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. 2023 Jul 17;10(10):6845–6855. doi: 10.1002/nop2.1933

Factors affecting unmet medical needs of patients with diabetes: A population‐based study

Ji Young Kim 1, Youngran Yang 2,✉
PMCID: PMC10495713  PMID: 37461150

Abstract

Aims

The purpose of this study was to identify unmet medical needs and related factors in patients with diabetes.

Design

A cross‐sectional study.

Methods

Participants included 2269 diabetes patients aged >19 years by using data from the National Health and Nutrition Examination Surveys. A complex sample design multiple logistic regression analysis was performed.

Results

The study found that 8.7% of diabetes patients experienced unmet medical needs, and it was found to be higher for those who thought their self‐assessed health status was unhealthy and often felt stressed in their daily life. Gender and education level had a moderating effect on income level on unmet medical needs experience.

Conclusion

These findings have important implications for nursing practice in the management of diabetes. Nurses can develop targeted interventions that address the specific needs of patients who are at risk for unmet medical needs, particularly those from low‐income backgrounds. By considering the factors that contribute to unmet medical needs and the moderating effect of income level, nurses can improve patient outcomes and reduce the burden of diabetes.

Keywords: diabetes mellitus, healthcare, income, nursing, patients

1. BACKGROUND

Diabetes is a major health concern worldwide. As of 2019, a total of 463 million diabetics worldwide were estimated, with one in 11 adults suffering from diabetes, estimated to be 578 million in 2030 and more than 700 million in 2045 (International Diabetes Federation, 2019). Direct deaths from diabetes were estimated at 1.5 million, ranking ninth as the leading cause of death, with 48% of all deaths occurring before the age of 70, and the early mortality rate from diabetes increased by 5% from 2000 to 2016 (World Health Organization [WHO], 2021). Diabetes is a direct cause of death, such as through stroke and myocardial infarction, and causes severe disabilities, such as blindness, renal failure and foot amputation; however, it can be avoided or delayed by proper diet, physical activity, medication, regular examination and complications (World Health Organization, 2021). The United Nations aims to reduce the premature mortality rate from non‐communicable diseases (e.g. diabetes and hypertension) by one‐third by 2030 according to the Sustainable Development Goals (SDGs) Target 3.4. (United Nations, 2015). However, globally, there is a significant unmet need among 24%–54% of people with diabetes, who have not achieved or maintained glycaemic control, which is essential to reduce the risk of mortality (Raccah et al., 2017).

In Korea, Diabetes is the sixth cause of death (Statistics Korea, 2021) and the prevalence rate increased rapidly from 11.8% in 2012 to 13.8% in 2018, and about one in seven adults over the age of 30 suffer from diabetes (Korean Diabetes Association, 2020). Diabetes has a high mortality and prevalence rate and is emerging as a major health problem with a high social burden owing to a steady increase in the number of doctors and medical expenses of more than 10% every year (Korea Health Promotion Institute, 2021). Particularly, after the COVID‐19 pandemic, there was a decrease in healthy living practices for diabetes management, the medical use rate and public medical specialized projects targeting the vulnerable (Cheong et al., 2021). Unlike other diseases, in diabetes, active self‐management is very important during the prevalence period, and the use of medical services such as continuous outpatient treatment, hospitalization and emergencies is essential; therefore long‐term diabetes management and treatment lead to a burden on patients' medical expenses, especially in low‐income families (WHO, 2016).

The 1946 World Health Organization Constitution (WHO, 2017) presented health rights to enjoy the highest level of health as a basic right of all human beings. It stipulates that all countries should provide timely, acceptable and affordable health services to all citizens without discrimination based on race, age or other status, and control health determinants to enjoy the best health. However, realistic health and management of diabetes have a large gap based on income level. In low‐income situations, essential medical services cannot be used due to economic factors and transportation, delaying diabetes diagnosis, which leads to inappropriate diabetes treatment and management (Kang et al., 2019). As a result, low‐income diabetic patients have a low HbA1c maintenance rate (84.6% for the high‐income group vs. 49.6% for the low‐income group) (Choi et al., 2018), poor continuity of outpatient visits and high mortality (Korean Diabetes Association, 2007; Rawshani et al., 2016). Another study revealed that medical benefit recipients (income level 0 quintile) had lower HbA1c levels, medication compliance and treatment persistence than health insurance subscribers and had significantly higher hospitalization rates for various short‐ and long‐term complications and unregulated diabetes between 2016 and 2018 (Kang et al., 2019). The HbA1c test implementation rate, which is a basic routine monitoring test for evaluating the blood sugar level for the last 3 months, was 1.55 times higher in the highest income quintile (five quintiles) than in the lowest income quintile (zero quintile) (Kang et al., 2019). In Taiwan, despite the introduction of the national health insurance (NHI), the accompanying disease index (Charlson Comorbidity Index), one of the indicators for measuring comorbidities in the poverty group, was 11.0%; the incidence of diabetes was 20.4 per 1000 people, which was 1.5 times higher than that of the 9.2 in the high‐income group, 2.2 times higher hospitalization rate, a 60% lower outpatient visit rate (odds ratio [OR] 0.4), and low diabetes test rates (HbA1c, LDL cholesterol, triglycerides and retinopathy) (Hsu et al., 2012).

The gap in medical use can be evaluated in terms of examination, treatment and management. According to socioeconomic factors, the gap in hospitalization, outpatient service use and medical expenses can be analysed; however, it is difficult to know whether health needs are properly met to directly evaluate unmet medical needs (Kang et al., 2019).

Unmet medical needs refer to health conditions or diseases for which there are currently no satisfactory treatments available or where existing treatments are inadequate in terms of efficacy, safety, tolerability or convenience (Bennett et al., 2012). If a person in need of treatment at an appropriate time does not receive it and their unmet healthcare needs are high, the disease may worsen and complications may occur (Bennett et al., 2012). Korea's unmet healthcare needs vary greatly depending on the income level. The lowest‐income class had 16.4% of unmet medical needs, while the highest‐income class had 7.5%, with the gap reaching 8.9% (Kang et al., 2019), which is widening year by year. The COVID‐19 pandemic had the greatest impact on low‐income patients with diabetes, which significantly lowered their access to blood glucose testing compared to that before the pandemic (Patel et al., 2022).

The Andersen model or Behavioural Model of Service Utilization (Andersen & Newman, 1973) is a widely‐used framework for understanding healthcare utilization behaviour. The model posits that healthcare utilization is determined by a complex interplay of predisposing, enabling and need factors, which are related to personal characteristics and a range of contextual conditions. Predisposing factors include demographic and sociological characteristics that are inherent to individuals (e.g. gender, education and health beliefs), enabling factors refer to those that enable or hinder healthcare utilization (e.g. financial, family resources and access to care), and need factors to reflect the need for medical services, such as disability or disease (e.g. symptom severity) (Andersen, 2008). The Behavior Model of Service Utilization is used in research to analyse health outcomes, such as medical services, medical systems and health status, and provides a useful basis for predicting the use of medical services (Ferris et al., 2016; McDonald & Conde, 2010). Nursing plays a critical role in managing and treating diabetes, and addressing the unmet medical needs of patients with diabetes is a crucial aspect of providing quality care (Alshammari et al., 2021). While previous research has identified various factors that may contribute to unmet medical needs in patients with diabetes, there is a need for further population‐based studies that examine the prevalence and impact of these factors.

Our study builds on previous research that has identified various factors associated with unmet medical needs, including studies that have focused on the general population (Yoon et al., 2019) and specific subgroups of patients with diabetes, such as those on insulin therapy (Beljić Živković et al., 2019; Tien et al., 2019) or insured individuals with diabetes (Fitzpatrick et al., 2021). However, there is a need for further population‐based studies that examine the prevalence and impact of these factors in a more comprehensive manner. Our study aims to identify the factors associated with unmet medical needs in patients with diabetes and to explore the moderating effect of income level on these factors. By addressing this gap in knowledge, our study makes an original contribution to the existing literature and has important implications for nursing practice in the management of diabetes. Specifically, our findings can inform the development of evidence‐based interventions that address the unique needs of patients with diabetes and improve patient outcomes.

Thus, this study aimed to identify the unmet medical needs and related factors of diabetic patients based on the Behavior Model of Service Utilization and to identify the moderating effect of income level on gender, age, spouse, education level and region. The results of this study can be used as basic data for policies to reduce the gap in medical use in patients with diabetes, access affordable medical services in a timely manner without discrimination based on income, and ensure universal health coverage.

2. METHODS

2.1. Study design

This study is a secondary analysis of descriptive research using data from the 7th (2016–2018) and 8th (2019) National Health and Nutrition Surveys conducted by the Korea Centers for Disease Control and Prevention; the research model is shown in Figure 1.

FIGURE 1.

FIGURE 1

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Conceptual framework of this study.

The National Health and Nutrition Survey is a nationally representative survey based on a complex sample design that selects participants by stratifying the extraction frame criteria for a city, dong, eup/myeon, and housing types (general housing, apartment) and collects data by interviewers' visits, examinations or self‐entry.

This study used raw data which were provided to researchers for academic research, from the 7th (2016–2018) and 8th (2019) National Health Examination and Nutrition Survey conducted by the Korean Centers for Disease Control and Prevention, and the data were downloaded and used directly after obtaining approval for use by entering the researcher's e‐mail on the institution's website (http://knhanes.kdca.go.kr).

2.2. Participants

The study analysed data from the 7th (2016–2018) and 8th (2019) National Health Examination and Nutrition Survey, which involved 32,379 participants. From this sample, 2269 individuals aged 19 or older diagnosed with diabetes and with complete data on the relevant variables were included in the analysis. Individuals who were missing data on result variables, unmet healthcare items, modulating variables and income‐level items were excluded from the analysis.

To ensure that the study had adequate statistical power, power calculations using G*Power 3.1 were performed to determine the necessary sample size to detect an OR of 1.3 with a power of 0.80 and a type I error rate of 0.05. The prevalence of exposure was assumed to be 8.7%. The calculations indicated that a sample size of 1420 individuals would be necessary to meet the criteria. Since the current study analysed data from 2269 individuals, the sample size was sufficient to meet the power requirement and achieve the desired level of statistical significance.

2.3. Variable measurements

2.3.1. Outcome variable: Unmet medical needs

In this study, unmet medical needs of patients with diabetes were considered as the dependent variable, as answering ‘Yes’ to a question, ‘Have you ever experience that you could not go to a hospital when you wanted to go?’ In cases when the participants of medical services were unable to use the services due to various circumstances, ‘If you could not go to a hospital when you wanted to go, for what reason could you not go?’ They can choose a, reason from ‘have no time (because the door does not open at the time I want, because I cannot leave work, because there is no one to look after the child, etc.)’, ‘since the symptom was mild (it will get better with time)’, ‘for economic reasons (burden of medical expense)’, ‘due to inconvenient transportation of long distances’, ‘I do not want to wait for a long time at the hospital’, ‘because it is difficult to make a reservation at a hospital’, ‘because I am afraid to receive treatment (examination or treatment)’ or ‘other reasons’. The highest frequency response among these reasons, were ‘don't have time’, ‘mild symptom’ and ‘economic reasons’ when further analysis of each response was conducted, while the response from the remaining reasons was limited to analyse; hence, they were defined as ‘other reasons’.

2.3.2. Variables of the Behavior Model of Service Utilization

Predisposing factors

Gender, age, spouse, educational level and residential area were investigated as well. Age was reclassified to under 65 years of age and 65 years or older based on the age at the time of the survey, and spouse status was classified into ‘yes (marriage, cohabitation)’, and ‘no (unmarried, separated, bereavement, divorce)’. The level of education was classified as less than elementary school, middle school, high school and college graduates, and the residential areas were classified as urban (dong) and rural (eup/myeon).

Enabling factors

Health insurance, private medical insurance and economic activities were included as enabling factors. The types of health insurance were classified into national health insurance and medical aid, and private medical insurance was classified by subscription status. Economic activities were classified as ‘yes’ or ‘no’, in terms of having a job.

Need factors

Subjective health status, stress level, depression and the number of chronic diseases were investigated for need factors. Subjective health status was classified as healthy (very good, good), moderate or bad (bad, very bad). The stress level was classified as ‘feeling a lot’ and ‘feeling less’ by asking, ‘How much stress do you feel in your daily life?’. Depression was classified as being diagnosed by a doctor. The number of chronic diseases was reclassified to one, two and three or more according to the response to whether they had been diagnosed by a doctor with hypertension, dyslipidaemia, stroke, heart disease (myocardial infarction and angina), kidney failure and various cancers.

2.3.3. Moderation variable: Income level

Household survey data were used to determine household income levels during the health survey. Participants were classified into first (Q1), second (Q2), third (Q3) and fourth (Q4) quartiles according to the standard income quartiles of the sample household. For reference information, the criteria for basic livelihood recipients with a monthly household income below 50% of the South Korean national median income is 5.1 million Korean won for 2022, based on four members of the household (Statistics Korea, 2022).

2.4. Ethical considerations

All written consent forms were collected from the National Health Examination and Nutrition Survey, and data were de‐identified in compliance with the Personal Information Protection Act and the Statistics Act. The research ethics review committee of the Korea Centers for Disease Control and Prevention approved the tools used in the investigation. For the ethical consideration of this study, it was conducted after obtaining approval from the Bioethics Review Committee of REDACTED University (IRB number: 2021‐12‐016).

2.5. Data analysis

According to the analysis guidelines provided by the National Health Examination and Nutrition Survey, we used an analysis tool based on a composite sample design and included all missing data in the analysis variables to minimize the possibility of convenience in variance estimates (Data Solution, 2016). Our analysis consisted of three main steps. First, we calculated unweighted frequencies and weighted percentages through a complex sample frequency analysis to identify the characteristics of the study participants. Second, we conducted a Rao‐Scott test through a complex sample cross‐analysis to understand the differences in unmet medical experiences according to the characteristics of the study participants. Finally, we used a complex sample multiple logistic regression model to understand the moderating effect of income level on the unmet medical experience of patients with diabetes. Specifically, we conducted three logistic regression models. Model 1 included demographic and health predictors and chronic disease predictors. Model 2 included all the predictors in Model 1, plus income level as a moderating variable. In Model 3, interaction terms between income level and each of the demographic and health predictors were added to Model 2 to explore potential moderation effects.

IBM SPSS ver.26.0 statistical program (IBM Corp.) and EasyFlow Statistics macro (Lee, 2020) were employed for the data analysis, and the calculated OR and 95% confidence interval (CI), and the statistical significance test level was set to p‐value < 0.05.

3. RESULTS

3.1. Differences in unmet medical needs according to the characteristics of the study participants

Table 1 shows the differences in unmet medical needs according to the participant characteristics. Statistically significant differences were found in unmet medical needs according to sex, spouse presence, education level, residential area, health insurance type, subjective health status, stress and depression. Of the 2269 participants in the study, 8.7% experienced unmet medical needs; they included females (11.1%), those who did not have a spouse (13.8%), less than elementary school graduates (10.8%) and rural residents (12.4%). Beneficiaries (17.8%) of medical aid experienced a very high demand for unmet healthcare compared to national health insurance subscribers (8.0%), and the rate of unmet healthcare experience was significantly higher when they thought their usual health condition was poor (13.6%). The rate of unmet medical experiences was higher than that of those who felt a lot of stress in their daily lives (16.4%) and those who had depression (15.8%).

TABLE 1.

The differences of Unmet medical needs by characteristics (N = 2269).

Characteristics Categories Total Unmet medical needs F * (р)
Yes No
n (%) a n (%) a n (%) a
Total 2269 (100.0) 213 (8.7) 2056 (91.3)
Income level Q1 868 (33.1) 120 (13.4) 748 (86.6) 8.05 (<0.001)
Q2 600 (25.6) 48 (8.3) 552 (91.7)
Q3 435 (21.5) 23 (5.1) 412 (94.9)
Q4 366 (19.8) 22 (5.4) 344 (94.6)
Predisposing factors
Gender Male 1114 (52.2) 75 (6.6) 1039 (93.4) 11.29 (0.001)
Female 1155 (47.8) 138 (11.1) 1017 (88.9)
Age (year) <65 934 (51.7) 78 (7.9) 856 (92.1) 1.86 (0.173)
≥65 1335 (48.3) 135 (9.7) 1200 (90.3)
Spouse No 666 (28.0) 98 (13.8) 568 (86.2) 24.37 (<0.001)
Yes 1603 (72.0) 115 (6.7) 1488 (93.3)
Education ≤Elementary school 974 (37.3) 111 (10.8) 863 (89.2) 2.68 (0.047)
Middle school 374 (16.2) 24 (5.4) 350 (94.6)
High school 580 (29.1) 49 (8.5) 531 (91.5)
≥College 325 (17.4) 26 (7.2) 299 (92.8)
Residential area City 1673 (77.4) 142 (7.6) 1531 (92.4) 7.93 (0.005)
Rural 596 (22.6) 71 (12.4) 525 (87.6)
Enable factors
Health insurance type National health insurance 2057 (92.8) 171 (8.0) 1886 (92.0) 19.48 (<0.001)
Medical‐aid 193 (7.2) 39 (17.8) 154 (82.2)
Private health insurance No 1010 (38.7) 106 (9.9) 904 (90.1) 2.24 (0.135)
Yes 1248 (61.3) 107 (8.0) 1141 (92.0)
Economic activity No 1210 (48.6) 119 (9.3) 1091 (90.7) 0.89 (0.347)
Yes 1044 (51.4) 92 (8.1) 952 (91.9)
Need factors
Self‐assessed health status Unhealthy 886 (37.9) 133 (13.6) 753 (86.4) 22.26 (<0.001)
Average 1076 (48.8) 71 (6.7) 1005 (93.3)
Healthy 305 (13.4) 9 (2.3) 296 (97.7)
Stress Rarely 1736 (75.8) 127 (6.2) 1609 (93.8) 42.97 (<0.001)
Often 516 (24.2) 85 (16.4) 431 (83.6)
Depression No 2113 (93.2) 190 (8.2) 1923 (91.8) 7.16 (0.008)
Yes 156 (6.8) 23 (15.8) 133 (84.2)
Chronic diseases None 362 (17.5) 26 (6.9) 336 (93.1) 0.70 (0.550)
1 872 (38.0) 79 (8.9) 793 (91.1)
2 775 (33.9) 82 (9.7) 693 (90.3)
≥3 260 (10.6) 26 (7.8) 234 (92.2)
Reason for unmet health care needs None 2056 (91.3)
Lack of available time 61 (2.5)
Mild symptoms 36 (1.4)
Financial burden 70 (2.9)
Others 46 (1.9)
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a

Unweighted frequency (weighted %).

*

Rao‐Scott test.

3.2. Moderating effect of income level on unmet medical needs

Table 2 shows the results of verifying the moderating effect of income level on the effect of unmet healthcare experiences. In Model 1, the control variables such as predisposing, enabling and need factors were included and were statistically significant in the spouse, residential area, health insurance type, subjective health status and stress perception rate; the explanatory power of the model was 12.2% (Nagelkerke R 2 = 0.120). In Model 2, the additional income level, which is a moderating variable, was found to be statistically significant; the explanatory power of the model was 13.4% (Nagelkerke R 2 = 0.134), which was 1.4% higher than that of Model 1 (△Wald χ 2 = 114.43, p < 0.001). The lower the income level, the more unmet medical needs were identified. Finally, in Model 3, an additional interaction term between the predisposing factor variables and the moderating variable, income level, is added. In this case, subjective health status, stress perception rate, income level (Q3), female interaction item, income level (Q2) and middle school graduate interaction terms were statistically significant. The explanatory power of the model was 16.6% in Model 3 (Nagelkerke R 2 = 0.166), which was significantly increased by 3.2% compared with Model 2 (△Wald χ 2 = 154.22, p < 0.001).

TABLE 2.

Logistic regression model of Unmet medical needs (N = 2269).

Characteristics Categories Model 1 Model 2 Model 3
OR (95% CI) p‐Value OR (95% CI) p‐Value OR (95% CI) p‐Value
Gender Female 1.35 (0.90–2.02) 0.141 1.40 (0.94–2.09) 0.094 2.17 (0.68–6.91) 0.188
Age (year) ≥65 1.39 (0.90–2.15) 0.140 1.22 (0.79–1.90) 0.372 2.67 (0.73–9.83) 0.139
Spouse No 1.68 (1.14–2.48) 0.009 1.52 (1.03–2.26) 0.036 0.24 (0.03–2.25) 0.213
Education ≤Elementary school 1.54 (0.88–2.70) 0.578 1.41 (0.81–2.47) 0.225 2.88 (0.49–16.91) 0.242
Middle school 0.82 (0.50–1.34) 0.420 0.72 (0.44–1.19) 0.198 2.36 (0.46–12.00) 0.301
High school 0.83 (0.44–1.58) 0.133 0.65 (0.35–1.19) 0.161 2.14 (0.63–7.30) 0.226
Residential area Rural 1.71 (1.17–2.51) 0.006 1.61 (1.09–2.36) 0.016 1.40 (0.40–4.96) 0.602
Health insurance type Medical‐aid 1.91 (1.18–3.10) 0.008 1.53 (0.95–2.45) 0.081 1.56 (0.96–2.54) 0.073
Private health insurance No 0.89 (0.62–1.27) 0.522 0.76 (0.52–1.11) 0.159 0.74 (0.50–1.09) 0.126
Economic activity No 0.80 (0.51–1.26) 0.340 0.77 (0.49–1.21) 0.258 0.80 (0.50–1.28) 0.352
Self‐assessed health status Unhealthy 4.60 (2.06–10.28) <0.001 4.45 (2.00–9.93) <0.001 4.24 (1.89–9.54) <0.001
Average 1.61 (1.12–2.33) 0.011 1.56 (1.08–2.25) 0.019 1.55 (1.06–2.26) 0.024
Stress Often 2.41 (1.68–3.47) <0.001 2.47 (1.71–3.55) <0.001 2.53 (1.75–3.66) <0.001
Depression Yes 1.27 (0.71–2.26) 0.417 1.21 (0.68–2.16) 0.513 1.21 (0.68–2.14) 0.526
Chronic diseases 1 0.75 (0.36–1.57) 0.442 0.75 (0.36–1.55) 0.428 0.84 (0.40–1.75) 0.641
2 0.63 (0.36–1.12) 0.116 0.63 (0.36–1.11) 0.112 0.65 (0.36–1.15) 0.135
≥3 0.65 (0.35–1.20) 0.168 0.65 (0.35–1.20) 0.166 0.65 (0.35–1.21) 0.173
Income Q1 2.49 (1.29–4.77) 0.006 8.58 (0.67–109.83) 0.099
Q2 2.39 (1.26–4.55) 0.008 2.58 (0.30–22.01) 0.386
Q3 1.65 (1.02–2.66) 0.040 1.75 (0.64–4.74) 0.274
Interaction Income = Q1 × Female 1.30 (0.36–4.63) 0.690
Income = Q2 × Female 0.79 (0.24–2.57) 0.695
Income = Q3 × Female 0.32 (0.12–0.82) 0.018
Income = Q1 × ≥65 2.18 (0.51–9.27) 0.293
Income = Q2 × ≥65 0.76 (0.23–2.50) 0.654
Income = Q3 × ≥65 0.91 (0.34–2.46) 0.859
Income = Q1 × Spouse = no 0.17 (0.02–1.70) 0.132
Income = Q2 × Spouse = no 0.65 (0.17–2.45) 0.520
Income = Q3 × Spouse = no 2.42 (0.95–6.13) 0.063
Income = Q1 × ≤Elementary school 1.16 (0.16–8.29) 0.882
Income = Q2 × Middle school 0.07 (0.01–0.61) 0.015
Income = Q3 × High school 0.47 (0.11–1.96) 0.302
Income = Q1 × Rural 1.01 (0.25–4.09) 0.993
Income = Q2 × Rural 2.69 (0.75–9.65) 0.129
Income = Q3 × Rural 0.95 (0.39–2.34) 0.915
Nagelkerke R 2 0.120 0.134 0.166
Wald χ 2 (p) 114.07 (<0.001) 114.43 (<0.001) 154.22 (<0.001)
△Wald χ 2 0.36 (0.948) 39.79 (0.008)
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Abbreviations: CI, confidence interval; OR, odds ratio.

Note: Unmet medical needs (ref = yes), Gender (ref = male), Age (ref = <65), Spouse (ref = yes), Education (ref = ≥college), Residental area (ref = city), Health insurance type (ref = national health insurance), Private health insurance (ref = yes), Economic activity (ref = yes), Self‐assessed health status (ref = healthy), Stress (ref = rarely), Depression (ref = no), Chronic diseases(ref = none), Income (ref = Q4).

The unmet medical experience was 4.24 times (95% CI = 1.89–9.54) higher among people who perceived their health as poor. Furthermore, those who felt a lot of stress in their daily life had 2.53 times (95% CI = 1.75–3.66) more unmet medical needs than those who felt less stress. Exploring the moderating effect, gender and education level have a moderating effect on unmet medical experience, according to income level. When the income level was Q3 as compared to Q4, the effect of income level on females' unmet medical care was 0.32 times (95% CI = 0.12–0.82) less than that of men. In income level Q2 as compared to Q4, the effect of income level on unmet healthcare was 0.07 times (95% CI = 0.01–0.61) less in college graduates than in middle school graduates.

4. DISCUSSION

Among the core spirit of the UN SDGs and WHO's universalism principle are ‘leave no one behind’ and ‘health for all’. The increasing prevalence of diabetes worldwide and regular hospital visits for patients with diabetes are critical for adequate diabetes management and prevention of complications, ultimately decreasing the mortality associated with diabetes. This study aimed to understand the status of unmet medical needs and its influencing factors. The average percentage of unmet medical needs was 8.7%; however, the groups with the lowest income level (Q1) (13.4%), females (11.1%), those without a spouse (13.8%), residents in rural areas (12.4%), medical‐aid beneficiaries (17.8%), those with self‐assessed health status as unhealthy (13.6%), those who felt stressed often (16.4%) and those having depression (15.8%) showed a very high percentage of unmet medical needs. Therefore, these groups of patients with diabetes need special attention to meet their healthcare needs in the areas of monitoring their blood glucose level and health status, medication (insulin treatment and anti‐hyperglycaemic agents), routine screening for diabetic complications and management of emergent cases (e.g. hypoglycaemic or hyperglycaemic events). Financial burden was the main reason for unmet medical needs in this study (2.9%), and it was higher than that of people without diabetes (Lee & Kim, 2014). This is also consistent with previous studies reporting that low income is a major factor influencing unmet medical needs (Diamant et al., 2004; Herr et al., 2014; Ronksley et al., 2012). It is surprising that even though the medical aid system provides almost free treatment, medicine and consumable materials (self‐payment rate is about 1–2 USD for doctor consultation and purchase of medicines for each) in Korea, the unmet medical needs in this group were the highest at 17.8% (Health Insurance Review & Assessment Service [HIRA], 2022). It is necessary to understand unmet needs in areas other than medical expenses and medicine through in‐depth individual interviews and to implement related policies to solve these problems.

As the level of unmet healthcare needs increases among diabetic patients, the rates of health check‐ups and ophthalmic examinations decrease, and the likelihood of encountering healthcare professionals decreases (Cole & Nguyen, 2020). The diabetic foot amputation rate is 73% higher (Ntuli & Letswalo, 2023), and compared to patients with fulfilled healthcare utilization, patients with unmet needs have 1.29 times higher hospitalization rates, 1.75 times higher mortality rates and 1.34 times higher high‐cost utilization rates (Kim et al., 2006). Therefore, based on the findings of this study, in order to reduce diabetes‐related complications, disabilities, hospitalization rates and mortality rates, it is essential to support vulnerable diabetic patients experiencing unmet healthcare needs in accessing the necessary medical care.

In this study, based on the Andersen model, as a result of confirming the factors affecting unmet healthcare in diabetic patients, it was found that sex, spouse, education level and residential area were statistically significant among the predisposing factors. Female participants had a 1.7 times higher probability of unmet healthcare than males. This is consistent with previous studies that showed approximately 1.8 times higher unmet medical care in female diabetic patients (Jang et al., 2021) and that females had more hyperglycaemia in Europe/Latin America (Raccah et al., 2017). Furthermore, women's overall low‐income levels, low wages and positions at work, or primary responsibility for their family health, are often passed on to women. This dual role can be seen as an obstacle to the access and use of necessary medical services by increasing the burden on women, ultimately affecting health inequality (Armstrong et al., 2004; Bryant et al., 2009; Choi & Lee, 2015). Therefore, it is necessary to establish practical policies to identify causes and alleviate gender gaps to improve medical access through further research. Unmet healthcare was higher in groups without a spouse, which was consistent with previous studies showing that unmet medical care was high in single households and among the elderly living alone (Bosworth & Schaie, 1997; Choi & Lee, 2015).

In general, the level of education is related to health behaviour or knowledge of diseases, which affects health problems; when the educational background is relatively low, more unmet healthcare occurs due to negative attitudes toward disease management (Bryant et al., 2009; Lahelma et al., 2004; Lee & Kim, 2014). To use a satisfactory level of medical service, one must be able to select a medical institution that is necessary for them and receive appropriate medical services; however, a low level of education could limit the appropriate choice for information and medical services. Compared to urban residents, those living in rural areas experienced more unmet healthcare (Weathers et al., 2004), In a study by Kim and Hahm (2021), the unmet medical needs due to physical accessibility were 1.22 times higher in small and medium‐sized cities and 3.95 times higher in rural areas than in big cities. In a recent pooled study from 42 low‐ and middle‐income counties, rural residents had the lower achievement of performance measures of glycaemic, blood pressure and cholesterol control than urban residents, with a large gap between rural women and men (Flood et al., 2022). Global diabetes care programs should be strengthened by considering the challenges faced by rural populations.

In terms of enabling factors, it was found that the unmet medical experience was 2.2 times higher in medical aid recipients than in national health insurance subscribers. In the case of diabetes, the proportion of medical aid was twice as high as that of the non‐medical aid population, and the likelihood of chronic and severe diseases among medical aid recipients was higher than that of national health insurance subscribers. Hence, since the patients with diabetes have low socioeconomic levels and are likely to be vulnerable, the problem of health care inequality can intensify. Therefore, it is necessary to discuss reducing uninsured medical benefits and copay items for medical security for chronically ill patients among medical aid recipients.

Considering the need factors, subjective health status, stress and depression were identified as influencing factors of unmet healthcare, and in the final Model 3 of this study, subjective health status and stress were independent variables that significantly affected unmet medical care. The proportion of unmet medical needs was higher in the group perceived to be moderate or worse than in the group perceived to be good, which is consistent with previous studies (Hwang & Choi, 2015; Idler & Kasl, 1995; McDonald & Conde, 2010), indicating that the better the subjective health status, the more interested individuals are in health and good health care. Stress (Gao et al., 2013) and depression (Clignet et al., 2019; Rafael et al., 2015) are important risk factors for unmet medical needs, and the likelihood of reporting unmet medical care was 2.78 times higher than in situations when the perceived stress in daily life was severe, and 1.40 times higher for depressed patients (Park et al., 2016). Stress causes frustration by reducing or eliminating an individual's willingness to use health care, and a depressed person pays less attention to the positive and focuses on the negative. Thus, even if the desire for treatment is satisfied, it may act as a risk factor for not recognizing the situation or avoiding treatment, resulting in a high level of unmet medical needs. Hence, it is necessary to develop a strategy to prevent unmet medical needs through interventions that can evaluate mental health conditions and reduce stress and depression.

The results of this study showed that the interaction term of the final model had a moderating effect on the impact of unmet healthcare experience in women with upper‐middle‐income levels (Q3), and the risk of unmet medical experience was 0.32 times lower than that in men with upper‐middle‐income levels (Q3). The same low‐income class had a greater impact on the demand for unmet medical care because of the difference in income levels among men. Additionally, although it was not statistically significant, the risk of unmet healthcare was 1.3 times higher when the income was lower (Q1), and in a study by Bryant et al. (2009), females accounted for only 71% of male wages; hence, unmet medical care occurred due to economic reasons. In particular, diabetics has a 44% chance of experiencing unmet healthcare due to costs (Cole & Nguyen, 2020), and it is estimated that more than 90% of those with less than 138% of the federal poverty level have unmet social needs (Permanente, 2019). These needs increased the incidence of chronic diseases, such as diabetes, were twice as likely to use the emergency room for treatment, and were more likely to miss scheduled visits to the clinic (Berkowitz et al., 2016). As income levels have a decisive impact on the access to medical services, especially in low‐income groups and women, the risk of unmet healthcare needs increases. Therefore, the health system needs to prepare a system and policies to maximize the health of all patients, especially poor women who are in blind spots and are excluded from medical coverage.

Middle school graduates with lower middle‐income levels (Q2) had a moderating effect on the impact of unmet healthcare experiences, and the risk of unmet healthcare experiences was 0.07 times lower than those with lower middle‐income levels (Q2) and who are college graduates or higher. As college graduates and low‐income families are middle school graduates and have a greater impact on the demand for unmet medical care than low‐income families, the discrimination of academic background has weakened as college graduates become more common. Moreover, although not statistically significant, lower income levels (Q1) had a 1.16 times higher risk of unmet healthcare, and lower education levels as one of the factors indicating socioeconomic status, leading to lower access to healthcare (Bryant et al., 2009; Scheppers et al., 2006), and socioeconomically vulnerable groups tend to experience more health problems (Bryant et al., 2009). Conversely, the higher the educational level, the more experienced in unmet healthcare in some cases (Wu et al., 2005). Because college graduates with diabetes may have a greater impact on unmet healthcare demand than middle school graduates with diabetes, circumstances should be made for unmet medical care for patients with diabetes according to education level, and customized strategies should be established. Since income and education levels, which are objectified indicators, can closely represent socioeconomic status, it is hoped that these factors, in maintaining horizontal equity, can serve as new grounds for improving unmet healthcare for chronically ill patients, such as those with diabetes.

The number of recipients of Basic livelihood system benefits in South Korea compared to the entire nation increased from 1.5 million in 2010 to 1.8 million in 2019 (Statistics Korea Government Official Work Conference, 2021). Health Plan 2030 aims to reduce the gap between the healthy life of the top 20% of income level and the healthy life of the bottom 20% of income level to 7.6 years or less by 2030 in order to secure health equity between gender, class and region (Korea Health Promotion Institute, 2021). To achieve this goal and improve the health of low‐income patients with diabetes, strategies and healthcare interventions that promote diabetes self‐management and address the specific needs of low‐income patients are needed.

This study had several limitations. Due to the nature of the cross‐sectional study design, the results cannot guarantee causal effects on unmet medical needs entailing a prospective cohort study. Second, unmet medical needs were measured using a single question (yes, no), Second, unmet medical needs were measured using a single question (yes or no). Therefore, in future research, it is necessary to develop items that specifically reflect the medical demands and needs of diabetic patients and ensure their validity and reliability. Requiring further qualitative research to understand the meaning of unmet healthcare from the patients' perspectives using in‐depth interviews, and the development of diabetes‐related specific measurement with approved reliability and validity. Despite these limitations, this study is the first to understand the factors related to unmet medical needs among people with diabetes, using the Andersen model with a comprehensive sampling of the Korean population.

5. CONCLUSION

In conclusion, our population‐based study found that a significant proportion of patients with diabetes experienced unmet medical needs. This study suggests that interventions targeted toward patients who perceive their health status as unhealthy and who often feel stressed in their daily life may be particularly effective in addressing unmet medical needs. Additionally, our results suggest that gender and education level may moderate the effect of income level on unmet medical needs, indicating a need for tailored interventions for patients with varying demographic profiles. To ensure that patients with diabetes receive the support they need, active clinical dialogue will be necessary to develop targeted interventions, particularly for low‐income patients.

5.1. Implications

These findings have important implications for nursing practice and highlight the need for continued research to improve the care of patients with diabetes. Diabetes is prevalent among low‐income populations, with higher concentration observed (Moon et al., 2022). The unmet medical needs associated with diabetes are primarily linked to financial factors (Cole & Nguyen, 2020). Therefore, it is crucial to alleviate the financial burden on diabetes patients while also providing education to enhance their abilities. Healthcare professionals and service providers should identify the factors that adversely affect the healthcare service requirements of diabetes patients and address the social needs both within and outside the medical environment. By doing so, improved access to services can be achieved for diabetes patients who face challenges in achieving full recovery, ultimately leading to positive health outcomes. For example, nurses can provide education and support to help patients manage stress and depression, and can work with other healthcare providers to ensure that patients receive appropriate mental health services. In addition, our study highlights the need for nurses to consider the moderating effect of income level on unmet medical needs. Nurses can play a critical role in identifying patients who may be at risk for unmet medical needs due to their income level and developing interventions that address their specific needs. This may include connecting patients with community resources, such as social services or financial assistance programs, or developing care plans that consider the financial limitations of low‐income patients.

Furthermore, our study adds to what is already known about the factors that contribute to unmet medical needs in patients with diabetes. By identifying the specific factors that are associated with unmet medical needs in our population‐based sample, our study provides valuable information that can inform nursing practice and improve the care of patients with diabetes. Nurses can use this information to develop evidence‐based interventions that address the unique needs of their patients and improve patient outcomes.

In the future, it is important to deeply understand how unmet medical needs for persons with diabetes vary according to income level, gender and education level, through research involving individual interviews. Based on this understanding, there is a need to plan for specific and customized interventions.

AUTHOR CONTRIBUTIONS

Study design: Youngran Yang and Ji Young Kim. Data analysis: Ji Young Kim. Study supervision: Youngran Yang. Manuscript writing: Youngran Yang and Ji Young Kim. Critical revisions for important intellectual content: Youngran Yang and Ji Young Kim.

FUNDING INFORMATION

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2021R1A2C2092656).

CONFLICT OF INTEREST STATEMENT

The authors declare that they have no competing interests.

ETHICS STATEMENT

All procedures performed in the studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

CONSENT FOR PUBLICATION

Not applicable.

ACKNOWLEDGMENTS

Not applicable.

Kim, J. Y. , & Yang, Y. (2023). Factors affecting unmet medical needs of patients with diabetes: A population‐based study. Nursing Open, 10, 6845–6855. 10.1002/nop2.1933

No patient or public contribution

DATA AVAILABILITY STATEMENT

The datasets used and analysed during the current study are available from the corresponding author upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets used and analysed during the current study are available from the corresponding author upon reasonable request.

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