Multilevel Analysis of Health Care Service Utilization among Medical Aid Beneficiaries in Korea

Yang Heui Ahn; Ok Kyung Ham; Soo Hyun Kim; Chang Gi Park

doi:10.4040/jkan.2012.42.7.928

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Ahn, Ham, Kim, and Park: Multilevel Analysis of Health Care Service Utilization among Medical Aid Beneficiaries in Korea

Original Article

J Korean Acad Nurs 2012;42(7):928-935.

Published online: 12 December 2012

DOI: https://doi.org/10.4040/jkan.2012.42.7.928

Multilevel Analysis of Health Care Service Utilization among Medical Aid Beneficiaries in Korea

Yang Heui Ahn¹, Ok Kyung Ham², Soo Hyun Kim², Chang Gi Park³

¹Department of Nursing, Yonsei University Wonju College of Medicine, Wonju, Korea

²Department of Nursing, Inha University, Incheon, Korea

³Office of Global Health Leadership, University of Illinois at Chicago, Illinois, USA

Address reprint requests to : Ham, Ok Kyung Department of Nursing, Inha University, Inha-ro 100, Nam-gu, Incheon 402-751, Korea Tel: +82-32-860-8211 Fax: +82-32-874-5880 Email: okkyung@inha.ac.kr, okkyung7@hanmail.net

Received 31 May 2012 Revised 18 June 2012 Accepted 13 November 2012

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Purpose

The current study was done to identify individual- and group-level factors associated with health care service utilization among Korean medical aid beneficiaries by applying multilevel modeling.

Methods

Secondary data analysis was performed using data on health care service reimbursement and medical aid case management progress from 15,948 beneficiaries, and data from 229 regions were included in the analysis.

Results

Results of multilevel analysis showed an estimated intraclass correlation coefficient (ICC) of 18.1%, indicating that the group level accounted for 18.1% of the total variance in health care service utilization, and that beneficiaries within the region are more likely to share common features with regard to health care service utilization. At the individual level, existence of disability and types of medical aid beneficiaries showed a significant association, while, at the group level, social deprivation index, and the number of beneficiaries and case managers within the region showed a significant association with health care service utilization.

Conclusion

The significant influence of group level variables in health care service utilization found in this study indicate a need for group level approaches, such as policy change and/ or promotion of community awareness.

Keywords: Multilevel analysis, Medical assistance, Health services, Case management

References

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Figure 1.

Theoretical framework of the study based on ecological model.

Table 1.

General Characteristics of Medical Aid Beneficiaries (N=15,948)

Individual variable	Distribution	n (%)	M±SD	Range
Age (year)			66.93±12.51	11-100
Gender	Male	5,667 (35.5)
	Female	10,281 (64.5)
Marital status	Married/cohabitating	4,460 (28.0)
	Single	977 (6.1)
	Widowed/divorced/separated	6,171 (38.7)
	No response	4,340 (27.2)
Education (n=19,946)	None	5,660 (35.5)
	≤9 years	6,171 (38.7)
	≥10 years	2,243 (14.1)
	No response	1,874 (11.7)
Disability	Yes	6,702 (42.0)
	No	9,246 (58.0)
Beneficiary type	Type 1	11,255 (70.6)
	Type 2	1,737 (10.9)
	Person of national merit	1,358 (8.5)
	Other^a	1,598 (10.0)
Health care service utilization (days/6 months)			1,235.99±585.99	1–15,872

^a Included refugees, servicemen, victims of natural disasters etc.

Table 2.

Attributes of Group Level Characteristics according to the 229 Regions (N=15,948)

Group variables	Range	M±SD
SDI	−1.42-1.69	−0.10±0.83
Population	18,221-1,073,149	292,585.34±212,291.33
Number of beneficiaries	468-27,773	10,214.73±6,034.24
Number of case managers	1-10	3.40±2.78

SDI=Social deprivation index.

Table 3.

Multilevel Analysis of Health Care Service Utilization according to the 229 Regions (N=15,948)

Fixed effect	Null model		Model 1		Model 2
Fixed effect	Estimates	p	Estimates	p	Estimates	p
Individual level
Intercept	1,209.39	<.001	1,325.70	<.001	1,254.37	<.001
Age			0.77	.121	0.61	.258
Gender			−1.73	.890	−11.42	.395
Education			−27.06	.099	−19.12	.287
Marital status			19.21	.114	15.62	.232
Disability			−28.67	.014	−35.98	.005
Beneficiary type			−111.49	<.001	−97.17	<.001
Group level
SDI					−73.17	.044
Population					0.00	.678
Number of beneficiaries					0.02	.005
Number of case managers					−47.02	.007

Random effect	Variance component	p	Variance component	p	Variance component	p

Residual	313,859.17	<.001	320,318.59	<.001	285,288.65	<.001
Intercept (variance)	69,267.68	<.001	74,194.58	<.001	62,038.82	<.001
Deviance	247,649.77		165,740.62		124,927.58
χ²			81,909.15	<.001	40,813.04	<.001

SDI=Social deprivation index. Variables: Gender 1=male, 2=female; education 1=less than or equal to 9 years, 2=more than 9 years; marriage1=without spouse (single, divorces, widowed, or separated), 2=with spouse; disability 0=not disabled, 1=disabled; beneficiary type 1=type 1, 2=other (type 2, those for persons of national merit, refugees, servicemen, and victims of natural disasters).

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