Using time-series intervention analysis to understand U.S. Medicaid expenditures on antidepressant agents

Forecasting Ontario Oncology Drug Expenditures: A Hybrid Approach to Improving Accuracy

BACKGROUND

The Provincial Drug Reimbursement Program (PDRP) at Cancer Care Ontario (CCO) is responsible for monitoring actual and projected outpatient intravenous cancer drug spending in the province. We developed a hybrid forecasting approach combining automated time-series forecasting with expert-customizable input.

OBJECTIVE

Our objectives were to provide a flexible tool in which to incorporate multiple forecasts and to improve the accuracy of the resulting forecast.

METHODS

The approach employed linear and non-linear time-series techniques and a combined hybrid model incorporating both approaches. We developed an interactive tool that incorporated the statistical models and identified the best performing forecast according to standard goodness-of-fit measures. Model selection procedures considered both the amount of historical expenditure data available per drug policy and the individual policy contributions to the overall budget. The user was allowed to customize forecasts based on knowledge of external factors related to policy or price changes and new drugs that come to market RESULTS: A comparison of 2016/17 fiscal year expenditures showed that all policies with a significant contribution to the overall budget were forecast with < 4% error. Forecasting error was reduced by at least $Can5 million for the nine most expensive policies compared with expert opinion. This approach to drug budget forecasting was implemented in Ontario for the first time in the 2017/18 fiscal year, where 1% error was observed for the overall budget, corresponding to an overestimate of expenditures by $Can3.0 million.

CONCLUSION

We introduced a pragmatic approach for regular forecasting by budget holders in Ontario. Our approach to isolating 'big budget' from 'small budget' drugs using an 80-20 rule and providing multiple forecasts depending on the length of the drug expenditure histories is transferable to other jurisdictions.

The swine flu and its impacts on tourism in Brunei

This paper demonstrates how to disentangle the impacts of the swine flu on tourism in Brunei, which overlaps with the continued effects of the 2008 global financial crisis that occurred earlier using the auto regressive integrated moving average and intervention time series analysis methods. Estimating the impacts of the swine flu for the first 12 months' post-swine flu period, we have predicted the number of tourists by fitting two auto regressive integrated moving average models: one for the swine flu and another for the global financial crisis which occurred and affected the number of tourist arrivals; and one intervention time series analysis model. It is shown that the number of tourists have been reduced significantly due to both the swine flu and the global financial crisis, which is reconfirmed by testing the coefficients of the fitted intervention time series analysis model. It is found that a small country like Brunei lost nearly 30, 000 (15%) tourists and B$15 million dollars due to the swine flu during the first twelve months’ post-swine flu period.

How do GP practices and patient characteristics influence the prescription of antidepressants? A cross-sectional study

BACKGROUND

Under-prescription of antidepressants (ADs) among people meeting the criteria for major depressive episodes and excessive prescription in less symptomatic patients have been reported. The reasons influencing general practitioners' (GPs) prescription of ADs remain little explored. This study aimed at assessing the influence of GP and patient characteristics on AD prescription.

METHODS

This cross-sectional study was based on a sample of 816 GPs working within the main health care insurance system in the Seine-Maritime district of France during 2010. Only GPs meeting the criteria for full-time GP practice were included. The ratio of AD prescription to overall prescription volume, a relative measure of AD prescription level, was calculated for each GP, using the defined daily dose (DDD) concept. Associations of this AD prescription ratio with GPs' age, gender, practice location, number of years of practice, number of days of sickness certificates prescribed, number of home visits and consultations, number and mean age of registered patients, mean patient income, and number of patients with a chronic condition were assessed using univariate and multivariate analysis.

RESULTS

The high prescribers were middle-aged (40-59) urban GPs, with a moderate number of consultations and fewer low-income and chronic patients. GPs' workload (e.g., volume of prescribed drug reimbursement and number of consultations) had no influence on the AD prescription ratio. GPs with more patients with risk factors for depression prescribed fewer ADs, however, which could suggest the medications were under-prescribed among the at-risk population.

CONCLUSIONS

Our study described a profile of the typical higher AD prescriber that did not include heavy workload. In future work, a more detailed assessment of all biopsychosocial components of the consultation and other influences on GP behavior such as prior training would be useful to explain AD prescription in GP's practice.

Evidence-based prediction of statin use with lipid-panel data from the National Health and Nutrition Examination Survey

OBJECTIVES

This study compared actual use of individual statin drugs to expected use based on their efficacy and safety profiles.

METHODS

Five panels covering the years 1999 to 2008 from the National Health and Nutrition Examination Survey provided interview, demographic, and laboratory data for 8769 (365,503,838 weighted) people aged 20 years or older who were not taking a statin medication. An individual's risk for coronary heart disease and low-density lipoprotein (LDL) cholesterol goal were determined, following the Adult Treatment Panel III Cholesterol Guidelines. The percentage LDL cholesterol lowering required to reach his/her LDL cholesterol level goal was calculated. Depending on the amount of LDL cholesterol lowering needed and on if the individual had a liver condition (i.e., enhanced risk of rhabdomyolysis) statins were hypothetically prescribed. Predicted use was compared to actual use by U.S. Medicaid beneficiaries in the third quarter of 2009, obtained from the Medicaid State Drug Utilization Data maintained by the Centers for Medicare and Medicaid Services.

RESULTS

Results showed that 72.34% of the population was in the lowest coronary heart disease risk group and that 86.30% required no statin therapy. Among the people who did require LDL cholesterol lowering, a significant majority (37.3 million or 10.22% of the population) needed 30% lowering or less. Only 314,784 (0.09%) required LDL cholesterol lowering of greater than 60%. Utilization shares based on safety and efficacy were estimated at 19.26% (rosuvastatin), 18.67% (atorvastatin), 16.48% (simvastatin), 16.30% (lovastatin), 14.93% (pravastatin), and 14.36% (fluvastatin).

CONCLUSIONS

Actual statin use differed substantially from predicted use. It may be appropriate to develop and maintain policies that encourage use of less costly products that have essentially equivalent safety profiles and efficacy.