studied interventions, except for revision hip arthroplasty, was well below 50 000, this figure being sometimes cited in the literature as a threshold level for the cost-effectiveness of an intervention. Based on the present study it may be concluded that routine evaluation of the cost-utility of secondary health care is feasible and produces information essential for a rational and balanced allocation of scarce health care resources.

Computational Methods for Reconstruction and Analysis of Genome-Scale Metabolic Networks

Metabolism is the cellular subsystem responsible for generation of energy from nutrients and production of building blocks for larger macromolecules. Computational and statistical modeling of metabolism is vital to many disciplines including bioengineering, the study of diseases, drug target identification, and understanding the evolution of metabolism. In this thesis, we propose efficient computational methods for metabolic modeling. The techniques presented are targeted particularly at the analysis of large metabolic models encompassing the whole metabolism of one or several organisms. We concentrate on three major themes of metabolic modeling: metabolic pathway analysis, metabolic reconstruction and the study of evolution of metabolism. In the first part of this thesis, we study metabolic pathway analysis. We propose a novel modeling framework called gapless modeling to study biochemically viable metabolic networks and pathways. In addition, we investigate the utilization of atom-level information on metabolism to improve the quality of pathway analyses. We describe efficient algorithms for discovering both gapless and atom-level metabolic pathways, and conduct experiments with large-scale metabolic networks. The presented gapless approach offers a compromise in terms of complexity and feasibility between the previous graph-theoretic and stoichiometric approaches to metabolic modeling. Gapless pathway analysis shows that microbial metabolic networks are not as robust to random damage as suggested by previous studies. Furthermore the amino acid biosynthesis pathways of the fungal species Trichoderma reesei discovered from atom-level data are shown to closely correspond to those of Saccharomyces cerevisiae. In the second part, we propose computational methods for metabolic reconstruction in the gapless modeling framework. We study the task of reconstructing a metabolic network that does not suffer from connectivity problems. Such problems often limit the usability of reconstructed models, and typically require a significant amount of manual postprocessing. We formulate gapless metabolic reconstruction as an optimization problem and propose an efficient divide-and-conquer strategy to solve it with real-world instances. We also describe computational techniques for solving problems stemming from ambiguities in metabolite naming. These techniques have been implemented in a web-based sofware ReMatch intended for reconstruction of models for 13C metabolic flux analysis. In the third part, we extend our scope from single to multiple metabolic networks and propose an algorithm for inferring gapless metabolic networks of ancestral species from phylogenetic data. Experimenting with 16 fungal species, we show that the method is able to generate results that are easily interpretable and that provide hypotheses about the evolution of metabolism.