Trajectories of Self-rated Health among One-person Households: A Latent Class Growth Analysis

Eunjoo Kim; Hyang Kim; Ju Young Yoon

doi:10.12799/jkachn.2019.30.4.449

Journal List > J Korean Acad Community Health Nurs > v.30(4) > 1142083

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Kim, Kim, and Yoon: Trajectories of Self-rated Health among One-person Households: A Latent Class Growth Analysis

Original Article

J Korean Acad Community Health Nurs 2019;30(4):449-459.

Published online: 12 December 2019

DOI: https://doi.org/10.12799/jkachn.2019.30.4.449

Trajectories of Self-rated Health among One-person Households: A Latent Class Growth Analysis

Eunjoo Kim¹, Hyang Kim¹, Ju Young Yoon²

¹Doctoral Student, College of Nursing, Seoul National University, Seoul, Korea

²Associate Professor, College of Nursing · Research Institute of Nursing Science, Seoul National University, Seoul, Korea

Corresponding author: Kim, Hyang College of Nursing, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul 03080, Korea. Tel: +82-2-740-8456, Fax: +82-2-766-1852, E-mail start2010@snu.ac.kr

Received 10 June 2019 Revised 7 October 2019 Accepted 7 November 2019

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 aim of this study is to explore different types of self-rated health trajectories among one-person households in Korea.

Methods

We used five time-point data derived from Korea Health Panel (2011~2015). A latent growth curve modeling was used to assess the overall feature of self-rated health trajectory in one-person households, and a latent class growth modeling was used to determine the number and shape of trajectories. We then applied multinomial logistic regression on each class to explore the predicting variables.

Results

We found that the overall slope of self-rated health in one-person households decreases. In addition, latent class analysis demonstrated three classes: 1) High-Decreasing class (i.e., high intercept, significantly decreasing slope), 2) Moderate-Decreasing class (i.e., average intercept, significantly decreasing slope), and 3) Low-Stable class (i.e., low intercept, flat and nonsignificant slope). The multinomial logistic regression analysis showed that the predictors of each class were different. Especially, one-person households with poor health condition early were at greater risk of being Low-Stable class compared with High-Decreasing class group.

Conclusion

The findings of this study demonstrate that more attentions to one-person households are needed to promote their health status. Policymakers may develop different health and welfare programs depending on different characteristics of one-person household trajectory groups in Korea.

Keywords: One-person household, Subjective health, Latent class analysis

References

1. Byun M. Single person household and urban policy in Seoul. Korean Journal of Culture and Social Issues. 2015; 21(3):551–573.

2. Statistics Korea. 2015 pupulation and housing census result [Internet]. Daejeon: Statistics Korea;2016. [cited 2019 Jan 24] Available from:. http://kostat.go.kr/portal/korea/kor_nw/3/index.board?bmode=read&aSeq=356062&pageNo=&rowNum=10&amSeq=&sTarget=&sTxt=.

3. Organisation for Economic Co-operation and Development. OECD Family Database 2013 [Internet]. Paris: Organisation for Economic Co-operation and Development;2013. [cited 2019 Jan 24] Available from: www.oecd.org/social/family/database.

4. Yeung WJ, Cheung AK. Living alone: One-person households in Asia. Demographic Research. 2015; 32(40):1099–1112. https://doi.org/10.4054/DemRes.2015.32.40.

5. Kang EN, Lee MH. Single-person households in South Korea and their policy implications. Health and Welfare Policy Forum. 2016; 234:47–56.

6. Kang ET, Kang JK, Ma KR. Subjective well-being of one-person households: Focus on non-married and married one-person households. Journal of Social Science. 2016; 27(1):3–23. https://doi.org/10.16881/jss.2016.01.27.1.3.

7. Park BY, Kwon HJ, Ha MN, Burm EA. A comparative study on mental health between elderly living alone and elderly couples: Focus on gender and demographic characteristics. Journal of Korean Public Health Nursing. 2016; 30(2):195–205. https://doi.org/10.5932/JKPHN.2016.30.2.195.

8. Bailis DS, Segall A, Chipperfield JG. Two views of self-rated general health status. Social Science & Medicine. 2003; 56(2):203–217. https://doi.org/10.1016/S0277-9536(02)00020-5.

9. Benyamini Y, Idler EL, Leventhal H, Leventhal EA. Positive affect and function as influences on self-assessments of health: Expanding our view beyond illness and disability. The Journals of Gerontology: Series B. 2000; 55(2):107–116. https://doi.org/10.1093/geronb/55.2.P107.

10. Kaplan G, Baron-Epel O. What lies behind the subjective evaluation of health status? Social Science & Medicine. 2003; 56(8):1669–1676. https://doi.org/10.1016/S0277-9536(02)00179-X.

11. Simon JG, De Boer JB, Joung IMA, Bosma H, Mackenbach JP. How is your health in general? A qualitative study on self-assessed health. European Journal of Public Health. 2005; 15(2):200–208. https://doi.org/10.1093/eurpub/cki102.

12. Ha JK, Lee S. The effect of health-related habitual consumption and lifetime on subjective health of one person households: Focusing on comparison between non-one person. Family and Environment Research. 2017; 55(2):141–152. https://doi.org/10.6115/fer.2017.011.

13. Kim EG, Park SK. Comparison of health behaviors, disease prevalence between one-person women and multiple households women in Korea. Journal of Korean Public Health Nursing. 2016; 30(3):483–494. https://doi.org/10.5932/JKPHN.2016.30.3.483.

14. Kwon T. Trajectories of self-rated health and its preditors: Focus on life span. The Korean Journal of Humanities and the Social Sciences. 2011; 35(4):203–231.

15. McAvay G, Rodin J. Determinants of change in perceived health in a longitudinal study of older adults. Journal of Gerontology. 1992; 47(6):373–384. https://doi.org/10.1093/geronj/47.6.P373.

16. Han B. Depressive symptoms and self-rated health in community-dwelling older adults: A longitudinal study. Journal of the American Geriatrics Society. 2002; 50(9):1549–1556. https://doi.org/10.1046/j.1532-5415.2002.50411.x.

17. Kim JS, Cho B. Association between self-perceived health status and health related behavior in routine health examinees. Korean Journal of Family Medicine. 2010; 31(9):688–696. https://doi.org/10.4082/kjfm.2010.31.9.688.

18. Abu-Omar K, Rutten A, Robine JM. Self-rated health and physical activity in the European Union. Social and Preventive Medicine. 2004; 49(4):235–242. https://doi.org/10.1007/s00038-004-3107-x.

19. Ha Y, Park H. Association between obesity and self-rated health in Korean males and females. Journal of Korean Biological Nursing Science. 2012; 14(3):203–211. https://doi.org/10.7586/jkbns.2012.14.3.203.

20. Korean Ministy of Health & Welfare. Korean Institute for Health and Social Affairs. Social security factbook 2017. Sejong: Korean Ministy of Health & Welfare;2017. Dec. Report No.: 11-1352000-001385-10.

21. Kim YS, Yang YJ, Park HK, Kim JW. Development of physical activity guidelines and self-prescription guides for Korean. Research Report. Seoul: Korean Health Promotion Foundation;2012. Nov. Report No.: Health Promotion Research Project 12-37.

22. Mäntyselkä PT, Turunen JH, Ahonen RS, Kumpusalo EA. Chronic pain and poor self-rated health. The Journal of the American Medical Association. 2003; 290(18):2435–2442. https://doi.org/10.1001/jama.290.18.2435.

23. World Health Organization. The Asia-Pacific perspective: Redefining obesity and its treatment [Internet]. Sydney: Health Communications Australia;2000. [cited 2019 Jan 23]. Available from:. https://apps.who.int/iris/bitstream/handle/10665/206936/0957708211_eng.pdf?sequence=1.

24. Duncan TE, Duncan SC, Strycker LA. An introduction to latent variable growth curve modeling: Concepts, issues, and applications. 2nd ed. New York: Routledge;2006. p. 261.

25. Jung T, Wickrama KAS. An introduction to latent class growth analysis and growth mixture modeling. Social and Personality Psychology Compass. 2008; 2(1):302–317. https://doi.org/10.1111/j.1751-9004.2007.00054.x.

26. Kline RB. Principles and practice of structural equation modeling. 4th ed. New York: Guilford publications;2016. p. 445.

27. Sirola J, Tuppurainen M, Rikkonen T, Honkanen R, Koivu-maa-Honkanen H, Kroger H. Correlates and predictors of self-rated health and ambulatory status among elderly women: Cross-sectional and 10 years population-based cohort study. Maturitas. 2010; 65(3):244–252. https://doi.org/10.1016/j.maturitas.2009.11.014.

8. Roberts G. Age effects and health appraisal: A meta-analysis. The Journals of Gerontology. Series B. 1999; 54B(1):S24–S30. https://doi.org/10.1093/geronb/54B.1.S24.

29. Statistics Korea. 2010 pupulation and housing census result [Internet]. Daejeon: Korea Statistics;2011. [cited 2019 Jan 24]. Available from:. http://kostat.go.kr/portal/korea/kor_nw/1/10/1/index.board?bmode=read&aSeq=249070.

Figure 1.

Latent classes of self-rated health trajectories.

Table 1.

General Characteristics of One-person Households on 2011 KHP data (N=698)

Domains	Variables	Categories	One-person households	Self-rated health	t or F or r^‡	p
Domains	Variables	Categories	n^† (%)^‡ or M±SD‡	M±SD^‡	t or F or r^‡	p
Demographic· socio-economic characteristics	Gender	Male	157 (28.5)	3.41±0.99	-4.07	＜.001
		Female	541 (71.5)	3.04±0.94
	Age (year)		62.62±15.78		-.21	<.001
		≤29	18 (4.8)	4.12±0.77	6.95	< .001
		30~39	32 (8.2)	3.42±0.83
		40~49	31 (6.4)	3.20±0.83
		50~59	95 (16.2)	3.08±1.02
		60~69	170 (21.2)	3.21±0.96
		70~79	285 (35.2)	2.99±0.96
		80~89	67 (8.1)	2.97±0.89
	Education	≥ Elementary school	325 (51.3)	2.96±0.95	16.29	< .001
		Middle-High school	169 (32.9)	3.29±0.97
		≤ College	56 (15.7)	3.66±0.93
		Missing	148
	Marital status	Single^§	84 (19.4)	3.52±0.91	4.59	< .001
		Experience of being married^‖	614 (80.6)	3.06±0.96
	Economic activity	Active	287 (45.9)	3.37±0.90	-5.12	< .001
		Inactive	411 (54.1)	2.96±0.98
	Household income quantiles (weighted)	Lowest	369 (47.6)	2.97±0.99	7.98	< .001
		Second	167 (22.2)	3.10±0.92
		Third	73 (12.2)	3.40±0.84
		Fourth	51 (10.3)	3.47±0.94
		Highest Fifth	38 (7.7)	3.60±0.90
	Individual medical expenditure (KRW/year)		722,077.49±		-.20	< .001
			1,269,974.27
		Missing	1	3.24±0.94	5.00	< .001
	Medical security	National health insurance	581 (84.2)	2.69±0.97
		Medicaid	117 (15.8)
Health behavior	Current cigarette smoking	Yes	111 (19.4)	3.27±1.03	-1.50	.135
		No	587 (80.6)	3.12±0.95
	Excessive alcohol consumption	Yes	88 (17.6)	3.55±0.91	-4.62	<.001
		No	610 (82.4)	3.06±0.96
	Healthy physical activity possible	Yes	287 (40.3)	3.31±0.97	-3.28	.001
		No	411 (59.7)	3.04±0.95
Health state	Disabled	Yes	107 (14.7)	2.72±1.07	4.00	< .001
		No	591 (85.3)	3.22±0.93
	Chronic disease	Yes	617 (83.2)	3.05±0.96	5.80	< .001
		No	81 (16.8)	3.66±0.82
	Obesity	Yes	184 (25.5)	3.11±0.93	0.62	.533
		No	513 (74.5)	3.16±0.98
		Missing	1
	Depression	Yes	89 (11.8)	2.65±0.99	4.60	< .001
		No	609 (88.2)	3.22±0.94
	Pain/discomfort	Yes	379 (49.9)	2.77±0.92	10.17	< .001
		No	319 (50.1)	3.53±0.86
	Self-rated health		3.15±0.97
		Very good	38 (6.4)
		Good	215 (31.9)
		Fair	256 (36.0)
		Poor	163 (21.7)
		Very poor	26 (4.0)
		Missing	1

^† Unweighted;

^‡ Sample cross-sectional weights were applied for t-test, ANOVA test, Person r and percentages;

^§ Never married;

^‖ Married, widow, divorced, separated.

Table 2.

Trajectory of Self-rated Health among One-person Households (N=698)

Latent variables	Mean	SE	Model fit
Latent variables	Mean	SE	x² (p)	CFI	NFI	TLI	RMSEA
Initial intercept	3.09*	0.03	24.78 (.037)	.99	.97	.99	.03
Slope	-0.05*	0.01	24.78 (.037)	.99	.97	.99	.03

CFI=comparative fit index; NFI=normed fit index; TLI=Tucker-Lewis index; RMSEA=root mean square error of approximation; *p＜.001.

Table 3.

Determining the Number of Latent Classes according to Self-rated Health Development Trajectory (N=698)

Categories		2-class model	3-class model	4-class model	5-class model
Model fit	AIC	8,104.20	7,988.71	7,991.44	7,990.61
	BIC	8,149.68	8,047.84	8,064.21	8,077.02
	Adjusted BIC	8,117.93	8,006.56	8,013.41	8,016.69
n (%) according to	Class 1	375 (53.7)	165 (23.6)	1 (0.1)	0 (0.0)
latent class	Class 2	323 (46.3)	310 (44.4)	328 (47.0)	231 (33.1)
	Class 3		223 (31.9)	204 (29.2)	163 (23.4)
	Class 4			165 (23.6)	12 (1.7)
	Class 5				292 (41.8)

AIC=Akaike information criterion; BIC=Bayesian information criterion.

Table 4.

Multinomi al Logistics Regression (N N=698)

Domains	Variables	Class 1: Low-Stable class (n=165)				Class 2: Moderate-Decreasing class (n=310)
Domains	Variables	OR	95% CI	Wald x²	p	OR	95% CI	Wald x²	p
Demographic·	Age (year)	0.99	0.95~1.03	0.40	.530	1.01	0.99~1.04	0.79	.375
socio-economic characteristics	Household income quantiles	1.03	0.74~1.44	0.04	.848	1.06	0.86~1.30	0.30	.583
	Gender	2.79	1.05~7.46	4.19	.041	1.44	0.82~2.55	1.58	.208
	(ref. male)
	Marital status	0.96	0.20~4.71	0.00	.958	2.93	1.17~7.35	5.22	.022
	(ref. experience of being married)
	Education
	≤Elementary school	0.11	0.01~1.24	3.17	.075	0.91	0.39~2.17	0.04	.838
	Middle~High school	0.26	0.02~2.73	1.28	.259	0.89	0.41~1.94	0.09	.769
	(ref.≥college)
	Economic activity	0.43	0.21~0.88	5.37	.021	0.94	0.57~1.54	0.06	.809
	(ref. inactive)
	Medical security	1.81	0.79~4.13	1.97	.160	1.19	0.58~2.42	0.22	.639
	(ref. national health insurance)
Health behavior	Current cigarette smoking	6.44	2.30~18.04	12.54	＜.001	1.44	0.75~2.77	1.22	.270
	(ref. no)
	Excessive alcohol consumption	0.21	0.05~0.82	5.04	.025	0.58	0.31~1.12	2.65	.103
	(ref. no)
	Healthy physical activity possible	0.32	0.17~0.59	13.32	＜.001	0.57	0.37~0.86	6.99	.008
	(ref. no)
Health state	Disabled	3.43	1.46~8.04	8.02	.005	1.45	0.70~2.99	1.01	.316
	(ref. no)
	Chronic disease	14.93	1.68~133.02	5.87	.015	3.08	1.53~6.19	9.95	.002
	(ref. no)
	Obesity	1.43	0.75~2.71	1.18	.278	0.97	0.61~1.55	0.01	.912
	(ref. no)
	Depression	6.51	2.67~15.87	16.96	＜.001	1.96	0.92~4.19	3.01	.083
	(ref. no)
	Pain/discomfort	10.03	4.94~20.33	40.84	＜.001	2.25	1.45~3.49	12.92	＜.001
	(ref. no)

Reference group=Class 3: High-Decreasing class (n=223); OR=odds ratio; CI=confidence interval; ref.=reference.

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