2024 年 47 巻 12 号 p. 2032-2040
Although medication oversupply results in waste of medications, triggers of medication oversupply remain unclear. This nationwide retrospective cohort study aimed to identify associated factors and causes of chronic disease medication oversupply in Japan by quantitative and qualitative analyses. Data of financial year 2019 from a large insurance claims database were used. Excess days, which represent medication oversupply in days, were calculated for each of the major five classes of chronic disease medications. For each class, the two cohorts were formed, consisting of individuals aged ≥55 years with either excessive oversupply or normal supply according to excess days. Oversupply-associated factors were subjected to quantitative analyses using logistic regression models, which included excessive oversupply vs. normal supply as an outcome variable. A qualitative analysis to identify causes of oversupply was performed by reviewing the medication history of 50 individuals randomly selected from each excessive oversupply cohort, and causes were classified into seven categories. Oversupply-associated factors in all classes were greater frequency of early supply (≥6 vs. <3 times/10 supplies, odds ratio (OR) 5–9 for all classes), inpatient prescription (included vs. not included, OR 3–5), and higher number of concomitant ingredients (≥16 vs. 1–5 ingredients, OR 3–5). The most common category of causes for oversupply in all classes was the “early supply of medications prescribed by a single facility.” The factors and causes of oversupply might reflect the unique features, rules, and customs of Japan’s healthcare system. This finding might help in developing measures for reducing medication oversupply.
Medication oversupply, in which patients receive medications exceeding their needs,1) is a worldwide issue.2–8) Oversupply results in unused or misused medications and wasting of national healthcare costs. The potential reasons for the increased healthcare costs are reported to be associated with medication oversupply, such as the increase in hospitalization and emergency department visits,3–5) deteriorated health conditions caused by inappropriate medication use,6) and increase in total patients out-of-pocket healthcare expenditure.4,5,7,8)
Most of the studies on medication oversupply have investigated the quantity of oversupply and its related factors.1,3,5,7–11) Studies1,3,5,8–11) reported that medication oversupply was observed in 10–40% of patients and was associated with factors such as hospitalization, polypharmacy, and insurance types. However, these results largely depended on the study methods including the definition of medication oversupply and study settings. Despite the relatively few studies on medication oversupply in Japan,12–14) two13,14) out of the three studies focused only on one factor that could lead to medication oversupply, namely, the overlap of multiple prescriptions caused by multiple prescribers and medical facilities. To our knowledge, only our study12) more comprehensively discussed problems of medication oversupply considering various associated factors.
Our previous study, which comprehensively explored the oversupply of chronic medications in Japan,12) revealed an oversupply of medications in 16% of the patients, with the conservative estimation of oversupply under a strict definition of medication oversupply (i.e., defining the same nonproprietary name and the same specification [the same ingredient] as the same medication, that is, not treated as the same medication if switching to a different nonproprietary name [e.g., switching amlodipine besilate to azelnidipne] and different specifications [e.g., switching amlodipine besilate 2.5 mg to amlodipine besilate 5 mg] to avoid physicians’ intentional switching and increasing dosage as medication oversupply, respectively). However, its related factors and causes in the context of the Japanese healthcare system have not been clarified yet. Japan’s prescription system differs from those overseas in terms of accessibility to medical facilities and insurance coverage, which is characterized by easy access to medical facilities and the universal health insurance system. In addition, attitudes and norms toward hospital visit and medications among Japanese might be different than those in other countries. Therefore, identifying the associated factors and causes of medication oversupply and addressing this problem are important to reduce unnecessary healthcare spending. This nationwide retrospective cohort study aimed to identify associated factors and causes of the oversupply of chronic disease medications in Japan by quantitative and qualitative analyses.
This nationwide retrospective cohort study was conducted using the DeSC database owned by DeSC Healthcare, Inc. This database covers approximately 10 million people, which is approximately 10% of the total population in Japan as of 2022 and contains a large number of claims from older patients. Database characteristics were provided previously.12,15,16)
Similar to our previous study that quantified medication oversupply using the same dataset,12) the present study was performed in accordance with the Act on the Protection of Personal Information, Ethical Guidelines for Medical and Biological Research Involving Human Subjects, and Data License Agreement of DeSC Healthcare, Inc. Ethics review was deemed unnecessary by the Ethics Committee of Tokushima Bunri University. The need for informed consent was waived because anonymized data were used.
Study PopulationThe study population consisted of two cohorts, namely, the excessive oversupply cohort and the normal supply cohort, which were defined according to the excess days of medication oversupply (Fig. 1). Excess days were defined as the number of days in that the total days of medication supply during the prescription persistence was over the period during which those prescription medications should have been used up. Period A, which the last days of supply during the prescription persistence were subtracted from the number of days of persistence, was employed because determining whether the most recently prescribed medications were already used was not possible.12,17) Thus, excess days were calculated as the difference between period A and the total days of supply during period A.
MPR, medication position ratio; Rx, prescription.
Initially, to form the two cohorts, patients aged ≥55 years who received ingredients of the following five classes of chronic disease medications during financial year (FY) 2019 (from April 1, 2019 to March 31, 2020) were identified: third-generation calcium antagonists (CABs), angiotensin 2 receptor blockers (ARBs), statins (STAs), dipeptidyl peptidase-4 inhibitors (DPPs), and biguanides (BIGs) (Supplementary Table S1), of which detailed eligible criteria for this first identification were described previously.12) In addition to the inclusion criteria employed in our previous study, the criterion “patients with ≥300 d of period A” was added, and the base population of the present study consisted all patients who met these inclusion criteria.
Then, excess days of oversupplied medications were calculated. To conservatively estimate oversupply, the same ingredients, not to the same medication classes, were treated as the same medication under the strict definition as in our previous study.12) Because the length of period A varied among patients, excess days during period A were converted into a yearly basis, and excess days per year were calculated.
Finally, two cohorts were formed: excessive oversupply cohort of patients with ≥30 excess days/year and normal supply cohort of patients with within ±15 d/year. Moreover, ≥30 excess days/year was defined as excessive oversupply referring to previous studies.12,18) In many existing studies,1,3–5,7–12,18) chronic disease medication oversupply is estimated based on the medication position ratio (MPR, Fig. 1), not excess days, and oversupply is defined as either MPR >1.1 or MPR >1.2. Because excess days indicate the number of days of supply, excess days are more comprehensible; therefore, this study employed excess days as an indicator of oversupply.
Quantitative and Qualitative AnalysesQuantitative analysis to identify factors associated with oversupply, and qualitative analysis to identify causes contributing to oversupply were conducted.
(1) Quantitative AnalysisLogistic regression models with excessive oversupply vs. normal supply as an outcome variable (dichotomous) were used to analyze oversupply-associated factors, and odds ratios (ORs) for medication oversupply were calculated for each of the five medication classes. Explanatory variables were chosen based on previous reports,3–5,7–10,13,14,18) review of prescriptions and dispensing history of the qualitative analysis as described below, and authors’ clinical experience in Japan. Sex and age (55–64, 65–74, 75–84, or ≥85 years) were included as demographic variables in the models. The following seven variables that are related to medication class and prescription were also included: (1) inclusion of inpatient prescription (yes/no), (2) number of medical facilities that issued prescriptions (1 or ≥2), (3) number of community pharmacies that dispensed medications (0, 1, or ≥2), (4) use of generic medications (yes/no), (5) medications to be taken multiple times a day (yes/no, i.e., whether the ingredient is instructed by guidelines to be taken multiple times a day), (6) difference in days between early and scheduled supplies (continuous values), and (7) frequency of early supply per 10 supplies (<3, ≥3 and <6, or ≥6 times). The following two variables related to concomitant medications were included: (1) number of concomitant ingredients (1–5, 6–10, 11–15, or ≥16) and (2) use of each of the following concomitant medication classes (yes/no): antidiabetic agents (Anatomical Therapeutic Chemical Classification: A10), antithrombotic agents (B01), diuretics (C03), beta-blockers (C07), calcium-channel blockers (C08), renin–angiotensin system agents (C09), lipid-modifying agents (C10), antineoplastic agents (L01), nonsteroidal anti-inflammatory agents (M01A), antipsychotics (N05B), hypnotics and sedatives (N05C), antidementia agents (N06D), and obstructive airway disease agents (R03)). Because CABs and ARBs are to be taken once daily, the variable “medications to be taken multiple times a day” was not included in models for CABs and ARBs. Because DPPs have no generic products, the variable “use of generic medications” was not included in the models for DPPs. Given that concomitant medication classes were included as adjustment variables in models, each model of the targeted five medication classes did not include the targeted class. For example, CABs were not included as a concomitant medication in the model for CABs.
(2) Qualitative AnalysisCauses of medication oversupply were identified and categorized by reviewing prescriptions and dispensing history of 50 patients randomly selected from the excessive oversupply cohort of each of the five classes. From the following seven categories of causes of medication oversupply, the category that had the strongest relation (main causes of medication oversupply) and categories that were considered correlated (all causes of medication oversupply) were selected: (1) early supply of medications prescribed by a single facility, (2) duplicate of the same prescription issued by a single facility on the same day, (3) supply of medications prescribed by a single facility at multiple dispensing facilities, (4) supply of medications prescribed by multiple facilities at multiple dispensing facilities, (5) duplicated prescriptions between outpatient and inpatient prescriptions, (6) duplicated prescriptions issued by a single facility during hospitalization, and (7) duplicated prescriptions due to intrahospital transfer during hospitalization. The categories were identified and selected independently by two researchers. For discrepancies between judgments by the two researchers, a discussion was held inviting other researchers.
Statistical AnalysesAnalyses were performed using SAS version 9.4 (SAS Institute, Cary, NC, U.S.A.). Chi-square and Mann–Whitney U tests were used to compare categorical and continuous variables with nonparametric distribution, respectively. All p-values were two-tailed with significance defined as p-value <0.05.
The base population of each of the five classes was composed of 428923 patients for CABs, 450862 for ARBs, 478653 for STAs, 175498 for DPPs, and 81283 for BIGs (Fig. 2). The proportion of patients in the excessive oversupply cohort (i.e., patients with ≥30 excess days/year) for each base population was 0.8–1.2% (Fig. 2).
ARB, angiotensin II receptor blocker; ATC, Anatomical Therapeutic Chemical Classification; BIG, biguanide; CAB, third-generation calcium blocker; DPC, diagnosis procedure combination; DPP, dipeptidyl peptidase-4 inhibitor; STA, statin. a Clinical service identification codes 21–28 mean that the medications were dispensed for medical, DPC, or dental claims. b Patients dispensed multiple medications of different classes were counted in each class. c Base population. d Proportion for base population.
Patient characteristics of both cohorts are shown in Table 1. Proportions of male were similar among cohorts of all medication classes and were approximately 40% for CABs, ARBs, and STAs, and approximately 50% for DPPs and BIGs. All cohorts included approximately 85–95% of patients aged ≥65 years. Proportions of older people (≥75 years), which nearly equal the proportion of patients with insurance under the Medical Care System for the Elderly Aged ≥75 Years, were higher in the excessive oversupply cohorts than in the normal cohorts for all medication classes, with approximately 10% difference among cohorts of all classes. Proportions of patients who had only outpatient prescriptions for targeted medication classes were lower for the excessive oversupply cohorts for all classes, and the difference among cohorts was approximately 15–25% depending on medication classes.
| CABs | ARBs | STAs | DPPs | BIGs | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Excessive oversupplya) n = 4634 n (%) | Normalb) n = 274543 n (%) | p-Valuec) | Excessive oversupplya) n = 3848 n (%) | Normalb) n = 294341 n (%) | p-Valuec) | Excessive oversupplya) n = 3791 n (%) | Normalb) n = 295349 n (%) | p-Valuec) | Excessive oversupplya) n = 1619 n (%) | Normalb) n = 116797 n (%) | p-Valuec) | Excessive oversupplya) n = 983 n (%) | Normalb) n = 50543 n (%) | p-Valuec) | |
| Sex | |||||||||||||||
| Male | 1866 (40.3) | 117813 (42.9) | <0.001 | 1632 (42.4) | 134754 (45.8) | <0.001 | 1425 (37.6) | 111585 (37.8) | 0.81 | 824 (50.9) | 62687 (53.7) | 0.026 | 524 (53.3) | 26991 (53.4) | 0.95 |
| Female | 2768 (59.7) | 156730 (57.1) | 2216 (57.6) | 159587 (54.2) | 2366 (62.4) | 183764 (62.2) | 795 (49.1) | 54110 (46.3) | 459 (46.7) | 23552 (46.6) | |||||
| Age (years) | |||||||||||||||
| 55–64 | 382 (8.2) | 23663 (8.6) | <0.001 | 318 (8.3) | 27506 (9.3) | <0.001 | 346 (9.1) | 28079 (9.5) | <0.001 | 148 (9.1) | 11672 (10.0) | <0.001 | 140 (14.2) | 7044 (13.9) | <0.001 |
| 65–74 | 1412 (30.5) | 106666 (38.9) | 1186 (30.8) | 118731 (40.3) | 1217 (32.1) | 127165 (43.1) | 518 (32.0) | 51204 (43.8) | 447 (45.5) | 27084 (53.6) | |||||
| 75–84 | 1786 (38.5) | 95573 (34.8) | 1526 (39.7) | 101899 (34.6) | 1565 (41.3) | 103049 (34.9) | 675 (41.7) | 40308 (34.5) | 313 (31.8) | 13778 (27.3) | |||||
| ≥85 | 1054 (22.7) | 48641 (17.7) | 818 (21.3) | 46205 (15.7) | 663 (17.5) | 37056 (12.6) | 278 (17.2) | 13613 (11.7) | 83 (8.4) | 2637 (5.2) | |||||
| Insurance | |||||||||||||||
| Society-managed health insurance | 73 (1.6) | 5843 (2.1) | <0.001 | 84 (2.2) | 6724 (2.3) | <0.001 | 79 (2.1) | 7128 (2.4) | <0.001 | 25 (1.5) | 2581 (2.2) | <0.001 | 21 (2.1) | 1485 (2.9) | <0.001 |
| National Health Insurance | 1581 (34.1) | 120126 (43.8) | 1304 (33.9) | 134471 (45.7) | 1378 (36.4) | 142066 (48.1) | 582 (36.0) | 57266 (49.0) | 534 (54.3) | 31644 (62.6) | |||||
| Medical Care System for the Elderly Aged ≥75 Years | 2980 (64.3) | 148574 (54.1) | 2460 (63.9) | 153146 (52.0) | 2334 (61.6) | 146155 (49.5) | 1012 (62.5) | 56950 (48.8) | 428 (43.5) | 17414 (34.5) | |||||
| Inpatient/outpatient prescription for targeted medication classes | |||||||||||||||
| Only outpatient prescription | 3757 (81.1) | 263856 (96.1) | <0.001 | 3177 (82.6) | 285331 (96.9) | <0.001 | 2797 (73.8) | 283510 (96.0) | <0.001 | 1110 (68.6) | 110800 (94.9) | <0.001 | 809 (82.3) | 48865 (96.7) | <0.001 |
| Only inpatient prescription | 4 (0.1) | 447 (0.2) | 5 (0.1) | 335 (0.1) | 1 (0.0) | 234 (0.0) | 4 (0.3) | 215 (0.2) | 3 (0.3) | 67 (0.1) | |||||
| Both | 873 (18.8) | 10240 (3.7) | 666 (17.3) | 8675 (3.0) | 993 (26.2) | 11605 (3.9) | 505 (31.2) | 5782 (5.0) | 171 (17.4) | 1611 (3.2) | |||||
| Period A for targeted medication classesd) | |||||||||||||||
| Median days (IQR) | 336 (322–345) | 336 (326–343) | 0.011e) | 335 (322–345) | 336 (325–343) | 0.002e) | 335 (322–343) | 336 (324–343) | <0.001e) | 335 (321–343) | 336 (323–343) | <0.001e) | 335 (320–343) | 336 (322–343) | 0.031e) |
| Concomitant medication classes, ATC | |||||||||||||||
| Antidiabetic agents, A10 | 973 (21.0) | 59298 (21.6) | 0.32 | 892 (23.2) | 72658 (24.7) | 0.032 | 1064 (28.1) | 82953 (28.1) | 0.98 | NA | NA | NA | NA | ||
| Antithrombotic agents, B01 | 1460 (31.5) | 71756 (26.1) | <0.001 | 1289 (33.5) | 84362 (28.7) | <0.001 | 1570 (41.4) | 98641 (33.4) | <0.001 | 668 (41.3) | 37335 (32.0) | <0.001 | 389 (39.6) | 13923 (27.6) | <0.001 |
| Diuretics, C03 | 858 (18.5) | 36443 (13.3) | <0.001 | 856 (22.3) | 44517 (15.1) | <0.001 | 890 (23.5) | 38920 (13.2) | <0.001 | 390 (24.1) | 17045 (14.6) | <0.001 | 158 (16.1) | 5309 (10.5) | <0.001 |
| Beta-blockers, C07 | 866 (18.7) | 39611 (14.4) | <0.001 | 805 (20.9) | 48138 (16.4) | <0.001 | 927 (24.5) | 52795 (17.9) | <0.001 | 344 (21.3) | 18415 (15.8) | <0.001 | 177 (18.0) | 6730 (13.3) | <0.001 |
| Calcium-channel blockers, C08 | NA | NA | 2073 (53.9) | 140889 (47.9) | <0.001 | 1896 (50.0) | 134832 (45.7) | <0.001 | 795 (49.1) | 49339 (42.2) | <0.001 | 454 (46.2) | 20003 (39.6) | <0.001 | |
| Renin–angiotensin system agents, C09 | 2641 (57.0) | 162292 (59.1) | 0.004 | NA | NA | 2031 (53.6) | 154294 (52.2) | 0.10 | 883 (54.5) | 61673 (52.8) | 0.16 | 572 (58.2) | 26998 (53.4) | 0.003 | |
| Lipid-modifying agents, C10 | 2105 (45.4) | 131877 (48.0) | <0.001 | 1878 (48.8) | 149980 (51.0) | 0.008 | NA | NA | 954 (58.9) | 68105 (58.3) | 0.62 | 656 (66.7) | 31028 (61.4) | <0.001 | |
| Antineoplastic agents, L01 | 46 (1.0) | 861 (0.3) | <0.001 | 24 (0.6) | 714 (0.2) | <0.001 | 28 (0.7) | 658 (0.2) | <0.001 | 15 (0.9) | 384 (0.3) | <0.001 | 5 (0.5) | 110 (0.2) | 0.056 |
| NSAIDs, M01A | 1338 (28.9) | 50083 (18.2) | <0.001 | 1033 (26.9) | 50100 (17.0) | <0.001 | 1027 (27.1) | 48857 (16.5) | <0.001 | 395 (24.4) | 17301 (14.8) | <0.001 | 204 (20.8) | 6984 (13.8) | <0.001 |
| Antipsychotics, N05B | 991 (21.4) | 30477 (11.1) | <0.001 | 816 (21.2) | 31008 (10.5) | <0.001 | 782 (20.6) | 33388 (11.3) | <0.001 | 217 (13.4) | 8524 (7.3) | <0.001 | 97 (9.9) | 3099 (6.1) | <0.001 |
| Hypnotics and sedatives, N05C | 1684 (36.3) | 50315 (18.3) | <0.001 | 1391 (36.2) | 50952 (17.3) | <0.001 | 1455 (38.4) | 53774 (18.2) | <0.001 | 517 (31.9) | 18343 (15.7) | <0.001 | 232 (23.6) | 6406 (12.7) | <0.001 |
| Antidementia agents, N06D | 266 (5.7) | 11271 (4.1) | <0.001 | 232 (6.0) | 10313 (3.5) | <0.001 | 197 (5.2) | 9029 (3.1) | <0.001 | 108 (6.7) | 3917 (3.4) | <0.001 | 51 (5.2) | 1046 (2.1) | <0.001 |
| Obstructive airway disease agents, R03 | 546 (11.8) | 28190 (10.3) | <0.001 | 472 (12.3) | 30234 (10.3) | <0.001 | 465 (12.3) | 29830 (10.1) | <0.001 | 180 (11.1) | 10345 (8.9) | 0.002 | 90 (9.2) | 4112 (8.1) | 0.25 |
ARB, angiotensin II receptor blocker; ATC, Anatomical Therapeutic Chemical Classification; BIG, biguanide; CAB, 3rd generation calcium blocker; DPP, dipeptidyl peptidase-4 inhibitor; IQR, interquartile range; NA, not applicable; NSAID, nonsteroidal anti-inflammatory drug; STA, statin. a) Patients with ≥30 excess days/year. b) Patients with within ±15 excess days/year. c) Chi-square tests. d) See Fig. 1 for period A. e) Mann–Whitney U tests.
Figure 3 and Supplementary Table S2 show crude (Supplementary Table S2) and adjusted ORs (Fig. 3, Supplementary Table S2) for medication oversupply. Significant factors that were associated with oversupply in common among all medication classes were as follows: inclusion of inpatient prescription (adjusted ORs 3–5 for reference of not included, depending on classes), number of medical facilities that issued prescriptions (adjusted ORs 1–2 for ≥2 medical facilities for reference of one facility), difference in days between early and scheduled supplies (adjusted ORs 1–2 for 1-d increment), frequency of early supply per 10 supplies (adjusted OR 5–9 for ≥6 times/10 supplies for reference of <3 times/10 supplies), and number of concomitant ingredients (adjusted OR 3–5 for ≥16 ingredients for reference of 1–5 ingredients). For the four medication classes, not for BIGs, the number of community pharmacies that dispensed medications (adjusted ORs approximately 1.5 for ≥2 community pharmacies for a reference of 0 community pharmacy, i.e., dispensing at hospitals or clinics instead of at community pharmacies) was also associated with oversupply. Medications to be taken multiple times a day were significantly associated with STAs and DPPs but not with BIGs.
AdOR, adjusted odds ratio; ARB, angiotensin II receptor blocker; ATC, Anatomical Therapeutic Chemical Classification; BIG, biguanide; CAB, third-generation calcium blocker; CI, confidence interval; DPP, dipeptidyl peptidase-4 inhibitor; NSAID, nonsteroidal anti-inflammatory drug; STA, statin. a Adjusted for listed variables, except for nonapplicable variables: medications to be taken multiple times a day and calcium-channel blockers (CABs) in the model for CABs; medication to be taken multiple times a day and renin–angiotensin system agents in the model for ARBs, lipid-modifying agents in the model for STAs, use of generic medications, and antidiabetic agents in the model for DPPs; and antidiabetic agents in the model for BIGs. b Calculated as the “(total number of days earlier than scheduled supply during period A)/(number of supply times during period A − 1).” The first supply time was subtracted from the denominator. See Fig. 1. for period A. c Calculated as the “(total number of early supply times during period A) × 10/(number of supply times during period A − 1). The first supply time was subtracted from the denominator. See Fig. 1. for period A. d The number included ingredient of the targeted medication classes.
The results of the qualitative analysis are shown in Figs. 4 and 5. As the main causes of oversupply, i.e., the category among the seven categories that had the strongest relation (Fig. 4), “early supply of medications prescribed by a single facility” was the most prominent, with the highest proportion of patients in four medication classes, except for BIGs (highest in ARBs at 66% and lowest in DPPs and BIGs at 38%). This was followed by “duplicate of the same prescription issued by a single facility on the same day” (highest in BIGs at 52%, and lowest in ARBs and STAs at 16%). “Duplicated prescriptions between outpatient and inpatient prescriptions” accounted for 8–28% (highest in DPPs at 28% and lowest in BIGs at 8%).
a One cause per patient was chosen from seven categorized causes.
a Multiple causes per patient were chosen from seven categorized causes.
Among all causes (Fig. 5), the factor contributing to oversupply the most was “early supply of medications prescribed by a single facility” in all the five medication classes. Although subsequent causes varied depending on medication classes, duplicate of the same prescription issued by a single facility on the same day, supply of medications prescribed by multiple facilities at multiple dispensing facilities, and duplicated prescriptions between outpatient and inpatient prescriptions had relatively high proportions.
This study used quantitative and qualitative analyses and identified the factors and causes related to the oversupply of chronic disease medications in Japan. Both analyses indicated that “supplying medications earlier than scheduled” demonstrated considerable relationship with medication oversupply (i.e., “difference in days between early and scheduled supplies” and “frequency of early supply per 10 supplies” as factors and “early supply of medications prescribed by a single facility” as the cause). This was followed by “inclusion of inpatient prescription” (“inclusion of inpatient prescription” as the factor and “duplicated prescriptions between outpatient and inpatient prescriptions” as the cause), and “prescribing and dispensing by multiple facilities” (“number of medical facilities that issued prescriptions” and “number of community pharmacies that dispensed medications” as factors and “supply of medications prescribed by multiple facilities at multiple dispensing facilities” as the cause). Both quantitative and qualitative analyses indicated that “number of concomitant ingredients” and “duplicate of the same prescription issued by a single facility on the same day” were related to oversupply, respectively. Consideration of measures to reduce medication oversupply based on the present findings might help in restraining healthcare expenditures and improving medication errors and misuses in Japan and countries with similar contexts.
Among the factors and causes of medication oversupply identified in this study, “inclusion of inpatient prescription” and “number of concomitant ingredients” have already been reported in existing overseas studies.3,5,9,18) On the contrary, no overseas studies have indicated “supplying medications earlier than scheduled” and “prescribing and dispensing by multiple facilities” as associated factors suggesting that these factors might be distinctive for Japan.
“Supplying medications earlier than scheduled” as well as “prescribing and dispensing by multiple facilities,” which were identified by both quantitative and qualitative analyses, are considered reflective of the unique features of the nation’s healthcare system, specifically universal health insurance and free access to medical facilities and community pharmacies. Andersson et al.10) and Dilokthornsakul et al.18) pointed out the association between the exemption from medical expenses and medication oversupply in studies in Sweden and Thailand, respectively. Chen et al. discussed that the lack of a referral system and easy access to physicians may cause medication oversupply in Taiwan.4)
In Japan, where patients have access to any healthcare facilities at any time, patients can visit not only a medical facility with an appointment but also a facility without an appointment on their preferred day and receive new prescription even if they still have many unused medications at home. Moreover, physicians occasionally issue longer prescription durations than the actual hospital visit intervals in the case medications are lost. Consequently, the difference between prescription durations and actual hospital visit intervals potentially results in medication oversupply. Even if that difference per prescription is small, the accumulation of such small medication oversupply results in large medication oversupply, particularly for chronic disease medications. For instance, if the prescription duration is 30 d for 28 d of actual hospital visit interval, medications for 1 month will be oversupplied in a year ([30 − 28 d] × 12 times/year = 24 d/year). Thus, to avoid medication oversupply before it occurs and follow up patients who have already been oversupplied, a system that enables the estimation of the amount of medications remaining in patient’s home based on the amount of medications supplied and hospital visits and shares the estimated information not only with patients but also with their families, physicians, pharmacists, and caregivers must be established. If such a system is effective, physicians can adjust the duration of prescription at the time the physician writes the prescription (i.e., before medications are supplied), and family, pharmacists, and caregivers can check the discrepancy between the estimated and actual amounts of medications remaining in the patient’s home. Chen et al.4) reported the need for coordinated care via electronic health records to avoid medication oversupply. Hsu et al.19) stated that a prescription system involving shared medication records across medical facility boundaries, which detects potential duplicated medications and issues an alert when prescribing, would help physicians screen doctor-shopping patients.
In Japan, patients and healthcare staff, whose medical and medication costs are mostly covered by insurance, may be less aware of the medication costs than people in countries with higher out-of-pocket medication expenses. Japan sometimes experiences natural disasters such as typhoons and earthquakes. Patients may feel anxious if they do not have extra medications for emergencies. A certain amount of medication should remain with patients; however, excessive supply must be avoided to not result in the wasting of national healthcare costs. Healthcare providers should promote patients’ understanding of the clinical and economic consequences of medication oversupply to reduce medication waste and risks of misuse.
The variable “inclusion of inpatient prescription,” which was identified in the quantitative and qualitative analyses, is potentially due to transitioning from outpatient to inpatient. In Japan, if a patient is hospitalized, medications that are supplied by outpatient prescriptions are often newly prescribed at the hospital where the patient is admitted from the following main reasons: avoidance of medication misuse and existence of a rule that medications used during hospitalization are principally prescribed at the hospital where the patient is admitted.20) If the hospitalization is scheduled in advanced, medications until the start date of hospitalization are supplied as an outpatient prescriptions. However, if a patient who has received a prescription for a long medication duration for a chronic condition is admitted through unscheduled hospitalization, the outpatient and inpatient prescriptions will overlap because the medications are supplied by inpatient prescriptions even if patients had medications from outpatient prescriptions at home.
The limitations of this study should be recognized. First, factors and causes of chronic disease medication oversupply vary depending on what is treated as the same medication (ingredients or beneficial effect classes), indicators of oversupply (MPR or excess days), and cutoff values for indicators. The difference in these definitions may have caused discrepancies in the associated factors and causes of oversupply between the present study and other studies. Most studies,1,5,7–10,18) which are conducted overseas, defined medication classification based on beneficial effect classes and employed an MPR cutoff of 1.2, which is the most commonly used cutoff value. However, the present study adopted the definition as ingredient unit to conservatively estimate oversupply and the cutoff of 30 excess days/year than the MPR. An MPR depends on the length of period A (denominator in the MPR calculation, Fig. 1); for example, an MPR of 1.2 during 1 year means 73 excess days/year (365 d × 1.2 − 365 d = 73 d).12) We consider that 73 d/year of reserve medications kept by the patients is too many, and 30 d/year of the reserve medication would be sufficient; thus, in this study, we chose 30 excess days/year as the cutoff values of oversupply. Although 30 excess days/year approximates an MPR of 1.1 ([365 d + 30 d]/365 d = 1.08), we believe that excess days are more comprehensible than MPR (see Materials and Methods). Second, medication oversupply can be overestimated for medications to be taken multiple times a day12) because oversupply is calculated based on the overlapped days of prescriptions (Fig. 1). For instance, BIGs, which are medications intended to be taken multiple times a day, are sometimes prescribed as different in Japan, i.e., two medications for morning and evening use, which lead to the overlapped days. Therefore, the result of the qualitative analysis would indicate that the variable “duplicate of the same prescription issued by a single facility on the same day” was a distinctive cause of oversupply of BIGs. However, the result of the quantitative analysis for the BIG model did not indicate that the adjusted OR of “medications to be taken multiple times a day” was significant. These results imply that other factors, such as “supplying medications earlier than scheduled,” had a greater effect on oversupply than “medications to be taken multiple times a day.” Third, this study did not perform analyses according to the degree of medication oversupply. Further studies are needed to analyze whether the factors and causes of oversupply change or remain unchanged, depending on the degree. Fourth, more medications than estimated based on excess days remain in patients’ hands if they do not take the medication as directed. However, patients’ actual medication use is unfathomable from health claims data. Finally, factors, such as financial situation and health literacy, which are not included in the claims data, were not considered in this study.
The results of this study presented that “supplying medications earlier than scheduled” was the primary factor of medication oversupply for older people taking chronic disease medications. In addition, “inclusion of inpatient prescription,” “prescribing and dispensing by multiple facilities,” and “number of concomitant ingredients” were related to medication oversupply. The factors and causes might reflect the unique features, rules, and customs of the nation’s healthcare system. Consideration of measures to reduce medication oversupply based on the present findings would help in restraining healthcare expenditures in Japan and countries with similar contexts.
We sincerely thank DeSC Healthcare, Inc., for providing the data for this study. We also thank MH and MI for their assistance with the investigation.
The authors declare no conflict of interest.
This article contains supplementary materials.
