Cost of antipsychotic polypharmacy in the treatment of schizophrenia

<p>Abstract</p> <p>Background</p> <p>This study compared the costs of antipsychotic polypharmacy for patients who initiated on 1 of the 3 most commonly prescribed atypical antipsychotics – olanzapine, quetiapine, or risperidone.</p> <p>Methods</p> <p>Data were drawn from a large, prospective, naturalistic, multi-site, nonrandomized study of treatment for schizophrenia in the United States conducted between July 1997 and September 2003. Participants who were initiated on olanzapine (N = 405), quetiapine (N = 115), or risperidone (N = 276) were followed for 1 year post initiation and compared on: (a) average daily cost of the index antipsychotic while on the index antipsychotic, (b) average daily cost of the coprescribed antipsychotics while on the index antipsychotic, (c) average daily cost of the index antipsychotic and the coprescribed antipsychotics while on the index antipsychotic, (d) total annual cost of antipsychotic medications prescribed in the year following initiation on the index antipsychotic, using propensity score-adjusted bootstrap resampling method. Average daily antipsychotic costs and total annual antipsychotic costs were also estimated using more recent (2004) antipsychotic drug prices.</p> <p>Results</p> <p>During the 1 year following initiation on the index antipsychotic, the total average daily cost of the index antipsychotic was higher for quetiapine ($15.33) than olanzapine ($13.90, p < .05) and risperidone ($11.04, p < .01), although the average daily cost of the index antipsychotic was higher for olanzapine ($10.08) than risperidone ($6.74, p < .01) or quetiapine ($6.63, p < .01). Lower total average daily costs were observed in risperidone than olanzapine or quetiapine. Significantly lower average daily cost of concomitant antipsychotic medications for olanzapine ($3.82) compared to quetiapine ($8.70, p < .01) or risperidone-initiated patients ($4.30, p < .01) contributed to the lower average daily cost of all antipsychotic medication for olanzapine-initiated patients. Each dollar spent on the index antipsychotic was accompanied by spending an additional$1.31 on concomitant antipsychotics for quetiapine compared to $0.64 for risperidone and$0.38 for olanzapine-initiated patients. A separate intent-to-treat analysis of the total annual antipsychotic cost found a significantly higher total annual antipsychotic cost for quetiapine-initiated patients ($5320) compared to olanzapine ($4536, p < .01) or risperidone ($3813, p < .01).</p> <p>Conclusion</p> <p>Prevalent antipsychotic polypharmacy adds substantial cost to the treatment of schizophrenia. Comparison of medication costs need to address the costs of all antipsychotics. A better understanding of concomitant antipsychotic costs provides a more accurate portrayal of antipsychotic medication costs in the treatment of schizophrenia.</p

Determinants of subject visit participation in a prospective cohort study of HTLV infection

<p>Abstract</p> <p>Background</p> <p>Understanding participation in a prospective study is crucial to maintaining and improving retention rates. In 1990–92, following attempted blood donation at five blood centers, we enrolled 155 HTLV-I, 387 HTLV-II and 799 HTLV seronegative persons in a long-term prospective cohort.</p> <p>Methods</p> <p>Health questionnaires and physical exams were administered at enrollment and 2-year intervals through 2004. To examine factors influencing attendance at study visits of the cohort participants we calculated odds ratios (ORs) with generalized estimated equations (GEE) to analyze fixed and time-varying predictors of study visit participation.</p> <p>Results</p> <p>There were significant independent associations between better visit attendance and female gender (OR = 1.31), graduate education (OR = 1.86) and income > $75,000 (OR = 2.68). Participants at two centers (OR = 0.47, 0.67) and of Black race/ethnicity (OR = 0.61) were less likely to continue. Higher subject reimbursement for interview was associated with better visit attendance (OR = 1.84 for$25 vs. $10). None of the health related variables (HTLV status, perceived health status and referral to specialty diagnostic exam for potential adverse health outcomes) significantly affected participation after controlling for demographic variables.</p> <p>Conclusion</p> <p>Increasing and maintaining participation by minority and lower socioeconomic status participants is an ongoing challenge in the study of chronic disease outcomes. Future studies should include methods to evaluate attrition and retention, in addition to primary study outcomes, including qualitative analysis of reasons for participation or withdrawal.</p

Use of machine learning algorithms to classify binary protein sequences as highly-designable or poorly-designable

<p>Abstract</p> <p>Background</p> <p>By using a standard Support Vector Machine (SVM) with a Sequential Minimal Optimization (SMO) method of training, Naïve Bayes and other machine learning algorithms we are able to distinguish between two classes of protein sequences: those folding to highly-designable conformations, or those folding to poorly- or non-designable conformations.</p> <p>Results</p> <p>First, we generate all possible compact lattice conformations for the specified shape (a hexagon or a triangle) on the 2D triangular lattice. Then we generate all possible binary hydrophobic/polar (H/P) sequences and by using a specified energy function, thread them through all of these compact conformations. If for a given sequence the lowest energy is obtained for a particular lattice conformation we assume that this sequence folds to that conformation. Highly-designable conformations have many H/P sequences folding to them, while poorly-designable conformations have few or no H/P sequences. We classify sequences as folding to either highly – or poorly-designable conformations. We have randomly selected subsets of the sequences belonging to highly-designable and poorly-designable conformations and used them to train several different standard machine learning algorithms.</p> <p>Conclusion</p> <p>By using these machine learning algorithms with ten-fold cross-validation we are able to classify the two classes of sequences with high accuracy – in some cases exceeding 95%.</p

Mapping recent information behavior research: an analysis of co-authorship and cocitation networks

There has been an increase in research published on information behavior in recent years, and this has been accompanied by an increase in its diversity and interaction with other fields, particularly information retrieval (HR). The aims of this study are to determine which researchers have contributed to producing the current body of knowledge on this subject, and to describe its intellectual basis. A bibliometric and network analysis was applied to authorship and co-authorship as well as citation and co-citation. According to these analyses, there is a small number of authors who can be considered to be the most productive and who publish regularly, and a large number of transient ones. Other findings reveal a marked predominance of theoretical works, some examples of qualitative methodology that originate in other areas of social science, and a high incidence of research focused on the user interaction with information retrieval systems and the information behavior of doctors

Routine Outcomes Monitoring to Support Improving Care for Schizophrenia: Report from the VA Mental Health QUERI

In schizophrenia, treatments that improve outcomes have not been reliably disseminated. A major barrier to improving care has been a lack of routinely collected outcomes data that identify patients who are failing to improve or not receiving effective treatments. To support high quality care, the VA Mental Health QUERI used literature review, expert interviews, and a national panel process to increase consensus regarding outcomes monitoring instruments and strategies that support quality improvement. There was very good consensus in the domains of psychotic symptoms, side-effects, drugs and alcohol, depression, caregivers, vocational functioning, and community tenure. There are validated instruments and assessment strategies that are feasible for quality improvement in routine practice