The influence of prescriber and patient gender on the prescription of benzodiazepines: results from the Florida Medicaid Dataset.

BACKGROUND: Benzodiazepine (BZD) prescription rates have increased over the past decade in the United States. Available literature indicates that sociodemographic factors may influence diagnostic patterns and/or prescription behaviour. Herein, the aim of this study is to determine whether the gender of the prescriber and/or patient influences BZD prescription. METHODS: Cross-sectional study using data from the Florida Medicaid Managed Medical Assistance Program from January 1, 2018 to December 31, 2018. Eligible recipients ages 18 to 64, inclusive, enrolled in the Florida Medicaid plan for at least 1 day, and were dually eligible. Recipients either had a serious mental illness (SMI), or non-SMI and anxiety. RESULTS: Total 125 463 cases were identified (i.e., received BZD or non-BZD prescription). Main effect of patient and prescriber gender was significant F(1, 125 459) = 0.105, P = 0 .745, partial η2 < 0.001. Relative risk (RR) of male prescribers prescribing a BZD compared to female prescribers was 1.540, 95% confidence intervals (CI) [1.513, 1.567], whereas the RR of male patients being prescribed a BZD compared to female patients was 1.16, 95% CI [1.14, 1.18]. Main effects of patient and prescriber gender were statistically significant F(1, 125 459) = 188.232, P < 0.001, partial η2 = 0.001 and F(1, 125 459) = 349.704, P < 0.001, partial η2 = 0.013, respectively. CONCLUSIONS: Male prescribers are more likely to prescribe BZDs, and male patients are more likely to receive BZDs. Further studies are required to characterize factors that influence this gender-by-gender interaction.

A computational algorithm for personalized medicine in schizophrenia.

Despite advances in sequencing candidate genes and whole genomes, no method has accurately predicted who will or will not benefit from a specific antipsychotic medication among patients with schizophrenia. We propose a computational algorithm that utilizes a person-centered approach that directly identifies individual patients who will respond to a specific antipsychotic medication. The algorithm was applied to the data obtained from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study. The predictors were either (1) 13 single-nucleotide polymorphisms (SNPs) and 53 baseline variables or (2) 25 SNPs and the same 53 baseline variables, depending on the existing findings and data availability. The outcome variables were either (1) improvement in the Positive and Negative Syndrome Scale (PANSS) (Yes/No) or (2) completion of phase 1/1A (Yes/No). Each of those four predictor-outcome combinations was tried for each of the five antipsychotic medications (Perphenazine, Olanzapine, Quetiapine, Risperidone, and Ziprasidone), leading to 20 prediction experiments. For 18 out of 20 experiments, all three performance measures were greater than 0.50 (sensitivity 0.51-0.79, specificity 0.52-0.79, accuracy 0.52-0.74). Notably, the model provided a promising prediction for Ziprasidone for the case involving completion of phase 1/1A (Yes/No) predicted by 13 SNPs and 53 baseline variables (sensitivity 0.75, specificity 0.74, accuracy 0.74). The proposed algorithm simultaneously used both genetic information and clinical profiles to predict individual patients' response to antipsychotic medications. As the method is not disease-specific but a general algorithm, it can be easily adopted in many other clinical practices for personalized medicine.

Antipsychotic polypharmacy prescribing patterns and costs in the Florida adult and child Medicaid populations.

The rapid growth in the use of antipsychotic medications and their related costs have resulted in states developing programs to measure, monitor, and insure their beneficial relevance to public program populations. One such program developed in the state of Florida has adopted an evidence-based approach to identify prescribers with unusual psychotherapeutic prescription patterns and track their utilization and costs among Florida Medicaid patients. This study reports on the prescriber prescription and cost patterns for adults and children using three measures of unusual antipsychotic prescribing patterns: (1) two antipsychotics for 60 days (2AP60), (2) three antipsychotics for 60 days (3AP60), and (2) two antipsychotics for 90 or more days (2AP90). We find that over the four-year study period there were substantial increases in several aspects of the Florida Medicaid behavioral drug program. Overall, for adults and children, patient participation increased by 29 percent, the number of prescriptions grew by 30 percent, and the number of prescribers that wrote at least one prescription grew 48.5 percent, while Medicaid costs for behavioral drugs increased by 32 percent. But the results are highly skewed. We find that a relatively small number of prescribers account for a disproportionately large share of prescriptions and costs of the unusual antipsychotic prescriptions. In general, the top 350 Medicaid prescribers accounted for more than 70 percent of the unusual antipsychotic prescriptions, and we find that this disparity in unusual prescribing patterns appears to be substantially more pronounced in adults than in children prescribers. For just the top 13 adult and children prescribers, their practice patterns accounted for 11 percent to 21 percent of the unusual prescribing activity and, overall, these 13 top prescribers accounted for 13 percent of the total spent on antipsychotics by the Florida Medicaid program and 9.3 percent of the total expenditure by the state for all drugs. Our findings suggest that a strategy to monitor and ensure patient safety and prescribing patterns that targets a relatively small number of Medicaid providers could have a substantial benefit and prove to be cost effective.

Improving psychotherapeutic medication prescribing in Florida: implementation of the Florida Medicaid Drug Therapy Management Program (MDTMP).

This paper describes a program that was established by Florida Medicaid to improve the quality of prescribing of psychotherapeutic medications. It relates the process used for defining quality medication treatment including the definitions of unusual psychotherapeutic medication indicators (UPMI). It details the results of analysis of FY 2007-2008 pharmacy claims data using these indicators that enabled the Program to identify practices and prescribers that required targeted interventions. The most frequently triggered UPMI for adults involved the use of 2 or more antipsychotics for greater than 60 days; high doses of psychotherapeutic medications was the indicator most frequently triggered for children closely followed by the use of 2 or more antipsychotics for more than 45 days. Prescriptions that triggered UPMI were concentrated in a small number of prescribers. These results led to the Program focusing on these high frequency practices and on the prescribers most associated with them. They also led to the implementation of new quality improvement initiatives like the implementation of a psychiatric telephone consultation line for pediatricians who are treating children with serious emotional disturbances who do not have access to child psychiatrists.

Influence of source and type of admission on in-hospital mortality for coronary artery bypass surgery patients: national results from 1.7 million CABG patients, 1998 to 2002.

BACKGROUND: Hospitals identify the type of coronary artery bypass graft (CABG) surgery admission as routine, urgent, or emergency, and identify CABG admissions as elective, from another hospital, from a long-term healthcare facility, and an admission that results from an emergency room visit. No research has analyzed the importance of these admission characteristics on CABG outcomes. METHODS: Using the Nationwide Inpatient Sample data from the Healthcare Cost and Utilization Project database for 1998 through 2002, 1.7 million CABG admissions are analyzed using descriptive and logistic regression techniques to evaluate the extent of differences in in-hospital CABG mortality rates by the type of admission and the source of admission. RESULTS: While there has been 22% decline over our 5-year study period regardless of the source or type of admission, we find that a 2.5-fold difference exists in the in-hospital CABG mortality rates among categories that distinguish the type of admission and the source of admission in the nation's hospital. In 2002, CABG admissions that are routine elective-surgeries have a CABG mortality rate of 1.49%, while urgent CABG admissions from a long-term care facility have an in-hospital mortality rate that is 3.64%. CONCLUSIONS: While significant progress has been made lowering in-hospital CABG mortality, sizable differences still remain among the types and sources of CABG admissions. Clinical trial research on CABG patients where the end points include mortality need to be especially mindful of the potential differences and distortions in their data that can be generated due to these various sources of hospital admissions.