Does Medication Adherence Lead to Lower Healthcare Expenses for Patients With Diabetes? | Page 2
Published Online: August 13, 2013
Shou-Hsia Cheng, PhD; Chi-Chen Chen, PhD; and Chin-Hsiao Tseng, MD, PhD
Dependent Variables. The dependent variables were healthcare outcomes and healthcare expenses. Healthcare outcomes were based on whether the patient was hospitalized or had an ED for diabetes or cardiovascular/cerebrovascular conditions during each year of the study period; the definition has been used by Lau and Nau (2004).18 Three variables were used to measure healthcare expenses: expenses associated with oral antihyperglycemic medications, expenses for hospitalization or ED visits due to diabetes or cardiovascular/cerebrovascular conditions, and total healthcare expenses forall conditions incurred by the patients. Total healthcare expenses included expenses for ambulatory care, ED visit, hospitalization, laboratory tests, pharmaceuticals, and patient’s copayment. The healthcare expenses were adjusted for inflation by using the consumer price index to facilitate the comparison of figures from various years with those from 2009.
Independent Variables. The independent variables that we included in the analysis consisted of adherence to medications,the duration of diabetes, and the interaction of the duration and adherence to medications. Adherence to medications was measured using the medication possession ratio (MPR) which was based on filled prescriptions for 7 oral antihyperglycemic medications. According to the ATC (Anatomical Therapeutic Chemical) code, these medications included biguanides (A10BA), sulfonamides or urea derivatives (A10BB), combinations of oral blood glucose-lowering drugs (A10BD), alpha glucosidase inhibitors (A10BF), thiazolidinediones (A10BG), dipeptidyl peptidase 4 (DPP-4) inhibitors (A10BH), and other blood glucose–lowering drugs, excluding insulin (A10BX).
In this study, the MPR was calculated as the ratio of the number of days of prescribed medication divided by the total number of days in each study year. Refilled medication days for oral antihyperglycemic medications were also included. The days when patients were prescribed oral antihyperglycemic medications during a hospital stay were excluded from the denominator in the MPR calculation. In addition, patients might receive multiple medications for different numbers of days during a visit; the number of the longest days of the prescribed medications (usually for chronic conditions such as diabetes) in a visit is reported in the NHI claims data. Patients were considered to be adherent to oral antihyperglycemic medications if the MPR was equal to or larger than 80%. This cutoff point has been widely used in previous studies to categorize medication adherence.18,19,21,23
In addition, the duration of diabetes was divided into 2 categories: less than 5 years (the second to fourth year after the initial diagnosis) and 5 years or more (the fifth to seventh year after the initial diagnosis). Effect modification for the duration of diabetes was examined by the inclusion of an interaction term for medication adherence and the duration of diabetes.
Several confounding factors were controlled for in the regression models, including yearly time-dependent variables and time-independent variables. The time-dependent variables in these models included the characteristics of patients and healthcare providers. The characteristics of patients included age, sex, number of physician visits, hospitalizations in the previous year, diabetes complication severity index (DCSI),29 chronic illness with complexity (CIC) index,30 intensity of the diabetes drug regimen, the average number of medications per prescription, and enrollment in the NHI diabetes pay-for-performance (P4P) program.31,32
The DCSI contained 7 categories of complications: cardiovascular complications, nephropathy, retinopathy, peripheral vascular disease, stroke, neuropathy, and metabolic disorders. The CIC index was used to adjust for comorbidity of patients with multiple chronic diseases. This index contained information regarding non-diabetes physical illness complexity (including cancers, as well as gastrointestinal, musculoskeletal, and pulmonary diseases), diabetes-related complexity, microvascular complications, and mental illness/substance abuse complexity. We excluded diabetes-related complexity to avoid the duplication of the comorbidity effect presented by the DCSI index. In the analysis, we counted the number of comorbidities based on the CIC. The intensity of the diabetes drug regimen was indicated whether study subjects used oral monotherapy or oral combination therapy. The average number of medications per prescription was stratified by the mean number of medications per prescription (>3 medications, <3 medications). The healthcare provider characteristics included the accreditation level of the hospital most frequently visited for diabetes care (medical center, regional hospitals, district hospitals, clinics)33 and specialty of the physician most frequently seen for diabetes care (metabolism endocrinologist, others). The time-independent variable was the subject’s sex.
Statistical Analysis. Generalized estimating equations (GEEs) were used with various proper distributions. The analysis accounted for the intraclass correlation between repeated observations of the same subject.16,34 Based on the characteristics of the variables used herein, the likelihood of hospitalization and ED visits was analyzed using a binominal distribution. Additionally, values from the healthcare expenses were skewed to the right. Therefore, we used the GEE model with a logarithmic link function and a gamma distribution to analyze the skew of the healthcare expense data. A similar approach has been used in the econometric literature to assess healthcare costs.35
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