Abstract
Background
Type 2 diabetes mellitus (T2DM) and degenerative or mechanical spinal disorders frequently co-occur and amplify one another’s clinical and socioeconomic burden. T2DM has been associated with greater pain severity, prolonged disability, and higher reported risks of surgery and opioid use, although the underlying mechanisms remain hypothesized rather than established. In South Korea’s dual healthcare system, patients may access both Western medicine (WM) and Korean medicine (KM), yet national-level evidence on spine-T2DM multimorbidity care patterns is limited. This study examined 10-year healthcare utilization, expenditures, and medication use among patients with coexisting T2DM and degenerative or mechanical spinal disorders.
Methods
We conducted a retrospective study using the Health Insurance Review and Assessment Service-National Patient Sample (HIRA-NPS) from 2010 to 2019. Patients with both T2DM (E11) and at least one degenerative spinal diagnosis (M47, M48, M51, M54, S33) were included. KM users were defined as those with ≥ 1 KM claim per year. Outcomes included annual claim counts, expenditures, service categories, medication use, and facility type. Annual percent change (APC) was estimated using log-linear regression, and baseline characteristics were compared using standardized mean differences (SMDs).
Results
A total of 188,716 patients generated 9,590,400 claims over 10 years; 62.9% were KM users. KM users were more often female and slightly older; back pain (M54) showed the largest imbalance (SMD = 0.26). Total claims increased from 715,279 (2010) to 1,157,475 (2019). KM users had substantially more annual claims; yet per-patient expenditures were similar, reflecting reliance on lower-cost outpatient KM services, notably acupuncture. Medication use peaked in 2012 and declined thereafter following national drug pricing reforms. Non-users received fewer but higher-cost prescriptions, particularly for pain and inflammatory medications.
Conclusions
Adults with coexisting T2DM and degenerative spinal disorders demonstrate increasing and complex healthcare needs driven primarily by chronic pain rather than glycemic management alone. KM users engage in high-frequency, multimodal outpatient care at lower unit cost, whereas non-users rely more heavily on tertiary WM services and higher-cost pharmaceuticals. Korea’s dual healthcare system appears to support differentiated care pathways in this multimorbidity population. Findings underscore the need for integrated, longitudinal chronic care models that combine conservative pain management with diabetes care to reduce disability and long-term healthcare burden.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12913-026-14657-1.
Keywords: Type 2 diabetes, Spinal disorders, Multimorbidity, Korean medicine, Musculoskeletal pain, Healthcare utilization, Claims data, Health systems, Outpatient care, APC, SMD
Introduction
Type 2 diabetes mellitus (T2DM) is a rapidly growing global health challenge associated with substantial morbidity, mortality, and economic burden. Beyond its well-known microvascular and macrovascular complications, T2DM is increasingly recognized as a condition that adversely affects musculoskeletal health [1, 2]. Hyperglycemia, chronic inflammation, microvascular dysfunction, and the accumulation of advanced glycation end products accelerate tissue degeneration within intervertebral discs, ligaments, and surrounding spinal structures [3–6]. As a result, individuals with T2DM experience disproportionately higher rates of degenerative spinal conditions [7], including spondylosis, spinal stenosis, intervertebral disc disorders, and chronic back pain [8–10].
Emerging clinical evidence demonstrates that T2DM not only increases the likelihood of developing spinal disorders but also worsens their clinical course [11–13]. People with T2DM report higher pain intensity, greater disability, and slower recovery from musculoskeletal injury [14–17]. They face elevated risks of hospitalization, surgical intervention—particularly lumbar spinal fusion—and perioperative complications, contributing to prolonged rehabilitation [17, 18]. Additionally, chronic pain and comorbid metabolic and psychological factors place patients with T2DM at higher risk of long-term opioid use [19, 20]. The resulting disability and productivity loss are substantial, posing wide-reaching socioeconomic consequences.
In South Korea, where both T2DM prevalence [21] and spinal disease burden [22] are increasing, patients have the unique option of receiving care under a dual healthcare system that includes Western medicine (WM) and traditional Korean medicine (KM). KM is frequently used for managing musculoskeletal pain, offering non-pharmacologic conservative treatments such as acupuncture, Chuna manual therapy, and pharmacopuncture [23]. Prior studies have highlighted differences in the cost structures, care pathways, and utilization patterns between WM and KM users in a number of chronic diseases [24, 25]. However, although KM plays an important role in pain management, national-level evidence describing long-term healthcare utilization patterns among patients with coexisting T2DM and degenerative spinal disorders is limited.
To address this gap, we analyzed 10 years of nationally representative claims data to characterize healthcare utilization, expenditures, medication patterns, and KM use among adults with both T2DM and degenerative spinal disorders. Understanding how multimorbidity manifests within Korea’s dual healthcare system is crucial for guiding integrated chronic disease management and informing health policy.
Methods
Study design and data source
This repeated cross-sectional study analyzed data from the HIRA-NPS, a nationally representative database constructed from health insurance claims submitted by the medical institutions in Korea [26]. The HIRA-NPS uses stratified randomized sampling based on age and sex to extract approximately 3% of the national population annually (2% from 2019), with sampling weights provided by HIRA to maintain national representativeness. All personal identifiers are removed prior to release.
Data from January 1, 2010, to December 31, 2019, were used. Each record includes patient demographics, diagnoses coded using the International Classification of Diseases 10th Revision (ICD-10), procedures, institution type, prescription details, and reimbursed costs. The study was approved by the HIRA Deliberative Committee and received institutional review board exemption (JASENG 2024-11-006). All analyses were conducted in accordance with relevant regulations.
This study was performed in line with the principles of the Declaration of Helsinki. Ethical approval for an exemption was granted by the Institutional Review Board of Jaseng Hospital of Korean Medicine, Seoul, Republic of Korea (IRB No. JASENG 2024-11-006). Because this study used publicly available, fully de-identified secondary claims data, informed consent from individual participants was waived, in accordance with national regulations and institutional review board guidelines.
Study design and population
Patients were included in the study if they met two criteria within the same calendar year. First, they were required to have a principal diagnosis of type 2 diabetes mellitus, identified using the ICD-10 code E11 [27, 28]. Second, they had to have at least one claim indicating a degenerative spinal disorder. Eligible spinal diagnoses included spondylosis (M47), spinal stenosis (M48), intervertebral disc disorders (M51), back pain (M54), and lumbar sprain or strain (S33), including relevant subcategories [29]. These criteria ensured that all included patients had coexisting T2DM and clinically recognized spinal pathology during the study period.
Exclusion criteria were applied to improve data accuracy and ensure that only valid and analyzable records were retained. Records were excluded if they lacked a primary diagnosis or originated from institutions not eligible for analysis, such as dental clinics or long-term care hospitals. Claims with zero reimbursed cost or zero treatment days were removed, as were records missing information on institution type. After initial exclusions, patients who no longer met both qualifying diagnoses following data cleaning were also removed (Fig. 1). A final consolidated cohort was constructed and analyzed at both the patient level and the claim level.
Fig. 1.
Flowchart
Definition of Korean Medicine (KM) use
Patients were classified according to their use of Korean Medicine (KM) services within each calendar year. KM users were defined as individuals with at least one KM claim, which included services delivered in KM hospitals or KM clinics. Non-users were defined as patients with no KM claims during the same period. KM services encompassed acupuncture, moxibustion, Chuna manual therapy, pharmacopuncture, and reimbursable herbal preparations under the national insurance system. For KM users, Western medicine (WM) and KM claims were analyzed separately to distinguish patterns of dual system utilization. For clarity, WM in this study encompassed all reimbursed services delivered in tertiary or general hospitals, primary clinics, and other non-KM facilities, including consultations, diagnostic imaging and laboratory testing, pharmacotherapy, injection procedures, anesthesia, hospitalization, and surgical interventions (e.g., decompression, fusion, discectomy). Surgical procedures are captured within the inpatient and anesthesia categories presented in Table 3. Once a patient met both eligibility criteria within a calendar year, all of that patient’s reimbursed claims were retained for utilization analysis; consequently, not every captured claim was necessarily billed against the index T2DM or spinal diagnoses, and the analyzed utilization should be interpreted as overall care use among the multimorbid cohort rather than care exclusively for the index conditions.
Table 3.
Medical use patterns by service category among patients with type 2 diabetes, overall and stratified by KM utilization
| Service Category | Total Cost | Total claims | Cost by Non-Users | Claims by non-users | Cost by KM users | Claims by KM users | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Mean Annual Cost | APC | Mean Annual Claims | APC | Mean Annual Cost | APC | Mean Annual Claims | APC | Mean Annual Cost | APC | Mean Annual Claims | APC | |
| Consultation fees | 9,656.95 | 9.15% | 1,515,707.40 | 7.76% | 3,047.27 | 10.42% | 479,335.40 | 9.25% | 6,609.68 | 8.55% | 1,036,372.00 | 7.05% |
| Inpatient care | 3,703.98 | 13.35% | 63,851.90 | 10.83% | 1,440.57 | 13.67% | 24,181.10 | 11.71% | 2,263.41 | 13.14% | 39,670.80 | 10.26% |
| Medication fees | 1,432.54 | 3.85% | 406,279.50 | 7.80% | 596.36 | 3.62% | 125,314.70 | 5.44% | 836.18 | 4.00% | 280,964.80 | 8.99% |
| Injection fees | 5,524.81 | 8.76% | 1,206,198.10 | 6.25% | 1,560.88 | 7.61% | 242,323.90 | 6.24% | 3,963.93 | 9.23% | 963,874.20 | 6.25% |
| Physical therapy | 1,234.96 | 7.67% | 489,007.90 | 3.80% | 443.17 | 10.37% | 172,548.40 | 5.43% | 791.79 | 6.13% | 316,459.50 | 2.88% |
| Anesthesia fees | 7,913.43 | 11.87% | 193,512.70 | 8.98% | 3,087.52 | 12.44% | 72,810.90 | 9.57% | 4,825.91 | 11.50% | 120,701.80 | 8.62% |
| Laboratory tests | 6,703.11 | 12.50% | 1,479,719.10 | 8.89% | 2,575.80 | 13.17% | 555,439.50 | 9.87% | 4,127.31 | 12.07% | 924,279.60 | 8.29% |
| Imaging diagnostics | 3,280.69 | 9.67% | 178,397.90 | 4.07% | 1,270.07 | 10.54% | 64,794.50 | 4.81% | 2,010.62 | 9.11% | 113,603.40 | 3.64% |
| Other out-of-pocket 100% items | 94.6 | 43.44% | 10,524.70 | 60.33% | 38.03 | 38.83% | 3,794.70 | 59.09% | 56.57 | 47.97% | 6,730.00 | 61.09% |
Average annual costs are in USD; annual percent change represents average yearly change over 2010–2019
Variables and outcomes
Demographic and institutional characteristics included patient age (categorized into eight 10-year intervals), sex, and insurance type (National Health Insurance, Medicaid, or other). The type of healthcare institution visited was also assessed and categorized as tertiary or general hospital, clinic, KM hospital, or KM clinic.
Healthcare utilization outcomes included total annual claims, average claims per patient, distribution of outpatient versus inpatient service use, and—for KM users—the proportion of WM and KM claims. Expenditure-related outcomes included total annual healthcare costs, average cost per patient (converted to USD), and costs stratified by service categories such as consultation, anesthesia, or diagnostic procedures. Long-term expenditure trends were evaluated using the annual percent change (APC) over the 10-year study period.
Medications were grouped into 11 therapeutic categories (circulatory, neuropsychiatric, digestive, respiratory, pain/inflammatory, allergy, anti-infective, diabetes, lipids, muscle relaxants, other). Annual claim counts, average claims per patient, and average cost per claim were calculated.
Statistical analysis
Descriptive statistics were used to compare demographic distributions, utilization patterns, and expenditures between KM users and non-users. Annual percent change (APC) was calculated using log-linear regression of yearly total costs and claim counts for each service category. Given the descriptive objective and population-level focus, analyses were conducted at both claim-level and patient-level. Standardized mean differences (SMDs) were calculated for categorical variables using the multinomial distance metric [30]. All analyses used SAS 9.4 (SAS Institute, Cary, NC, USA).
Results
Study population
A total of 188,716 patients with type 2 diabetes mellitus (T2DM) and spinal disorders coded as M47/M48/M51/M54/S33 were identified from 2010 to 2019, generating 9,590,400 claims (Table 1). Of these, 118,672 patients (62.9%) used Korean Medicine (KM) services at least once annually. KM users were more likely to be female (60.1% vs. 50.4% among non-users) and older, with the highest prevalence in the 65–74 age group. Most encounters (98.8%) occurred in outpatient settings. Most baseline characteristics showed minimal imbalance (SMD < 0.10), except for sex (SMD = 0.14), lumbar sprain/strain (S33, SMD = 0.10), and dorsalgia (M54), which demonstrated a substantial imbalance (SMD = 0.26), indicating that KM users were more likely to present with pain-related diagnoses.
Table 1.
Baseline characteristics of degenerative spine disorder patients with type 2 diabetes (E11) and their annual utilization of Korean Medicine services
| Variable | All Patients | Non-users (KM Use = 0/year) |
KM users (KM Use ≥ 1/year) |
SMD |
|---|---|---|---|---|
| Patient-level | (n = 188,716) | (n = 70,044) | (n = 118,672) | |
| Sex | 0.137 | |||
| Male | 82,078 (43.49%) | 34,736 (49.59%) | 47,342 (39.89%) | |
| Female | 106,638 (56.51%) | 35,308 (50.41%) | 71,330 (60.11%) | |
| Age group | 0.035 | |||
| < 15 years | 27 (0.01%) | 19 (0.03%) | 8 (0.01%) | |
| 15–24 years | 360 (0.19%) | 185 (0.26%) | 175 (0.15%) | |
| 25–34 years | 1,791 (0.95%) | 734 (1.05%) | 1,057 (0.89%) | |
| 35–44 years | 8,680 (4.60%) | 3,413 (4.87%) | 5,267 (4.44%) | |
| 45–54 years | 28,291 (14.99%) | 10,528 (15.03%) | 17,763 (14.97%) | |
| 55–64 years | 51,831 (27.47%) | 19,683 (28.10%) | 32,148 (27.09%) | |
| 65–74 years | 60,532 (32.08%) | 21,104 (30.13%) | 39,428 (33.22%) | |
| ≥ 75 years | 37,204 (19.71%) | 14,378 (20.53%) | 22,826 (19.23%) | |
| Insurance type | 0.036 | |||
| National Health Insurance | 172,740 (91.53%) | 62,935 (89.85%) | 109,805 (92.53%) | |
| Medicaid | 15,417 (8.17%) | 6,793 (9.70%) | 8,624 (7.27%) | |
| Other | 559 (0.30%) | 316 (0.45%) | 243 (0.20%) | |
| Primary Spinal Diagnosis* | ||||
| M47 - Spondylosis | 29,701 (15.74%) | 11,140 (15.90%) | 18,561 (15.65%) | 0.003 |
| M48 - Spinal stenosis | 47,820 (25.34%) | 18,443 (26.34%) | 29,377 (24.77%) | 0.016 |
| M51 - Intervertebral disc disorders | 39,886 (21.14%) | 15,139 (21.61%) | 24,747 (20.86%) | 0.008 |
| M54 - Back pain/dorsalgia | 129,615 (68.65%) | 36,685 (52.38%) | 92,930 (78.34%) | 0.259 |
| S33 - Lumbar sprain/strain | 43,831 (23.23%) | 11,813 (16.87%) | 32,018 (26.99%) | 0.101 |
| All Claims |
Non-users (KM Use = 0/year) |
KM users (KM Use ≥ 1/year) |
||
| Claims-level | ( n = 9,590,400) | ( n = 2,807,578) | ( n = 6,782,822) | |
| Type of healthcare facility | 0.293 | |||
| Hospital level or above | 1,865,236 (19.45%) | 723,149 (25.76%) | 1,142,087 (16.84%) | |
| Clinic (WM) | 6,098,327 (63.59%) | 2,083,830 (74.22%) | 4,014,497 (59.19%) | |
| KM hospital | 36,516 (0.38%) | 599 (0.02%) | 35,917 (0.53%) | |
| KMclinic | 1,590,321 (16.58%) | - | 1,590,321 (23.45%) | |
| Type of treatment utilization | 0.007 | |||
| Outpatient | 9,475,532 (98.80%) | 2,763,735 (98.44%) | 6,711,797 (98.95%) | |
| Inpatient | 114,868 (1.20%) | 43,843 (1.56%) | 71,025 (1.05%) | |
| Year of treatment | 0.019 | |||
| 2010 | 715,279 (7.46%) | 202,120 (7.20%) | 513,159 (7.57%) | |
| 2011 | 776,904 (8.10%) | 227,350 (8.10%) | 549,554 (8.10%) | |
| 2012 | 880,591 (9.18%) | 258,989 (9.22%) | 621,602 (9.16%) | |
| 2013 | 915,677 (9.55%) | 263,076 (9.37%) | 652,601 (9.62%) | |
| 2014 | 946,950 (9.87%) | 260,574 (9.28%) | 686,376 (10.12%) | |
| 2015 | 981,532 (10.23%) | 271,319 (9.66%) | 710,213 (10.47%) | |
| 2016 | 1,044,268 (10.89%) | 304,337 (10.84%) | 739,931 (10.91%) | |
| 2017 | 1,091,174 (11.38%) | 328,414 (11.70%) | 762,760 (11.25%) | |
| 2018 | 1,080,550 (11.27%) | 330,302 (11.76%) | 750,248 (11.06%) | |
| 2019 | 1,157,475 (12.07%) | 361,097 (12.86%) | 796,378 (11.74%) |
*Percentages may exceed 100% within groups because patients may hold multiple spinal diagnoses during the study period
KM, Korean Medicine; WM, Western Medicine
Comorbidity profiles differed between WM- and KM-based encounters. Across all claims (Supplementary Table 1), back pain (M54) was the most common musculoskeletal diagnosis (12.3%), while hypertension (I10, 7.9%) and lipid disorders (E78, 3.3%) were the most prevalent systemic comorbidities, reflecting the cardiometabolic burden typical of patients with T2DM. In contrast, claims originating from KM institutions (Supplementary Table 2) were mostly musculoskeletal, with back pain accounting for 35.2% of diagnoses. The divergence suggests that KM visits in this population were primarily motivated by pain and functional complaints, with metabolic disease management remaining concentrated in WM settings.
Healthcare utilization
Total annual claims rose steadily from 715,279 in 2010 to 1,157,475 in 2019, alongside an increase in total expenditures from USD 25.5 million to USD 63.8 million, illustrating the growing healthcare burden posed by concurrent T2DM and spinal disorders. KM users demonstrated substantially higher utilization intensity, averaging 52–60 claims per patient annually compared to 36–43 among non-users. However, WM services remained the majority of claims even within the KM-user group, indicating that KM was used in addition to, rather than instead of, WM.
Both groups relied primarily on clinics (Table 2), but non-users made proportionally more visits to tertiary or general hospitals, whereas KM users more commonly engaged with primary care and KM clinics. This suggests distinct care-seeking patterns: KM users pursued more frequent, multi-modal treatment anchored in primary care, whereas non-users engaged more selectively but at higher-cost WM facilities.
Table 2.
Ten-year changes in the number of medical visits by KM utilization status and claim type
| Year | All patients | Non-Users | KM Users | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Total claims | Average claims per patient | Total claims | Average claims per patient | Total claims | Average claims per patient | |||||
| Total | WM | KM | Total | WM | KM | |||||
| 2010 | 715,279 | 51.19 | 202,120 | 40.03 | 513,159 | 389,908 | 123,251 | 57.51 | 43.7 | 13.81 |
| 2011 | 776,904 | 51.54 | 227,350 | 40.23 | 549,554 | 419,532 | 130,022 | 58.31 | 44.52 | 13.8 |
| 2012 | 880,591 | 54.13 | 258,989 | 43.33 | 621,602 | 478,382 | 143,220 | 60.4 | 46.48 | 13.92 |
| 2013 | 915,677 | 53.49 | 263,076 | 43.35 | 652,601 | 497,999 | 154,602 | 59.05 | 45.06 | 13.99 |
| 2014 | 946,950 | 53.65 | 260,574 | 42.27 | 686,376 | 523,341 | 163,035 | 59.76 | 45.56 | 14.19 |
| 2015 | 981,532 | 52.85 | 271,319 | 41.2 | 710,213 | 537,678 | 172,535 | 59.25 | 44.86 | 14.39 |
| 2016 | 1,044,268 | 51.61 | 304,337 | 40.37 | 739,931 | 560,659 | 179,272 | 58.29 | 44.16 | 14.12 |
| 2017 | 1,091,174 | 50.34 | 328,414 | 39.78 | 762,760 | 581,521 | 181,239 | 56.83 | 43.33 | 13.5 |
| 2018 | 1,080,550 | 46.59 | 330,302 | 36.81 | 750,248 | 570,202 | 180,046 | 52.76 | 40.1 | 12.66 |
| 2019 | 1,157,475 | 46.38 | 361,097 | 36.91 | 796,378 | 596,243 | 200,135 | 52.49 | 39.3 | 13.19 |
WM, Western Medicine; KM, Korean Medicine
Longitudinally, claim frequency per patient peaked earlier in the decade (2012–2013) and declined thereafter in both groups, though more markedly among non-users. KM users consistently maintained higher utilization across the entire period. Within KM users, WM claims per patient exceeded those of non-users every year, reaffirming that KM users maintained a dual-care pathway rather than substituting WM for KM.
Expenditures
Across both groups, consultation fees represented the largest expenditure category, reflecting the chronic and symptom-driven nature of spine-diabetes multimorbidity (Table 3). In non-users, anesthesia fees were the highest annual expenditure item, followed by consultation and diagnostic fees; diagnostic services exhibited the fastest growth (APC 13.2%) and the highest claim volume, indicative of a diagnostic- and procedure-intensive WM trajectory.
Among KM users, consultation again dominated, but injection fees, under which acupuncture is classified, showed the most pronounced differential compared to non-users, with 2.5-fold higher annual costs and nearly 4-fold higher claim counts. This aligns with the role of acupuncture as a core KM modality for musculoskeletal pain management in diabetic populations.
Although total expenditures were higher in KM users due to more frequent encounters, their average per-patient cost was comparable to that of non-users, reflecting substantially lower per-claim costs and a pattern of frequent but low-cost service use (Fig. 2).
Fig. 2.
Annual statistics for expenditures at the claim level
Medication use
Total medication claims increased annually (Fig. 3A), but average medication claims per patient peaked in 2012 and declined thereafter (Fig. 3B). This trend aligns temporally with nationwide pharmaceutical pricing reforms implemented in 2012, which reduced drug reimbursement prices and changed the relative incentives for prescribing (Supplementary Tables 4–5).
Fig. 3.
Medications prescribed to degenerative spine disorder patients with type II diabetes mellitus between 2010–2019. A. Total annual claims for medications. B. Average number of claims per patient per year. CIRC = Circulatory agents; NEURO = Neuropsychological agents; DIG = Digestive agents; RESP = Respiratory agents; PAIN/INFL = Pain and inflammatory agents; ALLERG = Anti-allergic agents; ANTI-INF = Anti-infective agents; DIAB = Antidiabetic agents; LIPID = Lipid-modifying agents; MRLX = Muscle relaxants; OTHER = Other medications
Digestive agents were the most frequently used medication category, followed by pain/inflammatory and circulatory agents—patterns consistent with high NSAID use, gastroprotective co-prescribing, and cardiovascular risk management in this dual-morbidity population. KM users had higher average claim counts per patient across nearly all medication categories except lipid-modifying agents, whereas non-users consistently incurred higher per-claim costs, especially for pain/inflammatory drugs. This contrast suggests that KM users receive smaller or less costly prescriptions during more frequent visits, while non-users obtain fewer but more expensive prescriptions.
Discussion
This nationwide 10-year analysis provides a comprehensive assessment to date of healthcare utilization patterns among Korean patients living with both T2DM and degenerative or mechanical spinal disorders—a high-need population whose dual morbidity amplifies pain, disability, and healthcare demands. The findings reveal several clinically and policy-relevant patterns that deepen our understanding of multimorbidity in Korea’s dual WM-KM healthcare system.
First, the steadily rising number of claims and total expenditures underscores the escalating burden of spine-T2DM multimorbidity on the healthcare system. T2DM has been associated with spinal degeneration, with proposed mechanisms including hyperglycemia-induced inflammation, microvascular compromise, and accumulation of AGEs, which may contribute to greater pain severity, more frequent chronic low back pain, and heightened pain-related disability; however, these mechanisms remain hypothesized rather than established [31, 32]. Biological mechanisms linking hyperglycemia, systemic inflammation, and advanced glycation end products to accelerated disc degeneration and neuropathic pain help explain why individuals with T2DM require prolonged and intensive care for spinal symptoms [24]. These biological mechanisms help explain the substantial and sustained demand for outpatient care observed in this study, as chronic spinal pain appears to be the dominant factor influencing healthcare visit frequency, rather than glycemic control alone. The near-exclusive reliance on outpatient services reinforces that long-term symptom management dominates the care needs of this group.
Second, the divergent patterns of care between KM users and non-users highlight characteristic care-seeking behaviors shaped by symptom burden, perceived treatment effectiveness, and accessibility. KM users pursued 1.3–1.4 times more annual encounters, engaged more frequently with primary care and KM clinics, and utilized low-cost conservative modalities that are particularly relevant for chronic pain and functional limitations such as acupuncture. This pattern can be conceptualized as “high-frequency, low-cost maintenance care” in contrast to the “low-frequency, high-cost repair care” observed among non-users. Regular primary care engagement has been associated with improved chronic disease management outcomes, including fewer emergency department visits and hospitalizations [33],, suggesting that frequent monitoring and non-pharmacologic interventions may mitigate the long-term consequences of chronic pain and multimorbidity. This distinction aligns with evidence that patients with T2DM and chronic pain increasingly seek non-pharmacologic therapies to manage symptoms while avoiding medication side effects, particularly opioid dependence [34]. The predominance of outpatient visits further suggests that chronic pain management, rather than acute episodic events, drives the majority of healthcare utilization in this cohort.
By contrast, non-users engaged more selectively with WM, relying more heavily on tertiary hospitals, advanced diagnostics, and higher-cost procedures. This “low-frequency, high-cost repair care” pattern may reflect delayed care-seeking or disease pregression necessitating intensive intervention. Literature have documented that T2DM significantly increases the risk of perioperative complications, including surgical site infections, reoperations, prolonged hospitalization, and mortality following spinal surgery [35, 36]. Additionally, T2DM patients undergoing spine surgery demonstrate slower postoperative recovery and worse functional outcomes compared to non-diabetic patients [16, 37]. Furthermore, diabetes is associated with significantly higher rates of prolonged postoperative opioid use,, with diabetes conferring over a two-fold increased risk [38, 39]. These findings suggest that WM-centric care pathways emphasizing late-stage surgical interventions may inadvertently expose patients to greater perioperative risks and higher likelihood of chronic opioid dependence.
Third, despite higher total utilization, KM users exhibited similar per-patient expenditures compared to non-users. This reflects the predominance of low-unit-cost services in KM (e.g., acupuncture), which may mitigate cost escalation even with frequent encounters. The markedly higher injection-fee category among KM users, which captures acupuncture, is consistent with KM’s traditional role in managing chronic spinal pain, and prior studies in diabetic populations have associated acupuncture with improvements in neuropathy symptoms [40], circulation, and potentially cardiovascular outcomes [41]. These findings suggest that KM may play a stabilizing role in multimorbidity management by providing ongoing symptom relief while reducing reliance on high-cost interventions.
Medication patterns further illustrate the differential management strategies: KM users had higher prescription frequency but lower per-prescription costs, whereas non-users obtained fewer but costlier medications, especially NSAIDs and other pain medications. The high use of digestive agents across all groups reflects the gastrointestinal side effects of chronic NSAID therapy—another indicator of the pain-centric nature of healthcare utilization in this population. The decline in medication claims following 2012 is temporally aligned with the national drug pricing reform of that year; because the present analysis does not employ a quasi-experimental design, this observation should be interpreted as temporal coincidence rather than as evidence of a policy effect, particularly in populations with chronic pain and multimorbidity.
From a health-system perspective, these patterns suggest that Korea’s dual medical system may inherently support differentiated care models for spine-T2DM multimorbidity. KM users receive frequent, conservative, non-invasive care whose association with function, pain control, and downstream utilization of high-intensity interventions could not be assessed in the present descriptive analysis and should be regarded as a hypothesis-generating observation warranting prospective evaluation. Non-users rely more on WM procedural and pharmacologic strategies that, while necessary for some, may increase exposure to surgery, opioids, and complications—clinical trajectories already known to be more severe in diabetic patients [42–44]. Integrated KM-WM care pathways warrant further evaluation as a hypothesis-generating direction; the present descriptive analysis cannot establish whether such pathways would reduce surgery, opioid escalation, or other clinical endpoints.
Nevertheless, several limitations constrain causal inference. The analysis was restricted to reimbursed services, excluded non-covered KM treatments, and relied on cross-sectional annual cohorts rather than patient-level longitudinal trajectories. Disease severity, functional status, and patient-reported outcomes were unavailable, and KM-use assignment was based on ≥ 1 annual visit, which may underrepresent dose-response effects. More rigorous designs such as matched cohort analyses, longitudinal models, and outcome-focused evaluations of hospitalization, surgery, or cardiovascular events are needed to explore causal relationships. Several additional caveats should be highlighted. First, HIRA-NPS is a stratified random sample (approximately 2–3% of the national population) drawn on age and sex, not on KM utilization; the observed proportion of KM users (62.9%) should therefore be interpreted as a within-cohort estimate among multimorbid patients enriched for active spinal complaints, and not as a national prevalence of KM use. Second, because no matching, weighting, or multivariable adjustment was applied, the observed differences between KM users and non-users are descriptive: the dorsalgia imbalance (SMD = 0.26) suggests that KM users represent a more pain-dominant subgroup, and group differences may largely reflect underlying patient characteristics rather than care-pathway effects. Third, the cohort is defined operationally by ICD-10 codes M47, M48, M51, M54, and S33, which capture a heterogeneous range of mechanical and degenerative spinal conditions; M54 (back pain) and S33 (sprain/strain) dominate, and the dataset does not allow imaging-based confirmation of structural disc degeneration, which is also commonly observed in asymptomatic older adults. The label “degenerative spinal disorders” should therefore be read as a coding-based proxy rather than a confirmed pathological entity. Finally, the “low-cost” and “high-cost” descriptors used throughout refer strictly to per-claim reimbursed amounts and do not adjust for disease severity, visit complexity, or non-reimbursed services; expenditure comparisons should be interpreted accordingly.
Despite these limitations, the findings have important implications for chronic disease management and policy. As the population ages and the prevalence of both T2DM and spinal disorders continues to rise, healthcare systems must accommodate increasing multimorbidity. The heavy outpatient burden, frequent pain-related prescribing, and large proportion of conservative care underscore the need for integrated, multimodal, and longitudinal care approaches. Policies that strengthen KM-WM coordination, broaden access to non-pharmacologic pain management, and promote early conservative treatment may improve quality of life, reduce high-risk interventions, and help contain long-term healthcare costs for this high-need group.
In summary, Korea’s dual medical system provides valuable complementary treatment avenues for patients with T2DM and spinal disorders. KM plays a substantial role in conservative pain management and may offer an option of long-term multimorbidity care. Expanded integration of KM into chronic care pathways warrants consideration for future health policy and reimbursement strategies.
Electronic Supplementary Material
Below is the link to the electronic supplementary material.
Acknowledgements
The authors would like to thank the Health Insurance Review and Assessment Service (HIRA) of South Korea for providing access to the HIRA-NPS database. We also acknowledge the contributions of all healthcare providers and patients whose data contributed to this research.
Abbreviations
- T2DM
Type 2 diabetes mellitus
- KM
Korean Medicine
- WM
Western Medicine
- HIRA-NPS
Health Insurance Review and Assessment Service-National Patient Sample
- ICD-10
International Classification of Diseases 10th Revision
- SMD
Standardized Mean Difference
- APC
Annual Percent Change
- AGEs
Advanced Glycation End Products
- NSAIDs
Nonsteroidal Anti-Inflammatory Drugs
Author contributions
DJK: Conceptualization, Data curation, Writing—original draft. JL: Methodology, Formal analysis, Writing—review & editing. YJL: Validation, Writing—review & editing, Supervision. IHH: Conceptualization, Writing—review & editing, Supervision. YSL: Conceptualization, Methodology, Formal analysis, Data curation, Writing—review & editing, Funding acquisition, Project administration. All authors read and approved the final manuscript.
Funding
This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: RS-2024-00444687). The study sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Data availability
The datasets used and/or analyzed during the current study are available from the Health Insurance Review and Assessment Service (HIRA) of South Korea. Access to HIRA-NPS data is subject to approval by the HIRA Deliberative Committee and compliance with their data use policies. Researchers wishing to access the data should apply directly to HIRA (https://opendata.hira.or.kr/home.do).
Declarations
Ethics approval and consent to participate
This study was performed in line with the principles of the Declaration of Helsinki. Ethical approval for an exemption was granted by the Institutional Review Board of Jaseng Hospital of Korean Medicine, Seoul, Republic of Korea (IRB No. JASENG 2024-11-006). Because this study used publicly available, fully de-identified secondary claims data, informed consent from individual participants was waived, in accordance with national regulations and institutional review board guidelines.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
In-Hyuk Ha, Email: hanihata@gmail.com.
Ye-Seul Lee, Email: yeseul.j.lee@gmail.com.
References
- 1.Karlsson FA, Berne C, Björk E, Kullin M, Li Z, Ma JY, et al. Beta-cell activity and destruction in type 1 diabetes. Ups J Med Sci. 2000;105(2):85–95. 10.1517/03009734000000056. PubMed PMID: 11095106. [DOI] [PubMed]
- 2.Reed J, Bain S, Kanamarlapudi V. A review of current trends with type 2 diabetes epidemiology, aetiology, pathogenesis, treatments and future perspectives. Diabetes Metab Syndr Obes. 2021;14:3567–602. Epub 20210810. 10.2147/dmso.S319895. PubMed PMID: 34413662; PubMed Central PMCID: PMC8369920. [DOI] [PMC free article] [PubMed]
- 3.American Diabetes A. 2. Classification and Diagnosis of Diabetes. Diabetes Care. 2017;40(Suppl 1):S11–24. 10.2337/dc17-S005. PubMed PMID: 27979889. [DOI] [PubMed] [Google Scholar]
- 4.Cole JB, Florez JC. Genetics of diabetes mellitus and diabetes complications. Nat Rev Nephrol. 2020;16(7):377–90. 10.1038/s41581-020-0278-5. Epub 20200512. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9(th) edition. Diabetes Res Clin Pract. 2019;157:107843. 10.1016/j.diabres.2019.107843. Epub 20190910. [DOI] [PubMed] [Google Scholar]
- 6.Bae JH, Han KD, Ko SH, Yang YS, Choi JH, Choi KM, et al. Diabetes Fact Sheet in Korea 2021. Diabetes Metab J. 2022;46(3):417–26. 10.4093/dmj.2022.0106. Epub 20220525. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Hoy D, March L, Brooks P, Blyth F, Woolf A, Bain C, et al. The global burden of low back pain: estimates from the Global Burden of Disease 2010 study. Ann Rheum Dis. 2014;73(6):968–74. 10.1136/annrheumdis-2013-204428. Epub 20140324. [DOI] [PubMed] [Google Scholar]
- 8.Knezevic NN, Candido KD, Vlaeyen JWS, Van Zundert J, Cohen SP. Low back pain. Lancet. 2021;398(10294):78–92. 10.1016/S0140-6736(21)00733-9. Epub 20210608. [DOI] [PubMed] [Google Scholar]
- 9.Gugliotta M, da Costa BR, Dabis E, Theiler R, Juni P, Reichenbach S, et al. Surgical versus conservative treatment for lumbar disc herniation: a prospective cohort study. BMJ Open. 2016;6(12):e012938. 10.1136/bmjopen-2016-012938. Epub 20161221. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Costa F, Oertel J, Zileli M, Restelli F, Zygourakis CC, Sharif S. Role of surgery in primary lumbar disk herniation: WFNS spine committee recommendations. World Neurosurg X. 2024;22:100276. 10.1016/j.wnsx.2024.100276. Epub 20240223. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Broz K, Walk RE, Tang SY. Complications in the spine associated with type 2 diabetes: the role of advanced glycation end-products. Med Nov Technol Devices. 2021;11. 10.1016/j.medntd.2021.100065. Epub 20210302. [DOI] [PMC free article] [PubMed]
- 12.Jacob L, Rathmann W, Koyanagi A, Haro JM, Kostev K. Association between type 2 diabetes and chronic low back pain in general practices in Germany. BMJ Open Diabetes Res Care. 2021;9(1). 10.1136/bmjdrc-2021-002426. PubMed PMID: 34266855; PubMed Central PMCID: PMC8286747. [DOI] [PMC free article] [PubMed]
- 13.Carvalho ESAP, Ferreira ML, Ferreira PH, Harmer AR. Does type 2 diabetes increase the risk of musculoskeletal pain? Cross-sectional and longitudinal analyses of UK biobank data. Semin Arthritis Rheum. 2020;50(4):728–34. 10.1016/j.semarthrit.2020.05.007. Epub 20200526. [DOI] [PubMed] [Google Scholar]
- 14.Takeda K, Watanabe K, Nori S, Yamane J, Kono H, Yokogawa N, et al. Influence of Diabetes Mellitus on Neurological Recovery in Older Patients With Cervical Spinal Cord Injury Without Bone Injury: A Retrospective Multicenter Study. Global Spine J. 2025;15(4):2274–85. Epub 20241104. 10.1177/21925682241297587. PubMed PMID: 39494742; PubMed Central PMCID: PMC11559898. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Chen Y, Yang L, Gao X, Tang A, He H, Xiong C, et al. The Impact of Diabetes Mellitus on Patient-Reported Outcomes of Chronic Low Back Pain with Modic Changes at One Year: A Prospective Cohort Study. Global Spine J. 2025;15(2):722–30. Epub 20231012. 10.1177/21925682231206962. PubMed PMID: 37824194; PubMed Central PMCID: PMC11877571. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Luo W, Sun RX, Jiang H, Ma XL. The effect of diabetes on perioperative complications following spinal surgery: a meta-analysis. Ther Clin Risk Manag. 2018;14:2415–23. Epub 20181212. 10.2147/TCRM.S185221. PubMed PMID: 30587998; PubMed Central PMCID: PMC6296189. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Wilson C, Czernik PJ, Elgafy H, Khuder S, Serdahely K, Rowland A, et al. Diabetes increases risk of lumbar spinal fusion complications: association with altered structure of newly formed bone at the fusion site. JBMR Plus. 2024;8(6):ziae053. 10.1093/jbmrpl/ziae053. Epub 20240415. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Chang Y, Chi KY, Song J, Chen CM, Lin HM. Association of GLP-1 receptor agonist use with risk of lumbar degenerative disease and spine surgery in patients with type 2 diabetes: a propensity score-matched cohort study. Global Spine J. 2025:21925682251383170. Epub 20250924. 10.1177/21925682251383170. PubMed PMID: 40988481; PubMed Central PMCID: PMC12460268. [DOI] [PMC free article] [PubMed]
- 19.Kowalski C, Ridenour R, McNutt S, Ba D, Liu G, Bible J, et al. Risk factors for prolonged opioid use after spine surgery. Global Spine J. 2023;13(3):683–8. Epub 20210415. 10.1177/21925682211003854. PubMed PMID: 33853404; PubMed Central PMCID: PMC10240594. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Schwartz CE, Borowiec K, Rapkin BD, Finkelstein JA, Sutherland T, Caan OB, et al. Opioid-use disorder and reported pain after spine surgery: Risk-group patterns in cognitive-appraisal processes in a longitudinal cohort study. N Am Spine Soc J. 2025;22:100605. PubMed PMID: 40256050; PubMed Central PMCID: PMC12008593. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Park SE, Ko S-H, Kim JY, Kim K, Moon JH, Kim NH, et al. Diabetes Fact Sheets in Korea 2024. Diabetes Metab J. 2025;49(1):24–33. 10.4093/dmj.2024.0818. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Choi Y, Leigh J-H, Jeon J, Lee GJ, Shin H-I, Bang MS. Trends in the Incidence and Etiology of Non-Traumatic Spinal Cord Injury in Korea: A Nationwide Population-Based Study From 2007 to 2020. jkms. 2023;38(18):e158–0. 10.3346/jkms.2023.38.e158. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Jung B, Bae S, Kim S. Use of western medicine and traditional Korean medicine for joint disorders: a retrospective comparative analysis based on Korean nationwide insurance data. Evid Based Complement Alternat Med. 2017;2017(1):2038095. 10.1155/2017/2038095. [DOI] [PMC free article] [PubMed]
- 24.Goo B, Jo M-g, Kim E-J, Lee H-J, Kim J-S, Nam D, et al. editors. Korean medicine clinical practice guidelines for lumbar herniated intervertebral disc in adults: based on grading of recommendations assessment, development and evaluation (GRADE). Healthcare: MDPI; 2022. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Hyun EH, Lee H-Y, Kim HW, Kim HM, Kim EJ, Kim SJ, et al. Clinical and cost-effectiveness of collaborative traditional Korean and Western medicine treatment for low back pain: A protocol for a prospective observational exploratory study. Medicine. 2018;97(39):e12595. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Cheon C, Jang B-H, Ko S-GA, Review of Major Secondary Data Resources Used for Research in Traditional Korean Medicine. Perspect Integr Med. 2023;2(2):77–85. 10.56986/pim.2023.06.002. [Google Scholar]
- 27.Hong JS, Kang HC. Body mass index and all-cause mortality in patients with newly diagnosed type 2 diabetes mellitus in South Korea: a retrospective cohort study. BMJ Open. 2022;12(4):e048784. 10.1136/bmjopen-2021-048784. Epub 20220401. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Lee WR, Lee GM, Son N, Han KT, Chun S, Son Y, et al. Is avoidable diabetes-related hospitalization in older patients with type 2 diabetes mellitus associated with increased health expenditure? A nationwide retrospective cohort study in South Korea. Prev Med Rep. 2025;49:102946. PubMed PMID: 39807182; PubMed Central PMCID: PMC11729008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Baek GG, Ha IH, Lee YJ, Shin YJ, Shin BC. Analysis of the utilisation of Chuna manual therapy for musculoskeletal disorders after its coverage under national health insurance in Korea: a retrospective analysis. BMJ Open. 2025;15(8):e094099. 10.1136/bmjopen-2024-094099. Epub 20250808. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat Med. 2009;28(25):3083–107. 10.1002/sim.3697. PubMed PMID: 19757444; PubMed Central PMCID: PMC3472075. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Huebschmann AG, Regensteiner JG, Vlassara H, Reusch JE. Diabetes and advanced glycoxidation end products. Diabetes Care. 2006;29(6):1420–32. 10.2337/dc05-2096. Epub 2006/05/30. [DOI] [PubMed] [Google Scholar]
- 32.Park C-H, Min K-B, Min J-Y, Kim DH, Seo KM, Kim D-K. Strong association of type 2 diabetes with degenerative lumbar spine disorders. Sci Rep. 2021;11(1):16472. 10.1038/s41598-021-95626-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.O’Connor PJ. Greater diabetes-care contact frequency is associated with more rapid achievement of care goals but interpretation of finding requires consideration of patient adherence and motivation. Evid Based Med. 2012;17(4):120–1. 10.1136/ebmed-2011-100415. Epub 20120314. [DOI] [PubMed] [Google Scholar]
- 34.Blaibel D, Fernandez CJ, Pappachan JM. Non-pharmacological interventions for diabetic peripheral neuropathy: Are we winning the battle? World J Diabetes. 2024;15(4):579–85. 10.4239/wjd.v15.i4.579. PubMed PMID: 38680695; PubMed Central PMCID: PMC11045424. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Luo M, Cao Q, Wang D, Tan R, Shi Y, Chen J, et al. The impact of diabetes on postoperative outcomes following spine surgery: A meta-analysis of 40 cohort studies with 2.9 million participants. Int J Surg. 2022;104:106789. 10.1016/j.ijsu.2022.106789. Epub 20220731. [DOI] [PubMed] [Google Scholar]
- 36.Drayton DJ, Birch RJ, D’Souza-Ferrer C, Ayres M, Howell SJ, Ajjan RA. Diabetes mellitus and perioperative outcomes: a scoping review of the literature. Br J Anaesth. 2022;128(5):817–28. 10.1016/j.bja.2022.02.013. Epub 20220314. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Liu Y, Ban DX, Kan SL, Cao TW, Feng SQ. The Impact of Diabetes Mellitus on Patients Undergoing Cervical Spondylotic Myelopathy: A Meta-Analysis. Eur Neurol. 2017;77(1–2):105–12. Epub 20161221. 10.1159/000453547. PubMed PMID: 27997913. [DOI] [PubMed] [Google Scholar]
- 38.Song W, Shibuya N, Jupiter DC. Association Between Opioid Use and Diabetes in Patients With Ankle Fracture Repair. Foot Ankle Int. 2022;43(4):560–7. Epub 20211201. 10.1177/10711007211058157. PubMed PMID: 34852657. [DOI] [PubMed] [Google Scholar]
- 39.Lawal OD, Gold J, Murthy A, Ruchi R, Bavry E, Hume AL, et al. Rate and Risk Factors Associated With Prolonged Opioid Use After Surgery: A Systematic Review and Meta-analysis. JAMA Netw Open. 2020;3(6):e207367. 10.1001/jamanetworkopen.2020.7367. Epub 20200601. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Lan L, Wang L, Sadeghirad B, Tang J, Liu Y, Couban RJ, et al. Acupuncture for the management of chronic diabetic peripheral neuropathy: a systematic review and meta-analysis of randomized controlled trials. Curr Pain Headache Rep. 2025;29(1):74. Epub 20250412. 10.1007/s11916-025-01386-z. PubMed PMID: 40220243. [DOI] [PubMed]
- 41.Jung H, Won T, Kim GY, Jang J, Yeo S, Lim S. Efficacy of acupuncture on cardiovascular complications in patients with diabetes mellitus in Korea: A nationwide retrospective cohort. J Integr Med. 2023;21(2):176–83. 10.1016/j.joim.2023.01.007. Epub 20230202. [DOI] [PubMed] [Google Scholar]
- 42.Zammit A, Coquet J, Hah J, El Hajouji O, Asch SM, Carroll I, et al. Postoperative opioid prescribing patients with diabetes: Opportunities for personalized pain management. PLoS ONE. 2023;18(8):e0287697. 10.1371/journal.pone.0287697. Epub 20230824. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Zhang X, Hou A, Cao J, Liu Y, Lou J, Li H, et al. Association of Diabetes Mellitus With Postoperative Complications and Mortality After Non-Cardiac Surgery: A Meta-Analysis and Systematic Review. Front Endocrinol (Lausanne). 2022;13:841256. PubMed PMID: 35721703; PubMed Central PMCID: PMC9204286. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Khalid SI, Wang R, Tiefenbach J, Abou-Mrad T, Massaad E, Elsamadicy A, et al. Impact of prediabetes on postoperative outcomes following short-segment transforaminal lumbar interbody fusions. N Am Spine Soc J. 2025;24:100811. 10.1016/j.xnsj.2025.100811. Epub 20251006. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the Health Insurance Review and Assessment Service (HIRA) of South Korea. Access to HIRA-NPS data is subject to approval by the HIRA Deliberative Committee and compliance with their data use policies. Researchers wishing to access the data should apply directly to HIRA (https://opendata.hira.or.kr/home.do).