Predicting suicides after outpatient mental health visits in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS).

The 2013 US Veterans Administration/Department of Defense Clinical Practice Guidelines (VA/DoD CPG) require comprehensive suicide risk assessments for VA/DoD patients with mental disorders but provide minimal guidance on how to carry out these assessments. Given that clinician-based assessments are not known to be strong predictors of suicide, we investigated whether a precision medicine model using administrative data after outpatient mental health specialty visits could be developed to predict suicides among outpatients. We focused on male nondeployed Regular US Army soldiers because they account for the vast majority of such suicides. Four machine learning classifiers (naive Bayes, random forests, support vector regression and elastic net penalized regression) were explored. Of the Army suicides in 2004-2009, 41.5% occurred among 12.0% of soldiers seen as outpatient by mental health specialists, with risk especially high within 26 weeks of visits. An elastic net classifier with 10-14 predictors optimized sensitivity (45.6% of suicide deaths occurring after the 15% of visits with highest predicted risk). Good model stability was found for a model using 2004-2007 data to predict 2008-2009 suicides, although stability decreased in a model using 2008-2009 data to predict 2010-2012 suicides. The 5% of visits with highest risk included only 0.1% of soldiers (1047.1 suicides/100 000 person-years in the 5 weeks after the visit). This is a high enough concentration of risk to have implications for targeting preventive interventions. An even better model might be developed in the future by including the enriched information on clinician-evaluated suicide risk mandated by the VA/DoD CPG to be recorded

An Alternative Method for Solving a Certain Class of Fractional Kinetic Equations

An alternative method for solving the fractional kinetic equations solved earlier by Haubold and Mathai (2000) and Saxena et al. (2002, 2004a, 2004b) is recently given by Saxena and Kalla (2007). This method can also be applied in solving more general fractional kinetic equations than the ones solved by the aforesaid authors. In view of the usefulness and importance of the kinetic equation in certain physical problems governing reaction-diffusion in complex systems and anomalous diffusion, the authors present an alternative simple method for deriving the solution of the generalized forms of the fractional kinetic equations solved by the aforesaid authors and Nonnenmacher and Metzler (1995). The method depends on the use of the Riemann-Liouville fractional calculus operators. It has been shown by the application of Riemann-Liouville fractional integral operator and its interesting properties, that the solution of the given fractional kinetic equation can be obtained in a straight-forward manner. This method does not make use of the Laplace transform.Comment: 7 pages, LaTe

Dutch healthcare reform: did it result in performance improvement of health plans? A comparison of consumer experiences over time

<p>Abstract</p> <p>Background</p> <p>Many countries have introduced elements of managed competition in their healthcare system with the aim to accomplish more efficient and demand-driven health care. Simultaneously, generating and reporting of comparative healthcare information has become an important quality-improvement instrument. We examined whether the introduction of managed competition in the Dutch healthcare system along with public reporting of quality information was associated with performance improvement in health plans.</p> <p>Methods</p> <p>Experiences of consumers with their health plan were measured in four consecutive years (2005-2008) using the CQI^®health plan instrument 'Experiences with Healthcare and Health Insurer'. Data were available of 13,819 respondents (response = 45%) of 30 health plans in 2005, of 8,266 respondents (response = 39%) of 32 health plans in 2006, of 8,088 respondents (response = 34%) of 32 health plans in 2007, and of 7,183 respondents (response = 31%) of 32 health plans in 2008. We performed multilevel regression analyses with three levels: respondent, health plan and year of measurement. Per year and per quality aspect, we estimated health plan means while adjusting for consumers' age, education and self-reported health status. We tested for linear and quadratic time effects using chi-squares.</p> <p>Results</p> <p>The overall performance of health plans increased significantly from 2005 to 2008 on four quality aspects. For three other aspects, we found that the overall performance first declined and then increased from 2006 to 2008, but the performance in 2008 was not better than in 2005. The overall performance of health plans did not improve more often for quality aspects that were identified as important areas of improvement in the first year of measurement. On six out of seven aspects, the performance of health plans that scored below average in 2005 increased more than the performance of health plans that scored average and/or above average in that year.</p> <p>Conclusion</p> <p>We found mixed results concerning the effects of managed competition on the performance of health plans. To determine whether managed competition in the healthcare system leads to quality improvement in health plans, it is important to examine whether and for what reasons health plans initiate improvement efforts.</p

Relationship between functional fitness, medication costs and mood in elderly people

Objective: to verify if functional fitness (FF) is associated with the annual cost of medication consumption and mood states (MSt) in elderly people. Methods: a cross-sectional study with 229 elderly people aged 65 years or more at Santa Casa de Misericórdia de Coimbra, Portugal. Seniors with physical and psychological limitations were excluded, as well as those using medication that limits performance on the tests. The Senior Fitness Test was used to evaluate FF, and the Profile of Mood States - Short Form to evaluate the MSt. The statistical analysis was based on Mancova, with adjustment for age, for comparison between men and women, and adjustment for sex, for comparison between cardiorespiratory fitness quintiles. The association between the variables under study was made with partial correlation, controlling for the effects of age, sex and body mass index. Results: an inverse correlation between cardiorespiratory fitness and the annual cost of medication consumption was found (p < 0.01). FF is also inversely associated with MSt (p < 0.05). Comparisons between cardiorespiratory fitness quintiles showed higher medication consumption costs in seniors with lower aerobic endurance, as well as higher deterioration in MSt (p < 0.01). Conclusion: elderly people with better FF and, specifically, better cardiorespiratory fitness present lower medication consumption costs and a more positive MSt

Using a summary measure for multiple quality indicators in primary care: the Summary QUality InDex (SQUID)

BACKGROUND: Assessing the quality of primary care is becoming a priority in national healthcare agendas. Audit and feedback on healthcare quality performance indicators can help improve the quality of care provided. In some instances, fewer numbers of more comprehensive indicators may be preferable. This paper describes the use of the Summary Quality Index (SQUID) in tracking quality of care among patients and primary care practices that use an electronic medical record (EMR). All practices are part of the Practice Partner Research Network, representing over 100 ambulatory care practices throughout the United States. METHODS: The SQUID is comprised of 36 process and outcome measures, all of which are obtained from the EMR. This paper describes algorithms for the SQUID calculations, various statistical properties, and use of the SQUID within the context of a multi-practice quality improvement (QI) project. RESULTS: At any given time point, the patient-level SQUID reflects the proportion of recommended care received, while the practice-level SQUID reflects the average proportion of recommended care received by that practice's patients. Using quarterly reports, practice- and patient-level SQUIDs are provided routinely to practices within the network. The SQUID is responsive, exhibiting highly significant (p < 0.0001) increases during a major QI initiative, and its internal consistency is excellent (Cronbach's alpha = 0.93). Feedback from physicians has been extremely positive, providing a high degree of face validity. CONCLUSION: The SQUID algorithm is feasible and straightforward, and provides a useful QI tool. Its statistical properties and clear interpretation make it appealing to providers, health plans, and researchers