Predicting risk of hospitalisation: a retrospective population-based analysis in a paediatric population in Emilia-Romagna, Italy.

OBJECTIVES: Develop predictive models for a paediatric population that provide information for paediatricians and health authorities to identify children at risk of hospitalisation for conditions that may be impacted through improved patient care. DESIGN: Retrospective healthcare utilisation analysis with multivariable logistic regression models. DATA: Demographic information linked with utilisation of health services in the years 2006-2014 was used to predict risk of hospitalisation or death in 2015 using a longitudinal administrative database of 527 458 children aged 1-13 years residing in the Regione Emilia-Romagna (RER), Italy, in 2014. OUTCOME MEASURES: Models designed to predict risk of hospitalisation or death in 2015 for problems that are potentially avoidable were developed and evaluated using the C-statistic, for calibration to assess performance across levels of predicted risk, and in terms of their sensitivity, specificity and positive predictive value. RESULTS: Of the 527 458 children residing in RER in 2014, 6391 children (1.21%) were hospitalised for selected conditions or died in 2015. 49 486 children (9.4%) of the population were classified in the \u27At Higher Risk\u27 group using a threshold of predicted risk \u3e2.5%. The observed risk of hospitalisation (5%) for the \u27At Higher Risk\u27 group was more than four times higher than the overall population. We observed a C-statistic of 0.78 indicating good model performance. The model was well calibrated across categories of predicted risk. CONCLUSIONS: It is feasible to develop a population-based model using a longitudinal administrative database that identifies the risk of hospitalisation for a paediatric population. The results of this model, along with profiles of children identified as high risk, are being provided to the paediatricians and other healthcare professionals providing care to this population to aid in planning for care management and interventions that may reduce their patients\u27 likelihood of a preventable, high-cost hospitalisation

Mortality Differences Between Traditional Medicare and Medicare Advantage: A Risk-Adjusted Assessment Using Claims Data.

Medicare Advantage (MA) has grown rapidly since the Affordable Care Act; nearly one-third of Medicare beneficiaries now choose MA. An assessment of the comparative value of the 2 options is confounded by an apparent selection bias favoring MA, as reflected in mortality differences. Previous assessments have been hampered by lack of access to claims diagnosis data for the MA population. An indirect comparison of mortality as an outcome variable was conducted by modeling mortality on a traditional fee-for-service (FFS) Medicare data set, applying the model to an MA data set, and then evaluating the ratio of actual-to-predicted mortality in the MA data set. The mortality model adjusted for clinical conditions and demographic factors. Model development considered the effect of potentially greater coding intensity in the MA population. Further analysis calculated ratios for subpopulations. Predicted, risk-adjusted mortality was lower in the MA population than in FFS Medicare. However, the ratio of actual-to-predicted mortality (0.80) suggested that the individuals in the MA data set were less likely to die than would be predicted had those individuals been enrolled in FFS Medicare. Differences between actual and predicted mortality were particularly pronounced in low income (dual eligibility), nonwhite race, high morbidity, and Health Maintenance Organization (HMO) subgroups. After controlling for baseline clinical risk as represented by claims diagnosis data, mortality differences favoring MA over FFS Medicare persisted, particularly in vulnerable subgroups and HMO plans. These findings suggest that differences in morbidity do not fully explain differences in mortality between the 2 programs

Erosion of Empathy in Primary Care Trainees

Objective: To evaluate if empathy among physician residents (trainees) differs dependent on training year and to assess trainees\u27 characteristics associated with higher empathy scores. Poster presented at 2016 ISPOR conference in Washington DC.https://jdc.jefferson.edu/jcphposters/1006/thumbnail.jp

Did a physician-targeted intervention that reduced potentially inappropriate prescribing to elderly patients also reduce related hospitalizations?

Objectives: To determine whether a general practitioner focused intervention aimed at decreasing PIM prescribing in the elderly can decrease the risk of PIM-related hospitalizations. Poster presented at 2016 ISPOR conference in Washington DC.https://jdc.jefferson.edu/jcphposters/1005/thumbnail.jp

Results of search for magnetized quark-nugget dark matter from radial impacts on Earth

Magnetized Quark Nuggets (MQNs) are a recently proposed dark-matter candidate consistent with the Standard Model and with Tatsumi's theory of quark-nugget cores in magnetars. Previous publications have covered their formation in the early universe, aggregation into a broad mass distribution before they can decay by the weak force, interaction with normal matter through their magnetopause, and first observation consistent MQNs, i.e. a nearly tangential impact limiting their surface-magnetic-field parameter B_o from Tatsumi's values of 0.1 to 10.0 TT to new value of 1.65 TT +/- 21%. The MQN mass distribution and interaction cross section depend strongly on B_o. Their magnetopause is much larger than their geometric dimensions and can cause sufficient energy deposition to form non-meteorite craters, which are reported approximately annually. We report computer simulations of the MQN energy deposition in water-saturated peat, soft sediments, and granite and report results from excavating such a crater. Five points of agreement between observations and hydrodynamic simulations of an MQN impact support this second observation consistent with MQN dark matter and suggest a method for qualifying additional MQN events. The results also redundantly constrain B_o to greater than 0.4 TT.Comment: 30 pages, 13 figures, submitted to Univers

Factors Affecting Bait Site Visitation: Area of Influence of Baits

ABSTRACT Baiting is a fundamental strategy for the global management of wild pigs (Sus scrofa); however, little information exists on how anthropogenic bait affects wild pig movements on a landscape. We investigated factors that are important in determining the spatial area of attraction for wild pigs to bait (‘area of influence’ of a bait site) using data from Global Positioning System (GPS) collars and locations of bait sites. We monitored movements of wild pigs in 2 distinct study areas in the United States from February to September 2016 and used locational data using GPS collars to analyze the influence of habitat quality (dependent on site), home range size, number of bait sites in the home range, distance to a bait site, and sex in relation to movement in time and space. We determined the average area of influence by calculating the area of a circle with the radius as the average maximum distance travelled by wild pigs to reach a bait site. The average area of influence for our bait sites was 6.7 km2 (or a radius of approximately 1.5 km), suggesting a bait spacing of approximately 1.5 km would be adequate to capture visitation by most wild pigs and a spacing of 3 km could allow substantial visitation while minimizing redundant effort depending on the spatial structure of the populations. Eighty percent of wild pigs first visited bait sites within 8.9 days after bait deployment; and they visited earlier when their home range size was larger. As the number of bait sites in an individual’s home range increased, individual pigs visited more bait sites, and the probability of a visit increased dramatically up to approximately 5 bait sites and much less thereafter. Wild pigs travelled farther distances to visit bait sites in lower quality habitat. Our results support the hypothesis that habitat quality can mediate the efficacy of baiting programs for wildlife by influencing their movement patterns and motivation to use anthropogenic resources. Our results suggest wild pigs will travel extensively within their home range to visit bait sites, and that in lower quality habitat, most animals will find bait sites more quickly. Determining the area of influence of bait sites can increase the efficacy of planning and monitoring management programs. Our study provides new information to help managers plan baiting designs to attract the greatest number of pigs

Cost of Illness (COI) Study: A Review of Methods

Presentation: 26:3