Validation of the sheep as a large animal model for the study of vertebral osteoporosis

Rats have long been the animal of choice for research in the field of osteoporosis. In the search for a complementary large animal model the sheep appears useful but hitherto the extent of bone loss from the spine has failed to reach a level that is generally accepted as osteoporotic in humans. Osteoporosis was induced in ten sheep using ovariectomy, low calcium diet and steroid injection for 6 months. Bone samples of iliac crest (IC), lumbar spine (LS), and proximal femur (PF) from the osteoporotic sheep were compared with those from four normal sheep using densitometry, histomorphometry, biochemistry and basic mechanical testing. The differences were examined using an analysis of variance with Tukey–Kramer test. Overall, the bone mineral density at LS and PF decreased more than 25% after treatment. Trabecular bone volume decreased by 29.2, 33.4 and 42.6% in IC, LS and PF, respectively. The failure load of the LS in axial compression was reduced to 2,003 from 6,140 N. The extent of bone loss was sufficient to categorise these sheep as osteoporotic although the pattern of bone loss varied between sites. Reduced mechanical competence in LS confirmed the suitability of this model for evaluation of potential treatments for osteoporosis

Decision-making in healthcare: a practical application of partial least square path modelling to coverage of newborn screening programmes

<p>Abstract</p> <p>Background</p> <p>Decision-making in healthcare is complex. Research on coverage decision-making has focused on comparative studies for several countries, statistical analyses for single decision-makers, the decision outcome and appraisal criteria. Accounting for decision processes extends the complexity, as they are multidimensional and process elements need to be regarded as latent constructs (composites) that are not observed directly. The objective of this study was to present a practical application of partial least square path modelling (PLS-PM) to evaluate how it offers a method for empirical analysis of decision-making in healthcare.</p> <p>Methods</p> <p>Empirical approaches that applied PLS-PM to decision-making in healthcare were identified through a systematic literature search. PLS-PM was used as an estimation technique for a structural equation model that specified hypotheses between the components of decision processes and the reasonableness of decision-making in terms of medical, economic and other ethical criteria. The model was estimated for a sample of 55 coverage decisions on the extension of newborn screening programmes in Europe. Results were evaluated by standard reliability and validity measures for PLS-PM.</p> <p>Results</p> <p>After modification by dropping two indicators that showed poor measures in the measurement models’ quality assessment and were not meaningful for newborn screening, the structural equation model estimation produced plausible results. The presence of three influences was supported: the links between both stakeholder participation or transparency and the reasonableness of decision-making; and the effect of transparency on the degree of scientific rigour of assessment. Reliable and valid measurement models were obtained to describe the composites of ‘transparency’, ‘participation’, ‘scientific rigour’ and ‘reasonableness’.</p> <p>Conclusions</p> <p>The structural equation model was among the first applications of PLS-PM to coverage decision-making. It allowed testing of hypotheses in situations where there are links between several non-observable constructs. PLS-PM was compatible in accounting for the complexity of coverage decisions to obtain a more realistic perspective for empirical analysis. The model specification can be used for hypothesis testing by using larger sample sizes and for data in the full domain of health technologies.</p