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Polypharmacy poses a growing challenge to healthcare systems because of its association with adverse effects and the misuse of medication. This study aimed to identify the sociodemographic factors and comorbidities associated with polypharmacy.
Methods
We selected patients aged ≥30 years registered in the National Health Information Database in 2018 who were prescribed at least one medication for ≥180 days. Multivariate logistic regression analyses were performed to evaluate the associations between polypharmacy, sociodemographic characteristics, and comorbidities.
Results
Polypharmacy was significantly associated with increasing age, with the strongest association observed in adults aged ≥65 years. Compared with medical aid recipients, higher-income groups had a weaker association with polypharmacy. After adjusting for covariates, significant associations with polypharmacy were found for specific comorbidities, such as Parkinson disease (odds ratio [OR], 3.804; 95% confidence interval [CI], 3.733–3.876; P<0.001) and chronic ischemic heart disease (OR, 3.199; 95% CI, 3.178–3.221; P<0.001).
Conclusion
These findings may help reduce the burden of polypharmacy by facilitating the development of targeted strategies tailored to patients.
Polypharmacy, defined as the concurrent use of five or more medications [1-3], has become a major public concern because of the growing older adult population with multimorbidity [4]. It is associated with adverse drug reactions, dizziness, increased hospitalization risk, and mortality [5-7]. The World Health Organization emphasizes the need for the safe use of medications through effective management strategies for polypharmacy in various countries [8]. While some countries have implemented evidence-based systems to improve medication practices for polypharmacy in patients [9], others, such as Korea, lack systematic approaches to deal with polypharmacy.
Inappropriate polypharmacy involves the excessive prescription of medications. Effective management aims to reduce this by ensuring medications are prescribed rationally, considering individual patient characteristics and evidence-based practices [10]. The focus should be on the appropriateness of medications, rather than solely on the number of medications, especially for patients who require multiple medications for effective treatment [9]. Additionally, with limited healthcare resources, prioritization is crucial to identify patients who would benefit the most from polypharmacy management. Standardized criteria, especially in resource-limited settings, can help to determine the potential benefits of medication reviews.
Current research on polypharmacy in Korea has mainly focused on high-risk groups, such as older adults and severely ill patients, primarily reporting the prevalence and mortality rates of polypharmacy in this patient population [11-13]. Investigating various factors associated with polypharmacy across the entire adult population can aid in developing targeted and efficient management strategies for those at higher risk. Therefore, this study aimed to identify the sociodemographic factors and medical conditions associated with polypharmacy in the Korean adult population.
Methods
This study used data from the National Health Insurance Service (NHIS) customized database (DB), which contains comprehensive healthcare utilization information for the Korean population. The NHIS customized DB, based on a fee-for-service healthcare delivery model, covers approximately 97% of the Korean population and includes demographic characteristics, medical treatment records, prescription records, and medical care institution data, making it one of the most extensive real-world healthcare databases in South Korea [14,15]. This resource is based on a fee-for-service healthcare delivery model [14]. The National Health Insurance Program in South Korea, a cornerstone of the nation’s social security system, ensures nearly universal participation [15].
The study was conducted in accordance with the principles of the Declaration of Helsinki. Given the specific nature of the database, the requirement for written informed consent was waived by the Institutional Review Board. Ethical approval for this study was granted by the Institutional Review Board (2003-010-19308).
Study population
We extracted data from the NHIS customized DB, initially identifying adults aged ≥30 years who were registered between January 1 and December 31, 2018. Because of data processing limitations, we obtained a representative 30% sample using a simple random sampling method. From this sample, we selected patients who received at least one medication prescription during the study period as our study aimed to identify the determinants of polypharmacy among those requiring pharmacological treatment. To focus on regular medication use patterns, we included only patients who had received prescriptions for at least 180 days. We analyzed oral medications prescribed in both outpatient and inpatient settings, as these are standard treatments for chronic diseases and provided consistent prescription records. Patients with missing or invalid medication data were excluded.
Variables
Sociodemographic data included sex, age, income level, and disability status (registered with the Korea National Disability Registration System) [16]. Income levels were categorized into quartiles based on National Health Insurance Program subscriber data, with medical aid recipients classified separately [14]. In Korea, the medical aid program supports individuals with limited financial resources and those requiring substantial medical care, making this group a meaningful reference category for our analyses [15,17].
Clinical information, including comorbidities and prescribed medications, was collected annually [14]. Comorbidities were defined using the International Classification of Diseases, 10th revision (ICD-10) codes as follows: hypertension (I10‒15), hyperlipidemia (E78), knee arthrosis (M15‒M19), diabetes mellitus (E10‒E14), chronic ischemic heart disease (I20, I21, I25), liver disease (K70, K73, K74, K76, K713‒K717, K727), depression (F32, F33), asthma and chronic obstructive pulmonary disease (J40‒J47), osteoporosis (M80‒M82), chronic kidney disease (N18, N19), chronic stroke (I60‒I64, I69, G45), dementia (F00‒F03, G30, G31, R54, F051), anxiety (F40, F41), Parkinson disease (G20‒G22), cancer (C00‒C97, D37‒D48), and chronic gastritis and gastroesophageal reflux disease (K20, K21, K25‒K27, K29‒K31). Each medication was categorized and coded based on its primary active ingredients. Polypharmacy was defined as the concurrent use of five or more medications with different main ingredients, following previously established definitions in the literature [1-3].
Statistical analyses
For all analyses, categorical variables are expressed as frequencies and percentages, and continuous variables are expressed as means±standard deviation. The chi-square test or Student t-test was used to compare the characteristics between patients with and without polypharmacy. Multivariate logistic regression analyses were performed to evaluate the association between polypharmacy and sociodemographic factors or comorbidities, after adjusting for other confounders. All statistical analyses were performed using SAS ver. 9.4 (SAS Institute Inc.), and P-values <0.05 were considered statistically significant.
Results
The final analysis for this study included 3,988,076 patients
who were prescribed medications. Among these, 1,258,702 (31.6%) met the criteria for polypharmacy. The polypharmacy group had a higher mean age, more medical aid recipients, and a higher prevalence of comorbidities (all P<0.001) (Table 1).
Among the patients with polypharmacy, the five most frequently prescribed medications were aspirin, atorvastatin, metformin, rosuvastatin, and glimepiride. Age-specific analysis revealed higher prescription rates for cardiovascular medications in older adults (≥65 years) than in younger patients (30–64 years), particularly for aspirin (32.6% vs. 24.4%) and atorvastatin (27.0% vs. 23.5%). In contrast, antidiabetic medications had higher prescription rates in younger patients. Rosuvastatin showed similar prescription rates across all age groups (16.0%) (Table 2).
Table 3 shows the associations between sociodemographic factors, comorbidities, and polypharmacy. After adjusting for covariates, the analysis showed increasing odds ratios (ORs) with advancing age, from 1.637 (95% confidence interval [CI], 1.627–1.647) for ages 65–74 years to 2.592 (95% CI, 2.560–2.625) for ages ≥85 years. All studied comorbidities were significantly associated with polypharmacy (P<0.001), with particularly strong associations observed for Parkinson disease (OR, 3.804; 95% CI, 3.733–3.876) and chronic ischemic heart disease (OR, 3.199; 95% CI, 3.178–3.221) (Table 3).
Discussion
This study investigated the factors associated with polypharmacy in a large national database encompassing adults aged ≥30 years (n=3,988,076). Unlike previous studies that focused mainly on older adults or specific populations, this study analyzed polypharmacy across a broader spectrum. We found a 31.5% prevalence of polypharmacy among adults who received at least one prescribed medication, with a significantly stronger association among older adults than younger adults. To the best of our knowledge, this is the first study to identify the multifactorial determinants of polypharmacy among adults aged ≥30 years in a large national population database.
Our analysis demonstrated several significant demographic patterns associated with the risk of polypharmacy. We observed that the ORs increased substantially for those aged ≥85 years (OR, 2.592; 95% CI, 2.560–2.625). This age-related increase likely reflects both the accumulation of chronic conditions and age-related changes in the metabolism and clearance of medication. Moreover, previous studies in Korea have shown that approximately half of the population aged ≥65 years receives multidrug prescriptions [18]. These findings align with previous research showing that older age, living in a nursing home, and cancer survival are associated with an increased prevalence of polypharmacy [1,19].
Socioeconomic factors are associated with the risk of polypharmacy. Similar to the findings from the United States, we observed a significant association between polypharmacy and medical aid receipt in Korea [20]. This relationship likely reflects both the higher disease burden among lower-income populations and their increased access to healthcare services through public assistance programs. Furthermore, we found a negative association between polypharmacy and high-income levels. These findings underscore the importance of considering socioeconomic factors, including access to healthcare through programs such as Medicaid, when developing strategies to manage polypharmacy within healthcare systems. Regarding the demographic characteristics, we found notable differences in sex and disability status. Female patients had a lower likelihood of polypharmacy than male patients, after adjusting for other factors. This finding contrasts with those of previous studies and warrants further investigation into sex-specific prescription patterns and healthcare utilization in Korea. The strong association between disability status and polypharmacy underscores the complex medical needs of this vulnerable population and emphasizes the importance of comprehensive medication reviews for disabled individuals.
Our study revealed a strong positive correlation between polypharmacy and various chronic conditions. This pattern has been observed across various conditions ranging from common chronic diseases to more severe conditions that require complex management. The particularly strong association with Parkinson disease likely reflects the complex symptomatic management required for this condition, as well as the frequent presence of comorbid conditions in these patients. Similarly, the high OR for chronic ischemic heart disease may be attributed to the need for multiple cardiovascular medications for secondary prevention and symptom management, along with treatment for commonly co-occurring conditions. These findings align with those of previous research demonstrating links between polypharmacy and cardiovascular, metabolic, and chronic kidney diseases [21].
Our findings regarding medication patterns have important implications for clinical practice and policy development. The predominance of cardiovascular and metabolic medications in patients with polypharmacy suggests that patients with these comorbidities should be prioritized in medication management programs. This approach aligns with healthcare resource optimization efforts, because identifying high-risk groups enables more targeted interventions. Although these medications are often essential for disease management, systematic medication reviews in these patient populations may help mitigate risks, while preserving therapeutic benefits.
Additionally, we observed a significant association between polypharmacy and psychiatric conditions, such as depression and anxiety. Although the overall prevalence of these conditions was lower, our findings are partially supported by those of prior studies. For example, a Chinese study reported an association between polypharmacy and depression in older adults, but not in all age groups. Interestingly, the same study found no association with anxiety. Another US study suggested a link between polypharmacy and psychological distress in African Americans [22]. These discrepancies highlight the potential influence of factors such as population demographics, healthcare systems, and research settings. The relationship between psychiatric conditions and polypharmacy warrants particular attention, given the potential for cognitive side effects and drug interactions with psychotropic medications.
The findings of this study advocate for a multifaceted approach to polypharmacy management that considers the diverse characteristics and needs of patients. Several international programs have demonstrated success in optimizing medication use for polypharmacy patients through patient-centered clinical management [23]. These programs emphasize comprehensive medication reviews and appropriate adjustments, rather than simply reducing the number of medications [23]. Our research highlights the potential benefits of implementing such targeted interventions tailored to specific patient groups based on factors such as age, income level, and medical conditions. This approach can promote patient-centered care, while addressing the potential overprescription of psychiatric medications observed in some patients.
Our findings have important implications for clinical practice and policies. Healthcare providers should be particularly attentive to the risk of polypharmacy among older adults and medical aid recipients. The development of targeted medication review programs for these high-risk groups may help optimize medication use and reduce potential adverse effects. Given the limitations of healthcare resources, prioritizing individuals with the characteristics identified in this study for medication optimization programs will ensure efficient resource allocation and maximize the impact on those who need them the most.
This study had some limitations. First, owing to its cross-sectional design, we could not establish cause-and-effect relationships between the factors analyzed and polypharmacy. For instance, polypharmacy itself may influence a patient’s clinical status. Second, our database did not include information on residential areas or lifestyle factors that may have influenced medication use patterns. Although these factors may offer additional insights, they were not captured in the NHIS customized DB, which primarily contains healthcare utilization and prescription data for most patients. Third, the data originated from the National Health Insurance Program, which is primarily designed for billing purposes [14]. Fourth, our analytical approach examined associations between individual comorbidities and polypharmacy, but did not incorporate the cumulative number of comorbidities as a predictor variable, despite evidence that the comorbidity count directly contributes to polypharmacy risk in both theoretical frameworks and clinical practice [2,8]. Despite these limitations, the program’s large size, national representation, and real-world data provide valuable insights [14]. Future research should address these limitations using several approaches. Prospective studies investigating the temporal relationship between sociodemographic factors, comorbidities, and the development of polypharmacy may help establish causality. Studies analyzing how the comorbidity count influences polypharmacy risk may establish threshold values to guide intervention strategies. Additionally, research comparing polypharmacy patterns between specific disease groups and matched controls may provide further insights into disease-specific medication needs. Incorporating residential patterns and lifestyle factors through linkage with health examinations or survey data may also enhance our understanding of environmental and behavioral influences on polypharmacy.
Despite these limitations, this study identified key determinants of polypharmacy across a wide range of adults. These findings can inform the development of targeted management strategies to optimize medication use in different patient groups.
In conclusions, polypharmacy is a growing global public health concern. This study shed light on the multifaceted factors that influence its prevalence across a broad adult population. By identifying key determinants linked to sociodemographic characteristics and clinical conditions, our findings can inform the development of targeted interventions to optimize medication use and reduce the burden of polypharmacy.
Article Information
Conflict of interest
No potential conflict of interest relevant to this article was reported.
Funding
This research was supported by a Chung-Ang University research grant for 2023 (grant no., 20230027) and by the National Health Insurance Service (grant no., 2020-2-0006).
Data availability
Data of this research are available from the corresponding author upon reasonable request.
Author contribution
Conceptualization: THG, JHK. Data curation: THG. Formal analysis: WYS, THG. Funding acquisition: JHK. Investigation: all authors. Methodology: all authors. Project administration: JHK. Resources: THG, JHK. Software: WYS, THG. Supervision: JHK. Validation: THG, JHK. Visualization: WYS, THG. Writingoriginal draft: WYS, THG. Writing–review & editing: all authors. Final approval of the manuscript: all authors.
Table 1.
Sociodemographic characteristics and comorbidities of the study patients (n=3,988,076)
Values are presented as number (%) for categorical variables using the chi-square test and mean±standard deviation for continuous variables using the t-test.
a)P-values <0.05 were considered statistically significant.
Table 2.
Prevalence of the most frequently used medications in patients with polypharmacy
a)Analyzed using a multivariate logistic regression analysis.
b)Adjusted for all sociodemographic variables and comorbidities, including sex, age, medical aid status, income level, disability, hypertension, dyslipidemia, knee arthrosis, diabetes mellitus, CIHD, liver disease, depression, asthma, COPD, osteoporosis, chronic kidney disease, chronic stroke, dementia, anxiety, Parkinson disease, cancer, and chronic gastritis or GERD.
References
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Sociodemographic determinants and comorbidities associated with polypharmacy among the adult population in Korea: a nationwide claim analysis
Graphical abstract
Graphical abstract
Sociodemographic determinants and comorbidities associated with polypharmacy among the adult population in Korea: a nationwide claim analysis
Characteristic
Polypharmacy
Nonpolypharmacy (n=2,729,374)
P-valuea)
No. of medications prescribed
6.70±1.60
3.40±1.21
<0.001
Male sex
610,568 (48.5)
1,262,573 (46.3)
<0.001
Age (y)
69.0±12.2
61.5±12.6
<0.001
Income level
<0.001
Medical aid benefit recipient
146,902 (11.8)
98,138 (3.7)
Lowest
215,983 (17.4)
503,680 (18.7)
Second
187,423 (15.1)
479,372 (17.8)
Third
257,756 (20.8)
631,674 (23.5)
Highest
434,177 (35.0)
978,460 (36.4)
Disability
265,637 (21.1)
240,196 (8.8)
<0.001
Comorbidity
Hypertension
978,586 (77.8)
1,607,192 (58.9)
<0.001
Hyperlipidemia
942,549 (74.9)
1,555,806 (57.0)
<0.001
Knee arthrosis
433,584 (34.5)
607,003 (22.2)
<0.001
Diabetes mellitus
677,442 (53.8)
741,052 (27.2)
<0.001
Chronic ischemic heart disease
317,109 (25.2)
190,528 (7.0)
<0.001
Liver disease
328,433 (26.1)
662,401 (24.3)
<0.001
Depression
246,570 (19.6)
201,386 (7.4)
<0.001
Asthma/chronic obstructive pulmonary disease
432,327 (34.4)
706,374 (25.9)
<0.001
Osteoporosis
257,279 (20.4)
375,535 (13.8)
<0.001
Chronic kidney disease
76,328 (6.1)
36,986 (1.4)
<0.001
Chronic stroke
223,422 (17.8)
134,675 (4.9)
<0.001
Dementia
221,508 (17.6)
177,162 (6.5)
<0.001
Anxiety
276,063 (21.9)
300,558 (11.0)
<0.001
Parkinson disease
55,940 (4.4)
25,115 (0.9)
<0.001
Cancer
148,150 (11.8)
297,387 (10.9)
<0.001
Chronic gastritis/gastroesophageal reflux disease
1,084,011 (86.1)
2,171,600 (79.6)
<0.001
No.
Medication
Prevalence
P-valuea)
Total (n=1,258,702)
30–64 y (n=438,799)
≥65 y (n=819,903)
1
Aspirin (100 mg/tablet)
375,093 (29.8)
107,067 (24.4)
267,288 (32.6)
<0.001
2
Atorvastatin
324,745 (25.8)
102,942 (23.5)
221,210 (27.0)
<0.001
3
Metformin
252,999 (20.1)
95,088 (21.7)
157,585 (19.2)
<0.001
4
Rosuvastatin
201,392 (16.0)
70,208 (16.0)
131,430 (16.0)
0.785
5
Glimepiride
164,890 (13.1)
64,942 (14.8)
99,946 (12.2)
<0.001
Variable
Unadjusted model
Adjusted modelb)
OR (95% CI)
P-value
OR (95% CI)
P-value
Sex
Male
1
1
Female
0.912 (0.909–0.916)
<0.001
0.784 (0.780–0.788)
<0.001
Age (y)
30–64
1
1
65–74
2.536 (2.523–2.549)
<0.001
1.637 (1.627–1.647)
<0.001
75–84
4.130 (4.106–4.155)
<0.001
2.257 (2.240–2.274)
<0.001
≥85
4.113 (4.072–4.155)
<0.001
2.592 (2.560–2.625)
<0.001
Income level
Medical aid benefit recipient
1
1
Lowest
0.288 (0.285–0.291)
<0.001
0.398 (0.393–0.402)
<0.001
Second
0.265 (0.262–0.267)
<0.001
0.393 (0.389–0.398)
<0.001
Third
0.279 (0.277–0.282)
<0.001
0.385 (0.381–0.389)
<0.001
Highest
0.304 (0.301–0.306)
<0.001
0.358 (0.355–0.362)
<0.001
Disability
2.877 (2.860–2.894)
<0.001
1.831 (1.819–1.845)
<0.001
Comorbidity
Hypertension
2.938 (2.924–2.952)
<0.001
2.023 (2.012–2.035)
<0.001
Dyslipidemia
2.134 (2.125–2.144)
<0.001
1.585 (1.576–1.594)
<0.001
Knee arthrosis
1.837 (1.828–1.846)
<0.001
1.210 (1.203–1.217)
<0.001
Diabetes mellitus
3.223 (3.208–3.237)
<0.001
2.725 (2.711–2.740)
<0.001
CIHD
4.923 (4.894–4.952)
<0.001
3.199 (3.178–3.221)
<0.001
Liver disease
1.054 (1.048–1.059)
<0.001
0.825 (0.820–0.830)
<0.001
Depression
2.695 (2.678–2.711)
<0.001
2.222 (2.204–2.239)
<0.001
Asthma/COPD
1.484 (1.477–1.491)
<0.001
1.133 (1.126–1.139)
<0.001
Osteoporosis
1.652 (1.643–1.662)
<0.001
1.291 (1.281–1.300)
<0.001
Chronic kidney disease
5.107 (5.038–5.178)
<0.001
2.731 (2.687–2.776)
<0.001
Chronic stroke
4.615 (4.585–4.646)
<0.001
2.557 (2.537–2.578)
<0.001
Dementia
3.082 (3.059–3.106)
<0.001
1.180 (1.169 –1.192)
<0.001
Anxiety
2.112 (2.100–2.123)
<0.001
1.476 (1.466–1.486)
<0.001
Parkinson disease
5.337 (5.256–5.420)
<0.001
3.804 (3.733–3.876)
<0.001
Cancer
0.966 (0.959–0.973)
<0.001
1.046 (1.037–1.055)
<0.001
Chronic gastritis/GERD
1.560 (1.551–1.569)
<0.001
1.295 (1.287–1.304)
<0.001
Table 1. Sociodemographic characteristics and comorbidities of the study patients (n=3,988,076)
Values are presented as number (%) for categorical variables using the chi-square test and mean±standard deviation for continuous variables using the t-test.
P-values <0.05 were considered statistically significant.
Table 2. Prevalence of the most frequently used medications in patients with polypharmacy
Values are presented as number of frequency (%).
P-values <0.05 were considered statistically significant.
Table 3. Association of polypharmacy with sociodemographic factors and comorbiditiesa)
Analyzed using a multivariate logistic regression analysis.
Adjusted for all sociodemographic variables and comorbidities, including sex, age, medical aid status, income level, disability, hypertension, dyslipidemia, knee arthrosis, diabetes mellitus, CIHD, liver disease, depression, asthma, COPD, osteoporosis, chronic kidney disease, chronic stroke, dementia, anxiety, Parkinson disease, cancer, and chronic gastritis or GERD.
