This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on https://publichealth.jmir.org, as well as this copyright and license information must be included.
There are regional gaps in the access to medical services for patients with chronic kidney disease (CKD), and it is necessary to reduce those gaps, including the gaps involving medical costs.
This study aimed to analyze regional differences in the medical costs associated with CKD in the South Korean population.
This longitudinal cohort study included participants randomly sampled from the National Health Insurance Service-National Sample Cohort of South Korea. To select those who were newly diagnosed with CKD, we excluded those who were diagnosed in 2002-2003 and 2018-2019. A total of 5903 patients with CKD were finally included. We used a marginalized two-part longitudinal model to assess total medical costs.
Our cohort included 4775 (59.9%) men and 3191 (40.1%) women. Of these, 971 (12.2%) and 6995 (87.8%) lived in medically vulnerable and nonvulnerable regions, respectively. The postdiagnosis costs showed a significant difference between the regions (estimate: –0.0152, 95% confidence limit: –0.0171 to –0.0133). The difference in medical expenses between the vulnerable and nonvulnerable regions showed an increase each year after the diagnosis.
Patients with CKD living in medically vulnerable regions are likely to have higher postdiagnostic medical expenses compared to those living in regions that are not medically vulnerable. Efforts to improve early diagnosis of CKD are needed. Relevant policies should be drafted to decrease the medical costs of patients with CKD disease living in medically deprived areas.
Chronic kidney disease (CKD) has been known as a causative factor for early death and is being recognized as a global health problem [
In the case of chronic diseases, the effective implementation of medical care, management, and prevention of these diseases are important for social integration [
Kim et al [
The data for this study were obtained from the National Health Insurance Service- National Sample Cohort (NHIS-NSC). The NHIS-NSC data were collected by random sampling of medical claims, covering 2% of the South Korean population. The period during 2002 to 2003 was designated as a washout period to account for the effects of other existing diseases that might influence the results associated with the hypothesized relationship. To select those who were newly diagnosed with CKD, we excluded those who were diagnosed with CKD in 2002-2003 and 2018-2019. From these data, we extracted 10,019 cases diagnosed with CKD (International Classification of Diseases, 10th revision; code: N18). After excluding patients who were diagnosed with CKD during the excluded period, a total of 7966 individuals were included in the final study (vulnerable regions: n=971, 12.2%; nonvulnerable regions: n=6995, 87.8%).
This study was reviewed and approved by the International Review Board of Yonsei University’s Health System (Y-2020-0031) and adheres to the tenets of the Declaration of Helsinki. The NHIS-NSC data do not contain any identifying information; thus additional approval was not required.
The variable of interest in this study was the region. Position value for relative composite (PARC) indicators were used to categorize the health care level by region in South Korea; the analysis methods have been explained in detail in previous studies [
The dependent variable of this study was the total medical cost. Based on the time of diagnosis, the medical expenses were calculated monthly for 24 months before diagnosis and 24 months after diagnosis. Medical expenses before death were also included.
In addition, analyses that included sex, age, income, and social security type as independent variables were performed. Social security was categorized into health insurance (eg, corporate or regional) and medical aid. One of the social security systems in South Korea, National Health Insurance Service (NHIS), covers the entire population except for medical aid beneficiaries. Since the South Korean government provides insurance for the poor, it offers a medical aid program to people unable to pay for their own health care coverage. The medical aid program beneficiaries are financed by both the local and central government and are a part of the South Korean public assistance system [
To overcome the limitations posed by a conventional two-part model, we used a marginalized two-part (MTP) longitudinal model that directly parameterizes the marginal mean of
We redefined
The percentage of the study population with 0 expenditure over the study period was 29%, creating the need for a two-part model. To assess the effect of the regional difference on medical costs, we fit the MTP model with explanatory variables in the binary and overall mean components. In addition, we used segmented regression analysis for powerful estimation of intervention effects in an interrupted time series. The full model was constructed as follows:
In the above, 0 ≤
We fit the MTP model using the %MTPmle SAS macro program that correctly estimates model parameters through the likelihood function and can be maximized using SAS PROC NLMIXED. In addition, we chose a model type that used a continuous distribution in the second part of the MTP model: the gamma distribution.
We analyzed the descriptive statistics of the medical costs of CKD by region (
We analyzed the summary statistics for the semicontinuous outcome at each time point (
We calculated the overall means and 95% confidence limits of the MTP model parameters (
We mathematically expressed the MTP model–estimated slope effects of differences in diagnosis and region (
We used the MTP model to estimate the effect of region on medical expenses over time (
The overall mean of the values with log scale in
Descriptive statistics of the medical costs of chronic kidney disease analyzed by region.
Characteristics | Region | |||||||||
|
Total (N=7966, 100), n (%) | Vulnerable regions (n=971, 12.2), n (%) | Nonvulnerable regions (n=6995, 87.8), n (%) |
|
||||||
|
.40 | |||||||||
|
Male | 4775 (59.9) | 570 (58.7) | 4250 (60.1) |
|
|||||
|
Female | 2790 (40.1) | 401 (41.3) | 2790 (39.9) |
|
|||||
|
<.001 | |||||||||
|
<30 | 559 (7.0) | 56 (5.8) | 503 (7.2) |
|
|||||
|
30-39 | 742 (9.3) | 79 (7.5) | 669 (9.3) |
|
|||||
|
40-49 | 1303 (16.4) | 124 (9.5) | 1179 (16.9) |
|
|||||
|
50-59 | 1769 (22.2) | 215 (22.1) | 1554 (22.2) |
|
|||||
|
60-69 | 2451 (30.8) | 343 (35.3) | 2108 (30.1) |
|
|||||
|
70-79 | 1106 (13.9) | 154 (15.9) | 952 (13.6) |
|
|||||
|
>80 | 36 (0.5) | 6 (0.6) | 30 (0.4) |
|
|||||
|
.15 | |||||||||
|
NHIa, employed, or self-employed | 3799 (47.7) | 442 (45.5) | 3357 (48) |
|
|||||
|
Medical aid | 4167 (52.3) | 529 (54.5) | 3638 (52) |
|
|||||
|
<.001 | |||||||||
|
Low | 1094 (13.7) | 132 (13.6) | 962 (13.8) |
|
|||||
|
Middle | 3534 (44.4) | 488 (50.3) | 3046 (43.6) |
|
|||||
|
High | 3338 (41.9) | 351 (36.2) | 2987 (42.7) |
|
|||||
|
<.001 | |||||||||
|
0 | 1645 (20.7) | 163 (16.8) | 1482 (21.2) |
|
|||||
|
1 | 2300 (28.9) | 266 (27.4) | 2034 (29.1) |
|
|||||
|
>2 | 4021 (50.5) | 542 (55.8) | 3479 (50) |
|
aNHI: National Health Insurance.
bCCI: Charlson comorbidity index.
Summary statistics for the semicontinuous outcome (expenditures) at each time point. Monthly expenditures were calculated by converting South Korean won to US dollars.
Characteristics | Overall expenditures | Expenditures before diagnosis | Expenditures after diagnosis | |||||
|
Mean (SD) | Mean (SD) | Mean (SD) | |||||
|
.12 | .67 | .02 | |||||
|
Vulnerable region | 313 (411) |
|
209 (303) |
|
416 (682) |
|
|
|
Nonvulnerable region | 291 (390) |
|
214 (379) |
|
368 (586) |
|
|
|
.10 | .76 | .05 | |||||
|
Male | 288 (382) |
|
212 (377) |
|
363 (570) |
|
|
|
Female | 303 (408) |
|
215 (361) |
|
390 (639) |
|
|
|
.01 | <.001 | .13 | |||||
|
<30 | 266 (414) |
|
170 (426) |
|
362 (614) |
|
|
|
30-39 | 266 (420) |
|
162 (452) |
|
370 (637) |
|
|
|
40-49 | 294 (441) |
|
195 (427) |
|
393 (666) |
|
|
|
50-59 | 311 (411) |
|
230 (353) |
|
392 (617) |
|
|
|
60-69 | 306 (369) |
|
236 (339) |
|
375 (573) |
|
|
|
70-79 | 274 (320) |
|
216 (294) |
|
332 (502) |
|
|
|
>80 | 250 (254) |
|
237 (296) |
|
262 (361) |
|
|
|
.11 | .35 | .13 | |||||
|
NHIa, employed, or self-employed | 286 (392) |
|
209 (365) |
|
363 (597) |
|
|
|
Medical aid | 300 (394) |
|
217 (376) |
|
384 (599) |
|
|
|
.02 | .52 | .01 | |||||
|
Low | 318 (384) |
|
225 (372) |
|
411 (598) |
|
|
|
Middle | 298 (396) |
|
213 (356) |
|
382 (609) |
|
|
|
High | 282 (392) |
|
210 (385) |
|
353 (586) |
|
|
|
<.001 | <.001 | <.001 | |||||
|
0 | 173 (311) |
|
108 (278) |
|
237 (472) |
|
|
|
1 | 265 (385) |
|
177 (316) |
|
354 (622) |
|
|
|
>2 | 359 (413) |
|
278 (418) |
|
441 (621) |
|
aNHI: National Health Insurance.
bCCI: Charlson comorbidity index.
Overall means and 95% CIs of the marginalized two-part model parameters.
Characteristics | Parameter | Estimate | 95% confidence limit | |
|
||||
|
Intercept |
|
–0.4169 | (–0.4501, –0.3835) |
|
Diagnosis |
|
–0.03515 | (–0.0668, –0.0035) |
|
Region |
|
0.1875 | (0.1533, 0.2218) |
|
Time |
|
0.0260 | (0.0244, 0.0275) |
|
Diagnosis×region |
|
–0.1124 | (–0.1839, –0.0408) |
|
Diagnosis×after time |
|
–0.0888 | (–0.0910, –0.0867) |
|
Region×after time |
|
–0.0151 | (–0.0172, –0.0130) |
|
Diagnosis×region×after time |
|
0.0112 | (0.0091, 0.0133) |
|
Age |
|
0.0116 | (0.0161, 0.0171) |
|
Sex |
|
–0.0816 | (–0.0962, –0.0671) |
|
CCIa |
|
0.3348 | (0.3257, 0.3440) |
|
||||
|
Intercept |
|
11.1894 | (11.1561, 11.2226) |
|
Diagnosis |
|
0.0123 | (–0.0119, 0.0366) |
|
Region |
|
–0.0356 | (–0.0611, –0.0101) |
|
Time |
|
0.0652 | (0.0641, 0.0664) |
|
Diagnosis×region |
|
0.0884 | (0.0332, 0.1435) |
|
Diagnosis×after time |
|
–0.0825 | (–0.08430, –0.0807) |
|
Region×after time |
|
0.0178 | (0.0159, 0.0197) |
|
Diagnosis×region×after time |
|
–0.0152 | (–0.0171, –0.0133) |
|
Age |
|
–0.0002 | (–0.0006, 0.0003) |
|
Sex |
|
–0.0635 | (–0.0762, –0.0507) |
|
CCI |
|
0.3734 | (0.3650, 0.3818) |
aCCI: Charlson comorbidity index.
Marginalized two-part model–estimated slope effects of differences in diagnosis and region (mathematical expression).
Characteristics | Before | After | After-before |
Vulnerable region |
|
|
|
Nonvulnerable region |
|
|
|
Difference of regions |
|
|
|
Marginalized two-part model–estimated effects of region over time.
|
1 year | 2 years | 3 yearsa | 4 yearsa | 5 yearsa |
Difference (%) | 0.26 | 0.52 | 0.78 | 1.05 | 1.31 |
aThe data used in the marginalized two-part model analysis are for 2 years before and 2 years after diagnosis. The estimated effects at 3, 4, and 5 years are predicted values.
Model of the estimated log (mean expenditures) and 95% CIs for the chronic kidney disease costs.
In this study, we examined the differences in medical costs of CKD among the regions of South Korea. The study results demonstrated that there were differences in postdiagnosis medical costs for patients with CKD depending on the region. Patients living in medically vulnerable areas had higher medical costs than patients living in nonvulnerable areas.
These findings are consistent with those of previous studies on chronic obstructive pulmonary disease, as our analysis on this disease (
Geographical location plays an important role in the treatment of CKD. Some treatments, such as hospital-based hemodialysis, may not be feasible in rural areas. For patients who are dependent on ambulance service, transportation also becomes an important issue [
Since there is an increase in postdiagnosis medical expenses for CKD in vulnerable areas, necessary policies should be implemented to lower the burden of this condition. In addition, efforts to increase early diagnosis of CKD are needed. Currently, most cases of CKD are detected during the course of treating other health problems rather than because of any CKD symptoms. Often, the early stages of CKD show no symptoms, and discovery is made only when conditions become severe. As shown previously [
Our study has some important limitations. First, we were not able to perform a random slope analysis in the study because SAS software does not currently provide the necessary program. In future studies, using another statistical program would avoid this issue. Second, since the data set is a collection of medical-claim bills, it is highly likely that the actual number of patients with CKD and their actual burdens are higher than the reported numbers. In general, the number of patients with CKD can be said to represent a pyramid with ESRD at its peak, although the number of patients with CKD based on their treatment performance with health insurance and medical benefits shows the opposite picture [
However, there are some major strengths to our study. To the best of our knowledge, this study is the first to investigate the regional differences in the medical costs of CKD in the South Korean population using a TPM model. In addition, since all South Korean citizens are obligated to enroll in the NHIS, the NHIS data sets provide nationally representative data.
Our findings suggest that patients with CKD living in medically vulnerable regions are more likely to have increased postdiagnosis medical expenses compared with those living in medically nonvulnerable regions. Over time, the differences in medical expenses are likely to increase substantially. Policies are needed to decrease the medical bills of patients with CKD living in medically deprived areas.
Overall means and 95% CIs of the marginalized two-part model parameters for chronic obstructive pulmonary disease.
chronic kidney disease
end-stage renal disease
marginalized two-part
National Health Insurance Service-National Sample Cohort
National Health Insurance Service
position value for relative composite
This research was supported by a grant (HI20C1130) of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health and Welfare, South Korea. This work was also supported by the National Research Foundation of Korea (NRF; grant 2022R1F1A1062794) funded by the Korea government (Ministry of Science and ICT [information and communication technology]).
The funding organizations 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.
All data are available in the database of the Korean National Health Insurance Sharing Service and can be accessed upon reasonable request.
MP and CY conceptualized and designed the study; CY contributed to the acquisition, analysis, and interpretation of data as well as statistical analysis; MP drafted the manuscript; SIJ, ECP, and CMN were in charge of the administrative, technical, or material support of the study; YH and SIJ supervised the study; all authors contributed to the critical revision of the manuscript for important intellectual content.
None declared.