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

摘要/Abstract

摘要：

目的: 探讨中国城镇化与居民健康相关行为的关系,理清城镇化对健康行为的影响。方法: 基于中国健康与养老追踪调查(China Health and Retirement Longitudinal Study, CHARLS)在2011、2013和2015年均被追踪到的数据构建个体面板,采用面板数据固定效应模型分析城镇化水平对戒烟、饮酒、睡眠质量和锻炼行为的影响,利用人口密度、人均国内生产总值(gross domestic product, GDP)、第二三产业产值占比代表城镇化水平。结果: 2011—2015年中国城镇化水平呈增加趋势,表现为人口密度、人均GDP和第二三产业产值占比的逐年增长。戒烟率随人均GDP水平、第二三产业产值占比水平的增加均有所提高。饮酒率以低水平为对照,人均GDP水平在45 000元以上的地区饮酒率提高2.49%,第二三产业产值占比为80%~85%的地区饮酒率降低2.48%,人口密度的差异无统计学意义。睡眠质量随人均GDP水平的增加而降低,人均GDP在93%以上的地区睡眠质量好的比低水平降低3.71%,人口密度水平和第二三产业产值占比的差异无统计学意义。锻炼率以低水平为对照,人口密度在400~600人/平方公里和800人/平方公里以上的地区锻炼率分别降低5.2%和7.7%,人均GDP在25 000~35 000元和45 000元以上的地区锻炼率分别降低3.26%和3.73%,第二三产业产值占比在93%以上的地区锻炼率降低10.68%。结论: 城镇化不同维度对不同健康行为的影响不同,戒烟率随城镇化水平增加而上升,与人均GDP和第二三产业产值占比有关;锻炼率随城镇化的增加而降低,与三个维度均有关系;睡眠质量好的概率随城镇化水平的增加而降低,与人均GDP关系更为密切;饮酒率与城镇化水平无明显相关趋势,还需要进一步研究。

关键词: 城镇化, 健康行为, 实证研究

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

中图分类号:

R193

何珊,简伟研. 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[J]. 北京大学学报（医学版）, 2022, 54(2): 261-266.

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.

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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^***