Abstract
目的
抑郁症状的流行已经成为重大的公共卫生问题,研究人格特征与抑郁症状变化之间的关系,并进一步探讨其中的城乡差异,不仅有利于了解抑郁症状在中国的流行趋势,而且能够为政府部门制定个性化的心理健康预防战略提供有利参考。
方法
基于中国家庭追踪调查2018年和2020年的数据,对16 198名18岁以上中国居民进行单因素分析。人格特质有尽责性、外向性、宜人性、神经质和开放性五个维度,按照2018年和2020年抑郁症状的变化情况将16 198名居民分为健康组、抑郁好转组、抑郁恶化组和持续抑郁组。控制性别和教育等因素后采用多项Logistic回归模型检验人格特征是否与抑郁症状的改变相关,并分析了城乡差异在其中的交互作用。
结果
人格特质的五个维度均与抑郁症状的变化呈现显著的相关性,尽责性、外向性、宜人性与抑郁症状呈负相关,而神经质和开放性呈正相关。城乡差异可以调节人格特质与抑郁症状之间的关系,与城市居民相比,农村居民在神经质(OR = 1.14; 95%CI:1.00~1.30) 和抑郁好转组,以及尽责性(OR = 0.79;95%CI:0.68~0.93)和持续抑郁组之间表现出更强的相关性。
结论
人格特质与抑郁症状的变化相关,尽责性、外向性、宜人性与抑郁症状呈负相关,而神经质和开放性呈正相关。抑郁症状好转与神经质呈正相关,持续抑郁与尽责性呈负相关。农村居民的人格特征和持续性抑郁症状以及好转的抑郁症状之间有更强的联系,因此,在中国成年人的心理健康干预和预防计划中,应该进一步考虑人格特质和具有城乡差异的特定策略;同时,本研究结果需要其他针对于独立人群的研究验证。
Keywords: 抑郁症状, 城乡差异, 人格特征, 中国成年人
Abstract
Objective
The prevalence of depressive symptoms has become a significant public health issue in China. Research on the relationship between personality traits and changes in depressive symptoms, as well as further exploration of urban-rural differences, not only benefits for the understanding of the prevalence trend of depression in China, but also provides a useful reference for the government to develop personalized mental health prevention strategies.
Methods
Based on the data from the China Family Panel Studies in 2018 and 2020, a univariate analysis was conducted on 16 198 Chinese residents aged 18 years and above. Five dimensions of personality traits were conscientiousness, extraversion, agreeableness, neuroticism and openness. In the study, 16 198 residents were divided into "keep good group", "better group", "worse group" and "keep bad group" according to the changes in depressive symptoms in 2018 and 2020. After controlling for factors, such as gender and education, multinomial Logistic regression analysis was used to examine whether personality traits were associated with changes in depressive symptoms. In addition, we evaluated whether urban-rural and personality traits interacted to influence depressive symptoms.
Results
The five dimensions of personality traits were significantly correlated with changes in depressive symptoms. Conscientiousness, extroversion, and agreeableness were negatively associated with depressive symptoms, while neuroticism and openness were positively related. Urban and rural differences moderated the relationship between personality traits and depressive symptoms. Compared with urban residents, rural residents showed stronger correlations between neuroticism (OR=1.14; 95%CI: 1.00-1.30) and the group of depression-recovery, as well as conscientiousness (OR=0.79;95%CI: 0.68-0.93) and the group of persistent-depression.
Conclusion
The study finds that personality traits have a significant correlation with changes in depressive symptoms, with certain traits showing a negative or positive relationship. Specifically, higher levels of conscientiousness, extraversion, and agreeableness are associated with lower levels of depressive symptoms, while higher levels of neuroticism and openness are associated with higher levels of depressive symptoms. In addition, the study finds that rural residents have a stronger association between their personality traits and persistent or improved depressive symptoms, which highlights the need for tailoring mental health intervention and prevention programs that should take into account personality traits and urban-rural differences in China. By developing targeted strategies that are sensitive to personality differences and geographic disparities, policymakers and mental health professionals can help prevent and reduce the incidence of depressive symptoms, ultimately improving the overall well-being of Chinese adults. Meanwhile, additional studies in independent populations are needed to corroborate the findings of this study.
Keywords: Depressive symptoms, Urban-rural differentials, Personality traits, Chinese adults
全世界约有3.5亿人有抑郁症状,抑郁症状的广泛流行无疑是一个重大的公共卫生问题[1]。根据全球疾病负担(Global Burden of Disease,GBD) 研究数据显示,从1990年到2017年,抑郁症状的病例数从1.72亿增加到2.58亿[2]。随着经济增长和生活方式的改变,中国抑郁症的患病率正在急剧上升,这对中国居民的健康发展构成了极大威胁。人格特质,如有关人的认知、情感和行为等,对于增加抑郁症状等精神病理状态的倾向至关重要。国际上最流行的人格理论是20世纪80年代建立的大五人格模型[3]。神经质与负面情绪有关,如焦虑、恐惧和愤怒等。外向性是一种活跃和善于交际的倾向。开放性是指倾向于非传统的想法和经历不同的情绪。亲和性是指人际关系特征,如利他和合作的倾向。尽责性是具有坚持、组织和目标导向的行为[4]。国际上已开展了大量关于人格特质与抑郁症状关系的研究,Fournier等[5]发现人格特征与抑郁症状的发生、复发、症状的严重程度和持续时间等密切相关。先前的研究发现人格特质的变化对抑郁症状的变化有影响[6],然而,部分研究者认为人格特质是一个相对稳定的结构,因此,人格特质与抑郁症状之间的关系尚未达成统一的结论[7]。
在中国,抑郁症状在地区间存在较大差异,特别是在城市和农村地区,农村居民的抑郁症状水平显著高于城市居民[8]。目前,中国有关大五人格的城乡差异研究尚不多见,以往的研究发现,城市居民和农村居民的人格特质存在一定差异[9],而肖凌燕[10]在对大学生人格特质的研究中则未观察到城乡差异,因此,城乡差异在人格特质与抑郁症状关系之间的作用仍需要进一步探究。本研究利用具有全国代表性的家庭调查数据,构建了2018年和2020年的面板数据,之后采用多项Logistic回归模型检验人格特征是否与抑郁症状的改变相关,同时探究其中是否存在城乡差异。
1. 资料与方法
1.1. 资料来源
本研究数据来源于中国家庭追踪调查(China Family Panel Studies,CFPS),利用受访者的唯一编码,构建了2018年和2020年的面板数据。纳入标准: (1)18岁及以上成年人;(2)2018年和2020年均完成调查的受访者。排除标准: (1)2018年年龄在18岁以下者;(2)数据有缺失值者。最终纳入了16 198名受访者作为本次研究对象。
1.2. 变量选择
1.2.1. 因变量
本研究使用2018年至2020年受访者抑郁症状的变化作为结果变量。CFPS调查中使用流调抑郁自评量表简版8项(Centre for Epidemiological Studies Depression Scale-8 items, CES-D8) 来测量受访者过去一周的抑郁情况,CES-D8量表共有8个条目(含2项反向计分条目),每项分别赋值0~3分(0, < 1 d;1,1~2 d;2,3~4 d;3,5~7 d),总分为0~24分,分数越高表明抑郁症状越严重。本研究以8分为分界点,根据受访者的量表总分将受访者分为两大类,健康状态≤8分,抑郁症状> 8分[11-12],克朗巴哈系数(Cronbach’s α)为0.778。随后,根据居民2018年和2020年抑郁症状评分的变化,将所有样本的结果分为4组:健康组(2018年和2020年都保持健康);抑郁好转组(2018年抑郁,但2020年健康);抑郁恶化组(2018年健康,但2020年抑郁);持续抑郁组(2018年和2020年都保持抑郁)。
1.2.2. 自变量
2018年CFPS调查使用大五人格量表收集受访者的人格特征数据[13],该量表包含15个项目,每项分别赋值1~5分,克朗巴哈系数为0.737。根据以往的研究,将所有项目分为尽责性、外向性、开放性、神经质和宜人性五个维度,并计算每个维度的总分作为五个主要的连续自变量。
1.2.3. 控制变量
本研究将以下人口统计学和社会经济学因素作为控制变量纳入分析:地区(西部/中部/东部/东北部);居住地(城市/农村);性别(女性/男性);年龄(18~30岁/>30~45岁/> 45岁);婚姻状况(已婚/未婚);当前就业状况(工作/失业);教育(小学/中学/高中/大学及以上);慢性病(是/否);健康状况(差/一般/良好);收入状况(低等收入/中低收入/中等收入/中高收入/高收入)。
1.3. 统计学分析
使用Stata 16.0版本进行数据整理分析。抑郁症状的改变是因变量,基线(2018年)的人格特质是自变量,基线社会人口学变量是控制变量。采用描述性统计分析来描述受试者的分布情况,并使用卡方检验来探讨每个自变量是否与抑郁症状的变化相关。采用多项Logistic回归模型分析自变量,特别是人格特征是否与抑郁症状的变化有关。此外,还分析了城乡对人格特征与抑郁症状关系的交互作用。检验水准α=0.05。
2. 结果
2.1. 社会人口学特征
最终样本包括16 198名居民,大多数受访者来自我国东部。居民年龄中位数为48岁,平均年龄为(47.24±0.12)岁。本次调查的居民主要来自城镇(51.55%),其余48.45%来自农村。2018年,健康人群和抑郁人群的比例分别为70.01%和29.99%。与2018年相比,2020年心理健康人数减少(66.08%),抑郁人数增加(33.92%),详见表 1。
表 1.
社会人口学特征
Sociological-demographic characteristics of the sample
Characteristics | n (%) | Rural, n | Urban, n | P |
Gender | ||||
Female | 8 178 (50.49) | 3 906 | 4 272 | 0.077 |
Male | 8 020 (49.51) | 3 942 | 4 078 | |
Age/years | ||||
18-30 | 2 338 (14.43) | 1 019 | 1 319 | < 0.001 |
>30-45 | 4 512 (27.86) | 2 012 | 2 500 | |
>45 | 9 348 (57.71) | 4 817 | 4 531 | |
Marital status | ||||
Married | 13 955 (86.15) | 6 874 | 7 081 | < 0.001 |
Unmarried | 2 243 (13.85) | 974 | 1 269 | |
Working status | ||||
Unemployed | 3 501 (21.61) | 1 216 | 2 285 | < 0.001 |
Employed | 12 697 (78.39) | 6 632 | 6 065 | |
Education | ||||
Primary school | 6 392 (39.46) | 4 098 | 2 294 | < 0.001 |
Middle school | 5 010 (30.93) | 2 459 | 2 551 | |
High school | 3 805 (23.49) | 1 139 | 2 666 | |
College degree or above | 991 (6.12) | 152 | 839 | |
Chronic diseases | ||||
No | 13 448 (83.02) | 6 478 | 6 970 | 0.115 |
Yes | 2 750 (16.98) | 1 370 | 1 380 | |
Self-evaluated income status | ||||
Low | 1 730 (10.68) | 826 | 904 | < 0.001 |
Low-middle | 2 854 (17.62) | 1 278 | 1 576 | |
Middle | 7 860 (48.52) | 3 634 | 4 226 | |
Middle-high | 2 116 (13.06) | 1 097 | 1 019 | |
High | 1 638 (10.12) | 1 013 | 625 | |
Self-reported health status | ||||
Very good | 2 124 (13.11) | 979 | 1 145 | < 0.001 |
Quite good | 7 017 (43.32) | 3 099 | 3 918 | |
Good | 2 326 (14.36) | 1 120 | 1 206 | |
General | 2 161 (13.34) | 1 192 | 969 | |
Bad | 2 570 (15.87) | 1 458 | 1 112 | |
Depression status (2018) | ||||
Depression | 4 858 (29.99) | 2 544 | 2 314 | < 0.001 |
Healthy | 11 340 (70.01) | 5 304 | 6 036 | |
Depression status (2020) | ||||
Depression | 5 495 (33.92) | 2 889 | 2 606 | < 0.001 |
Healthy | 10 703 (66.08) | 4 959 | 5 744 | |
Total | 16 198 | 7 848 | 8 350 |
2.2. 自变量与抑郁症状的相关性分析
表 2展示了自变量与抑郁症状变化之间的相关性结果,性别、慢性病、自评健康状况与居民抑郁症状的改变显著相关(P < 0.05)。
表 2.
心理健康状态变化的影响因素分析
Analysis results of risk factors on depression status
Characteristics | Keep good, n | Better, n | Worse, n | Keep bad, n | P |
Gender | |||||
Female | 3 663 | 1 324 | 1 639 | 1 552 | < 0.001 |
Male | 4 676 | 1 040 | 1 362 | 942 | |
Township | |||||
Rural | 3 805 | 1 223 | 1 499 | 1 321 | < 0.001 |
Urban | 4 534 | 1 141 | 1 502 | 1 173 | |
Age/years | |||||
18-30 | 1 260 | 343 | 441 | 294 | < 0.001 |
>30-45 | 2 491 | 630 | 816 | 575 | |
>45 | 4 588 | 1 391 | 1 744 | 1 625 | |
Marital status | |||||
Married | 7 311 | 2 006 | 2 595 | 2 043 | < 0.001 |
Unmarried | 1 028 | 358 | 406 | 451 | |
Working status | |||||
Unemployed | 1 701 | 506 | 649 | 645 | < 0.001 |
Employed | 6 638 | 1 858 | 2 352 | 1 849 | |
Education | |||||
Primary school | 2 886 | 1 006 | 1 274 | 1 226 | < 0.001 |
Middle school | 2 712 | 712 | 907 | 679 | |
High school | 2 158 | 509 | 653 | 485 | |
College degree or above | 583 | 137 | 167 | 104 | |
Chronic diseases | |||||
No | 7 266 | 1 870 | 2 468 | 1 844 | < 0.001 |
Yes | 1 073 | 494 | 533 | 650 | |
Self-evaluated income status | |||||
Low | 743 | 271 | 363 | 353 | < 0.001 |
Low-middle | 1 361 | 433 | 581 | 479 | |
Middle | 4 277 | 1 116 | 1 367 | 1 100 | |
Middle-high | 1 153 | 281 | 374 | 308 | |
High | 805 | 263 | 316 | 254 | |
Self-reported health status | |||||
Very good | 1 038 | 318 | 426 | 342 | < 0.001 |
Quite good | 3 731 | 1 049 | 1 279 | 958 | |
Good | 1 425 | 276 | 412 | 213 | |
General | 1 323 | 265 | 369 | 204 | |
Bad | 822 | 456 | 515 | 777 | |
Personality traits | |||||
Conscientiousness | - | - | - | - | < 0.001 |
Extraversion | - | - | - | - | < 0.001 |
Openness | - | - | - | - | < 0.001 |
Neuroticism | - | - | - | - | < 0.001 |
Agreeableness | - | - | - | - | < 0.001 |
Total | 8 339 | 2 364 | 3 001 | 2 494 |
2.3. 人格特征与抑郁症状变化的多元Logistic回归分析结果
表 3展示了多元Logistic回归模型分析的结果,在控制混杂因素的情况下,人格特征与抑郁症状的改变相关,其中开放性和神经质显著相关,具体而言,尽责性(OR = 0.91; 95%CI:0.84~0.98)、外向性(OR = 0.90; 95%CI : 0.85~0.96)和宜人性(OR = 0.89; 95%CI:0.83~0.96)是保护因素,而神经质(OR = 1.45; 95%CI : 1.35~1.57)和开放性(OR = 1.18; 95%CI : 1.11~1.26)是持续抑郁症状的危险因素。
表 3.
人格特征与抑郁症状变化的多元Logistic回归分析结果
Results of multinominal logistic regression analysis on the relationship between personality traits and changes in depression symptoms
Characteristics | Better | Worse | Keep bad | |||||
OR a | 95%CI | OR a | 95%CI | OR a | 95%CI | |||
*P < 0.05;**P < 0.01;***P < 0.001. a, compared to the keep good group (n=8 339). OR, odds ratio; CI, confidence interval; Ref, reference. | ||||||||
Township (Ref: Urban) | ||||||||
Rural | 1.19*** | 1.08-1.31 | 1.10 | 1.01-1.21 | 1.19*** | 1.08-1.31 | ||
Gender (Ref: Female) | ||||||||
Male | 0.63*** | 0.57-0.70 | 0.67*** | 0.61-0.73 | 0.51*** | 0.46-0.56 | ||
Age (Ref: 18-30)/years | ||||||||
30-45 | 1.20 | 0.99-1.55 | 1.01 | 0.86-1.26 | 1.49* | 1.15-1.92 | ||
> 45 | 1.18 | 0.98-1.45 | 0.82 | 0.68-1.01 | 1.36 | 0.99-1.76 | ||
Marital status(Ref: Unmarried) | ||||||||
Married | 1.41*** | 1.23-1.62 | 1.20** | 1.05-1.36 | 1.83*** | 1.61-2.09 | ||
Work conditions(Ref: Unemployed) | ||||||||
Employed | 1.12 | 0.99-1.27 | 1.08 | 0.96-1.21 | 1.03 | 0.92-1.16 | ||
Education(Ref: Primary) | ||||||||
Middle school | 0.88* | 0.78-0.99 | 0.84** | 0.76-0.94 | 0.78*** | 0.70-0.88 | ||
High school | 0.81** | 0.70-0.93 | 0.78*** | 0.69-0.88 | 0.71*** | 0.62-0.82 | ||
College degree or above | 0.82 | 0.66-1.03 | 0.76** | 0.61-0.93 | 0.59*** | 0.46-0.76 | ||
Chronic diseases(Ref: No) | ||||||||
Yes | 1.39*** | 1.22-1.58 | 1.19** | 1.05-1.35 | 1.40*** | 1.24-1.59 | ||
Self-evaluated incomestatus (Ref: Low) | ||||||||
Low-middle | 0.92 | 0.76-1.11 | 0.89 | 0.75-1.05 | 0.85 | 0.71-1.01 | ||
Middle | 0.77** | 0.65-0.92 | 0.69*** | 0.59-0.80 | 0.67*** | 0.57-0.78 | ||
Middle-high | 0.71*** | 0.58-0.87 | 0.69*** | 0.58-0.83 | 0.67*** | 0.55-0.81 | ||
High | 0.87 | 0.70-1.07 | 0.78* | 0.64-0.94 | 0.66*** | 0.54-0.82 | ||
Self-reported health status(Ref: Bad) | ||||||||
Very good | 0.65*** | 0.54-0.77 | 0.72*** | 0.61-0.85 | 0.43*** | 0.37-0.51 | ||
Quite good | 0.61*** | 0.52-0.70 | 0.64*** | 0.56-0.74 | 0.36*** | 0.32-0.41 | ||
Good | 0.41*** | 0.34-0.50 | 0.54*** | 0.46-0.64 | 0.21*** | 0.18-0.26 | ||
General | 0.40*** | 0.33-0.48 | 0.51*** | 0.43-0.61 | 0.21*** | 0.17-0.25 | ||
Personality traits | ||||||||
Conscientiousness | 1.02 | 0.94-1.11 | 0.91* | 0.84-0.98 | 0.94 | 0.86-1.02 | ||
Extraversion | 1.01 | 0.94-1.07 | 0.99 | 0.94-1.04 | 0.90*** | 0.85-0.96 | ||
Openness | 1.10** | 1.03-1.17 | 1.06* | 1.00-1.12 | 1.18*** | 1.11-1.26 | ||
Neuroticism | 1.32*** | 1.23-1.41 | 1.24*** | 1.16-1.31 | 1.45*** | 1.35-1.57 | ||
Agreeableness | 1.03 | 0.94-1.13 | 0.89** | 0.83-0.96 | 0.93 | 0.85-1.01 | ||
Constant | 0.69* | 0.50-0.94 | 0.94 | 0.70-1.26 | 1.27 | 0.93-1.74 |
2.4. 城乡与人格特征对抑郁症状变化的交互作用
表 4展示了城乡和人格对抑郁症状交互作用的回归结果,在持续抑郁组中,城乡和尽责性(OR = 0.79; 95%CI:0.68~0.93)观察到显著的交互效应,而城乡和神经质(OR = 1.14;95%CI: 1.00~1.30)则在每个亚组中都存在显著的交互效应。与城市居民相比,农村居民在尽责性和持续抑郁组之间表现出更强的相关性,且农村居民在神经质和各个组之间都表现出较强关联性。
表 4.
城乡和人格对抑郁症状交互作用的回归结果
Regression results of the interaction effect between urban-rural disparity and personality traits on the changes in depression symptoms
Characteristics | Better | Worse | Keep bad | |||||
OR a | 95%CI | OR a | 95%CI | OR a | 95%CI | |||
*P < 0.05; **P < 0.01; ***P < 0.001. a, compared to the keep good group (n=8 339). OR, odds ratio; CI, confidence interval. | ||||||||
Township | ||||||||
Rural | 1.24*** | 1.13-1.36 | 1.17*** | 1.07-1.27 | 1.30*** | 1.19-1.43 | ||
Personality traits | ||||||||
Conscientiousness | 1.02 | 0.91-1.15 | 0.89* | 0.80-0.98 | 0.98 | 0.87-1.09 | ||
Extraversion | 1.00 | 0.92-1.09 | 0.97 | 0.90-1.05 | 0.89** | 0.82-0.96 | ||
Openness | 1.06 | 0.98-1.16 | 1.02 | 0.94-1.10 | 1.07 | 0.98-1.17 | ||
Neuroticism | 1.28*** | 1.17-1.41 | 1.23*** | 1.13-1.34 | 1.48*** | 1.34-1.64 | ||
Agreeableness | 1.02 | 0.90-1.14 | 0.95 | 0.86-1.05 | 0.93 | 0.83-1.04 | ||
Township × conscientiousness | 0.91 | 0.77-1.08 | 0.98 | 0.85-1.13 | 0.79** | 0.68-0.93 | ||
Township × extraversion | 1.00 | 0.89-1.13 | 1.05 | 0.94-1.16 | 1.02 | 0.91-1.15 | ||
Township × openness | 0.92 | 0.82-1.05 | 0.94 | 0.84-1.04 | 0.92 | 0.81-1.04 | ||
Township × neuroticism | 1.14* | 1.00-1.30 | 1.09 | 0.96-1.23 | 1.11 | 0.96-1.28 | ||
Township × agreeableness | 1.07 | 0.90-1.28 | 0.90 | 0.78-1.04 | 1.10 | 0.93-1.31 | ||
Constant | 0.25*** | 0.24-0.27 | 0.34*** | 0.32-0.36 | 0.26*** | 0.24-0.28 |
3. 讨论
本研究利用具有全国代表性的数据分析了中国成年人的人格特征和抑郁症状变化之间的关系,研究结果表明尽责性与持续的抑郁症状有关,神经质与抑郁症状好转有关。此外,相较于城市居民,农村居民在人格特征和抑郁症状改变之间的关联性更强。
本研究结果表明,神经质、开放性和抑郁症状改变之间存在正相关关系,这与以往的研究结果一致[14]。众所周知,高度神经质的人更容易悲伤、焦虑、自我意识较为脆弱,更容易经历负面情绪[15]。开放性被分为六个层面:幻想、美学、感觉、行动、想法和价值观。有幻想的人可能会患上抑郁症,因为他们的理想状态与其实际状态差异较大,从而更容易导致失望和悲伤[16],因此,情绪管理良好和相对保守的个性或许能在一定程度上保护人们免受抑郁症状加重的风险。责任心较差、外向性较差、宜人性较差的人或许更容易罹患抑郁症状,这与以前的研究结果一致[17]。尽责性似乎与抑郁症状呈负相关,尽责意味着更强的自我调节能力和调节情绪的能力,它可以降低内化问题的风险[14]。外向性意味着更好的人际关系和积极情绪,这说明外向的人更可能经历较少的压力事件和更多的积极情绪[18]。先前的研究表明,宜人性较强可以增强个体适应新环境的能力,这可能有助于减少压力和规避抑郁症状[17]。Seligman[19]研究发现人格特质与那些抑郁症状恶化和持续抑郁的人显著相关,可能是因为这些人早期经历了抑郁发作,抑郁症状在心理健康领域十分常见,以至于曾将其描述为心理学中的“普通感冒”,因此,人格特征可能会在很长一段时间内继续影响个人,而研究人员、医疗保健专业人员和公众应该对其进行持续监测和加强心理健康服务。
城乡差异在人格特征和抑郁症状的变化之间有一定的调节作用,这与Verheij[20]的研究结果一致,他们的研究也发现城乡差异和人格特质与抑郁症状之间存在显著的交互作用。近年来,我国城乡居民的心理健康问题不断恶化,大量居民患有抑郁症或者其他心理健康问题,其中社会环境不平等和劳动力流失等因素导致农村居民受到心理疾病的严重影响,农村居民更有可能保持抑郁症状,这与常韵琪等[21]的研究结果一致,可能与城市居民相比,农村居民获得的社会资源和机会有限,从而消极情绪可能通过多种途径导致抑郁[22]。大量的研究表明,经济水平、社会福利、医疗服务和基础设施等都会加剧城乡差距,放大抑郁症状的不平等[21]。实际上,造成城乡居民抑郁症状差异的因素还有很多,例如社会支持、住房条件和户籍状况等,还需要研究人员进一步探索[23-24]。
本研究的局限性主要体现在以下三方面:第一,虽然此次使用了纵向数据进行了分析,但是得到的人格特质与抑郁症状之间的关系不足以证明其因果关系以及相关因素的中介作用,由居民进行的自评健康状况和抑郁症状以及人格特征结果可能会存在一定的不准确性,因此,可能会影响到研究结果的稳定性;第二,本研究采用的结果变量是根据同一研究对象在2018和2020两个年份抑郁状况评分的客观变化情况进行的分类,因此,研究结果可能存在偏倚,未来还需要采取更加准确且可靠的临床量表对心理抑郁情况的变化进行测量,并开展相关影响因素的探究,以增强研究结论的因果关系强度;第三,本研究基于CFPS数据开展,结果可以反映中国大部分居民的情况,但仍然需要通过开展不同独立人群的研究,以验证结果的稳健性,并为更有针对性地制定干预策略提供实际证据。
本研究使用2018年和2020年的CFPS数据构造了面板数据,分析了人格特质和抑郁症状变化之间的关系,并分析了城乡差异在其中的交互作用,发现在控制个体层面上的人口经济学因素的前提下,人格特质与抑郁症状的变化显著相关,尽责性、外向性、宜人性与抑郁症状呈负相关,而神经质和开放性呈正相关。此外,抑郁症状好转与神经质正相关,持续抑郁与尽责性负相关。农村居民的人格特征和持续性抑郁症状以及好转的抑郁症状之间有更强的关联,因此,在中国成年人的心理健康干预和预防计划中,特别是在新型冠状病毒肺炎疫情大流行的背景下,应该考虑人格和城乡差异,制定特定的健康策略。
Funding Statement
国家社会科学基金重大项目(22 & ZD143)
Supported by the Major Program of the National Social Science Foundation of China (22 & ZD143)
References
- 1.过 伟峰, 曹 晓岚, 盛 蕾, et al. 抑郁症中西医结合诊疗专家共识. 中国中西医结合杂志. 2020;40(2):141–148. [Google Scholar]
- 2.Liu QQ, He HR, Yang J, et al. Changes in the global burden of depression from 1990 to 2017: Findings from the Global Burden of Disease study. J Psychiatr Res. 2020;126:134–140. doi: 10.1016/j.jpsychires.2019.08.002. [DOI] [PubMed] [Google Scholar]
- 3.Markon KE, Krueger RF, Watson D. Delineating the structure of normal and abnormal personality: An integrative hierarchical approach. J Pers Soc Psychol. 2005;88(1):139–157. doi: 10.1037/0022-3514.88.1.139. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Chioqueta AP, Stiles TC. Personality traits and the development of depression, hopelessness, and suicide ideation. Pers Indivd Dif. 2005;38(6):1283–1291. doi: 10.1016/j.paid.2004.08.010. [DOI] [Google Scholar]
- 5.Fournier J, Jones N, Chase H, et al. Personality dysfunction in depression and individual differences in effortful emotion regulation. Biol Psychiatry. 2017;81(Suppl 10):S336–S337. [Google Scholar]
- 6.Naragon-Gainey K, Watson D. Consensually defined facets of personality as prospective predictors of change in depression symptoms. Assessment. 2014;21(4):387–403. doi: 10.1177/1073191114528030. [DOI] [PubMed] [Google Scholar]
- 7.Panaite V, Rottenberg J, Bylsma LM. Daily affective dynamics predict depression symptom trajectories among adults with major and minor depression. Affec Sci. 2020;1(3):186–198. doi: 10.1007/s42761-020-00014-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.李 磊, 马 孟园, 彭 红叶, et al. 中国农村地区老年人抑郁症状发生情况及影响因素研究. 中国全科医学. 2021;24(27):3432–3438. doi: 10.12114/j.issn.1007-9572.2021.00.577. [DOI] [Google Scholar]
- 9.张 海钟. 人格心理的城乡跨文化实证研究十年成果综述. 社科纵横. 2006;21(3):125–127. [Google Scholar]
- 10.肖 凌燕. 基于"大五"人格理论的大学生人格结构分析. 黑龙江教育(高教研究与评估) 2011;(9):27–29. [Google Scholar]
- 11.Radloff LS. The CES-D scale: A self-report depression scale for research in the general population. Appl Psychol Meas. 1977;1(3):385–401. doi: 10.1177/014662167700100306. [DOI] [Google Scholar]
- 12.Feeney J, Kenny R. Aair cortisol as a risk marker for increased depressive symptoms among older adults during the COVID-19 pandemic. Psychoneuroendocrinology. 2022;143:105847. doi: 10.1016/j.psyneuen.2022.105847. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Hahn E, Gottschling J, Spinath FM. Short measurements of personality: Validity and reliability of the GSOEP Big Five Inventory (BFI-S) J Res Pers. 2012;46(3):355–359. doi: 10.1016/j.jrp.2012.03.008. [DOI] [Google Scholar]
- 14.Smith KA, Barstead MG, Rubin KH. Neuroticism and conscientiousness as moderators of the relation between social withdrawal and internalizing problems in adolescence. J Youth Adolesc. 2017;46(4):772–786. doi: 10.1007/s10964-016-0594-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Guo P, Cui J, Wang Y, et al. Spontaneous microstates related to effects of low socioeconomic status on neuroticism. Sci Rep. 2020;10(1):1–8. doi: 10.1038/s41598-019-56847-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Khoo S, Simms LJ. Links between depression and openness and its facets. Personal Ment Health. 2018;12(3):203–215. doi: 10.1002/pmh.1417. [DOI] [PubMed] [Google Scholar]
- 17.Kim SE, Kim HN, Cho J, et al. Direct and indirect effects of five factor personality and gender on depressive symptoms mediated by perceived stress. PLoS One. 2016;11(4):e0154140. doi: 10.1371/journal.pone.0154140. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Chen J, Qiu L, Ho MHR. A meta-analysis of linguistic markers of extraversion: Positive emotion and social process words. J Res Pers. 2020;89:104035. doi: 10.1016/j.jrp.2020.104035. [DOI] [Google Scholar]
- 19.Seligman ME. Fall into helplessness. Psychol Today. 1973;7(1):43–48. [Google Scholar]
- 20.Verheij RA. Explaining urban-rural variations in health: A review of interactions between individual and environment. Soc Sci Med. 1996;42(6):923–935. doi: 10.1016/0277-9536(95)00190-5. [DOI] [PubMed] [Google Scholar]
- 21.常 韵琪, 郑 晓, 李 咪咪, et al. 老年慢性病患者抑郁状态及影响因素城乡差异研究. 中国全科医学. 2021;24(10):1254–1259. [Google Scholar]
- 22.Hyde JS, Mezulis AH, Abramson LY. The ABCs of depression: Integrating affective, biological, and cognitive models to explain the emergence of the gender difference in depression. Psychol Rev. 2008;115(2):291–313. doi: 10.1037/0033-295X.115.2.291. [DOI] [PubMed] [Google Scholar]
- 23.Guo J, Guan LD, Fang LM, et al. Depression among Chinese older adults: A perspective from Hukou and health inequities. J Affect Disord. 2017;223:115–120. doi: 10.1016/j.jad.2017.07.032. [DOI] [PubMed] [Google Scholar]
- 24.Fang MW, Mirutse G, Guo L, et al. Role of socioeconomic status and housing conditions in geriatric depression in rural China: A cross-sectional study. BMJ Open. 2019;9(5):e024046. doi: 10.1136/bmjopen-2018-024046. [DOI] [PMC free article] [PubMed] [Google Scholar]