Knowledge change regarding osteoporosis prevention: translating recommended guidelines into user-friendly messages within a community forum.

Osteoporosis is a skeletal disorder characterised by low bone mineral density and increased fracture risk. Nationally the total costs of this chronic disease are currently estimated at $2.754 billion annually. Effective public health messages providing clear recommendations are vital in supporting prevention efforts. This research aimed to investigate knowledge change associated with the translation of preventive guidelines into accessible messages for the community

A Spoonful of Math Helps the Medicine Go Down: An Illustration of How Healthcare can Benefit from Mathematical Modeling and Analysis

<p>Abstract</p> <p>Objectives</p> <p>A recent joint report from the Institute of Medicine and the National Academy of Engineering, highlights the benefits of--indeed, the need for--mathematical analysis of healthcare delivery. Tools for such analysis have been developed over decades by researchers in Operations Research (OR). An OR perspective typically frames a complex problem in terms of its essential mathematical structure. This article illustrates the use and value of the tools of operations research in healthcare. It reviews one OR tool, queueing theory, and provides an illustration involving a hypothetical drug treatment facility.</p> <p>Method</p> <p>Queueing Theory (QT) is the study of waiting lines. The theory is useful in that it provides solutions to problems of waiting and its relationship to key characteristics of healthcare systems. More generally, it illustrates the strengths of modeling in healthcare and service delivery.</p> <p>Queueing theory offers insights that initially may be hidden. For example, a queueing model allows one to incorporate randomness, which is inherent in the actual system, into the mathematical analysis. As a result of this randomness, these systems often perform much worse than one might have guessed based on deterministic conditions. Poor performance is reflected in longer lines, longer waits, and lower levels of server utilization.</p> <p>As an illustration, we specify a queueing model of a representative drug treatment facility. The analysis of this model provides mathematical expressions for some of the key performance measures, such as average waiting time for admission.</p> <p>Results</p> <p>We calculate average occupancy in the facility and its relationship to system characteristics. For example, when the facility has 28 beds, the average wait for admission is 4 days. We also explore the relationship between arrival rate at the facility, the capacity of the facility, and waiting times.</p> <p>Conclusions</p> <p>One key aspect of the healthcare system is its complexity, and policy makers want to design and reform the system in a way that affects competing goals. OR methodologies, particularly queueing theory, can be very useful in gaining deeper understanding of this complexity and exploring the potential effects of proposed changes on the system without making any actual changes.</p

Effects of the mu-opioid receptor antagonist GSK1521498 on hedonic and consummatory eating behaviour: a proof of mechanism study in binge-eating obese subjects.

The opioid system is implicated in the hedonic and motivational processing of food, and in binge eating, a behaviour strongly linked to obesity. The aim of this study was to evaluate the effects of 4 weeks of treatment with the mu-opioid receptor antagonist GSK1521498 on eating behaviour in binge-eating obese subjects. Adults with body mass index ⩾30 kg m−2 and binge eating scale scores ⩾19 received 1-week single-blind placebo run-in, and were then randomized to 28 days with either 2 mg day−1 GSK1521498, 5 mg day−1 GSK1521498 or placebo (N=21 per arm) in a double-blind parallel group design. The outcome measures were body weight, fat mass, hedonic and consummatory eating behaviour during inpatient food challenges, safety and pharmacokinetics. The primary analysis was the comparison of change scores in the higher-dose treatment group versus placebo using analysis of covariance at each relevant time point. GSK1521498 (2 mg and 5 mg) was not different from placebo in its effects on weight, fat mass and binge eating scores. However, compared with placebo, GSK1521498 5 mg day−1 caused a significant reduction in hedonic responses to sweetened dairy products and reduced calorific intake, particularly of high-fat foods during ad libitum buffet meals, with some of these effects correlating with systemic exposure of GSK1521498. There were no significant effects of GSK1521498 2 mg day−1 on eating behaviour, indicating dose dependency of pharmacodynamics. GSK1521498 was generally well tolerated and no previously unidentified safety signals were detected. The potential for these findings to translate into clinically significant effects in the context of binge eating and weight regain prevention requires further investigation

Detecting autozygosity through runs of homozygosity: A comparison of three autozygosity detection algorithms

<p>Abstract</p> <p>Background</p> <p>A central aim for studying runs of homozygosity (ROHs) in genome-wide SNP data is to detect the effects of autozygosity (stretches of the two homologous chromosomes within the same individual that are identical by descent) on phenotypes. However, it is unknown which current ROH detection program, and which set of parameters within a given program, is optimal for differentiating ROHs that are truly autozygous from ROHs that are homozygous at the marker level but vary at unmeasured variants between the markers.</p> <p>Method</p> <p>We simulated 120 Mb of sequence data in order to know the true state of autozygosity. We then extracted common variants from this sequence to mimic the properties of SNP platforms and performed ROH analyses using three popular ROH detection programs, PLINK, GERMLINE, and BEAGLE. We varied detection thresholds for each program (e.g., prior probabilities, lengths of ROHs) to understand their effects on detecting known autozygosity.</p> <p>Results</p> <p>Within the optimal thresholds for each program, PLINK outperformed GERMLINE and BEAGLE in detecting autozygosity from distant common ancestors. PLINK's sliding window algorithm worked best when using SNP data pruned for linkage disequilibrium (LD).</p> <p>Conclusion</p> <p>Our results provide both general and specific recommendations for maximizing autozygosity detection in genome-wide SNP data, and should apply equally well to research on whole-genome autozygosity burden or to research on whether specific autozygous regions are predictive using association mapping methods.</p

Measuring and Modeling Risk Using High-Frequency Data

Measuring and modeling financial volatility is the key to derivative pricing, asset allocation and risk management. The recent availability of high-frequency data allows for refined methods in this field. In particular, more precise measures for the daily or lower frequency volatility can be obtained by summing over squared high-frequency returns. In turn, this so-called realized volatility can be used for more accurate model evaluation and description of the dynamic and distributional structure of volatility. Moreover, non-parametric measures of systematic risk are attainable, that can straightforwardly be used to model the commonly observed time-variation in the betas. The discussion of these new measures and methods is accompanied by an empirical illustration using high-frequency data of the IBM incorporation and of the DJIA index

Effect of vitamin D on bone mineral density of elderly patients with osteoporosis responding poorly to bisphosphonates

BACKGROUND: Bisphosphonates are indicated in the prevention and treatment of osteoporosis. However, bone mineral density (BMD) continues to decline in up to 15% of bisphosphonate users. While randomized trials have evaluated the efficacy of concurrent bisphosphonates and vitamin D, the incremental benefit of vitamin D remains uncertain. METHODS: Using data from the Canadian Database of Osteoporosis and Osteopenia (CANDOO), we performed a 2-year observational cohort study. At baseline, all patients were prescribed a bisphosphonate and counseled on vitamin D supplementation. After one year, patients were divided into two groups based on their response to bisphosphonate treatment. Non-responders were prescribed vitamin D 1000 IU daily. Responders continued to receive counseling on vitamin D. RESULTS: Of 449 patients identified, 159 were non-responders to bisphosphonates. 94% of patients were women. The mean age of the entire cohort was 74.6 years (standard deviation = 5.6 years). In the cohort of non-responders, BMD at the lumbar spine increased 2.19% (p < 0.001) the year after vitamin D was prescribed compared to a decrease of 0.55% (p = 0.36) the year before. In the cohort of responders, lumbar spine BMD improved 1.45% (p = 0.014) the first year and 1.11% (p = 0.60) the second year. The difference between the two groups was statistically significant the first year (p < 0.001) but not the second (p = 0.60). Similar results were observed at the femoral neck but were not statistically significant. CONCLUSION: In elderly patients with osteoporosis not responding to bisphosphonates, vitamin D 1000 IU daily may improve BMD at the lumbar spine

Markov Chain Monte Carlo and the Application to Geodetic Time Series Analysis

The time evolution of geophysical phenomena can be characterised by stochastic time series. The stochastic nature of the signal stems from the geophysical phenomena involved and any noise, which may be due to, e.g., un-modelled effects or measurement errors. Until the 1990's, it was usually assumed that white noise could fully characterise this noise. However, this was demonstrated to be not the case and it was proven that this assumption leads to underestimated uncertainties of the geophysical parameters inferred from the geodetic time series. Therefore, in order to fully quantify all the uncertainties as robustly as possible, it is imperative to estimate not only the deterministic but also the stochastic parameters of the time series. In this regard, the Markov Chain Monte Carlo (MCMC) method can provide a sample of the distribution function of all parameters, including those regarding the noise, e.g., spectral index and amplitudes. After presenting the MCMC method and its implementation in our MCMC software we apply it to synthetic and real time series and perform a cross-evaluation using Maximum Likelihood Estimation (MLE) as implemented in the CATS software. Several examples as to how the MCMC method performs as a parameter estimation method for geodetic time series are given in this chapter. These include the applications to GPS position time series, superconducting gravity time series and monthly mean sea level (MSL) records, which all show very different stochastic properties. The impact of the estimated parameter uncertainties on sub-sequentially derived products is briefly demonstrated for the case of plate motion models. Finally, the MCMC results for weekly downsampled versions of the benchmark synthetic GNSS time series as provided in Chapter 2 are presented separately in an appendix

Genomic comparisons reveal biogeographic and anthropogenic impacts in the koala (Phascolarctos cinereus): a dietary-specialist species distributed across heterogeneous environments

The Australian koala is an iconic marsupial with highly specific dietary requirements distributed across heterogeneous environments, over a large geographic range. The distribution and genetic structure of koala populations has been heavily influenced by human actions, specifically habitat modification, hunting and translocation of koalas. There is currently limited information on population diversity and gene flow at a species-wide scale, or with consideration to the potential impacts of local adaptation. Using species-wide sampling across heterogeneous environments, and high-density genome-wide markers (SNPs and PAVs), we show that most koala populations display levels of diversity comparable to other outbred species, except for those populations impacted by population reductions. Genetic clustering analysis and phylogenetic reconstruction reveals a lack of support for current taxonomic classification of three koala subspecies, with only a single evolutionary significant unit supported. Furthermore, similar to 70% of genetic variance is accounted for at the individual level. The Sydney Basin region is highlighted as a unique reservoir of genetic diversity, having higher diversity levels (i.e., Blue Mountains region; AvHe(corr)-0.20, PL% = 68.6). Broad-scale population differentiation is primarily driven by an isolation by distance genetic structure model (49% of genetic variance), with clinal local adaptation corresponding to habitat bioregions. Signatures of selection were detected between bioregions, with no single region returning evidence of strong selection. The results of this study show that although the koala is widely considered to be a dietary-specialist species, this apparent specialisation has not limited the koala's ability to maintain gene flow and adapt across divergent environments as long as the required food source is available