Accuracy of telepsychiatric assessment of new routine outpatient referrals

<p>Abstract</p> <p>Background</p> <p>Studies on the feasibility of telepsychiatry tend to concentrate only on a subset of clinical parameters. In contrast, this study utilises data from a comprehensive assessment. The main objective of this study is to compare the accuracy of findings from telepsychiatry with those from face to face interviews.</p> <p>Method</p> <p>This is a primary, cross-sectional, single-cluster, balanced crossover, blind study involving new routine psychiatric referrals. Thirty-seven out of forty cases fulfilling the selection criteria went through a complete set of independent face to face and video assessments by the researchers who were blind to each other's findings.</p> <p>Results</p> <p>The accuracy ratio of the pooled results for DSM-IV diagnoses, risk assessment, non-drug and drug interventions were all above 0.76, and the combined overall accuracy ratio was 0.81. There were substantial intermethod agreements for Cohen's kappa on all the major components of evaluation except on the Risk Assessment Scale where there was only weak agreement.</p> <p>Conclusion</p> <p>Telepsychiatric assessment is a dependable method of assessment with a high degree of accuracy and substantial overall intermethod agreement when compared with standard face to face interview for new routine outpatient psychiatric referrals.</p

Assessment of possible impact of a health promotion program in Korea from health risk trends in a longitudinally observed cohort

BACKGROUND: Longitudinally observed cohort data can be utilized to assess the potential for health promotion and healthcare planning by comparing the estimated risk factor trends of non-intervened with that of intervened. The paper seeks (1) to estimate a natural transition (patterns of movement between states) of health risk state from a Korean cohort data using a Markov model, (2) to derive an effective and necessary health promotion strategy for the population, and (3) to project a possible impact of an intervention program on health status. METHODS: The observed transition of health risk states in a Korean employee cohort was utilized to estimate the natural flow of aggregated health risk states from eight health risk measures using Markov chain models. In addition, a reinforced transition was simulated, given that a health promotion program was implemented for the cohort, to project a possible impact on improvement of health status. An intervened risk transition was obtained based on age, gender, and baseline risk state, adjusted to match with the Korean cohort, from a simulated random sample of a US employee population, where a health intervention was in place. RESULTS: The estimated natural flow (non-intervened), following Markov chain order 2, showed a decrease in low risk state by 3.1 percentage points in the Korean population while the simulated reinforced transition (intervened) projected an increase in low risk state by 7.5 percentage points. Estimated transitions of risk states demonstrated the necessity of not only the risk reduction but also low risk maintenance. CONCLUSIONS: The frame work of Markov chain efficiently estimated the trend, and captured the tendency in the natural flow. Given only a minimally intense health promotion program, potential risk reduction and low risk maintenance was projected

Prostaglandin treatment is associated with a withdrawal of progesterone and androgen at the receptor level in the uterine cervix

Treatment with prostaglandin(PG)-E2 is clinically efficient for cervical priming. The aim of this study was to evaluate the impact of PG-E2 on the expression of the progesterone (PR), androgen (AR) and glucocorticoid (GR) receptors in human uterine cervix in prolonged pregnancy

The challenge for genetic epidemiologists: how to analyze large numbers of SNPs in relation to complex diseases

Genetic epidemiologists have taken the challenge to identify genetic polymorphisms involved in the development of diseases. Many have collected data on large numbers of genetic markers but are not familiar with available methods to assess their association with complex diseases. Statistical methods have been developed for analyzing the relation between large numbers of genetic and environmental predictors to disease or disease-related variables in genetic association studies. In this commentary we discuss logistic regression analysis, neural networks, including the parameter decreasing method (PDM) and genetic programming optimized neural networks (GPNN) and several non-parametric methods, which include the set association approach, combinatorial partitioning method (CPM), restricted partitioning method (RPM), multifactor dimensionality reduction (MDR) method and the random forests approach. The relative strengths and weaknesses of these methods are highlighted. Logistic regression and neural networks can handle only a limited number of predictor variables, depending on the number of observations in the dataset. Therefore, they are less useful than the non-parametric methods to approach association studies with large numbers of predictor variables. GPNN on the other hand may be a useful approach to select and model important predictors, but its performance to select the important effects in the presence of large numbers of predictors needs to be examined. Both the set association approach and random forests approach are able to handle a large number of predictors and are useful in reducing these predictors to a subset of predictors with an important contribution to disease. The combinatorial methods give more insight in combination patterns for sets of genetic and/or environmental predictor variables that may be related to the outcome variable. As the non-parametric methods have different strengths and weaknesses we conclude that to approach genetic association studies using the case-control design, the application of a combination of several methods, including the set association approach, MDR and the random forests approach, will likely be a useful strategy to find the important genes and interaction patterns involved in complex diseases