Prevalence and Predictors of Hypoglycemia in South Korea

June 12, 2018
Sun-Young Park, PhD

Eun Jin Jang, PhD

Ju-Young Shin, PhD

Min-Young Lee, PhD

Donguk Kim, PhD

Eui-Kyung Lee, PhD

Volume 24, Issue 6

The prevalence and predictors of hypoglycemia in South Korean patients with type 2 diabetes were evaluated using a nationwide healthcare database.


Objectives: This study aimed to identify the prevalence and predictors of hypoglycemia in patients with type 2 diabetes (T2D) using South Korea’s nationwide healthcare database.

Study Design: Retrospective cohort and nested case-control analyses were conducted to estimate the prevalence and predictors of hypoglycemia, respectively.

Methods: A cohort of 2,273,481 patients with T2D was followed to estimate the 1-year prevalence of hypoglycemia. Total hypoglycemia was identified using outpatient, inpatient, or emergency department visit data containing a diagnosis code for hypoglycemia. Severe hypoglycemia was defined as an event with inpatient admission or emergency care. Within the T2D cohort, cases with hypoglycemia were identified, and up to 4 controls were randomly selected after matching by sex, age, and cohort entry date. Possible predictive factors included insurance type, medical institution type, hypoglycemic history, antidiabetic drugs, Charlson Comorbidity Index score, and diabetic complications. We conducted conditional logistic regression analyses to estimate adjusted odds ratios (aORs) and 95% CIs to identify predictors of hypoglycemia.

Results: The prevalences of total and severe hypoglycemia were 1.38% and 0.96%, respectively. Those with a history of hypoglycemia had the highest risk for a further hypoglycemic event (aOR, 16.71; 95% CI, 15.62-17.88). Use of combination therapy with insulin and sulfonylurea was highly associated with severe hypoglycemia (aOR, 15.09; 95% CI, 13.60-16.74). Among diabetic complications, the presence of nephropathy was the greatest predictive factor (aOR, 1.79; 95% CI, 1.73-1.85).

Conclusions: Patients with a history of hypoglycemia or receiving combined antidiabetic therapy must be appropriately managed to achieve optimal glycemic control without significant risk of hypoglycemia.

Am J Manag Care. 2018;24(6):278-286Takeaway Points

Our study evaluated the prevalence and predictors of hypoglycemia among patients with type 2 diabetes (T2D) using the nationwide South Korean healthcare database.

  • The prevalence of total and severe hypoglycemia in patients with T2D was 1.38% and 0.96%, respectively.
  • A history of hypoglycemia was a strong predictive factor, approximately 16.71 times higher, for a further hypoglycemic event, and combination therapy of insulin and sulfonylurea was highly associated with severe hypoglycemia.
  • Patients with hypoglycemic history or having combined antidiabetic therapy need to be appropriately managed to achieve optimal glycemic control without significant risk of hypoglycemia.

Maintaining adequate glycemic control in patients with diabetes is essential for preventing micro- and macrovascular complications and premature death. However, intensive glycemic control can increase the risk of hypoglycemia.1 Aggressively achieving an optimal glycated hemoglobin (A1C) level is associated with a 3-fold increased risk of hypoglycemia.2 Hypoglycemia is a potentially severe adverse effect of antidiabetic treatment and can negatively impact physical, mental, and social aspects of a patient’s life. Short-term effects of hypoglycemia include an increased risk of accidents or falls and decreased cognitive function. Long-term consequences include decreased productivity, increased risk for cardiovascular diseases, and fear of future episodes.3 Furthermore, hypoglycemia can lead to a significant cost burden on healthcare systems and society.4,5 Patients who experience hypoglycemia are less satisfied with, and less likely to adhere to, antidiabetic treatment, which threatens the efficacy of treatment.6,7 Therefore, preventing hypoglycemia is important in diabetes management.

Previous studies’ results have documented possible predictors of hypoglycemia, including a past history of hypoglycemic events, antidiabetic drugs, and diabetic complications. A history of hypoglycemia is known as the most important risk factor for subsequent hypoglycemic events.8 Hypoglycemia can occur as a side effect of some antidiabetic drugs that increase insulin production.9 Associated comorbidities (eg, liver or kidney disorders and micro- or macrovascular complications), behavioral risk factors (eg, alcohol consumption, exercise, and missed meals), and diabetes duration are also common risk factors for hypoglycemia.4,10

Hypoglycemia is a well-recognized complication in patients with type 1 diabetes (T1D), but it is often underestimated in patients with type 2 diabetes (T2D).11,12 Previous studies on T2D have focused on patients using insulin or specific oral medications or who visited specific medical institutions, rather than on the population as a whole.10,13-15 Furthermore, heterogeneity of study design, data collection methods, and target population make comparison of findings difficult across studies. Therefore, it is necessary to identify the risk of hypoglycemia in the entire population with T2D to provide an efficient management plan for optimal glycemic control with the lowest possible risk of hypoglycemia. This study aimed to evaluate the prevalence and predictive factors of hypoglycemia among patients with T2D using nationwide claims data.


Data Source

We utilized the nationwide healthcare database of the Health Insurance Review and Assessment Service (HIRA; Seoul, South Korea) between January 1, 2011, and December 31, 2013. HIRA is a governmental agency established to evaluate the accuracy of claims for National Health Insurance (covers approximately 96.7% of the overall population in South Korea) and National Medical Aid (covers approximately 3.3% of the population).16 The HIRA database is generated during claim reimbursement for healthcare services. This database is representative of the total population in South Korea and has advantages for generalization to the population. It includes information regarding demographic variables; all medical services provided, along with diagnostic codes (Korean Classification of Diseases version 7 [KCD-7]); and all prescription medications dispensed.

Patient records and information were anonymized and deidentified before the analysis. This study was approved by the institutional review board of Sungkyunkwan University. Informed consent was waived by the board.

Study Design and Patient Selection Criteria

Two methods were used to evaluate the prevalence and predictors of hypoglycemia among patients with T2D. First, a retrospective cohort study was used to estimate the 1-year prevalence of hypoglycemia in these patients. The study population included patients 20 years or older and diagnosed with T2D, defined as having a T2D diagnosis code (KCD-7 code E11) and receiving treatment with antidiabetic drugs. The index period for selecting patients was January 1, 2011, to December 31, 2012, to ensure a 1-year follow-up period for estimating the prevalence. We excluded patients diagnosed with T1D (KCD-7 code E10) or gestational diabetes (KCD-7 code O24).

Second, a nested case-control study was conducted to identify the predictors of hypoglycemia within the cohort with T2D. The cohort entry date was defined as the first date of receiving antidiabetic drugs, with a diagnosis of T2D between July 1, 2011, and December 31, 2013. A 6-month period was applied to assess possible predictive factors. Within the eligible cohort, we identified cases with hypoglycemia that occurred after the cohort entry date. For each case, we defined the index date as the date of the first eligible claim for a hypoglycemia-related visit. Controls were selected using individual matching. For each case, up to 4 controls matched for sex, age (±5 years), and cohort entry date were randomly selected without replication from patients with T2D without a diagnosis of hypoglycemia after the cohort entry date. The index date for each control was considered equal to the index date of their matched case.

Prevalence of Hypoglycemia

The 1-year prevalence of hypoglycemia was defined as the percentage of patients who experienced 1 or more hypoglycemic episode during the 1-year follow-up period after being screened for T2D. Total hypoglycemia was identified on the basis of the first medical encounter for hypoglycemia (outpatient, inpatient, or emergency department [ED] visits) containing any of the following KCD-7 diagnosis codes: E11.63 (T2D, with hypoglycemia), E12.63 (malnutrition-related diabetes mellitus, with hypoglycemia), E13.63 (other specified diabetes mellitus, with hypoglycemia), E14.63 (unspecified diabetes mellitus, with hypoglycemia), E16.0 (drug-induced hypoglycemia without coma), E16.1 (other hypoglycemia), or E16.2 (hypoglycemia, unspecified). Also, the setting of hypoglycemia diagnosis was used to capture information on the severity of hypoglycemia. Severe hypoglycemia was defined as an event with inpatient admission or emergency care. Annual prevalence was also presented from 2011 to 2013.

Potential Predictors of Hypoglycemia

We considered the following factors as potential predictors of hypoglycemia: insurance type, medical institution type, hypoglycemia history, antidiabetic drugs, Charlson Comorbidity Index (CCI) score, and diabetic complications.

Insurance type was classified as insured or medical aid beneficiaries. It was used as a proxy indicator of income levels because the medical aid program is intended for low-income households.17 The type of medical institution was categorized as clinic, hospital, general hospital, or tertiary hospital based on the most-visited medical institution during the treatment period. A history of hypoglycemia was defined as having a claim for an outpatient, inpatient, or ED visit for hypoglycemia occurring during the 6-month period before cohort entry.

Antidiabetic drugs were evaluated using prescription data from the 30 days preceding the index date. Medications included insulin, sulfonylureas, nonsulfonylurea secretagogues, biguanides, α-glucosidase inhibitors, dipeptidyl peptidase-4 (DPP-4) inhibitors, thiazolidinediones, glucagon-like peptide-1 (GLP-1) analogues, and sodium glucose cotransporter-2 (SGLT-2) inhibitors. Thiazolidinediones, GLP-1 analogues, and SGLT-2 inhibitors were combined into 1 category (other monotherapy) due to limited sample size. When using 2 or more multiple drug combinations, we categorized with and without insulin and/or sulfonylureas.

The CCI score, a weighted index that accounts for the number and severity of comorbidities, was used as a general marker of comorbidity. The score was assessed using algorithms by Quan et al during the 6-month period before the index date.18 Diabetic complications were assessed using methods published by Park et al during the 6-month period prior to the index date.19 We defined diabetic complications as having at least 2 outpatient visits or an inpatient visit with a hospital stay of 2 or more days, including diagnosis for the following complications: retinopathy, nephropathy, neuropathy, peripheral vascular disease, cerebrovascular disease, or cardiovascular disease.

Statistical Analysis

In the analysis of prevalence, baseline characteristics were expressed as means and SDs or frequencies and percentages. We measured 1-year prevalence of hypoglycemia over 3 years, and each year for 2011, 2012, and 2013. We also conducted subgroup analysis to identify differences in prevalence according to age group, sex, insurance type, medical institution type, antidiabetic drugs, CCI score, and diabetic complications.

In the analysis of predictors, descriptive statistics were used to compare the baseline characteristics between selected cases and matched controls. For categorical variables, frequencies and percentages were reported. Differences between cases and controls were analyzed using Pearson’s χ2 test (or Fisher’s exact test). For continuous variables, means and SDs were presented. Mean differences between groups were analyzed using Student’s t test. We conducted a conditional logistic regression to identify independent predictors of hypoglycemia. Conditional logistic regression analysis is a method used to minimize bias arising from unconditional logistic analysis of matched data.20,21 Adjusted odds ratios (aORs) are presented together with 95% CIs and P values. The goodness of fit of the model was evaluated using the Pearson’s χ2 test statistic (χ2) and likelihood ratio test statistic (G2). Statistical significance was determined at the .05 level. All statistical analyses were performed using SAS, version 9.3 (SAS Institute Inc; Cary, North Carolina).


Baseline Patient Characteristics

In the analysis of prevalence, 2,273,481 patients with T2D were selected for the eligible cohort (Figure). The baseline characteristics of the cohort are summarized in Table 1. The mean age was 60.79 years, and 54.02% were men. For most patients (81.44%), 2 or more medications were prescribed, of whom 45.68% were prescribed sulfonylureas and medications other than insulin.

In the analysis of predictors, 61,060 of 2,479,265 patients with T2D were identified as having hypoglycemia. All cases except 1 (61,059 cases) were matched with 244,233 controls (Figure). The baseline characteristics of the groups showed statistically significant differences, except for the type of insurance (Table 2). There were 20,944 patients with severe hypoglycemia and 83,776 matched controls. The baseline characteristics of patients with severe hypoglycemia were similar to those of patients with total hypoglycemia.

Prevalence and Predictive Factors of Hypoglycemia

Table 3 [part A and part B] presents the prevalence of hypoglycemia. The 1-year prevalence of total hypoglycemia was 1.38%. The prevalence by year was the highest in 2012, at 1.41%, and the annual prevalence between 2011 and 2013 was between 1.35% and 1.41%, similar to the 1-year prevalence. The prevalence of severe hypoglycemia was 0.96%, and the prevalence between 2011 and 2013 ranged from 0.90% to 0.96%.

The results for predictors of hypoglycemia are presented in Table 4. With enrollees who mainly visited clinics as the reference group, the aORs for hypoglycemia were 1.96 (95% CI, 1.89-2.03), 1.67 (95% CI, 1.63-1.72), and 1.15 (95% CI, 1.11-1.20) for those mainly visiting a hospital, general hospital, and tertiary hospital, respectively. A previous history of hypoglycemia was associated with the highest risk of a future hypoglycemic event (aOR, 16.71; 95% CI, 15.62-17.88). Insulin and sulfonylurea combination therapy had an aOR of 10.06, which was confirmed to be the strongest predictor among all of the antidiabetic agents analyzed. Of the monotherapy medications evaluated, insulin (aOR, 5.22; 95% CI, 4.87-5.59), sulfonylureas (aOR 1.70; 95% CI, 1.60-1.80), and nonsulfonylurea secretagogues (aOR 2.19; 95% CI, 1.94-2.46) were associated with an increased risk of hypoglycemia. Biguanides (P = .259), α-glucosidase inhibitors (P = .442), DPP-4 inhibitors (P = .605), and other monotherapy (P = .545) had no effect on hypoglycemia. Compared with enrollees having a CCI score of 0, a CCI score of 1, 2, and 3 or higher increased the hypoglycemia risk. The presence of a claim for diabetic complications, including retinopathy (aOR, 1.05; 95% CI, 1.02-1.08), nephropathy (aOR, 1.79; 95% CI, 1.73-1.85), neuropathy (aOR, 1.04; 95% CI, 1.02-1.07), cerebrovascular disease (aOR, 1.28; 95% CI, 1.24-1.32), and cardiovascular disease (aOR, 1.17; 95% CI, 1.15-1.20), was associated with an increased risk of hypoglycemia.

Analysis of the predictors affecting severe hypoglycemia was mostly similar to the results for total hypoglycemia. However, biguanide monotherapy was associated with a significantly lower risk of severe hypoglycemia compared with no evidence of antidiabetic drug availability (aOR, 0.89; 95% CI, 0.81-0.98). The presence of retinopathy and peripheral vascular disease had no impact on the occurrence of severe hypoglycemia (P = .362 and P = .208, respectively).


We evaluated the prevalence and predictors of hypoglycemia among patients with T2D using the nationwide South Korean healthcare database. A history of hypoglycemia and the combined use of antidiabetic drugs were strong predictors of hypoglycemia. Karter et al reported a hypoglycemia risk stratification tool, which referred to previous episodes of hypoglycemia and insulin or sulfonylurea use as criteria indicating high risk of hypoglycemia, in line with our findings.22 These predictors need to be adequately addressed to achieve optimal glycemic control without the significant risk of hypoglycemia.

The prevalence of total hypoglycemia was 1.38%, lower than previously reported. Quilliam et al and Bron et al reported that the prevalence in patients with T2D receiving oral diabetic therapy was 2.29% and 3.50%, respectively.11,14 Our prevalence in patients with T2D taking insulin and sulfonylurea was higher (3.2%), but still lower than that found in a US study that reported a prevalence of 6.27% among patients with T2D who started treatment with insulin glargine.13 In comparison with results from other countries, our lower results may be due to the small number of patients seeking medical care even though hypoglycemia occurred, which might be due to their unawareness or concealment of hypoglycemia. Underreporting of hypoglycemic events limits the ability of physicians to take appropriate action to effectively manage hypoglycemia, which can increase the risk of severe hypoglycemia. Henderson et al reported that impaired symptomatic awareness is associated with a 9-fold increased risk of severe hypoglycemic events in patients with T2D.23 This highlights the importance of patient education about early recognition of hypoglycemic symptoms, various preventive strategies, and available treatment options. A structured education program can improve hypoglycemia awareness and the self-management ability of patients.24

A history of hypoglycemia was the strongest predictor of hypoglycemia recurrence, at 16.71 times. In the study from Xie et al, previous inpatient/ED and outpatient hypoglycemia were associated with ORs of 6.55 and 7.65, respectively, for subsequent hypoglycemic episodes.13 Our estimated ratio was more than twice as high as those values. These results suggest that more rigorous management of recurrent hypoglycemia is needed in South Korea. Patients with a history of hypoglycemia should be educated in hypoglycemic management skills, including periodic self-monitoring, rechecking blood glucose after a hypoglycemic episode, and dose adjustment of their medication.

Of all of the antidiabetic regimens analyzed, combination therapy of insulin and sulfonylurea was associated with the highest risk of hypoglycemia. For severe hypoglycemia, this combination therapy was the strongest predictor, surpassing even a history of hypoglycemia (aORs, 15.09 vs 7.74, respectively). Insulin has been reported as the most common predictor for risk of hypoglycemia in diabetic patients.25 Sulfonylurea is also known to frequently induce hypoglycemia because it promotes insulin secretion regardless of blood sugar levels. Thus, this combination is becoming less favored by clinicians and sulfonylurea is recommended to be avoided when prandial or premixed insulin is used.26 Careful choice of glucose-lowering medication, by considering the risk of hypoglycemia, is necessary to maintain good glycemic control.27,28 Physicians should refine the treatment strategy for each patient using an individualized approach to achieve glycemic targets.

Comorbidity burden and diabetic complications have considerable influence on hypoglycemia. Among the complications, the risk of nephropathy was the greatest. Factors that are associated with an increased risk of hypoglycemia in diabetic nephropathy include reduced renal gluconeogenesis, deranged metabolic pathways, and prolonged clearance of hypoglycemic agents.29 In addition, patients undergoing dialysis may have an increased risk of hypoglycemia due to malnutrition caused by uremia and a lack of calories from vomiting.30 Accurate diagnosis and treatment of complications in diabetic patients are needed to prevent hypoglycemia. Patients with T2D who also have nephropathy should be carefully monitored by classifying them as a group at high risk for hypoglycemia.

Strengths and Limitations

Our study had several strengths. First, our results provide evidence for establishing effective strategies to obtain adequate glycemic control while minimizing the risk of hypoglycemia in patients with T2D. Second, our study utilized a nationwide healthcare database that covers the entire South Korean population. This database contains comprehensive information about medical services and reflects the actual healthcare environment, unlike primary data collected under strict controls. As a result, this study provides real-world evidence based on analysis of the data of a large representative population.

Although nationwide healthcare databases offer comprehensive information, there are some limitations. First, we could not identify mild or self-treated hypoglycemic events requiring nonmedical management. Second, because the data do not address unobserved variables, such as lifestyle, patient genetic factors, and clinical severity, we could not directly assess the association with known risk factors of hypoglycemia, including body mass index, alcohol use, exercise, diet, and A1C. Third, the accuracy of diagnosis has been an issue owing to the nature of claims data, which are collected for the purpose of reimbursing healthcare services rather than for research. In claims data, a certain diagnosis in a patient would not necessarily mean that he or she has the disease corresponding to the diagnosis. However, according to validity analysis of the diagnostic code, the accordance rate of diagnosis codes among diabetic patients was 87.2% and 72.3% for inpatients and outpatients, respectively.31 Furthermore, this study defined the target population by supplementing the diagnosis code with disease-specific interventions to address possible discrepancies between the diagnosis and actual health condition. Fourth, overdiagnosis may be a possibility in the outpatient diagnosis of hypoglycemia, especially among patients with a history of hypoglycemia. We aimed to estimate the representative prevalence and predictive factors of total hypoglycemia; thus, total hypoglycemia, including outpatient, inpatient, or ED visits, was analyzed apart from severe hypoglycemia. However, the diagnosis code of hypoglycemia is unlikely to have remained in the data because it is not a chronic disease. A result similar to ours, when the cohort was restricted to patients without hypoglycemia before the cohort entry date, also supports that overdiagnosis might not be an issue in our study (results not shown). However, careful approach should be taken in the interpretation of our results, considering the possibility of inaccurate diagnoses in the claims data. Fifth, this study is derived from the Korean population. Thus, direct extrapolation of the results to other populations should be exercised with caution.


Our study showed that a history of hypoglycemia and having combined antidiabetic drugs were the strongest predictors of hypoglycemia. These factors need to be adequately addressed to achieve optimal glycemic control without significant risk of hypoglycemia. Hypoglycemia is considered a serious barrier for physicians and patients to overcome while they strive to safely achieve optimal glycemic goals. Therefore, identification and prevention of predictors for hypoglycemia are important clinical issues for patients with T2D. This study can be used to improve the effectiveness of diabetes treatment through more effective management of hypoglycemia.Author Affiliations: School of Pharmacy, Sungkyunkwan University (SYP, JYS, MYL, EKL), Gyeonggi-do, South Korea; Department of Information Statistics, College of Natural Science, Andong National University (EJJ), Andong, South Korea; Department of Statistics, Sungkyunkwan University (DK), Seoul, South Korea.

Source of Funding: None.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (SYP, EJJ, JYS); acquisition of data (SYP, MYL, DK); analysis and interpretation of data (SYP, EJJ, MYL, DK); drafting of the manuscript (SYP, JYS); critical revision of the manuscript for important intellectual content (JYS, EKL); statistical analysis (EJJ, DK); obtaining funding (EKL); administrative, technical, or logistic support (MYL); and supervision (EKL).

Address Correspondence to: Eui-Kyung Lee, PhD, School of Pharmacy, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do 16419, South Korea. Email:

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