Associations of subjective perceptions and income change with transitions in usual source of care among Chinese residents: A study based on China Family Panel Studies

  • Chunchun XU 1 ,
  • Weiyan JIAN , 1, 2, *
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  • 1. Department of Health Policy and Management, Peking University School of Public Health, Beijing 100191, China
  • 2. Key Laboratory of Health System Reform and Governance, National Health Commission, Beijing 100191, China
JIAN Weiyan, e-mail,

Received date: 2026-02-26

  Online published: 2026-05-15

Supported by

Major Program of National Fund of Philosophy and Social Science of China(22&ZD143)

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All rights reserved. Unauthorized reproduction is prohibited.

Abstract

Objective: Using the China Family Panel Studies (CFPS, 2012-2022), this study aimed to characterize changes in the distribution of usual sources of care and in subjective perceptions among Chinese adults, and to examine the associations of subjective perceptions and income change with next-wave transitions in usual source of care. Methods: This was a retrospective longitudinal observational study based on adult CFPS data from 2012, 2014, 2016, 2018, 2020, and 2022. We first described temporal trends in three types of usual source of care (primary care, hospitals, and clinics), as well as trends in residents' subjective perceptions of healthcare providers, and in relative income change. We then constructed person-period samples from adjacent survey waves and analyzed two transition processes separately: outflow from primary care among baseline primary care users and inflow to primary care among baseline non-primary care users. Key predictors were prior-wave satisfaction, perceived medical competence, and relative income change; the income-change variable was defined based on changes in relative income group within the same wave and same province sample. Descriptive analyses applied cross-sectional weights; the main regressions were unweighted binary Logistic models with individual-level cluster-robust standard errors, reporting odds ratios (OR), 95% confidence intervals (95% CI), and P va-lues. Results: The pooled sample comprised 135 986 observations from 34 010 individuals. From 2012 to 2022, the proportion using primary care as the usual source of care declined from 43.49% to 30.34%, whereas the hospital share rose from 34.06% to 46.81%. The decline in primary care was steeper during 2012-2018 (43.49% to 33.72%) and persisted at a slower pace thereafter (33.72% to 30.34%). Across adjacent survey waves, primary care outflow increased from 35.47% to 45.22%, while primary care inflow decreased from 30.09% to 19.60%, indicating simultaneous increases in exits and decreases in entries. Subjective perceptions improved for all three provider types over time; however, the relative gap between primary care and hospitals widened on perceived medical competence, and primary care shifted from a slight advantage over clinics to a clear disadvantage in composite subjective perceptions. The proportion of residents with unchanged relative income group rose from 50.64% to 60.33%. In multivariable models, each one-unit increase in satisfaction with primary care was associated with 7.5% lower odds of leaving primary care (OR=0.925, P < 0.001). In contrast, each one-unit increase in perceived medical competence of non-primary care providers was associated with 5.3% lower odds of moving into primary care (OR=0.947, P < 0.001). Compared with stable relative income group, upward relative income-group mobility, particularly low-to-high movement, was associated with higher odds of outflow from primary care and lower odds of inflow to primary care (outflow OR=1.166; inflow OR=0.840), whereas downward relative income-group mobility, especially high-to-low movement, showed the opposite pattern (outflow OR=0.785; inflow OR=1.371). Conclusion: Primary care utilization in China continued to decline, with increased outflow from primary care and reduced inflow to primary care occurring simultaneously. Residents ' subjective perceptions were associated with different considerations in retention in versus movement into primary care: the former was more closely related to satisfaction, whereas the latter was more closely related to perceived medical competence. People with upward relative income-group mobility showed a lower inclination to use primary care. Hierarchical care policy should address both entry into and retention in primary care by strengthening continuity of care, reinforcing service capability and institutional design, and aligning payment incentives.

Cite this article

Chunchun XU , Weiyan JIAN . Associations of subjective perceptions and income change with transitions in usual source of care among Chinese residents: A study based on China Family Panel Studies[J]. Journal of Peking University(Health Sciences), 2026 , 58(3) : 455 -463 . DOI: 10.19723/j.issn.1671-167X.2026.03.003

建立分级诊疗制度是合理配置医疗资源的重要举措,基层医疗卫生服务是分级诊疗制度的基石[1]。自2009年深化医药卫生体制改革启动以来,政府对基层医疗卫生体系在人力资源、基础设施及卫生信息化等方面持续投入[2-3],但在实际就医行为中,患者持续流向医院而非基层医疗卫生机构[4-7]。在缺乏刚性首诊制度、居民可自由选择就医点的情境下,就医决策往往是对机构主观认知与可支付能力进行权衡的过程:主观认知反映患者对基层机构服务能力与就医体验的判断[8-9],而收入变化则可能通过影响居民对医疗服务价格的敏感程度而影响实际选择[10]
现有研究已从不同角度指出,患者主观认知、收入水平、家庭医生签约、服务需求特征以及多病共存等因素均与基层首诊、就医机构选择或医院利用相关[6, 8, 11-14],且已有部分纵向研究围绕就医机构选择的时间趋势进行了描述[4-5, 7]。然而,当前多数证据仍以横断面研究、单地区调查或政策阶段性评估为主,较少利用全国纵向数据同时考察主观认知和支付能力变化如何影响基层流出或流入的转移机制。本研究基于中国家庭追踪调查(China Family Panel Studies,CFPS)2012—2022年的六轮数据,描述居民通常就医点及核心解释变量的时间变化趋势,分析居民主观认知、收入变化与通常就医点转移的关联,以期为强化基层医疗服务体系建设和完善分级诊疗相关制度提供证据。

1 资料与方法

1.1 研究设计、数据来源与研究对象

本研究为基于公开匿名数据库的回顾性纵向观察研究,属于非干预性人群调查分析,数据来自CFPS 2012、2014、2016、2018、2020、2022年的六轮成年人样本。CFPS原始调查由项目执行机构按照伦理规范实施并取得受访者知情同意,本研究仅使用去标识化公开数据,不涉及新增受试者招募和干预,故免于再次伦理审批。
本研究设定两类分析样本。第一,在各轮调查成年人样本中描述通常就医点、居民主观认知与相对收入变化的分布及各指标2012—2022年的变化趋势;第二,构建2012→2014、2014→2016、2016→2018、2018→2020和2020→2022五个相邻调查区间配对的人-期观察样本(person-period data),用于进行通常就医点的转移机制与回归分析。人-期样本纳入条件为:相邻两轮调查均有通常就医点信息,且上一轮对通常就医点的满意度和医疗水平评价有效;排除核心变量缺失的记录后纳入分析。同一受访者可贡献多个区间观测。

1.2 变量定义

因变量为CFPS成年人问卷变量(QP601“通常去哪里看病”)所反映的通常就医点,其原始选项(编码)为综合医院(1)、专科医院(2)、社区卫生服务中心/乡镇卫生院(3)、社区卫生服务站/村卫生室(4)、诊所(5),本研究将其重编码为医院(1和2)、基层(3和4)和诊所(5)三类。在机制分析中进一步将通常就医点二分类为基层和非基层(医院+诊所),并构造四类就医点转移结局:持续基层、基层流出(从基层转为非基层)、持续非基层、基层流入(从非基层转为基层)。
核心解释变量为上一轮调查居民主观认知和家庭人均收入变化。主观认知:包括CFPS成年人问卷变量“对看病点条件满意度”(QP602)和“看病点医疗水平”(QP603),两变量均统一处理为1~5分,分值越高表示评价越高。家庭人均收入变化:根据同一轮调查的同一省份样本内家庭人均收入分布,将家庭人均收入划分为低、中、高3个相对收入组,在相邻两轮调查间构建6种相对收入变化(低→中、中→高、低→高、高→中、中→低、高→低),以相对收入组不变为参照;该指标反映的是个体在同一轮调查的同一省份样本中的相对收入位次变化,而非家庭绝对收入增长、购买力改善或经通货膨胀调整后的真实收入变动。另将家庭人均收入按国家统计局年度居民消费价格指数(consumer price index,CPI)折算为2022年不变价,并以连续真实收入变化和对数真实收入变化进行敏感性分析。控制变量依据Andersen医疗服务利用模型纳入倾向因素、使能因素和需求因素,并控制年份固定效应。主模型需求因素包含自评健康、慢性病和住院史;医疗支出变量在2012年缺少与2014—2022年一致的总额指标,且缺失率高,不纳入主模型,仅用于稳健性分析。

1.3 统计学处理与质量控制

样本特征分析采用未加权统计,通常就医点构成和主观认知均值采用横截面权重计算,相邻两轮调查之间的通常就医点转移比例及相对收入变化采用区间末期横截面权重进行描述。分类变量组间差异采用Pearson χ2检验,等级或偏态连续变量采用秩和检验。回归部分分别在上一轮调查通常就医点为基层和非基层两套子样本中建立二分类Logistic模型,报告优势比(odds ratio,OR)、95%置信区间(confidence interval,CI)和P值。本研究在进行回归分析时未进一步叠加复杂抽样权重、分层和整群设计参数,标准误按个体唯一标识进行聚类稳健估计,主结果以合并Logistic模型为主。另进行了广义估计方程(generalized estimating equations,GEE)、CPI调整真实收入以及以个体唯一标识为随机截距的Logistic敏感性分析。对总体参数推断均报告95% CI。为减少解释变量与结局变量同期测量可能带来的偏倚,满意度、医疗水平评价和大多数控制变量采用以上一轮调查(t-1期)的解释变量预测下一轮调查(t期)结局的时序设定,并执行统一缺失值清理、变量重编码和极端值核查流程。

2 结果

2.1 研究样本特征

总体上,主分析纳入样本具有较好的规模与可比性,2012年与2022年分别纳入记录23 716和13 707条,六轮调查合并后共135 986条记录、34 010名个体。与2012年相比,2022年样本社会经济特征呈现出城镇化与收入上升并行、健康服务需求同步增加的变化趋势(表 1)。
表1 主分析样本特征(未加权)

Table 1 Characteristics of the main analytic sample (unweighted)

Variable 2012 2022 Pooled sample (6 survey waves) P value
Records 23 716 13 707 135 986
Unique individuals 23 716 13 707 34 010
Female, n(%) 12 265 (51.72) 6 740 (49.17) 68 094 (50.07) < 0.001
Age, ${\bar x}$±s 46.61±15.64 47.31±16.08 47.17±16.23 < 0.001
Education levela, ${\bar x}$±s 0.74±0.92 1.22±1.10 0.92±1.01 < 0.001
Married/cohabiting, n(%) 19 686 (83.01) 10 484 (76.49) 108 156 (79.54) < 0.001
Urban residence, n(%) 10 557 (44.72) 7 311 (53.39) 65 472 (48.79) < 0.001
Eastern region, n(%) 8 789 (37.06) 5 009 (36.54) 49 833 (36.65) 0.321
Central region, n(%) 5 123 (21.60) 2 914 (21.26) 28 390 (20.88) 0.443
Western region, n(%) 6 259 (26.39) 3 836 (27.99) 38 544 (28.34) < 0.001
Northeastern region, n(%) 3 543 (14.94) 1 948 (14.21) 19 215 (14.13) 0.057
Household income per capita (median, CNY) 8 228.00 23 000.00 13 460.00 < 0.001
Low-income group, n(%) 8 092 (37.26) 4 400 (33.12) 47 316 (36.01) < 0.001
Middle-income group, n(%) 7 131 (32.83) 4 501 (33.88) 44 178 (33.63) 0.045
High-income group, n(%) 6 497 (29.91) 4 386 (33.01) 39 889 (30.36) < 0.001
Insured, n(%) 20 870 (88.22) 12 557 (92.97) 122 932 (90.99) < 0.001
Agricultural hukou, n(%) 17 025 (71.91) 9 728 (71.14) 98 477 (72.55) 0.118
Self-rated healthb, ${\bar x}$±s 3.21±1.20 2.91±1.18 3.05±1.22 < 0.001
Any chronic disease, n(%) 3 160 (13.33) 2 317 (16.91) 22 435 (16.50) < 0.001
Number of chronic diseases, ${\bar x}$±s 0.16±0.45 0.34±0.75 0.25 ± 0.60 < 0.001
Hospitalization in past 12 months, n(%) 2 121 (8.94) 1 501 (10.95) 15 361 (11.30) < 0.001

Categorical variables were compared using Pearson Chi-square tests; continuous or ordinal variables were compared using rank-sum tests. P values compare 2012 and 2022. a, education level: 0=primary school or below, 1=junior high, 2=senior high/technical secondary/vocational high school, 3=college or above. b, self-rated health: 1=best to 5=worst.

与2012年相比,在样本构成方面,2022年女性占比由51.72%降至49.17%,年龄均值由46.61岁升至47.31岁,教育程度均值由0.74升至1.22,城镇人口占比由44.72%增至53.39%(均P < 0.001);经济特征方面,家庭人均收入中位数由8 228元升至23 000元,高收入组占比由29.91%升至33.01%,低收入组占比由37.26%降至33.12%(均P < 0.001);健康与医疗利用方面,慢性病患病比例由13.33%升至16.91%,过去12个月住院比例由8.94%升至10.95%(均P < 0.001)。

2.2 核心变量描述性分析

2.2.1 通常就医点持续由基层向医院转移

通常就医点结构上,基层占比在2012—2022年持续下降,由2012年的43.49%降至2022年的30.34%,医院占比由34.06%升至46.81%,而诊所占比基本稳定(22.45%至22.86%,表 2)。进一步分析降幅,基层占比在2012—2018年下降更快(43.49%降至33.72%),2018年后继续下降但降幅趋缓(33.72%降至30.34%)。
表2 2012—2022年通常就医点加权构成比例

Table 2 Weighted composition of usual source of care, 2012-2022

Survey wave Hospital, % (95%CI) Primary care, % (95%CI) Clinic, % (95%CI)
2012 34.06 (33.34-34.78) 43.49 (42.74-44.25) 22.45 (21.81-23.08)
2014 39.24 (38.49-39.99) 43.32 (42.57-44.08) 17.44 (16.86-18.02)
2016 42.33 (41.55-43.11) 39.27 (38.49-40.04) 18.40 (17.79-19.02)
2018 44.28 (43.43-45.13) 33.72 (32.91-34.53) 22.00 (21.29-22.71)
2020 44.29 (43.29-45.29) 32.61 (31.67-33.55) 23.10 (22.25-23.95)
2022 46.81 (45.82-47.79) 30.34 (29.43-31.25) 22.86 (22.03-23.69)

CI, confidence interval.

2.2.2 基层就诊流出上升且流入下降

相邻两轮调查的通常就医点转移显示出基层流出增加而流入减少的结构性格局:在基线为基层的人群中,基层流出加权比例由2014年的35.47%升至2022年的45.22%,并在2018年后持续保持在45%左右的较高水平;在基线为非基层的人群中,基层流入加权比例由2014年的30.09%持续下降至2022年的19.60%(表 3)。上述结果表明,基层就诊比例下降不仅来自对非基层患者的吸引力不足,也源于基层患者的持续流失。
表3 各轮调查中与前一轮相比的基层流出与流入情况

Table 3 Primary care outflow and inflow in each survey wave compared with the previous wave

Survey wave Primary care outflow Stayed in primary care Primary care inflow Stayed in non-primary care
2014 3 718 (35.47) 7 017 (64.53) 4 304 (30.09) 8 677 (69.91)
2016 4 695 (40.74) 6 520 (59.26) 3 314 (25.56) 9 057 (74.44)
2018 4 245 (44.54) 5 137 (55.46) 3 162 (21.76) 10 264 (78.24)
2020 2 689 (45.22) 3 338 (54.78) 2 358 (21.46) 8 157 (78.54)
2022 2 041 (45.22) 2 460 (54.78) 1 944 (19.60) 7 262 (80.40)

Data are expressed as n(%).

2.2.3 居民对三类机构的主观认知整体提升

主观认知上,居民对三类机构的满意度与医疗水平评价均持续提升(表 4),以基层为例,满意度由3.456升至3.778,医疗水平由3.259升至3.532。横向比较结果表明,医院在满意度和医疗水平方面始终保持领先,且医疗水平与基层的差距有所扩大,差值从2012年的0.244增至2022年的0.295;基层主观认知在2012年微弱领先于诊所(3.357 vs. 3.350),但自2016年被诊所反超后,至2022年诊所已明显高于基层(3.742 vs. 3.656)。
表4 各类通常就医点主观认知加权指标

Table 4 Weighted perceived quality indicators by provider type

Survey wave Hospital Primary care Clinic
Sat Pmc Composite Sat Pmc Composite Sat Pmc Composite
2012 3.549 3.503 3.526 3.456 3.259 3.357 3.405 3.295 3.350
2014 3.559 3.528 3.543 3.434 3.254 3.344 3.379 3.263 3.320
2016 3.607 3.544 3.576 3.482 3.284 3.384 3.458 3.340 3.399
2018 3.647 3.606 3.626 3.583 3.376 3.480 3.601 3.443 3.522
2020 3.822 3.792 3.806 3.760 3.509 3.635 3.730 3.581 3.655
2022 3.839 3.827 3.832 3.778 3.532 3.656 3.810 3.673 3.742

Sat, satisfaction score; Pmc, perceived medical competence score; Composite, composite score, which is the average of satisfaction score and perceived medical competence score. Each score ranges from 1 (lowest) to 5 (highest), with higher scores indicating better evaluations.

2.2.4 相对收入变化以稳定为主、温和流动为辅

相对收入变化结构上,相邻两轮调查中,相对收入不变者占比始终最高,且由2012→2014年的50.64%升至2020→2022年的60.33%;相对收入上移和下移1档的人群(低收入组上移至中收入组、中收入组上移至高收入组、高收入组下移至中收入组、中收入组下移至低收入组)占比次之,在2020→2022年的配对样本中分别占比16.61%和17.51%;相对收入上移和下移2档的居民(高收入组下移至低收入组、低收入组上移至高收入组)的居民占比始终较低,约2.60%和2.96%(表 5)。这表明样本总体呈现出以稳定为主、温和流动为辅的收入变化格局。
表5 相邻两轮调查收入变化类型构成(末期权重)

Table 5 Distribution of income-change types across adjacent waves (end-wave weights)

Interval Weighted valid n Downward 2 levels/% Downward 1 level/% No change/% Upward 1 level/% Upward 2 levels/%
2012→2014 20 739 5.08 19.38 50.64 19.72 5.19
2014→2016 22 203 4.21 18.35 53.75 18.87 4.82
2016→2018 22 483 3.14 19.18 56.42 17.94 3.31
2018→2020 16 164 2.79 17.04 58.94 18.37 2.87
2020→2022 13 093 2.96 17.51 60.33 16.61 2.60

Valid n with income-group observed at both ends (unweighted) equals weighted valid n in all waves; therefore the unweighted column is omitted. Income change is defined here by relative income-group mobility across adjacent waves; “upward 1 level” indicates low to middle or middle to high, and “downward 1 level” indicates high to middle or middle to low; “upward 2 level” indicates low to high, and “downward 2 level” indicates high to low.

2.3 主观认知、收入变化与通常就医点转移的关联:二分类Logistic回归结果

总体上,在控制人口学、社会经济和健康需求等协变量后,主观认知和收入变化均与通常就医点转移显著相关,不同维度的主观认知在基层流出和基层流入两个决策环节呈现不同关联模式,收入变化则表现出明显的社会经济分层关联(图 1表 6)。
图1 两类就医点转移结局中核心解释变量的关联强度

Figure 1 Core predictor effects in two transition outcomes

Points indicate odd ratios(OR) and horizontal lines indicate 95% confidence intervals (CI); standard errors are clustered at the individual level. t-1 denotes the previous survey wave. Model 1 includes core predictors (satisfaction score, perceived medical competence score, and six income-change pathways based on relative income-group shifts; see Table 4 and Table 5 for detailed definitions of these terms) plus year fixed effects and baseline income group; Model 2 further adds predisposing factors (sex, age, education, marital status); Model 3 further adds enabling factors (urban residence, insurance, agricultural hukou, region); Model 4 further adds need factors (self-rated health, chronic disease, hospitalization). Medical expenditure is included only in sensitivity analyses.

表6 二分类Logistic回归主模型(Model 4)全变量估计结果

Table 6 Full variable estimates from binary Logistic Model 4

Variable Primary care inflow vs. stayed in non-primary care (n=56 750) Primary care outflow vs. stayed in primary care (n=39 831)
OR 95%CI P value OR 95%CI P value
Agricultural hukou 2.051 1.931-2.179 < 0.001 0.655 0.610-0.702 < 0.001
Perceived medical competence 0.947 0.919-0.977 < 0.001 0.996 0.965-1.028 0.797
Insurance coverage 1.109 1.034-1.190 0.004 0.859 0.791-0.933 < 0.001
Region: eastern 1.987 1.859-2.125 < 0.001 0.509 0.469-0.552 < 0.001
Region: central 1.888 1.756-2.031 < 0.001 0.563 0.516-0.614 < 0.001
Region: western 1.684 1.569-1.807 < 0.001 0.571 0.525-0.621 < 0.001
Urban residence 0.684 0.651-0.718 < 0.001 1.121 1.066-1.178 < 0.001
Female 0.929 0.889-0.970 < 0.001 1.065 1.018-1.115 0.006
Married/cohabiting 1.179 1.111-1.250 < 0.001 0.800 0.752-0.851 < 0.001
Year: 2016 0.737 0.695-0.782 < 0.001 1.385 1.308-1.467 < 0.001
Year: 2018 0.590 0.557-0.625 < 0.001 1.623 1.531-1.721 < 0.001
Year: 2020 0.571 0.537-0.608 < 0.001 1.611 1.508-1.722 < 0.001
Year: 2022 0.556 0.520-0.595 < 0.001 1.571 1.458-1.692 < 0.001
Age 1.010 1.008-1.012 < 0.001 0.987 0.986-0.989 < 0.001
Any chronic disease 0.897 0.845-0.952 < 0.001 1.057 0.991-1.128 0.092
Relative income-group up: middle to high 0.840 0.770-0.916 < 0.001 1.172 1.072-1.280 < 0.001
Relative income-group up: low to middle 0.878 0.814-0.948 < 0.001 1.089 1.012-1.172 0.023
Relative income-group up: low to high 0.840 0.752-0.939 0.002 1.166 1.044-1.302 0.007
Relative income-group down: middle to low 1.014 0.935-1.100 0.730 1.035 0.955-1.121 0.402
Relative income-group down: high to middle 1.397 1.286-1.519 < 0.001 0.839 0.764-0.922 < 0.001
Relative income-group down: high to low 1.371 1.224-1.536 < 0.001 0.785 0.694-0.888 < 0.001
Income group: middle 0.892 0.833-0.956 0.001 1.015 0.948-1.087 0.671
Income group: high 0.636 0.591-0.684 < 0.001 1.233 1.141-1.333 < 0.001
Education levela 0.821 0.800-0.844 < 0.001 1.042 1.011-1.075 0.008
Satisfaction 1.027 0.994-1.062 0.112 0.925 0.894-0.957 < 0.001
Self-rated healthb 0.958 0.940-0.976 < 0.001 1.031 1.011-1.051 0.002
Hospitalization in past 12 months 0.889 0.832-0.949 < 0.001 1.140 1.057-1.229 < 0.001

All predictors were measured at the baseline (the previous survey wave, denoted as t-1 in the Methods), except for the “Relative income-group” variables, which represent change from baseline to the current survey wave (t-1 to t). a, education level: 0=primary school or below, 1=junior high, 2=senior high/technical secondary/vocational high school, 3=college or above. b, self-rated health: 1=best to 5=worst.

从主观认知看,居民对基层医疗服务的满意度越高,基层流出的优势比越低(OR=0.925,P < 0.001),即满意度每提升1分,流出优势比下降约7.5%;而居民对基层医疗水平的评价与基层流出无关(OR=0.996,P=0.797)。相反,居民对非基层医疗水平的评价越高,基层流入的优势比越低(OR=0.947,P < 0.001),即非基层医疗机构的得分每提高1分,流入优势比下降约5.3%;而对非基层的满意度与基层流入无关(OR=1.027,P=0.112)。这一结果表明,居民对基层医疗机构的满意度主要与持续留在基层相关,而居民对非基层医疗机构医疗水平评价主要与是否流入基层相关。
从收入变化看,其与就医流向的关联更强,且相对收入变化变动幅度越大,关联越明显。与相对收入组不变者相比,相对收入组上移者,特别是从低收入组升至高收入组者,表现为更高的基层流出优势比和更低的基层流入优势比:基层流出优势比上升约16.6%(OR=1.166),基层流入优势比下降约16.0%(OR=0.840);相对收入组下移者,特别是从高收入组降至低收入组者,则表现为更低的基层流出优势比和更高的基层流入优势比:基层流出优势比下降约21.5%(OR=0.785),基层流入优势比上升约37.1%(OR=1.371)。
协变量结果同样呈现稳定方向:对于基线为基层的居民,城镇户口(OR=1.121)、受教育程度较高(OR=1.042)、高收入(OR=1.233)及有住院史(OR= 1.140)者表现为更高的基层流出优势比,而农业户口(OR=0.655)和参加医疗保险(OR=0.859)者则表现为更低的基层流出优势比;对于基线为非基层的居民,农业户口(OR=2.051)和参加医疗保险(OR=1.109)者表现为更高的基层流入优势比,而城镇户口(OR=0.684)、受教育程度较高(OR=0.821)和高收入(OR=0.636)者则表现为更低的基层流入优势比。

3 讨论

本研究基于CFPS 2012—2022年全国纵向数据发现,通常就医点为基层者占比持续下降,由43.49%降至30.34%,而医院占比则由34.06%升至46.81%。基层就诊占比下降不仅来自基层流入减少,也来自基层流出增加。尽管居民对基层的主观认知持续上升,但其与医院和诊所的相对差距仍有所扩大。回归结果显示,居民对就医点的主观认知与就医流向呈现差异化关联:居民对基层的高满意度与较低的基层流出优势比相关(OR=0.925),而对非基层医疗水平的高评价与较低的基层流入优势比相关(OR=0.947)。相对收入组上移与更高的基层流出优势比(OR=1.166)和更低的基层流入优势比(OR=0.840)相关,相对收入组下移则相反。同时,城镇、高教育水平和高收入居民更易从基层流出,农业户口与参加医疗保险的居民更倾向于流入基层。这些结果提示,即使居民对基层的总体主观认知有所提升,但较医院和诊所仍处于相对劣势位置,与居民社会经济相对位置提升所对应的偏好升级并行出现,导致“倒三角”问题尚未得到实质扭转[15-19]
主观认知与基层流出和流入的关联具有维度分化,基层需发挥自身服务优势,提升满意度, 减少可避免的患者流出。对于通常就医点为基层的居民而言,决策依据更多来自重复接触后的体验信息,因此沟通质量、便利性和关系连续性更可能与继续留在基层就医相关;对目前通常就医点不是基层的居民而言,信息不对称更强,基层是否具备有效解决健康问题的技术能力往往是重要判断信号。该结果与既有证据一致,即基层主观认知评价提升与医院利用下降相关;在可自由择医的情境下,患者常绕过基层选择高等级机构[6, 8]。国内关于社区居民服务需求、家庭医生签约和社区首诊的研究也显示,居民对全科服务的认知和信任程度、签约参与和制度安排均与基层首诊意向或实际就诊行为有关[11-12, 20]。因此,基层提升服务质量不宜简化为设备和项目扩张,而应坚持能力建设和体验治理并重,通过家庭医生签约、慢性病连续管理和可执行转诊协同,避免已经选择在基层就医的患者流出基层[21-26]
相对收入变化与就医流向存在稳定关联,这一模式可能反映了自由选择和质量偏好的共同作用,是服务质量之外另一项重要分层因素。按照Andersen模型,使能因素通过支付能力与资源可及性影响利用行为[27-28];在门诊缺乏强制守门的情境下,相对收入组上移可能伴随居民对更优卫生服务的支付意愿上升,对价格差异的敏感性相对降低,从而更倾向于选择医院就诊;相对收入组下移则可能因经济预算紧张而与更高的基层利用相关。对多病共存患者的研究同样提示,疾病负担、服务可及性和资源条件均与机构选择有关[13-14]。已有研究还表明,支付激励与居民对基层服务水平的主观认知和基层首诊制度应当协同设计,仅靠单一工具难以稳定改变就医流向[10, 29]。这表明基层对相对收入组下移人群具有重要的逆周期保障作用;同时,分级诊疗若仅依赖价格杠杆引导分层就医,仍难以稳定改变社会经济相对位置变化所对应的总体流向,必须把质量信号、转诊协同和支付激励作为组合政策[6, 10, 29]
加强基层能力建设不应只考核导入了多少患者,还应考核留住了多少居民。当前的瓶颈在于投入转化效率偏低,即硬件和项目投入增长快于居民主观认知与行为偏好的转变。现行政策已明确“基层首诊、双向转诊、急慢分治、上下联动”的方向[1, 30],后续宜按“先稳存量、再扩增量、再强协同”推进可操作路径:一是以家庭医生团队和医疗卫生服务共同体提升服务的连续性,降低可避免的流出;二是以医保支付和转诊激励强化上级下转责任与基层接续能力,提高有效流入。总体上,分级诊疗绩效评估应由“首诊导入”单指标,转向“流出率-流入率-留存率”联动评价。
本研究仍有若干局限。第一,描述性结果已加权,但主回归基于展开后的人-期样本,未与复杂抽样设计和正式面板权重一一对应,因此结果宜解释为分析样本内的关联;加权敏感性分析仅支持方向稳健,不能替代正式面板加权。第二,本研究中的收入变化为同一轮调查的同一省份样本内家庭人均收入的相对收入变化,不等同于绝对收入或真实购买力变化;虽经CPI调整的真实收入敏感性分析与其方向一致,但全国年度CPI仍难充分反映省际价格差异。第三,尽管采用上一轮调查(t-1期)的解释变量预测下一轮调查(t期)结局的时序设定,补做随机效应Logistic回归并支持主结论,但仍不能完全排除未观测混杂和测量误差导致的偏倚。
综上所述,我国基层就医利用持续下降,且基层流出增加和基层流入减少同时存在。居民主观认知在基层流出与流入中的关注点并不相同:留在基层与居民对基层的满意度更高相关,而居民转入基层与居民对非基层医疗机构医疗水平评价较低相关;相对收入组上移者对基层的利用倾向相对较弱。因此,分级诊疗政策不宜仅停留在首诊导入层面,而应同步关注“导入基层”和“留在基层”,围绕连续照护体验、基层服务能力与支付转诊协同等方面同步发力,推动基层从“能够接诊”进一步转向“能够留住并吸引居民”。

利益冲突  所有作者均声明不存在利益冲突。

作者贡献声明  许春春:研究设计,数据整理与统计分析,论文撰写;简伟研:研究构思与方案审定,方法学指导,论文修改和审定。所有作者均参与论文修改,并对最终文稿进行审读和确认。

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