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The American Journal of Managed Care November 2019
Population Health Screenings for the Prevention of Chronic Disease Progression
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Currently Reading
Prescribing Trend of Pioglitazone After Safety Warning Release in Korea
Han Eol Jeong, MPH; Sung-Il Cho, MD, ScD; In-Sun Oh, BA; Yeon-Hee Baek, BA; and Ju-Young Shin, PhD

Prescribing Trend of Pioglitazone After Safety Warning Release in Korea

Han Eol Jeong, MPH; Sung-Il Cho, MD, ScD; In-Sun Oh, BA; Yeon-Hee Baek, BA; and Ju-Young Shin, PhD
The pioglitazone safety warning issued in South Korea, which recommended prescribing with careful attention among those with high risk of bladder cancer, led to a moderate decrease in pioglitazone users.
Definition of Exposure and Outcome

Exposure was defined as “before” or “after,” relative to the intervention. The proportion of antidiabetic drug users was defined as the number of antidiabetic drug users divided by the total number of patients with diabetes. Use of the study drug, pioglitazone (anatomical therapeutic chemical classification system code, A10BG03), was compared with use of other antidiabetic drugs (comparators), which were classified as (1) rosiglitazone (A10BG02), (2) sulfonylurea derivatives (A10BB) and metformin (A10BA02), (3) dipeptidyl peptidase-4 (DPP-4) inhibitors (A10BH) and glucagon-like peptide-1 (GLP-1) analogues (A10BJ), and (4) insulin analogues (A10A). Metformin, the preferred initial treatment of diabetes, and sulfonylurea, which is used for second-line therapy together with metformin, were grouped because these 2 drug classes are regarded as the most cost-effective and widely prescribed treatment for diabetes.12 Additionally, because both DPP-4 inhibitors and GLP-1 analogues are incretin-based drugs with similar mechanisms of action, they were grouped together as well.13

Potential Confounders

Demographic variables, such as age and gender, were identified from the database. With regard to medical institution type (ie, institutions that patients visited for diabetes-related healthcare services), the following classification was applied: tertiary hospitals (≥500 beds), general hospitals (30-499 beds), and clinics (<30 beds). With regard to comorbidities, those with a history of ischemic heart disease (ICD-10 codes I24 and I25), myocardial infarction (I21), ischemic stroke (I63), hypertension (I10-I15), and cancer (C00-D49) were assessed in the whole study period (ie, the periods before and after the intervention).

Statistical Analysis

Age, gender, medical institution type, and comorbidities were presented as frequencies and proportions. The absolute standardized difference (aSD) was calculated for all categorical variables. The absolute change in drug users was calculated as the difference between the proportions before and after the intervention, whereas the relative change (%) was calculated by dividing the absolute change by the proportion of users before the intervention. 95% CIs were calculated for both absolute and relative changes. To estimate the impact of the intervention, the proportion of monthly antidiabetic drug users was analyzed via ordinary least-squares regression and maximum likelihood estimation.

A segmented regression model was designed using data available for 30 months prior to the intervention. Using preintervention data, the monthly rates over time were projected to predict what would have occurred without the intervention. The dependent variable was the proportion of antidiabetic drug users per 1000 patients with diabetes. Independent variables included time (months), intervention indicator, and time after the intervention (months). The intervention indicator variable was set as a dichotomous variable: 0 (before) or 1 (after). Time after the intervention was a continuous variable representing the number of months after the intervention and was set to 0 for all months prior to the intervention. The equation used for the regression model is as follows10,11:

Y = B0 + B1 × Time + B2 × Intervention + B3 × Time after intervention + e

The estimates for “intervention” and “time after intervention” are the main coefficients of interest from the segmented regression analysis, with the former measuring the level of change immediately after the intervention and the latter measuring the trend after the intervention. The assumption of autocorrelation for time-series data was assessed via Durbin-Watson statistics, and seasonality or stationarity was assessed using an augmented Dickey-Fuller test.14,15 In addition, the numbers of incident and prevalent pioglitazone users per 1000 patients with diabetes were calculated to compare and contrast their trends. Incident pioglitazone users were defined as those having no prescription for pioglitazone within the previous 12 months of the first occurrence of pioglitazone use. Prevalent pioglitazone users were defined as those having a prescription for pioglitazone in each respective month, regardless of their past use. To test the sensitivity and robustness of the study results, a 3-month lag was applied both before and after the intervention.

All statistical analyses were performed using the SAS Enterprise Guide statistical application program provided by the NHIS (release 9.71; SAS Institute Inc; Cary, North Carolina) and accessed through a virtual machine system. A 2-tailed P value <.05 or aSD <0.1 was considered statistically significant.


 
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