Prescription Patterns and Trends of Antiparkinsonian Drugs in Korean Patients With Parkinson’s Disease: A Real-World Data Analysis

Prescription Patterns and Trends of Antiparkinsonian Drugs in Korean Patients With Parkinson’s Disease: A Real-World Data Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Prescription Patterns and Trends of Antiparkinsonian Drugs in Korean Patients With Parkinson’s Disease: A Real-World Data Analysis Bora Yoon, Hwa Jung Kim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5214960/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract While guidelines exist for Parkinson's disease (PD) treatment, variations in real-world patient management highlight the need for understanding prescription practices, yet real-world data (RWD) are limited. We aimed to analyze prescribing trends in patients with PD using RWD. We used the Korean National Health Insurance Service database to identify 160,476 patients with PD from 2002 to 2019. We analyzed initial drug prescriptions, dosage patterns, combination therapies, and dosage adjustments. Group-based trajectory modeling identified distinct levodopa prescription trajectories. Most patients initiated monotherapy (66.3%), with levodopa being the predominant across all ages. The dopamine agonist (DA)/levodopa ratio decreased with age. The average initial levodopa dose in the monotherapy group was 300 mg, irrespective of age. In the polytherapy group (33.7%), over 90% of patients started with levodopa or DA combinations; 73.4% started with two drugs, 21.8% with three, and 4.8% with four or more. In the initial levodopa monotherapy group, levodopa dosage increases and add-on therapies were most common within the first year. Trajectory analysis revealed four distinct levodopa prescription patterns. This RWD analysis provides valuable insights into age-dependent prescription variations and the timing of medication changes or dosage increases in PD management, aiding clinicians in making informed, patient-centered treatment decisions. Biological sciences/Neuroscience Health sciences/Medical research Health sciences/Neurology Parkinson’s disease Antiparkinsonian drug Levodopa Prescription Real-world data Figures Figure 1 Figure 2 Figure 3 Introduction Parkinson's disease (PD) is a progressive neurological disorder characterized by the degeneration of dopaminergic neurons in the brain 1 . While PD is relatively uncommon among individuals younger than 50 years, its prevalence increases with advancing age, affecting millions of individuals worldwide 2 . PD presents complex, broad-spectrum of manifestations including well-known motor symptoms, such as resting tremors, rigidity, and bradykinesia, as well as various non-motor symptoms including hyposmia, sleep disorders, cognitive impairment, and psychiatric disturbances 3 . Effective management of PD necessitates a comprehensive approach that combines both pharmacological and nonpharmacological interventions aimed at alleviating symptoms and enhancing patients' quality of life. In particular, antiparkinsonian drugs play a pivotal role in symptom management and improving the overall condition. Antiparkinsonian drugs are categorized into several classes based on their mechanisms of action 4 . These include dopamine precursors (levodopa) and agonists (dopamine agonist; DA), monoamine oxidase-B (MAO-B) inhibitors, catechol-O-monoamine transferase (COMT) inhibitors, anticholinergics, and amantadine. Given the distinctive mechanisms of action of these drug classes, diverse effects and variable responses may emerge depending on their dosages or combination. Despite the existence of evidence-based treatment guidelines for PD 5 , their lack of patient specificity, necessitates clinicians to consider multiple factors when treating patients with PD in real clinical settings. Beyond the fundamental characteristics of the patient, including age, comorbidities, and concomitant medications, careful attention is warranted for the pharmacokinetic properties and potential side effects of the prescribed antiparkinsonian drug 6 . In addition, the age at disease onset and disease severity stand out as pivotal considerations in PD. The choice of antiparkinsonian drugs is notably influenced by the age at PD diagnosis, as treatment goals and preferences could vary between younger and older patients with PD. Disease severity further guides drug dosage, with higher doses or polytherapy more likely in advanced stages. Moreover, considering clinicians must weigh potential adverse events, drug interactions, and the potential long-term side effects of drugs, the practical application of these guidelines in real-world scenarios is intricate due to individual patient profiles, including comorbidities, genetic variations, economic considerations, and cultural disparities. This interplay between guideline recommendations and the practical realities of patient care underscores the need to explore real-world data (RWD) to uncover the intricacies of treatment decisions and their broader clinical implications. In the context of RWD playing a pivotal role in bridging the gap between guideline recommendations and real-world patient care, analyzing and comprehending the prescription patterns and trends of antiparkinsonian drugs in PD by evaluating RWD such as claims data are essential for optimizing treatment outcomes. It allows clinicians to gain insights into prescription practices in routine clinical settings and provides guidance on the most effective treatment strategies for diverse groups of patients with PD. The purpose of this study is to evaluate and analyze the prescription patterns of antiparkinsonian drugs in Korean patients with PD using RWD. By examining the initial drug selection based on the age of onset of disease, the prevalence of monotherapy, combination therapy patterns, and the dosage of levodopa or DA according to the age of onset, we aim to provide a comprehensive understanding of prescription practices and enhance the precision of treatment approaches in PD management. Methods This retrospective study was approved by the Institutional Review Board (IRB) of Konyang University Hospital (IRB No. KYUH 2023-06-027). All methods were performed in accordance with the relevant guidelines and regulations. As the KNHIS dataset is subject to strict confidentiality regulations mandated by the Korean government and the data provided to researchers have already been de-identified, the IRB of Konyang University Hospital waived the requirement for written informed consent in this study. Data source The data utilized in this research were acquired from the Korean National Health Insurance Service (KNHIS), an organization that offers coverage for approximately 97% of the Korean population. The study spanned from January 1, 2002, to December 31, 2019. The database contains information on individual demographics, socioeconomic status, diagnoses, procedures, prescription records, and direct medical expenses. All diagnoses are recorded using the 10th revision of the International Classification of Diseases (ICD-10) code 7 . In South Korea, an enrollment program for rare and intractable diseases (RIDs) has been implemented to alleviate the economic burden on patients seeking treatment for RIDs, and PD is registered as a RID. When a diagnosis of PD is registered, it is assigned a unique diagnostic code (V code) in addition to the ICD-10 code (PD: V124). A qualified clinician must meet specific comprehensive diagnostic criteria to determine eligibility for the V code to ensure a high level of reliability. Study design and study population We established a retrospective cohort consisting of patients diagnosed with PD utilizing the KNHIS database. The diagnosis was based on the ICD-10 code for PD (G20) and the RID code for PD (V124), and the index date was set as the date of initial PD diagnosis. A comprehensive pool of 240,391 PD patients was initially identified. Subsequent exclusions were made based on specific criteria: patients outside the age range of 40 to 90 years (n = 4,792), patients whose first PD claim was before January 1, 2003 (n = 16,228) for a wash-out period, and those who discontinued PD claims within 90 days of the index date (n = 22,862). For our drug-focused analyses, additional exclusions were applied: patients with no recorded PD drug claims (n = 6,017), those with incomplete prescription details (n = 2,348), and those with no claims of any PD drugs within a 3-year period before and after the initial PD diagnosis (n = 13,488). Finally, to enhance the accuracy of the prescription assessment, our analyses were limited to outpatient drug prescriptions. This resulted in a final cohort of 160,476 patients who were included in the present analysis. A detailed flowchart of patient selection is shown in Fig. 1 . Clinical variables Sociodemographic data including age, sex, residential area, socioeconomic status, age at the first health insurance claim bearing a diagnosis of PD, Charlson Comorbidity Index (CCI) score 8 , and various comorbidities constituted the basic demographics and clinical characteristics. As for information on pre-existing comorbidities, the following diagnosis codes were identified within the past 1 year from the initial claims date of PD: hypertension (ICD-10; I10–13, I15), DM (ICD-10; E10–14), hyperlipidemia (ICD-10; E78), stroke (ICD-10; I60–64, G45), CAD (ICD-10; I20, I21, I22), AF (ICD-10; I48), and chronic kidney disease (ICD-10, N18, N19, Z49, Z940, Z992). We categorized antiparkinsonian drugs that can be prescribed in Korea based on their drug class or individual composition. Our taxonomy included levodopa (benserazide/levodopa, carbidopa/levodopa, carbidopa/entacapone/levodopa), (non-ergot) DA (pramipexole, ropinirole), MAO-B inhibitors (rasagiline, selegiline, safinamide), COMT inhibitors (opicapone, entacapone), anticholinergic drugs (benztropine, trihexyphenidyl, procyclidine), and amantadine. Detailed drug information (ATC code) is presented in the Supplementary Table S1 online. Patients were classified into monotherapy and polytherapy (i.e., combination therapy that uses more than one drug class) groups by the starting combination of medications used. We identified the first prescription date for the antiparkinsonian drugs. We evaluated the frequency of initial prescription of individual drugs according to the age groups. In addition, the initial mean doses of levodopa or DA during 1st trimester were investigated in patients with PD who were prescribed monotherapy according to age group. We also examined the combination profile in patients with PD on levodopa polytherapy. We split the amount of levodopa taken in the first three months into five categories: equal to or less than 150 mg, 151–300 mg, 301–450 mg, 451–600 mg, and greater than 600 mg. When the patients’ levodopa dosage increased, we referred to this as an “increased dosage”. If they were prescribed any additional antiparkinsonian drug, we referred this as an “add-on drug”. We analyzed the patterns of increased dosages or add-on drugs according to age group and by the initially prescribed levodopa dosage. Statistical analyses Categorical variables are presented as counts/frequencies (%), while continuous variables are expressed as the mean ± standard deviation (SD), where appropriate. Categorical variables were compared using the Chi-square test and continuous variables were compared using Student’s t-tests. In addition, standardized mean differences (SMDs) were calculated to determine the balance of baseline covariates between the two groups 9 . In the analysis of medication dosages, the median and interquartile range (IQR) as well as the mean ± SD are presented due to the skewed distribution. We used group-based trajectory modeling (GBTM) to identify trajectories of levodopa prescription patterns in patients with PD. GBTM is an unsupervised model-based clustering technique that estimates potential trajectory groups for similar patterns of individuals over time through maximum likelihood estimation 10 . The individual probability of belonging to each trajectory was estimated for each patient and subsequently assigned to the trajectory with the highest probability. The GBTM analysis targeted patients who had a 3-year continuous follow-up after the index date and assessed levodopa prescription patterns at quarterly intervals from the index date. We conducted trajectory models with two to five groups and assessed the best model fit using the statistical indices of the Bayesian information criterion (BIC) and Akaike information criterion (AIC) and required a minimum of 5% of the number of individuals in each trajectory 11 . GBTM was performed using the latrend package in R, version 4.0.5 (R Foundation for Statistical Computing, Vienna, Austria; www.r-project.org ). Statistical analyses were conducted using SAS version 9.4 (SAS Institute, Cary, NC, USA) and R software version 4.0.5 (R Foundation for Statistical Computing, Vienna, Austria; www.r-project.org ). A p-value of less than 0.05 was considered statistically significant for all two-sided tests. Results Baseline characteristics The characteristics of a total of 160,476 patients diagnosed with PD are presented in Table 1 . These patients were categorized into two groups: 106,417 patients (66.3%) who received initial treatment with monotherapy and 54,059 (33.7%) who received polytherapy. Patients in the polytherapy group were younger at the initial health insurance claim for a PD diagnosis (69.7 years) than those in the monotherapy group (71.2 years) (p < 0.001). However, there was no statistically significant difference in sex distribution between the two groups, with 42.1% males in both groups. The age group distribution revealed a consistent decrease in the proportion of patients starting with polytherapy as age increased (Table 1 ). An analysis of risk factors and CCI scores indicated that patients with PD in the polytherapy group exhibited lower frequencies of all risk factors and lower mean CCI scores than those in the monotherapy group (Table 1 ). Table 1 Characteristics of 160,476 patients with PD Group Monotherapy Polytherapy p SMD Number 106,417 (66.3) 54,059 (33.7) Sex, male 44,751 (42.1) 22,752 (42.1) 0.898 0.001 Age group 40–49 2,432 (58.2) 1,745 (41.8) 50–59 9,187 (59.2) 6,327 (40.8) 60–69 27,376 (63.9) 15,471 (36.1) 70–79 49,300 (67.8) 23,362 (32.2) 80–89 18,122 (71.7) 7,154 (28.3) Age at PD diagnosis 71.22 ± 8.94 69.67 ± 9.36 < 0.001 0.170 Income quintiles < 0.001 0.056 Medicaid 10,570 (9.9) 4,906 (9.1) NA 2,193 (2.1) 1,080 (2.0) Q1(lowest) 12,178 (11.4) 6,520 (12.1) Q2 9,817 (9.2) 5,242 (9.7) Q3 13,017 (12.2) 7,113 (13.2) Q4 20,385 (19.2) 10,722 (19.8) Q5(highest) 38,257 (36.0) 18,476 (34.2) Residential area < 0.001 0.025 Seoul and capital area a 44,231 (41.6) 21,867 (40.5) Metropolitan city b 19,710 (18.5) 10,179 (18.8) Rural area 42,391 (39.8) 21,953 (40.6) NA 85 (0.1) 60 (0.1) CCI 5.50 ± 2.79 5.02 ± 2.87 < 0.001 0.167 Hypertension 70,658 (66.4) 33,376 (61.7) < 0.001 0.097 DM 42,745 (40.2) 20,640 (38.2) < 0.001 0.041 Hyperlipidemia 54,539 (51.3) 26,418 (48.9) < 0.001 0.048 Stroke 34,963 (32.9) 15,916 (29.4) < 0.001 0.074 CAD 18,282 (17.2) 8,479 (15.7) < 0.001 0.040 AF 4,277 (4.0) 1,875 (3.5) < 0.001 0.029 CKD 3,282 (3.1) 1,293 (2.4) < 0.001 0.042 Data are shown as the mean ± SD or number (%). P values were obtained by t -tests for continuous variables or Chi-square tests for categorical variables. a Seoul, Incheon, and Gyeonggi-do; b Busan, Daegu, Daejeon, Gwangju, Sejong, and Ulsan. PD, Parkinson's disease; SMD, standardized mean difference; NA, not applicable, Q, Quintile; CCI, Charlson Comorbidity Index, DM, diabetes mellitus; CAD, coronary artery disease; AF, atrial fibrillation, CKD, chronic kidney disease. Prescription patterns and trends of antiparkinsonian drugs The initial drug prescriptions in the monotherapy group, which accounted for 66.3% of patients, were examined: levodopa monotherapy consistently held the highest percentage across all age groups, and its use increased in older age groups (Table 2 ). In contrast, the percentage of DA monotherapy decreased with age, which results in a gradual decrease in the DA/levodopa ratio as age increased. In the monotherapy group, aside from levodopa or DA, anticholinergics were the second most frequently prescribed drugs across all age groups, followed by amantadine and MAO-B inhibitors. Monotherapy with drugs other than levodopa was relatively more common among patients in their 40s than in other age groups. Among the 54,059 patients with PD receiving polytherapy (33.7% of patients), the primary drug was levodopa or DA, accounting for over 90% of prescriptions across all age groups, and this showed a slightly increasing trend with advancing age. Table 2 Frequency of initial prescription of individual drugs in 16,0476 patients with PD Initial drug Monotherapy (n = 106,417, 66.3%) Polytherapy (n = 54,059, 33.7%) Age group LD DA DA/LD (ratio) MAOBI COMTI AC AMT Sum Any LD or DA 40–49 (n = 4,177) 1,156 (47.5) 567 (23.3) 0.49 172 (7.1) 2 (0.1) 336 (13.8) 199 (8.2) 2,432 (100) 1,745 1,604 (91.9) 50–59 (n = 15,514) 5,151 (56.1) 2,011 (21.9) 0.39 530 (5.8) 12 (0.1) 848 (9.2) 635 (6.9) 9,187 (100) 6,327 5,926 (93.7) 60–69 (n = 42,847) 18,524 (67.7) 4,325 (15.8) 0.23 1,009 (3.7) 21 (0.1) 2,258 (8.2) 1,239 (4.5) 27,376 (100) 15,471 14,852 (96.0) 70–79 (n = 72,662) 36,805 (74.7) 6,531 (13.2) 0.18 881 (1.8) 28 (0.1) 3,525 (7.2) 1,530 (3.1) 49,300 (100) 23,362 22,846 (97.8) 80–89 (n = 25,276) 14,260 (78.7) 2,167 (12.0) 0.15 227 (1.3) 10 (0.1) 1,041 (5.7) 417 (2.3) 18,122 (100) 7,154 7,055 (98.6) Sum 75,896 15,601 2,819 73 8,008 4,020 106,417 54,059 52,283 Any means any combination, LD or DA means the combination included LD or DAs. Values are represented as number (%). LD, levodopa; DA, dopamine agonist; MAOBI, monoamine oxidase-B inhibitor; COMTI, catechol-O-methyltransferase inhibitor; AC, anticholinergic; AMT, amantadine The initial levodopa doses during the 1st trimester in patients with PD who were prescribed monotherapy averaged approximately 300 mg across all age groups, with minimal variation between age groups (Table 3 ). Table 3 Initial mean doses of levodopa during the first trimester by age group in patients with PD receiving monotherapy Levodopa monotherapy (n = 75,896) Age group Number Mean (SD) Median [Q1 – Q3] 40s 1,156 293.5 (280.7) 220.9 [82.4–413.5] 50s 5,151 307.2 (451.8) 240.7 [98.9–417.6] 60s 18,524 293.5 (265.8) 230.8 [101.1–400.5] 70s 36,805 286.4 (283.6) 230.8 [105.5–391.2] 80s 14,260 275.2 (360.3) 215.4 [102.2–369.5] Values are presented as milligrams per day. SD, standard deviation; Q1, 1st quartile; Q3, 3rd quartile The combination profiles of 47,340 patients receiving polytherapy including levodopa demonstrated that upon the initial prescription, 73.4% received two drugs, 21.8% were prescribed three drugs, and only 4.8% were prescribed four or more drugs (Table 4 ). Among those on two-drug polytherapy, DA was the most frequently added in combination with levodopa, followed by COMT inhibitors, anticholinergics, amantadine, and MAO-B inhibitors (Table 4 ). Regarding three-drug combinations, the most frequent prescription was levodopa + DA + COMT inhibitor (6.4%) (Table 4 ). Furthermore, 16.2% of patients receiving three-drug combinations included combinations of levodopa + DA with other drugs (Table 4 ). Table 4 Combination profile in 47,340 patients on levodopa polytherapy Number % Two-drug therapy (Levodopa+) 73.4 DA 15,095 31.9 MAOBI 2,842 6.0 COMTI 6,279 13.3 AC 5,877 12.4 AMT 4,643 9.8 Three-drug therapy (Levodopa+) 21.8 DA + MAOBI 1,113 2.4 DA + COMTI 3,023 6.4 DA + AC 1,509 3.2 DA + AMT 2,001 4.2 MAOBI + COMTI 271 0.6 MAOBI + AC 314 0.7 MAOBI + AMT 441 0.9 COMTI + AC 633 1.3 COMTI + AMT 518 1.1 AC + AMT 479 1.0 Four or more drug therapy (Levodopa+) 4.8 DA + MAOBI + COMTI 252 0.5 DA + MAOBI + AC 117 0.2 DA + MAOBI + AMT 276 0.6 DA + COMTI + AC 421 0.9 DA + COMTI + AMT 594 1.3 DA + AC + AMT 198 0.4 MAOBI + COMTI + AC 32 0.1 MAOBI + COMTI + AMT 67 0.1 MAOBI + AC + AMT 40 0.1 COMTI + AC + AMT 79 0.2 DA + MAOBI + COMTI + AC 23 0.0 DA + MAOBI + COMTI + AMT 82 0.2 DA + MAOBI + AC + AMT 41 0.1 DA + COMTI + AC + AMT 69 0.1 MAOBI + COMTI + AC + AMT 3 0.0 DA + MAOBI + COMTI + AC + AMT 8 0.0 DA, dopamine agonist; MAOBI, monoamine oxidase-B inhibitor; COMTI, catechol-O-methyltransferase inhibitor; AC, anticholinergic; AMT, amantadine The patterns of levodopa dosage adjustments, including dosage increases and the addition of other drugs, were categorized by age group (Fig. 2 A) and initial prescription dosage of levodopa (Fig. 2 B); over the course of the first three years of observation, irrespective of age group, consistent trends emerged over time. The percentage of patients experiencing an increase in levodopa dosage or the addition of other drugs steadily increased throughout the observation period, with the most significant increase occurring within the first four quarters. Similarly, we observed a steady increase in the percentage of patients requiring an adjustment in levodopa dosage or the addition of other drugs, regardless of their initial levodopa dose. The most prominent changes occurred within the first four quarters (Fig. 2 A, B). Trends of levodopa prescription among patients with monotherapy by trajectory modeling In 52,919 individuals undergoing levodopa monotherapy with a complete 3-year follow-up, four distinct trajectories in levodopa prescription patterns emerged. These trajectories are as follows: Pattern A, characterized by a high starting dose with continuous increase and a slight late decrease (21%); Pattern B, exhibiting a high starting dose with an early peak followed by continuous decrease (11%); Pattern C, featuring a moderate starting dose with delayed increase and reaching a late maximum (28%); Pattern D, marked by a moderate starting dose with an early decrease (40%) (Fig. 3 A). Upon application of trajectory models based on age groups, similar patterns were observed across all age groups (Fig. 3 B-F). Discussion We have elucidated various aspects of antiparkinsonian drug prescription utilizing RWD: initial prescription preferences, dosage patterns, trends in combination therapies, and the propensity for dosage escalation or add-on therapies in Korean patients with PD. A high proportion of patients, approximately 66%, initiated treatment with monotherapy, which suggests that the disease severity may have been relatively mild at the time of the initial diagnosis or that clinicians may have been concerned about potential drug interactions or side effects associated with polytherapy, which influenced their choice of medication. When comparing the prescription patterns analyzed in Japan and China 12 – 15 , countries with a similar East Asian racial background, certain similarities and differences emerged. Consistent with our findings, levodopa was the most frequently prescribed drug, followed by non-ergot DAs 12 , 13 . Moreover, the trend of a higher proportion of levodopa prescriptions with increasing age, as observed in our Korean data, was also evident in these countries. The consistent preference for levodopa across all age groups underscores its enduring efficacy as the cornerstone of PD treatment. However, there were differences in the levodopa prescription rates. In Korea, over 50% of patients in all age groups except those in their 40s received levodopa prescriptions, with over 70% of patients aged 70 years and older receiving levodopa prescriptions. In contrast, the Japanese results showed lower levodopa prescription rates across all age groups than the Korean results, and even in patients with PD aged 70 years and older, the prescription rate did not exceed 60% 12,13 . In a Chinese RWD analysis focusing on patients with early-onset PD (EOPD) aged 50 years and younger, the most commonly prescribed medication was levodopa, accounting for 36% of prescriptions, followed by pramipexole, a DA, prescribed at 23%, resulting in a ratio of approximately 0.6 between these two medications. This pattern aligns with our findings: levodopa was also the most frequently prescribed drug; however, the relative prescription rate of DAs was higher in China than in Korea 14 . In our results, the proportion of patients receiving monotherapy was 66.3% across all ages, but in patients 50 years and younger, it was approximately 59%. This trend was similar, and the levodopa prescription rate in our study averaged approximately 33.5%, which closely resembles the rate of 32.0% observed in the Chinese data 14 . However, in Japanese claims data focusing on patients with young-onset PD aged 21 to 50 years, DAs were the most commonly prescribed drugs at 49.2%, which differs from our findings 15 . This difference may be due to the age range of our patient group, which started from the age of 40 years. DAs have demonstrated efficacy in delaying the introduction of levodopa therapy and the risk of motor complications and have thus become preferable drugs for the treatment of younger patients 16 . When dopaminergic therapy is necessary, DAs are usually initiated before levodopa in EOPD because they delay the onset of dyskinesia compared with levodopa 17 , 18 . Patients with EOPD are more likely to develop motor fluctuations and dyskinesia early in the course of levodopa treatment, which leads to some patients and clinicians to be hesitant to initiate levodopa therapy even though these patients experience troublesome symptoms 17 , 18 . This is consistent with our findings that a higher prevalence of DA monotherapy was observed in younger patients and that the ratio of DA monotherapy to levodopa monotherapy decreased with age, decreasing by approximately half in patients in their 40s. However, even in their 40s and 50s, the most frequently prescribed drug was levodopa, which could be related to differences in the fundamental therapeutic effects on the symptoms of PD. A more recent multicenter double-blind placebo-controlled delayed-start trial using carbidopa/levodopa in early PD showed no significant change in the rate of progression between early- or delayed-start groups, suggesting that levodopa does not have disease-modifying effect 19 . A 9-month study, called the “earlier versus later levodopa” trial found no evidence of levodopa toxicity; however, 16.5% of the patients in the 600-mg group developed dyskinesia 20 . Some argue that concerns about side effects, such as dyskinesia, have contributed to the insufficient utilization of levodopa therapy 21 , reflecting a slight shift in the perception of prescription patterns. The higher prescription rate of levodopa in our data compared to other Asian countries may be related to the shifts in perception, as our dataset contains data that is at least several years more recent. In the monotherapy subgroups, the initial levodopa dosage showed minimal variance regardless of age. Anticholinergics, excluding levodopa and DAs, were the next most frequently prescribed antiparkinsonian medication across all age groups, which is likely due to their efficacy against resting tremors, one of the main symptoms of PD 22 . Although MAO-B inhibitors are one treatment option for early-stage PD, the utilization of MAO-B inhibitors as initial monotherapy was relatively infrequent in Korea 5 . This finding could be attributed to the trend of patients seeking medical attention when they were already experiencing discomfort form motor symptoms or a decreased ability to perform activities of daily living as they had before. Consequently, the consideration of MAO-B inhibitor monotherapy alone might not align with the patients’ clinical needs at the time of diagnosis or might not be adequate to provide sufficient symptom relief. Given that patients in the polytherapy group were diagnosed with PD at a younger age than those in the monotherapy group and demonstrated fewer concurrent comorbid conditions, they may have been selected for polytherapy due to the lower potential risks associated with drug adverse events and interactions. Polytherapy predominantly consisted of combinations involving levodopa or DAs, exceeding 90% across all age groups. In terms of combination profiles, dual-drug therapy constituted 73.4%, with the most frequent combinations being levodopa + DAs, followed by levodopa + COMT inhibitors, and levodopa + anticholinergic drugs. levodopa + COMT inhibitor combinations are known to mitigate off-symptom occurrence and on-time dyskinesia by prolonging the efficacy of levodopa 5 , 23 . Conversely, the combination of levodopa + anticholinergics is favored for patients with PD with tremors. While the utilization of three-drug combinations was relatively limited at 21.8%, combinations featuring DAs accounted for 16.2% within this subgroup. Other drug combinations were minimally prescribed and likely adhered to general guideline recommendations. Upon examining the patterns of levodopa dosage increases or the addition of other drugs within the levodopa monotherapy group, we observed consistent trends over time, with prominent drug adjustments occurring in the first year, irrespective of patient age or the initial levodopa dosage. This contrasts with the general expectation for patients with PD to experience a “honeymoon period” characterized by a favorable drug response in the first year after treatment initiation. Our findings suggest that there might be few patients who experience a distinct honeymoon period in real clinical practice. However, clinicians in Korea could have adopted treatment approaches with a stronger emphasis on early symptom alleviation. The progressive increase in levodopa dosage and augmentation of therapy over time correspond to the gradual deterioration inherent to PD, which is a degenerative disorder. Our trajectory analysis revealed diverse medication management patterns, emphasizing the imperative of tailored treatment strategies aligned with individual patient trajectories. The identification of these four trajectories in levodopa prescription patterns highlights the nuanced and dynamic nature of medication management in PD. From these trajectories, we found various patterns based on the combination of the starting dose and compliance. Approximately 49% of patients with PD exhibited dose escalation, while the remaining 51% had dose reduction. The increase in dosage could either represent a snapshot of symptom alleviation over time or diverse trends of continuous adjustments, emphasizing the challenges in achieving optimal therapeutic responses. These findings underscore the complexity and intricate nature of long-term medication management in PD and call for further investigation into individualized approaches to enhance treatment efficacy. This study has several limitations. First, as with any retrospective analysis, there might have been limitations in the accuracy of recorded diagnoses and prescription details that could have led to misclassification or underreporting of certain variables. Second, while the KNHIS database provides a large and diverse dataset, it lacks certain clinical information that could influence prescription decisions, such as disease severity, treatment response, genetic factors, and specific clinical characteristics of individual patients. The absence of these factors might have impacted the precision and generalizability of the findings. Third, due to the observational nature of the study, causal relationships could not be definitely established. Moreover, the retrospective design limited the ability to establish temporal relationships between factors. The study design did not allow for control of potential confounding variables, which may have introduced unmeasured factors that influence prescription decisions and outcomes. Fourth, the study focused on the Korean population, and the observed prescription patterns might not be directly applicable to other populations with different healthcare systems, cultural factors, or patient characteristics. Finally, while the study evaluated prescription practices, it did not address patient preferences, treatment adherence, or physician rationale behind prescription decisions. These factors could play a substantial role in real-world treatment patterns and should be considered when interpreting the study findings. Despite these limitations, this study has a significant advantage in providing a practical and comprehensive analysis of drug prescription patterns in Korean patients with PD through RWD analysis. Our findings demonstrate that RWD analysis can bridge the gap between guideline recommendations and the complexities of actual patient care in clinical practice. Conclusion Our RWD analysis provides specific insights into how prescription patterns differ with age and when mediation adjustments or dosage increases typically occur in real practice. These insights may enable us to refine and optimize treatment approaches. Ultimately, our study can give clinicians the information needed to make informed, patient-centered decisions for managing patients with PD. Declarations Competing interests The authors declare no competing interests. Supplementary information The online version contains supplementary Table S1 . Funding The authors have no funding to report. Author Contribution YB and KHJ designed and conceptualized the study, analyzed and interpreted the data, reviewed all the drafts, and approved the final manuscript. KHJ performed statistical analysis. BY wrote the first draft of the manuscript. Acknowledgments The study was initiated at the Konyang University Hospital, and the author gratefully acknowledges the crucial role of the IRB of Konyang University Hospital in obtaining ethical approval for the study. Data Availability The data that support the findings of this study are available from the National Institutes of Health Stroke Scale (NIHSS), but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the corresponding authors upon reasonable request and with permission of (NIHSS). References Poewe, W. et al. Parkinson disease. Nat. Rev. Dis. Primers . 3 , 17013 (2017). Collaborators., G. P. S. D. Global, regional, and national burden of Parkinson's disease, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 17 , 939–953 (2018). Schapira, A. H. V., Chaudhuri, K. R. & Jenner, P. Non-motor features of Parkinson disease. Nat. Rev. Neurosci. 18 , 435–450 (2017). Mizuno, Y. in Definition and Classification of Parkinsonian Drugs in NeuroPsychopharmacotherapy . 2823–2852 (eds Riederer, P., Laux, G., Nagatsu, T., Le, W. & Riederer, C.) (Springer International Publishing, 2022). Fox, S. H. et al. International Parkinson and movement disorder society evidence-based medicine review: Update on treatments for the motor symptoms of Parkinson's disease. Mov. Disord . 33 , 1248–1266 (2018). Jankovic, J. & Tan, E. K. Parkinson's disease: etiopathogenesis and treatment. J. Neurol. Neurosurg. Psychiatry . 91 , 795–808 (2020). Quan, H. et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med. Care . 43 , 1130–1139 (2005). Charlson, M. E., Pompei, P., Ales, K. L. & MacKenzie, C. R. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J. Chronic Dis. 40 , 373–383 (1987). Austin, P. C. Using the Standardized Difference to Compare the Prevalence of a Binary Variable Between Two Groups in Observational Research. Commun. Stat. - Simul. Comput. 38 , 1228–1234 (2009). Nagin, D. S. & Odgers, C. L. Group-based trajectory modeling in clinical research. Annu. Rev. Clin. Psychol. 6 , 109–138 (2010). Nielsen, J. D. et al. Group-based Criminal Trajectory Analysis Using Cross-validation Criteria. Commun. Stat. - Theory Methods . 43 , 4337–4356 (2014). Suzuki, M., Arai, M., Hayashi, A. & Ogino, M. Prescription pattern of anti-Parkinson's disease drugs in Japan based on a nationwide medical claims database. eNeurologicalSci . 20 , 100257 (2020). Nakaoka, S. et al. Prescribing pattern of anti-Parkinson drugs in Japan: a trend analysis from 2005 to 2010. PLoS One . 9 , e99021 (2014). Liu, X. Q. et al. Real-World Prescription Patterns For Patients With Young-Onset Parkinson's Disease in China: A Trend Analysis From 2014 to 2019. Front. Pharmacol. 13 , 858139 (2022). Kasamo, S. et al. Real-world pharmacological treatment patterns of patients with young-onset Parkinson's disease in Japan: a medical claims database analysis. J. Neurol. 266 , 1944–1952 (2019). Cerri, S. & Blandini, F. An update on the use of non-ergot dopamine agonists for the treatment of Parkinson's disease. Expert Opin. Pharmacother . 21 , 2279–2291 (2020). Rascol, O. et al. A five-year study of the incidence of dyskinesia in patients with early Parkinson's disease who were treated with ropinirole or levodopa. N Engl. J. Med. 342 , 1484–1491 (2000). Mehanna, R. & Jankovic, J. Young-onset Parkinson's disease: Its unique features and their impact on quality of life. Parkinsonism Relat. Disord . 65 , 39–48 (2019). Verschuur, C. V. M. et al. Randomized Delayed-Start Trial of Levodopa in Parkinson's Disease. N Engl. J. Med. 380 , 315–324 (2019). Fahn, S. et al. Levodopa and the progression of Parkinson's disease. N Engl. J. Med. 351 , 2498–2508 (2004). Lang, A. E. & Marras, C. Initiating dopaminergic treatment in Parkinson's disease. Lancet . 384 , 1164–1166 (2014). Sy, M. A. C. & Fernandez, H. H. Pharmacological Treatment of Early Motor Manifestations of Parkinson Disease (PD). Neurotherapeutics . 17 , 1331–1338 (2020). Horstink, M. et al. Review of the therapeutic management of Parkinson's disease. Report of a joint task force of the European Federation of Neurological Societies (EFNS) and the Movement Disorder Society-European Section (MDS-ES). Part II: late (complicated) Parkinson's disease. Eur. J. Neurol. 13 , 1186–1202 (2006). Additional Declarations No competing interests reported. Supplementary Files SupplementarymaterialsTableS1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5214960","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":374650328,"identity":"e3877234-3c3b-439b-8c73-7e766289ff24","order_by":0,"name":"Bora Yoon","email":"","orcid":"","institution":"The Catholic University of Korea","correspondingAuthor":false,"prefix":"","firstName":"Bora","middleName":"","lastName":"Yoon","suffix":""},{"id":374650330,"identity":"bbf3de93-bed5-48a3-a6fd-23068c1b66ce","order_by":1,"name":"Hwa Jung Kim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIie3PMUvDQBTA8Xc8uCynfgGpXyFFOBFC+kFcLgTqJDh2KHhFqEs+QL6Eq/MdD3SRZg0o2CA4Z+zY67WLQ9K6Fbz/Eni8X+4OIBQ6wmLAmQEwcBoBIIjEzdhm0keY9oSjJ+MN0X8hQH7aS66imTYtfA44osH78yp9fiL3k2ly00WuC6ttCT+XHLnCUnzkL++ZI6/jO911sTrTJICyOYoYhSPSOMI0dZOvxpOHHVnksmr2kJp5oviWmFTW+05xN7dlTMO5ewsJkStZu1NU31veyC7bCV2cRY/2+6RIR7K6bZbtNOkkO7j9GFZA5jdV7/qvVjA6fDkUCoX+S2vLz2HMJmE29AAAAABJRU5ErkJggg==","orcid":"","institution":"Asan Medical Center, Ulsan University College of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Hwa","middleName":"Jung","lastName":"Kim","suffix":""}],"badges":[],"createdAt":"2024-10-07 02:53:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5214960/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5214960/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69356447,"identity":"37e3f20b-6d88-4f2c-9fa3-96ef022fedee","added_by":"auto","created_at":"2024-11-19 13:50:15","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":505762,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart of the study population.\u003c/strong\u003e PD, Parkinson’s disease; ICD-10\u003cem\u003e,\u003c/em\u003e International Classification of Diseases, 10\u003csup\u003eth\u003c/sup\u003e revision; RID, rare and intractable disease; KNHIS, Korean National Health Insurance Service.\u003c/p\u003e","description":"","filename":"Figure1600dpi.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5214960/v1/b100e058e9c2dc5ae9a105b2.jpg"},{"id":69356443,"identity":"3949d5e4-77e6-4586-8506-acab95e97f31","added_by":"auto","created_at":"2024-11-19 13:50:15","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":382715,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePatterns of increased levodopa dosage or addition of other drugs categorized by age group (A) and initially prescribed dosage of levodopa (B) during the first three years of the observation period. \u003c/strong\u003eQ denotes a quarter. In panel A, consistent trends are observed over time, irrespective of age group, with the percentage of patients experiencing an increase in levodopa dosage or the addition of other drugs steadily increasing throughout the observation period. Panel B similarly reveals a steady increase in the percentage of patients requiring an increase in levodopa dosage or the addition of other drugs, regardless of their initial levodopa dose. The slope of the percentage is steeper during the first four quarters in both panel A and B.\u003c/p\u003e","description":"","filename":"Figure2600dpi.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5214960/v1/f031c7067165a827da0db486.jpg"},{"id":69356445,"identity":"8ca38d85-9292-4787-b44e-99f022a1183e","added_by":"auto","created_at":"2024-11-19 13:50:15","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":843432,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGroup-based trajectory modeling.\u003c/strong\u003e These plots illustrate the longitudinal trends in levodopa prescription patterns over a 3-year period, spanning 12 quarters (A), as well as the variations in levodopa prescription patterns across different age groups (B-F). The numbers on the X-axis denote quarters, with four quarters corresponding to one year, while the Y-axis represents the mean dose of levodopa in gram per day. The distinct trajectories have been identified as follows: Group A (red), exhibiting a high initial dose with continuous increase followed by a slight late decrease; Group B (blue), displaying a high initial dose with an early peak and subsequent continuous decrease; Group C (green), featuring a moderate starting dose with delayed increase and reaching a late maximum; and Group D (yellow), characterized by a moderate starting dose with an early decrease.\u003c/p\u003e","description":"","filename":"Figure3600dpi.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5214960/v1/dfc56610825c1e4dc28755ba.jpg"},{"id":74986504,"identity":"2632c717-7312-4f34-b760-78fde8d7e5d8","added_by":"auto","created_at":"2025-01-29 06:16:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2696135,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5214960/v1/ace07784-2b8b-4658-aa9f-9fe3468db996.pdf"},{"id":69356446,"identity":"7e964dca-b4b6-42e4-a74a-0ed376377f5d","added_by":"auto","created_at":"2024-11-19 13:50:15","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20671,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarymaterialsTableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5214960/v1/280ef7641537c0e1ea9b44f7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prescription Patterns and Trends of Antiparkinsonian Drugs in Korean Patients With Parkinson’s Disease: A Real-World Data Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eParkinson's disease (PD) is a progressive neurological disorder characterized by the degeneration of dopaminergic neurons in the brain\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. While PD is relatively uncommon among individuals younger than 50 years, its prevalence increases with advancing age, affecting millions of individuals worldwide\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. PD presents complex, broad-spectrum of manifestations including well-known motor symptoms, such as resting tremors, rigidity, and bradykinesia, as well as various non-motor symptoms including hyposmia, sleep disorders, cognitive impairment, and psychiatric disturbances\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Effective management of PD necessitates a comprehensive approach that combines both pharmacological and nonpharmacological interventions aimed at alleviating symptoms and enhancing patients' quality of life. In particular, antiparkinsonian drugs play a pivotal role in symptom management and improving the overall condition.\u003c/p\u003e \u003cp\u003eAntiparkinsonian drugs are categorized into several classes based on their mechanisms of action\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. These include dopamine precursors (levodopa) and agonists (dopamine agonist; DA), monoamine oxidase-B (MAO-B) inhibitors, catechol-O-monoamine transferase (COMT) inhibitors, anticholinergics, and amantadine. Given the distinctive mechanisms of action of these drug classes, diverse effects and variable responses may emerge depending on their dosages or combination.\u003c/p\u003e \u003cp\u003eDespite the existence of evidence-based treatment guidelines for PD\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, their lack of patient specificity, necessitates clinicians to consider multiple factors when treating patients with PD in real clinical settings. Beyond the fundamental characteristics of the patient, including age, comorbidities, and concomitant medications, careful attention is warranted for the pharmacokinetic properties and potential side effects of the prescribed antiparkinsonian drug\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In addition, the age at disease onset and disease severity stand out as pivotal considerations in PD. The choice of antiparkinsonian drugs is notably influenced by the age at PD diagnosis, as treatment goals and preferences could vary between younger and older patients with PD. Disease severity further guides drug dosage, with higher doses or polytherapy more likely in advanced stages. Moreover, considering clinicians must weigh potential adverse events, drug interactions, and the potential long-term side effects of drugs, the practical application of these guidelines in real-world scenarios is intricate due to individual patient profiles, including comorbidities, genetic variations, economic considerations, and cultural disparities. This interplay between guideline recommendations and the practical realities of patient care underscores the need to explore real-world data (RWD) to uncover the intricacies of treatment decisions and their broader clinical implications. In the context of RWD playing a pivotal role in bridging the gap between guideline recommendations and real-world patient care, analyzing and comprehending the prescription patterns and trends of antiparkinsonian drugs in PD by evaluating RWD such as claims data are essential for optimizing treatment outcomes. It allows clinicians to gain insights into prescription practices in routine clinical settings and provides guidance on the most effective treatment strategies for diverse groups of patients with PD.\u003c/p\u003e \u003cp\u003eThe purpose of this study is to evaluate and analyze the prescription patterns of antiparkinsonian drugs in Korean patients with PD using RWD. By examining the initial drug selection based on the age of onset of disease, the prevalence of monotherapy, combination therapy patterns, and the dosage of levodopa or DA according to the age of onset, we aim to provide a comprehensive understanding of prescription practices and enhance the precision of treatment approaches in PD management.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis retrospective study was approved by the Institutional Review Board (IRB) of Konyang University Hospital (IRB No. KYUH 2023-06-027). All methods were performed in accordance with the relevant guidelines and regulations. As the KNHIS dataset is subject to strict confidentiality regulations mandated by the Korean government and the data provided to researchers have already been de-identified, the IRB of Konyang University Hospital waived the requirement for written informed consent in this study.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eThe data utilized in this research were acquired from the Korean National Health Insurance Service (KNHIS), an organization that offers coverage for approximately 97% of the Korean population. The study spanned from January 1, 2002, to December 31, 2019. The database contains information on individual demographics, socioeconomic status, diagnoses, procedures, prescription records, and direct medical expenses. All diagnoses are recorded using the 10th revision of the International Classification of Diseases (ICD-10) code\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In South Korea, an enrollment program for rare and intractable diseases (RIDs) has been implemented to alleviate the economic burden on patients seeking treatment for RIDs, and PD is registered as a RID. When a diagnosis of PD is registered, it is assigned a unique diagnostic code (V code) in addition to the ICD-10 code (PD: V124). A qualified clinician must meet specific comprehensive diagnostic criteria to determine eligibility for the V code to ensure a high level of reliability.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy design and study population\u003c/h3\u003e\n\u003cp\u003eWe established a retrospective cohort consisting of patients diagnosed with PD utilizing the KNHIS database. The diagnosis was based on the ICD-10 code for PD (G20) and the RID code for PD (V124), and the index date was set as the date of initial PD diagnosis. A comprehensive pool of 240,391 PD patients was initially identified. Subsequent exclusions were made based on specific criteria: patients outside the age range of 40 to 90 years (n\u0026thinsp;=\u0026thinsp;4,792), patients whose first PD claim was before January 1, 2003 (n\u0026thinsp;=\u0026thinsp;16,228) for a wash-out period, and those who discontinued PD claims within 90 days of the index date (n\u0026thinsp;=\u0026thinsp;22,862). For our drug-focused analyses, additional exclusions were applied: patients with no recorded PD drug claims (n\u0026thinsp;=\u0026thinsp;6,017), those with incomplete prescription details (n\u0026thinsp;=\u0026thinsp;2,348), and those with no claims of any PD drugs within a 3-year period before and after the initial PD diagnosis (n\u0026thinsp;=\u0026thinsp;13,488). Finally, to enhance the accuracy of the prescription assessment, our analyses were limited to outpatient drug prescriptions. This resulted in a final cohort of 160,476 patients who were included in the present analysis. A detailed flowchart of patient selection is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eClinical variables\u003c/h3\u003e\n\u003cp\u003eSociodemographic data including age, sex, residential area, socioeconomic status, age at the first health insurance claim bearing a diagnosis of PD, Charlson Comorbidity Index (CCI) score\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, and various comorbidities constituted the basic demographics and clinical characteristics. As for information on pre-existing comorbidities, the following diagnosis codes were identified within the past 1 year from the initial claims date of PD: hypertension (ICD-10; I10\u0026ndash;13, I15), DM (ICD-10; E10\u0026ndash;14), hyperlipidemia (ICD-10; E78), stroke (ICD-10; I60\u0026ndash;64, G45), CAD (ICD-10; I20, I21, I22), AF (ICD-10; I48), and chronic kidney disease (ICD-10, N18, N19, Z49, Z940, Z992).\u003c/p\u003e \u003cp\u003eWe categorized antiparkinsonian drugs that can be prescribed in Korea based on their drug class or individual composition. Our taxonomy included levodopa (benserazide/levodopa, carbidopa/levodopa, carbidopa/entacapone/levodopa), (non-ergot) DA (pramipexole, ropinirole), MAO-B inhibitors (rasagiline, selegiline, safinamide), COMT inhibitors (opicapone, entacapone), anticholinergic drugs (benztropine, trihexyphenidyl, procyclidine), and amantadine. Detailed drug information (ATC code) is presented in the Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e online. Patients were classified into monotherapy and polytherapy (i.e., combination therapy that uses more than one drug class) groups by the starting combination of medications used.\u003c/p\u003e \u003cp\u003eWe identified the first prescription date for the antiparkinsonian drugs. We evaluated the frequency of initial prescription of individual drugs according to the age groups. In addition, the initial mean doses of levodopa or DA during 1st trimester were investigated in patients with PD who were prescribed monotherapy according to age group. We also examined the combination profile in patients with PD on levodopa polytherapy. We split the amount of levodopa taken in the first three months into five categories: equal to or less than 150 mg, 151\u0026ndash;300 mg, 301\u0026ndash;450 mg, 451\u0026ndash;600 mg, and greater than 600 mg. When the patients\u0026rsquo; levodopa dosage increased, we referred to this as an \u0026ldquo;increased dosage\u0026rdquo;. If they were prescribed any additional antiparkinsonian drug, we referred this as an \u0026ldquo;add-on drug\u0026rdquo;. We analyzed the patterns of increased dosages or add-on drugs according to age group and by the initially prescribed levodopa dosage.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eCategorical variables are presented as counts/frequencies (%), while continuous variables are expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), where appropriate. Categorical variables were compared using the Chi-square test and continuous variables were compared using Student\u0026rsquo;s t-tests. In addition, standardized mean differences (SMDs) were calculated to determine the balance of baseline covariates between the two groups\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. In the analysis of medication dosages, the median and interquartile range (IQR) as well as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD are presented due to the skewed distribution.\u003c/p\u003e \u003cp\u003eWe used group-based trajectory modeling (GBTM) to identify trajectories of levodopa prescription patterns in patients with PD. GBTM is an unsupervised model-based clustering technique that estimates potential trajectory groups for similar patterns of individuals over time through maximum likelihood estimation\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The individual probability of belonging to each trajectory was estimated for each patient and subsequently assigned to the trajectory with the highest probability. The GBTM analysis targeted patients who had a 3-year continuous follow-up after the index date and assessed levodopa prescription patterns at quarterly intervals from the index date. We conducted trajectory models with two to five groups and assessed the best model fit using the statistical indices of the Bayesian information criterion (BIC) and Akaike information criterion (AIC) and required a minimum of 5% of the number of individuals in each trajectory\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. GBTM was performed using the latrend package in R, version 4.0.5 (R Foundation for Statistical Computing, Vienna, Austria; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.r-project.org\u003c/span\u003e\u003cspan address=\"http://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStatistical analyses were conducted using SAS version 9.4 (SAS Institute, Cary, NC, USA) and R software version 4.0.5 (R Foundation for Statistical Computing, Vienna, Austria; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.r-project.org\u003c/span\u003e\u003cspan address=\"http://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A p-value of less than 0.05 was considered statistically significant for all two-sided tests.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eThe characteristics of a total of 160,476 patients diagnosed with PD are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. These patients were categorized into two groups: 106,417 patients (66.3%) who received initial treatment with monotherapy and 54,059 (33.7%) who received polytherapy. Patients in the polytherapy group were younger at the initial health insurance claim for a PD diagnosis (69.7 years) than those in the monotherapy group (71.2 years) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, there was no statistically significant difference in sex distribution between the two groups, with 42.1% males in both groups. The age group distribution revealed a consistent decrease in the proportion of patients starting with polytherapy as age increased (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). An analysis of risk factors and CCI scores indicated that patients with PD in the polytherapy group exhibited lower frequencies of all risk factors and lower mean CCI scores than those in the monotherapy group (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of 160,476 patients with PD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonotherapy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePolytherapy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSMD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106,417 (66.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54,059 (33.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44,751 (42.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22,752 (42.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,432 (58.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,745 (41.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9,187 (59.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,327 (40.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27,376 (63.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15,471 (36.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49,300 (67.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23,362 (32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80\u0026ndash;89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18,122 (71.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,154 (28.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at PD diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.22\u0026thinsp;\u0026plusmn;\u0026thinsp;8.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.67\u0026thinsp;\u0026plusmn;\u0026thinsp;9.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome quintiles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicaid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10,570 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,906 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,193 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,080 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1(lowest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12,178 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,520 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9,817 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,242 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,017 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,113 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20,385 (19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,722 (19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ5(highest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38,257 (36.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18,476 (34.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidential area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeoul and capital area\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44,231 (41.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21,867 (40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetropolitan city\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19,710 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,179 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42,391 (39.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21,953 (40.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.50\u0026thinsp;\u0026plusmn;\u0026thinsp;2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.02\u0026thinsp;\u0026plusmn;\u0026thinsp;2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70,658 (66.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33,376 (61.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42,745 (40.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20,640 (38.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54,539 (51.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26,418 (48.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34,963 (32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15,916 (29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18,282 (17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,479 (15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,277 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,875 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,282 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,293 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eData are shown as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or number (%). \u003cem\u003eP\u003c/em\u003e values were obtained by \u003cem\u003et\u003c/em\u003e-tests for continuous variables or Chi-square tests for categorical variables.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eSeoul, Incheon, and Gyeonggi-do; \u003csup\u003eb\u003c/sup\u003eBusan, Daegu, Daejeon, Gwangju, Sejong, and Ulsan.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003ePD, Parkinson's disease; SMD, standardized mean difference; NA, not applicable, Q, Quintile; CCI, Charlson Comorbidity Index, DM, diabetes mellitus; CAD, coronary artery disease; AF, atrial fibrillation, CKD, chronic kidney disease.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePrescription patterns and trends of antiparkinsonian drugs\u003c/h3\u003e\n\u003cp\u003eThe initial drug prescriptions in the monotherapy group, which accounted for 66.3% of patients, were examined: levodopa monotherapy consistently held the highest percentage across all age groups, and its use increased in older age groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In contrast, the percentage of DA monotherapy decreased with age, which results in a gradual decrease in the DA/levodopa ratio as age increased. In the monotherapy group, aside from levodopa or DA, anticholinergics were the second most frequently prescribed drugs across all age groups, followed by amantadine and MAO-B inhibitors. Monotherapy with drugs other than levodopa was relatively more common among patients in their 40s than in other age groups. Among the 54,059 patients with PD receiving polytherapy (33.7% of patients), the primary drug was levodopa or DA, accounting for over 90% of prescriptions across all age groups, and this showed a slightly increasing trend with advancing age.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFrequency of initial prescription of individual drugs in 16,0476 patients with PD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInitial drug\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e \u003cp\u003eMonotherapy (n\u0026thinsp;=\u0026thinsp;106,417, 66.3%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003ePolytherapy\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;54,059, 33.7%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDA/LD\u003c/p\u003e \u003cp\u003e(ratio)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMAOBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCOMTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAny\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eLD or DA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;4,177)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,156 (47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e567 (23.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e172 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e336 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e199 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2,432 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1,745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1,604 (91.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;15,514)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,151 (56.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,011 (21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e530 (5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e848 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e635 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9,187 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6,327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5,926 (93.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;69\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;42,847)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18,524 (67.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,325 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,009 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,258 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,239 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e27,376 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15,471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e14,852 (96.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;79\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;72,662)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36,805 (74.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,531 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e881 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3,525 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,530 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e49,300 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23,362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e22,846 (97.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80\u0026ndash;89\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;25,276)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14,260 (78.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,167 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e227 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,041 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e417 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18,122 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7,154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7,055 (98.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75,896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15,601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8,008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4,020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e106,417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e54,059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e52,283\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003eAny means any combination, LD or DA means the combination included LD or DAs. Values are represented as number (%).\u003c/p\u003e \u003cp\u003eLD, levodopa; DA, dopamine agonist; MAOBI, monoamine oxidase-B inhibitor; COMTI, catechol-O-methyltransferase inhibitor; AC, anticholinergic; AMT, amantadine\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe initial levodopa doses during the 1st trimester in patients with PD who were prescribed monotherapy averaged approximately 300 mg across all age groups, with minimal variation between age groups (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInitial mean doses of levodopa during the first trimester by age group in patients with PD receiving monotherapy\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eLevodopa monotherapy (n\u0026thinsp;=\u0026thinsp;75,896)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian [Q1 \u0026ndash; Q3]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e293.5 (280.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e220.9 [82.4\u0026ndash;413.5]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e307.2 (451.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e240.7 [98.9\u0026ndash;417.6]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18,524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e293.5 (265.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e230.8 [101.1\u0026ndash;400.5]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36,805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e286.4 (283.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e230.8 [105.5\u0026ndash;391.2]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14,260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e275.2 (360.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e215.4 [102.2\u0026ndash;369.5]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eValues are presented as milligrams per day.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eSD, standard deviation; Q1, 1st quartile; Q3, 3rd quartile\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe combination profiles of 47,340 patients receiving polytherapy including levodopa demonstrated that upon the initial prescription, 73.4% received two drugs, 21.8% were prescribed three drugs, and only 4.8% were prescribed four or more drugs (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Among those on two-drug polytherapy, DA was the most frequently added in combination with levodopa, followed by COMT inhibitors, anticholinergics, amantadine, and MAO-B inhibitors (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Regarding three-drug combinations, the most frequent prescription was levodopa\u0026thinsp;+\u0026thinsp;DA\u0026thinsp;+\u0026thinsp;COMT inhibitor (6.4%) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Furthermore, 16.2% of patients receiving three-drug combinations included combinations of levodopa\u0026thinsp;+\u0026thinsp;DA with other drugs (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCombination profile in 47,340 patients on levodopa polytherapy\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTwo-drug therapy (Levodopa+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15,095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAOBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOMTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThree-drug therapy (Levodopa+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDA\u0026thinsp;+\u0026thinsp;MAOBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDA\u0026thinsp;+\u0026thinsp;COMTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDA\u0026thinsp;+\u0026thinsp;AC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDA\u0026thinsp;+\u0026thinsp;AMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAOBI\u0026thinsp;+\u0026thinsp;COMTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAOBI\u0026thinsp;+\u0026thinsp;AC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAOBI\u0026thinsp;+\u0026thinsp;AMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOMTI\u0026thinsp;+\u0026thinsp;AC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOMTI\u0026thinsp;+\u0026thinsp;AMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u0026thinsp;+\u0026thinsp;AMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFour or more drug therapy (Levodopa+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDA\u0026thinsp;+\u0026thinsp;MAOBI\u0026thinsp;+\u0026thinsp;COMTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDA\u0026thinsp;+\u0026thinsp;MAOBI\u0026thinsp;+\u0026thinsp;AC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDA\u0026thinsp;+\u0026thinsp;MAOBI\u0026thinsp;+\u0026thinsp;AMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDA\u0026thinsp;+\u0026thinsp;COMTI\u0026thinsp;+\u0026thinsp;AC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDA\u0026thinsp;+\u0026thinsp;COMTI\u0026thinsp;+\u0026thinsp;AMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDA\u0026thinsp;+\u0026thinsp;AC\u0026thinsp;+\u0026thinsp;AMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAOBI\u0026thinsp;+\u0026thinsp;COMTI\u0026thinsp;+\u0026thinsp;AC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAOBI\u0026thinsp;+\u0026thinsp;COMTI\u0026thinsp;+\u0026thinsp;AMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAOBI\u0026thinsp;+\u0026thinsp;AC\u0026thinsp;+\u0026thinsp;AMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOMTI\u0026thinsp;+\u0026thinsp;AC\u0026thinsp;+\u0026thinsp;AMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDA\u0026thinsp;+\u0026thinsp;MAOBI\u0026thinsp;+\u0026thinsp;COMTI\u0026thinsp;+\u0026thinsp;AC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDA\u0026thinsp;+\u0026thinsp;MAOBI\u0026thinsp;+\u0026thinsp;COMTI\u0026thinsp;+\u0026thinsp;AMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDA\u0026thinsp;+\u0026thinsp;MAOBI\u0026thinsp;+\u0026thinsp;AC\u0026thinsp;+\u0026thinsp;AMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDA\u0026thinsp;+\u0026thinsp;COMTI\u0026thinsp;+\u0026thinsp;AC\u0026thinsp;+\u0026thinsp;AMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAOBI\u0026thinsp;+\u0026thinsp;COMTI\u0026thinsp;+\u0026thinsp;AC\u0026thinsp;+\u0026thinsp;AMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDA\u0026thinsp;+\u0026thinsp;MAOBI\u0026thinsp;+\u0026thinsp;COMTI\u0026thinsp;+\u0026thinsp;AC\u0026thinsp;+\u0026thinsp;AMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eDA, dopamine agonist; MAOBI, monoamine oxidase-B inhibitor; COMTI, catechol-O-methyltransferase inhibitor; AC, anticholinergic; AMT, amantadine\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe patterns of levodopa dosage adjustments, including dosage increases and the addition of other drugs, were categorized by age group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) and initial prescription dosage of levodopa (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB); over the course of the first three years of observation, irrespective of age group, consistent trends emerged over time. The percentage of patients experiencing an increase in levodopa dosage or the addition of other drugs steadily increased throughout the observation period, with the most significant increase occurring within the first four quarters. Similarly, we observed a steady increase in the percentage of patients requiring an adjustment in levodopa dosage or the addition of other drugs, regardless of their initial levodopa dose. The most prominent changes occurred within the first four quarters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eTrends of levodopa prescription among patients with monotherapy by trajectory modeling\u003c/h3\u003e\n\u003cp\u003eIn 52,919 individuals undergoing levodopa monotherapy with a complete 3-year follow-up, four distinct trajectories in levodopa prescription patterns emerged. These trajectories are as follows: Pattern A, characterized by a high starting dose with continuous increase and a slight late decrease (21%); Pattern B, exhibiting a high starting dose with an early peak followed by continuous decrease (11%); Pattern C, featuring a moderate starting dose with delayed increase and reaching a late maximum (28%); Pattern D, marked by a moderate starting dose with an early decrease (40%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Upon application of trajectory models based on age groups, similar patterns were observed across all age groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe have elucidated various aspects of antiparkinsonian drug prescription utilizing RWD: initial prescription preferences, dosage patterns, trends in combination therapies, and the propensity for dosage escalation or add-on therapies in Korean patients with PD.\u003c/p\u003e \u003cp\u003eA high proportion of patients, approximately 66%, initiated treatment with monotherapy, which suggests that the disease severity may have been relatively mild at the time of the initial diagnosis or that clinicians may have been concerned about potential drug interactions or side effects associated with polytherapy, which influenced their choice of medication. When comparing the prescription patterns analyzed in Japan and China\u003csup\u003e\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, countries with a similar East Asian racial background, certain similarities and differences emerged. Consistent with our findings, levodopa was the most frequently prescribed drug, followed by non-ergot DAs\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Moreover, the trend of a higher proportion of levodopa prescriptions with increasing age, as observed in our Korean data, was also evident in these countries. The consistent preference for levodopa across all age groups underscores its enduring efficacy as the cornerstone of PD treatment.\u003c/p\u003e \u003cp\u003eHowever, there were differences in the levodopa prescription rates. In Korea, over 50% of patients in all age groups except those in their 40s received levodopa prescriptions, with over 70% of patients aged 70 years and older receiving levodopa prescriptions. In contrast, the Japanese results showed lower levodopa prescription rates across all age groups than the Korean results, and even in patients with PD aged 70 years and older, the prescription rate did not exceed 60%\u003csup\u003e12,13\u003c/sup\u003e. In a Chinese RWD analysis focusing on patients with early-onset PD (EOPD) aged 50 years and younger, the most commonly prescribed medication was levodopa, accounting for 36% of prescriptions, followed by pramipexole, a DA, prescribed at 23%, resulting in a ratio of approximately 0.6 between these two medications. This pattern aligns with our findings: levodopa was also the most frequently prescribed drug; however, the relative prescription rate of DAs was higher in China than in Korea\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In our results, the proportion of patients receiving monotherapy was 66.3% across all ages, but in patients 50 years and younger, it was approximately 59%. This trend was similar, and the levodopa prescription rate in our study averaged approximately 33.5%, which closely resembles the rate of 32.0% observed in the Chinese data\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. However, in Japanese claims data focusing on patients with young-onset PD aged 21 to 50 years, DAs were the most commonly prescribed drugs at 49.2%, which differs from our findings\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. This difference may be due to the age range of our patient group, which started from the age of 40 years.\u003c/p\u003e \u003cp\u003eDAs have demonstrated efficacy in delaying the introduction of levodopa therapy and the risk of motor complications and have thus become preferable drugs for the treatment of younger patients\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. When dopaminergic therapy is necessary, DAs are usually initiated before levodopa in EOPD because they delay the onset of dyskinesia compared with levodopa\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Patients with EOPD are more likely to develop motor fluctuations and dyskinesia early in the course of levodopa treatment, which leads to some patients and clinicians to be hesitant to initiate levodopa therapy even though these patients experience troublesome symptoms\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. This is consistent with our findings that a higher prevalence of DA monotherapy was observed in younger patients and that the ratio of DA monotherapy to levodopa monotherapy decreased with age, decreasing by approximately half in patients in their 40s. However, even in their 40s and 50s, the most frequently prescribed drug was levodopa, which could be related to differences in the fundamental therapeutic effects on the symptoms of PD. A more recent multicenter double-blind placebo-controlled delayed-start trial using carbidopa/levodopa in early PD showed no significant change in the rate of progression between early- or delayed-start groups, suggesting that levodopa does not have disease-modifying effect\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. A 9-month study, called the \u0026ldquo;earlier versus later levodopa\u0026rdquo; trial found no evidence of levodopa toxicity; however, 16.5% of the patients in the 600-mg group developed dyskinesia\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Some argue that concerns about side effects, such as dyskinesia, have contributed to the insufficient utilization of levodopa therapy\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, reflecting a slight shift in the perception of prescription patterns. The higher prescription rate of levodopa in our data compared to other Asian countries may be related to the shifts in perception, as our dataset contains data that is at least several years more recent. In the monotherapy subgroups, the initial levodopa dosage showed minimal variance regardless of age.\u003c/p\u003e \u003cp\u003eAnticholinergics, excluding levodopa and DAs, were the next most frequently prescribed antiparkinsonian medication across all age groups, which is likely due to their efficacy against resting tremors, one of the main symptoms of PD\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Although MAO-B inhibitors are one treatment option for early-stage PD, the utilization of MAO-B inhibitors as initial monotherapy was relatively infrequent in Korea\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. This finding could be attributed to the trend of patients seeking medical attention when they were already experiencing discomfort form motor symptoms or a decreased ability to perform activities of daily living as they had before. Consequently, the consideration of MAO-B inhibitor monotherapy alone might not align with the patients\u0026rsquo; clinical needs at the time of diagnosis or might not be adequate to provide sufficient symptom relief.\u003c/p\u003e \u003cp\u003eGiven that patients in the polytherapy group were diagnosed with PD at a younger age than those in the monotherapy group and demonstrated fewer concurrent comorbid conditions, they may have been selected for polytherapy due to the lower potential risks associated with drug adverse events and interactions. Polytherapy predominantly consisted of combinations involving levodopa or DAs, exceeding 90% across all age groups. In terms of combination profiles, dual-drug therapy constituted 73.4%, with the most frequent combinations being levodopa\u0026thinsp;+\u0026thinsp;DAs, followed by levodopa\u0026thinsp;+\u0026thinsp;COMT inhibitors, and levodopa\u0026thinsp;+\u0026thinsp;anticholinergic drugs. levodopa\u0026thinsp;+\u0026thinsp;COMT inhibitor combinations are known to mitigate off-symptom occurrence and on-time dyskinesia by prolonging the efficacy of levodopa\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Conversely, the combination of levodopa\u0026thinsp;+\u0026thinsp;anticholinergics is favored for patients with PD with tremors. While the utilization of three-drug combinations was relatively limited at 21.8%, combinations featuring DAs accounted for 16.2% within this subgroup. Other drug combinations were minimally prescribed and likely adhered to general guideline recommendations.\u003c/p\u003e \u003cp\u003eUpon examining the patterns of levodopa dosage increases or the addition of other drugs within the levodopa monotherapy group, we observed consistent trends over time, with prominent drug adjustments occurring in the first year, irrespective of patient age or the initial levodopa dosage. This contrasts with the general expectation for patients with PD to experience a \u0026ldquo;honeymoon period\u0026rdquo; characterized by a favorable drug response in the first year after treatment initiation. Our findings suggest that there might be few patients who experience a distinct honeymoon period in real clinical practice. However, clinicians in Korea could have adopted treatment approaches with a stronger emphasis on early symptom alleviation. The progressive increase in levodopa dosage and augmentation of therapy over time correspond to the gradual deterioration inherent to PD, which is a degenerative disorder.\u003c/p\u003e \u003cp\u003eOur trajectory analysis revealed diverse medication management patterns, emphasizing the imperative of tailored treatment strategies aligned with individual patient trajectories. The identification of these four trajectories in levodopa prescription patterns highlights the nuanced and dynamic nature of medication management in PD. From these trajectories, we found various patterns based on the combination of the starting dose and compliance. Approximately 49% of patients with PD exhibited dose escalation, while the remaining 51% had dose reduction. The increase in dosage could either represent a snapshot of symptom alleviation over time or diverse trends of continuous adjustments, emphasizing the challenges in achieving optimal therapeutic responses. These findings underscore the complexity and intricate nature of long-term medication management in PD and call for further investigation into individualized approaches to enhance treatment efficacy.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, as with any retrospective analysis, there might have been limitations in the accuracy of recorded diagnoses and prescription details that could have led to misclassification or underreporting of certain variables. Second, while the KNHIS database provides a large and diverse dataset, it lacks certain clinical information that could influence prescription decisions, such as disease severity, treatment response, genetic factors, and specific clinical characteristics of individual patients. The absence of these factors might have impacted the precision and generalizability of the findings. Third, due to the observational nature of the study, causal relationships could not be definitely established. Moreover, the retrospective design limited the ability to establish temporal relationships between factors. The study design did not allow for control of potential confounding variables, which may have introduced unmeasured factors that influence prescription decisions and outcomes. Fourth, the study focused on the Korean population, and the observed prescription patterns might not be directly applicable to other populations with different healthcare systems, cultural factors, or patient characteristics. Finally, while the study evaluated prescription practices, it did not address patient preferences, treatment adherence, or physician rationale behind prescription decisions. These factors could play a substantial role in real-world treatment patterns and should be considered when interpreting the study findings.\u003c/p\u003e \u003cp\u003eDespite these limitations, this study has a significant advantage in providing a practical and comprehensive analysis of drug prescription patterns in Korean patients with PD through RWD analysis. Our findings demonstrate that RWD analysis can bridge the gap between guideline recommendations and the complexities of actual patient care in clinical practice.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur RWD analysis provides specific insights into how prescription patterns differ with age and when mediation adjustments or dosage increases typically occur in real practice. These insights may enable us to refine and optimize treatment approaches. Ultimately, our study can give clinicians the information needed to make informed, patient-centered decisions for managing patients with PD.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eSupplementary information\u003c/h2\u003e \u003cp\u003eThe online version contains supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors have no funding to report.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYB and KHJ designed and conceptualized the study, analyzed and interpreted the data, reviewed all the drafts, and approved the final manuscript. KHJ performed statistical analysis. BY wrote the first draft of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003e The study was initiated at the Konyang University Hospital, and the author gratefully acknowledges the crucial role of the IRB of Konyang University Hospital in obtaining ethical approval for the study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the National Institutes of Health Stroke Scale (NIHSS), but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the corresponding authors upon reasonable request and with permission of (NIHSS).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePoewe, W. et al. Parkinson disease. \u003cem\u003eNat. Rev. Dis. 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Neurol.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 1186\u0026ndash;1202 (2006).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Parkinson’s disease, Antiparkinsonian drug, Levodopa, Prescription, Real-world data","lastPublishedDoi":"10.21203/rs.3.rs-5214960/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5214960/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e While guidelines exist for Parkinson's disease (PD) treatment, variations in real-world patient management highlight the need for understanding prescription practices, yet real-world data (RWD) are limited. We aimed to analyze prescribing trends in patients with PD using RWD. We used the Korean National Health Insurance Service database to identify 160,476 patients with PD from 2002 to 2019. We analyzed initial drug prescriptions, dosage patterns, combination therapies, and dosage adjustments. Group-based trajectory modeling identified distinct levodopa prescription trajectories. Most patients initiated monotherapy (66.3%), with levodopa being the predominant across all ages. The dopamine agonist (DA)/levodopa ratio decreased with age. The average initial levodopa dose in the monotherapy group was 300 mg, irrespective of age. In the polytherapy group (33.7%), over 90% of patients started with levodopa or DA combinations; 73.4% started with two drugs, 21.8% with three, and 4.8% with four or more. In the initial levodopa monotherapy group, levodopa dosage increases and add-on therapies were most common within the first year. Trajectory analysis revealed four distinct levodopa prescription patterns. This RWD analysis provides valuable insights into age-dependent prescription variations and the timing of medication changes or dosage increases in PD management, aiding clinicians in making informed, patient-centered treatment decisions.\u003c/p\u003e","manuscriptTitle":"Prescription Patterns and Trends of Antiparkinsonian Drugs in Korean Patients With Parkinson’s Disease: A Real-World Data Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-19 13:50:10","doi":"10.21203/rs.3.rs-5214960/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8c7159a4-f303-4137-a899-3f443a096e14","owner":[],"postedDate":"November 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":39877021,"name":"Biological sciences/Neuroscience"},{"id":39877022,"name":"Health sciences/Medical research"},{"id":39877023,"name":"Health sciences/Neurology"}],"tags":[],"updatedAt":"2025-01-29T06:08:43+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-19 13:50:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5214960","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5214960","identity":"rs-5214960","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}