中国城镇化与健康相关行为关系分析:一项基于CHARLS数据的实证研究

doi:10.19723/j.issn.1671-167X.2022.02.010

Abstract

Abstract:

Objective: To explore the relationship between urbanization and residents’ health behaviors in China, and to clarify the impact of urbanization on health behaviors. Methods: Based on China Health and Retirement Longitudinal Study (CHARLS),we established a panel data fixed effect model tracked in 2011, 2013 and 2015 to analyze the effect of urbanization level on smoking cessation, drinking, sleep quality and physical exercise behavior. The ratio of population density, gross domestic product (GDP) per capita and output value of secondary and tertiary industries to GDP were used to represent the levels of urbanization. Results: From 2011 to 2015, China’s urbanization levels showed an increasing trend, which showed that the ratio of population density, GDP per capita and output value of secondary and tertiary industries in GDP increased year by year. Smoking cessation increased with the increase of GDP per capita and the proportion of the output value of secondary and tertiary industries. Compared with the low-level, the drinking rate in the regions with per capita GDP of more than 45 000 yuan increased by 2.49%, and the drinking rate in the regions with secondary and tertiary industries for 80%-85% decreased by 2.48%. However, there was no significant difference with population density. The sleep quality decreased with the increase of per capita GDP. In regions where per capita GDP was more than 93%, the sleep quality decreased by 3.71% compared with the low-level which was not significantly different from the ratio of population density and the output value of secondary and tertiary industries. For low contrast, the exercise rate in regions with the population density of 400-600 people/km²and over 800 people/km² was reduced by 5.2% and 7.7% respectively. The exercise rate in regions with per capita GDP of 25 000-35 000 yuan and over 45 000 yuan was reduced by 3.26% and 3.73% respectively. The exercise rate in regions with secondary and tertiary industries accounting for more than 93% of GDP was 10.68% lower than that of the low-level regions. Conclusion: Different dimensions of urbanization have different impacts on different health behaviors. The smoking cessation rate increases with the increase of urbanization level, which is related to the proportion of per capita GDP and the output value of secondary and tertiary industries. The exercise rate, related to the three dimensions, decreases with the increase of urbanization. Sleep quality is more closely related to per capita GDP, and the probability of good sleep quality decreases with the increase of urbanization level. However, there is no obvious trend between drinking rate and urbanization level, which needs further study.

Key words: Urbanization, Health behavior, Empirical research

CLC Number:

R193

HE Shan,JIAN Wei-yan. Analysis on the relationship between urbanization and health behavior in China: An empirical research based on China Health and Retirement Longitudinal Study (CHARLS)[J].Journal of Peking University (Health Sciences), 2022, 54(2): 261-266.

Figures/Tables 5

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References 29

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Indicators	Explanation
Healthy behavior variable
Smoking cessation	If quit smoking now=1, never quit=0
Drinking	If current drinking frequency >1 time per month, then drinking=1, else=0
Sleep quality	If current bad nights frequency <3 time per week, then sleep quality=1, else=0
Exercise	If current exercise time >10 min at a time, then exercise=1, else=0
Urbanization variable
Population density	Local population/local area (people/km²)
GDP per capita	GDP/local population (thousand yuan)
The output value of secondary and tertiary industries in GDP	Value of the secondary industry in GDP + value of the tertiary industry in GDP (%)
Personal characteristics variable
Age	Continuous variables, >45 years old
Gender	Male=1, female=0
Education level	Illiteracy=1, can read=2, primary school=3, junior high school and above=4
Marital status	Married=1, other=0

Indicators	Number
Age/years
45-49	2 931	22.27
50-59	4 996	37.96
60-69	3 568	27.11
70-79	1 375	10.45
≥80	292	2.22
Gender
Male	6 306	47.94
Female	6 848	52.06
Education level
Illiteracy	3 674	27.94
Can read	2 361	17.96
Primary school	2 948	22.42
Junior high school and above	4 166	31.68
Marital status
Married	11 624	88.31
Other	1 538	11.69

Variables	Year 2011	Year 2013	Year 2015
Smoking cessation/%	21.44	23.43	31.66
Drinking/%	33.43	34.16	35.33
Sleep quality/%	65.78	66.54	64.39
Exercise/%	90.06	89.77	88.24

Indicators	Year 2011			Year 2013			Year 2015
Indicators	n	Median	IQR	n	Median	IQR	n	Median	IQR
Population density/(people/km²)		411.96	443.72		425.66	442.73		425.14	457.09
Low (<200)	75	129.72	71.64	72	130.14	71.08	69	128.12	71.49
Mid-low (<400)	112	298.56	113.36	100	301.09	116.13	100	301.58	95.47
Middle (<600)	90	488.18	110.35	90	469.47	104.96	99	477.99	125.6
Mid-high (<800)	86	677.18	102.05	89	683.04	83.94	77	698.38	55.50
High (≥800)	50	940.21	136.71	59	990.94	182.95	65	932.90	160.23
GDP per capita/thousand yuan		22.07	18.99		36.42	24.68		44.30	31.17
Low (<15)	117	11.24	3.55	27	14.38	1.92	9	12.04	1.06
Mid-low (<25)	138	21.06	4.42	81	19.82	0.46	57	21.52	4.38
Middle (<35)	61	30.38	4.55	87	28.91	0.08	81	30.70	4.90
Mid-high (<45)	45	38.91	4.98	82	39.84	0.48	63	39.33	6.38
High (≥45)	52	63.43	19.56	133	65.08	1.45	200	61.51	43.06
The output value of secondary and tertiary industries in GDP/%		85.83	13.02		87.55	10.76		88.22	9.58
Low (<80)	114	75.81	6.31	90	74.52	5.74	63	75.30	3.15
Mid-low (<85)	81	81.60	2.54	72	83.75	0.96	75	83.56	1.91
Middle (<90)	72	87.99	1.88	72	86.09	2.98	81	87.62	2.40
Mid-high (<93)	57	95.51	1.13	78	91.72	1.68	75	91.95	1.83
High (≥93)	89	95.51	3.71	98	95.73	3.45	110	96.02	4.02

Items	Smoking cessation (1, Yes; 0, No)	Drinking (1, Yes; 0, No)	Sleep quality (1, Good; 0, Poor)	Exercise (1, Yes; 0, No)
Population density
Low	1	1	1	1
Mid-low	-0.021 2	0.027 3	0.021 9	-0.031 1
Middle	0.037 5	0.017 1	0.028 9	-0.052 0^*
Mid-high	-0.012 4	0.025 0	0.025 4	-0.044 1
High	0.065 1	0.008 6	-0.006 6	-0.077 0^**
GDP per capita
Low	1	1	1	1
Mid-low	0.051 2^***	0.004 5	-0.020 5^**	0.001 7
Middle	0.112 3^***	0.000 8	-0.026 4^**	-0.032 6^**
Mid-high	0.124 2^***	0.017 0	-0.021 1^*	-0.023 6
High	0.190 1^***	0.024 9^*	-0.037 1^***	-0.037 3^**
The output value of secondary and tertiary industries in GDP
Low	1	1	1	1
Mid-low	0.047 0^***	-0.024 8^*	-0.009 8	-0.009 6
Middle	0.107 9^***	-0.016 6	-0.009 7	0.004 0
Mid-high	0.162 3^***	-0.067 5	-0.005 5	-0.001 3
High	0.237 4^***	-0.039 6	-0.017 3	-0.106 8^***

Analysis on the relationship between urbanization and health behavior in China: An empirical research based on China Health and Retirement Longitudinal Study (CHARLS)

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