A Bayesian hierarchical model for characterizing the diffusion of new antipsychotic drugs.

New prescription medications are a primary driver of spending growth in the United States. For patients with severe mental illnesses, second-generation antipsychotic (SGA) medications feature prominently. However, many SGAs are costly, particularly before generic entry, and some may increase the risk of diabetes. Because physicians play a prominent role in new prescription adoption, understanding their prescribing behaviors is policy-relevant. Several features of prescription data, such as different antipsychotic choice sets over time, variable physician prescription volumes, and correlation among drug choices within physicians, complicate inferences. We propose a multivariate Bayesian hierarchical model with piecewise random effects to characterize the diffusion of new antipsychotic drugs. This model captures the complex prescriber-specific relationships among the different diffusion processes and takes advantage of the Bayesian paradigm to quantify uncertainty for all parameters straightforwardly. To evaluate the prescribing patterns for each physician, we propose various indices to identify early new SGA adopters. A sample of nearly 17,000 US physicians whose antipsychotic drug prescribing information was collected between January 1, 1997 and December 31, 2007 illustrates the methods. Determinants of high prescription rates and adoption speeds of new SGAs included physician sex, age, hospital affiliation, physician specialty, and office location. Large within- and between-provider variations in prescribing patterns of new SGAs were identified. Early adopters for one drug were not early adopters for another drug.

Analysis of significant factors for dengue fever incidence prediction.

BACKGROUND: Many popular dengue forecasting techniques have been used by several researchers to extrapolate dengue incidence rates, including the K-H model, support vector machines (SVM), and artificial neural networks (ANN). The time series analysis methodology, particularly ARIMA and SARIMA, has been increasingly applied to the field of epidemiological research for dengue fever, dengue hemorrhagic fever, and other infectious diseases. The main drawback of these methods is that they do not consider other variables that are associated with the dependent variable. Additionally, new factors correlated to the disease are needed to enhance the prediction accuracy of the model when it is applied to areas of similar climates, where weather factors such as temperature, total rainfall, and humidity are not substantially different. Such drawbacks may consequently lower the predictive power for the outbreak. RESULTS: The predictive power of the forecasting model-assessed by Akaike's information criterion (AIC), Bayesian information criterion (BIC), and the mean absolute percentage error (MAPE)-is improved by including the new parameters for dengue outbreak prediction. This study's selected model outperforms all three other competing models with the lowest AIC, the lowest BIC, and a small MAPE value. The exclusive use of climate factors from similar locations decreases a model's prediction power. The multivariate Poisson regression, however, effectively forecasts even when climate variables are slightly different. Female mosquitoes and seasons were strongly correlated with dengue cases. Therefore, the dengue incidence trends provided by this model will assist the optimization of dengue prevention. CONCLUSIONS: The present work demonstrates the important roles of female mosquito infection rates from the previous season and climate factors (represented as seasons) in dengue outbreaks. Incorporating these two factors in the model significantly improves the predictive power of dengue hemorrhagic fever forecasting models, as confirmed by AIC, BIC, and MAPE.

Segmentation of stunting, wasting, and underweight in Southeast Sulawesi using geographically weighted multivariate Poisson regression.

The health profile of Southeast Sulawesi Province in 2021 shows that the prevalence of stunting is 11.69 %, wasting 5.89 % and underweight 7.67 %. This relatively high figure should be immediately reduced to zero because it greatly affects the quality of human resources. Cases of stunting, wasting and underweight are an iceberg phenomenon, especially in Southeast Sulawesi. Therefore, it is necessary to research the number of cases of stunting, wasting and underweight in Southeast Sulawesi using GWMPR. The research results show that there is a trivariate correlation between the number of cases of stunting, wasting and underweight. The GWMPR model provides better results in modeling the number of stunting, wasting and underweight cases than the MPR model. The models produced for each sub-district are different from each other based on the predictor variables that have a significant effect and the estimated parameter values ​​for each sub-district. The segmentation of the number of stunting cases consists of 21 regional groups with 10 significant predictor variables, while the number of wasting cases consists of 10 regional groups with 9 significant predictor variables, while the number of underweight cases consists of 37 regional groups with 11 significant predictor variables. Therefore, policies on stunting, wasting, and underweight should be based on local conditions. 3 important components of this study: 1. GWMPR is the development of GWPR model when there are 2 or more response variables that are correlated. 2. GWMPR is a spatial model that considers geography. 3. Application of GWMPR to the analysis of the number of stunting, wasting, and underweight in Southeast Sulawesi province.