The Relationship Between Focal and Generalized Bone Loss in Rheumatoid Arthritis.

OBJECTIVE: To determine if there is an association between focal and systemic bone loss in patients with RA. METHODS: Bone loss is a hallmark finding in rheumatoid arthritis (RA) and manifests as localized, periarticular and systemic bone loss. RA patients were selected from the Consortium of Rheumatology Researchers of North America (CORRONA) database. Multiple logistic regression models were constructed to assess the association between the presence or absence of erosions and T-scores at the lumbar spine (LS) and total hips and adjusted for age, gender, body mass index (BMI), medications and disease activity indices. RESULTS: Data on erosions and T-scores were available in 3,898 and 5,099 subjects, respectively. Patients with erosions had a significantly lower LS T-scores (-0.9) compared to RA patients without erosions (p=0.0002). Similarly, the mean total hip T-scores were significantly lower in patients with (-1.4) compared to subjects without erosions (-1.0) (p<0.01). The odds of having no erosion increased by 21% for each 1-unit increase in LS T-score and 46% for each 1 unit increase in hip Tscore. Patients with erosions were significantly younger (p<0.01) had a lower BMI (p<0.01) and higher DAS28 scores than those without erosions. More patients with erosions were on anti-TNF therapy, disease modifying drugs and osteoporosis medications than patients without erosions (p<0.01, 0.003 and 0.0003). CONCLUSION: RA patients with bone erosions have significantly lower T-scores at the LS and hips compared with RA patients without erosions. These data suggest a relationship between localized and generalized bone loss in RA.

An optimisation model for regional integrated solid waste management I. Model formulation.

Increased environmental concerns and the emphasis on material and energy recovery are gradually changing the orientation of MSW management and planning. In this context, the application of optimisation techniques have been introduced to design the least cost solid waste management systems, considering the variety of management processes. This study presents a model that was developed and applied to serve as a solid waste decision support system for MSW management taking into account both socio-economic and environmental considerations. The model accounts for solid waste generation rates, composition, collection, treatment, disposal as well as potential environmental impacts of various MSW management techniques. The model follows a linear programming formulation with the framework of dynamic optimisation. The model can serve as a tool to evaluate various MSW management alternatives and obtain the optimal combination of technologies for the handling, treatment and disposal of MSW in an economic and environmentally sustainable way. The sensitivity of various waste management policies will be also addressed. The work is presented in a series of two papers: (I) model formulation, and (II) model application and sensitivity analysis.

An optimisation model for regional integrated solid waste management II. Model application and sensitivity analyses.

Increased environmental concerns and the emphasis on material and energy recovery are gradually changing the orientation of MSW management and planning. In this context, the application of optimisation techniques have been introduced to design the least cost solid waste management systems, considering the variety of management processes (recycling, composting, anaerobic digestion, incineration, and landfilling), and the existence of uncertainties associated with the number of system components and their interrelations. This study presents a model that was developed and applied to serve as a solid waste decision support system for MSW management taking into account both socio-economic and environmental considerations. The model accounts for solid waste generation rates, composition, collection, treatment, disposal as well as potential environmental impacts of various MSW management techniques. The model follows a linear programming formulation with the framework of dynamic optimisation. The model can serve as a tool to evaluate various MSW management alternatives and obtain the optimal combination of technologies for the handling, treatment and disposal of MSW in an economic and environmentally sustainable way. The sensitivity of various waste management policies is also addressed. The work is presented in a series of two papers: (I) model formulation, and (II) model application and sensitivity analysis.

Generation of correlated logistic-normal random variates for medical decision trees.

A Logistic-Normal random variable (Y) is obtained from a Normal random variable (X) by the relation Y = (ex)/(1 + ex). In Monte-Carlo analysis of decision trees, Logistic-Normal random variates may be used to model the branching probabilities. In some cases, the probabilities to be modeled may not be independent, and a method for generating correlated Logistic-Normal random variates would be useful. A technique for generating correlated Normal random variates has been previously described. Using Taylor Series approximations and the algebraic definitions of variance and covariance, we describe methods for estimating the means, variances, and covariances of Normal random variates which, after translation using the above formula, will result in Logistic-Normal random variates having approximately the desired means, variances, and covariances. Multiple simulations of the method using the Mathematica computer algebra system show satisfactory agreement with the theoretical results.