Gender Based Health Inequality and Impacting Factors

Mi Young Song; Woo Youn Lim; Jeung-Im Kim

doi:10.4069/kjwhn.2015.21.2.150

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Song, Lim, and Kim: Gender Based Health Inequality and Impacting Factors

Original Article

Korean Journal of Women Health Nursing 2015; 21(2): 150-159.

Published online: 27 June 2015

DOI: https://doi.org/10.4069/kjwhn.2015.21.2.150

Gender Based Health Inequality and Impacting Factors

Mi Young Song¹, Woo Youn Lim¹, Jeung-Im Kim²

¹Chungnam Women's Policy Development Institute, Gongju, Korea.

²School of Nursing, Soonchunhyang University, Cheonan, Korea.

Corresponding author: Kim, Jeung-Im. School of Nursing, Soonchunhyang University, 31 Suncheonhyang 6-gil, Dongnam-gu, Cheonan 330-930, Korea. Tel: +82-41-570-2493, Fax: +82-41-570-2498, jeungim@sch.ac.kr

Received 25 March 2015 Revised 3 June 2015 Accepted 16 June 2015

(open-access):

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

This study was aimed to identify gender-based health inequality and explore impacting factors on health inequality in one province in Korea.

Methods

This was an explanatory study using the secondary data on Chungnam province from the Fifth Community Health Survey from August 16 to Oct 31, 2012. Variables included in this analysis were education level, poverty, marital status, and residential community for socio-cultural characteristics and subjective health status as an indicator of health inequality. Data were analyzed by χ²-test, t-test, ANOVA, and multiple linear regression.

Results

There were gender inequalities and disparities in health, and these inequalities were greater in woman than in man (χ²=161.8, p<.001). The impacting factors were education level, poverty, marital status, and residential community, which was accounted for 22.6% of variances of health inequality. Among these variables, gender showed the largest influence in health inequalities.

Conclusion

To solve health inequalities, it should be considered gender differences based on social determinants of health. It is necessary to develop long term project based on these results and the social determinants model of World Health Organization.

Keywords: Health Inequality, Women, Gender

Figures and Tables

Figure 1

Analysis model based on gender approach (Song, Lim, & Kim, 2014).

Table 1

Sociocultural Characteristics and Health Inequality by Gender (N=1,598,762)

Variables	Classification	Total	Man	Woman	x² or t (p)
Variables	Classification	n (%)	n (%)	n (%)	x² or t (p)
Age (year)	19~44	750,692 (47.0)	396,212 (24.8)	354,480 (22.2)	11,290.2 (<.001)
	45~64	536,812 (33.6)	275,266 (17.2)	261,546 (16.4)
	65~74	177,345 (11.1)	78,960 (4.9)	98,385 (6.2)
	≥75	133,912 (8.4)	52,184 (3.3)	81,728 (5.1)
Education	Illiteracy	109,298 (6.8)	18,926 (1.2)	90,372 (5.7)	85,611.3 (<.001)
	Under elementary^†	264,739 (16.6)	100,769 (6.3)	163,970 (10.3)
	Middle/high school	672,015 (42.1)	353,704 (22.2)	318,311 (19.9)
	College/university	506,694 (31.8)	297,931 (18.7)	208,763 (13.1)
	Over graduate	44,072 (2.8)	30,361 (1.9)	13,711 (0.9)
Poverty	Poor	46,703 (2.9)	21,115 (1.3)	25,588 (1.6)	479.8 (<.001)
Poverty	Not poor	1,551,947 (97.1)	781,478 (48.9)	770,469 (48.2)	479.8 (<.001)
Marital status	Married	1,314,452 (82.2)	619,355 (38.7)	695,097 (43.5)	28,119.4 (<.001)
Marital status	Unmarried	284,310 (17.8)	183,268 (11.5)	101,042 (6.3)	28,119.4 (<.001)
Dwelling region	Dong	692,432 (43.3)	344,865 (21.6)	347,567 (21.7)	77.4 (<.001)
Dwelling region	Eup/Myeon	906,330 (56.7)	457,758 (28.6)	448,572 (28.1)	77.4 (<.001)
Health Inequality		2.72 (.930)	2.60 (.909)	2.84 (.936)	-161.9 (<.001)

^†Include Seodang.

Table 2

Differences in Health Status by Gender and Sociocultural Variables (N=1,598,762)

Variables	Classification	Man		Woman
Variables	Classification	M±SD	t or F (p)	M±SD	t or F (p)
Education	Illiteracy	3.44±0.99	22,021.3 (<.001)	3.67±0.89	56,279.4 (<.001)
	Under elementary (include Seodang)	3.14±0.98		3.31±0.91
	Middle/high school	2.64±0.87		2.68±0.82
	College/university	2.33±0.82		2.37±0.75
	Over graduate	2.52±0.77		2.38±0.70
	Subtotal	2.60±0.91		2.84±0.936
Poverty	Poor	3.14±1.15	69.1 (<.001)	3.42±0.94	101.0 (<.001)
	Not poor	2.59±0.89		2.82±0.93
	Subtotal	1.97±0.16		1.97±0.18
Marital status	Married	2.70±0.89	187.9 (<.001)	2.92±0.92	232.4 (<.001)
	Unmarried	2.26±0.90		2.27±0.82
	Subtotal	1.23±0.42		1.13±0.33
Life span	19~44	2.35±0.82	33,739.7 (<.001)	2.43±0.75	74,795.3 (<.001)
	45~64	2.69±0.85		2.90±0.86
	65~74	3.09±0.96		3.51±0.09
	≥75	3.33±1.00		3.60±0.93
	Subtotal	2.60±0.91		2.84±0.94
Dwelling region	Dong	2.57±0.86	-31.4 (<.001)	2.69±0.89	-123.5 (<.001)
Dwelling region	Eup/Myeon	2.63±0.94	-31.4 (<.001)	2.95±0.96	-123.5 (<.001)

Table 3

Gender Differences in Predictors of Health Inequality

Variables	Categories	Woman		Man
Variables	Categories	B (β)	t (p)	B (β)	t (p)
Education^†	Elementary school	.088 (.046)	4.19 (<.001)	.098 (.051)	3.80 (<.001)
	Middle/High school	.423 (.210)	13.48 (<.001)	.312 (.163)	8.70 (<.001)
	College/University	.523 (.215)	12.92 (<.001)	.503 (.235)	12.24 (<.001)
	Graduate school	.602 (.073)	6.92 (<.001)	.576 (.113)	8.58 (<.001)
Poverty^‡		-.295 (-.059)	-5.89 (<.001)	-.435 (-.081)	-6.87 (<.001)
Marital status^§		-.189 (-.055)	-4.83 (<.001)	-.186 (-.068)	-5.01 (<.001)
Life span^∥	45~64	-.229 (-.114)	-8.01 (<.001)	-.120 (-.061)	-3.79 (<.001)
	65~74	-.593 (-.238)	-14.81 (<.001)	-.374 (-.148)	-8.81 (<.001)
	≥75	-.690 (-.256)	-15.85 (<.001)	-.609 (-.206)	-12.50 (<.001)
Dwelling region^¶		.007 (.003)	.314 (.170)	.049 (.022)	1.84 (.103)
Regression constant		3.207		3.246
R²		.259		.164
F (p)		259.7 (<.001)		119.2 (<.001)

Dummy Variables: ^†Education (ineducation); ^‡Poverty (poverty); ^§Marital status (married); ^∥Life span (19~44); ^¶Dwelling region (rural).

Table 4

Impact of Gender on Health Inequality in addition to Education, Poverty, Marital Status, and Life Span

Health Inequality	Categories	B (β)	t (p)	B (β)	t (p)
Education^†	Elementary school	.092 (.048)	5.66 (<.001)
	Middle/High school	.387 (.197)	16.71 (<.001)
	College/University	.533 (.232)	18.93 (<.001)
	Graduate school	.602 (.095)	11.60 (<.001)
Poverty^‡		-.352 (-.068)	-8.92 (<.001)
Marital status^§		-.168 (-.055)	-6.37 (<.001)
Life span^∥	45~64	-.179 (-.090)	-8.56 (<.001)
	65~74	-.493 (-.195)	-17.16 (<.001)
	≥75	-.646 (-.229)	-20.13 (<.001)
Gender^¶				-.111 (-.057)	-7.24 (<.001)
Regression constant		3.274
R² (corrected R²)		.226 (.225)
F		394.1 (<.001)

Dummy Variables: ^†Education (ineducation); ^‡Poverty (poverty); ^§Marital status (married); ^∥Life span (19~44); ^¶Dwelling region (rural).

Notes

This study was based on the report of 2014 health inequality practice and solving strategies for women in Chungnam (Chungcheongnam-do Women's Policy Development Institute 2014-2).

Summary Statement

▪ What is already known about this topic?

There are gender inequalities in health worldwide. These problems occur sociologically rather than biologically.

▪ What this paper adds?

Gender was the most important factor to explain the disparities in health women were more likely to be poor, and have lower education levels, and lower health status than man.

▪ Implications for practice, education and/or policy

Toachieve equality in health, it is necessary to conduct long term project based on our results and the social determinants model of World Health Organization.

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