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Article(id=1240972416202109237, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240972413354176744, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202401329, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1705420800000, receivedDateStr=2024-01-17, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773800480174, onlineDateStr=2026-03-18, pubDate=1715270400000, pubDateStr=2024-05-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773800480174, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773800480174, creator=13701087609, updateTime=1773800480174, updator=13701087609, issue=Issue{id=1240972413354176744, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='9', pageStart='1537', pageEnd='1728', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773800479495, creator=13701087609, updateTime=1773800596829, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1240972905568334240, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240972413354176744, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1240972905568334241, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240972413354176744, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1609, endPage=1614, ext={EN=ArticleExt(id=1240972416516682058, articleId=1240972416202109237, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Study on the changes and influencing factors of hospital admissions of children with mental disorder in Beijing from 2014 to 2018, columnId=1228016568949474136, journalTitle=Modern Preventive Medicine, columnName=Child and Adolescent Health, Maternal and Child Health, runingTitle=null, highlight=null, articleAbstract=
Objective

To analyze the time trend and potential influencing factors of hospitalization of children with mental disorders in Beijing.

Methods

The information on hospital admissions of children with mental disorders living in Beijing from 2014 to 2018 was collected. Joinpoint regression model and grey correlation analysis were used to explore the changing trend of hospital admissions and the correlation intensity of the influencing factors.

Results

From 2014 to 2018, the number of hospitalized children with mental disorders showed an upward trend with an average annual change rate of 19.33%. In the subgroup, the number of hospitalized children with mood disorders increased most rapidly (average annual percentage rate change (AAPC) =34.39%), followed by female children (AAPC=31.97%). The results of grey correlation analysis showed that per capita disposable income (correlation degree 0.841) and per capita GDP (correlation degree 0.837) had the strongest correlation with the number of hospitalized children with mental disorders.

Conclusion

The number of hospitalized children with mental disorders increased significantly from 2014 to 2018, and the trend of hospitalization of children with mental disorders with different characteristics and the correlation order of influencing factors were different. We should focus on the strongly related factors of different children with mental disorders and strengthen the mental health education and prevention and control of children in Beijing so as to provide reference for the formulation of relevant policies.

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目的

分析北京市儿童精神障碍患者入院人数的时间变化趋势及潜在影响因素。

方法

收集2014—2018年北京安定医院儿童住院精神障碍患者中居住地为北京的入院人数，采用Joinpoint回归模型、灰色关联分析探究其入院人数变化趋势和各影响因素的关联强度。

结果

2014—2018年儿童精神障碍入院人数整体以平均每年19.33%的年度变化百分率呈现上升趋势，在亚组中患心境障碍的入院儿童数量上升最为迅速［年平均变化百分率（average annual percentage rate change, AAPC）=34.39%］、其次是女性患儿（AAPC=31.97%）。灰色关联分析结果显示，人均可支配收入（关联度0.841）和人均地区生产总值（关联度0.837）与儿童精神障碍入院人数的关联最强。

结论

2014—2018年儿童精神障碍入院人数明显上升，不同特征精神障碍患儿入院人数趋势及影响因素关联度排序不同，应重点关注不同儿童精神障碍患者的较强关联影响因素，针对性地加强北京市儿童精神健康教育及防控工作，为相关政策的制定提供参考。

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高琦，E-mail:
, copyrightStatement=本刊刊出的所有文章不代表中华预防医学会和本刊编委会的观点，除非特别声明。, copyrightOwner=中华预防医学会和四川大学华西公共卫生学院, extLink=null, articleAbsUrl=null, sourceXml=HFTCMpv82+HBk2WGfbZBhg==, magXml=qpKrkrD/9vWCypWoo1TYSg==, pdfUrl=null, pdf=p9izpBbOCwDcQq4vVml+GQ==, pdfFileSize=788909, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=M/GDFKgL8Pa8O1ZP2HwbTg==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=AZz6s2dj7OLuieIzu+U4Xw==, mapNumber=null, authorCompany=null, fund=null, authors=

姜震（1998—），女，硕士在读，研究方向：流行病与卫生统计学

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姜震（1998—），女，硕士在读，研究方向：流行病与卫生统计学

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姜震（1998—），女，硕士在读，研究方向：流行病与卫生统计学

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Advances in Clinical and Experimental Medicine, 2022, 31(10): 1139-1152., articleTitle=Association between the long-term exposure to air pollution and depression, refAbstract=null)], funds=[Fund(id=1240986268058841930, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972416202109237, awardId=81872688, language=CN, fundingSource=国家自然科学基金(81872688), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1240986257451446615, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972416202109237, xref=1., ext=[AuthorCompanyExt(id=1240986257464029530, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972416202109237, companyId=1240986257451446615, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China), AuthorCompanyExt(id=1240986257476612444, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972416202109237, companyId=1240986257451446615, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.首都医科大学公共卫生学院流行病与卫生统计学系，北京 100069)]), AuthorCompany(id=1240986257665356130, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972416202109237, xref=2., ext=[AuthorCompanyExt(id=1240986257677939046, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972416202109237, companyId=1240986257665356130, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.首都医科大学附属北京安定医院)])], figs=[ArticleFig(id=1240986265252852437, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972416202109237, language=EN, label=Figure 1, caption=Admissions of children in male, female, and total population, 2014—2018, figureFileSmall=wMGZaN1v6ta2wdybwFH//Q==, figureFileBig=jp4v0dBiRu+HtdPkfR7FCA==, tableContent=null), ArticleFig(id=1240986265512899293, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972416202109237, language=CN, label=图1, caption=2014—2018年男性、女性、总人群儿童新入院人数逐年分布图, figureFileSmall=wMGZaN1v6ta2wdybwFH//Q==, figureFileBig=jp4v0dBiRu+HtdPkfR7FCA==, tableContent=null), ArticleFig(id=1240986265814889197, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972416202109237, language=EN, label=Figure 2, caption=Admissions of children in different age groups and different types of disease, figureFileSmall=LABUIhHy+aQy26T6G6N0Cw==, figureFileBig=tdBMxi2yoMbmPtmItSjVtg==, tableContent=null), ArticleFig(id=1240986266028798709, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972416202109237, language=CN, label=图2, caption=不同年龄段、不同患病类型儿童新入院人数逐年分布图

注：图A为不同年龄段；图B不同患病类型。

, figureFileSmall=LABUIhHy+aQy26T6G6N0Cw==, figureFileBig=tdBMxi2yoMbmPtmItSjVtg==, tableContent=null), ArticleFig(id=1240986266179793663, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972416202109237, language=EN, label=Table 1, caption=

Joinpoint regression results of admissions of children with mental disorders with different characteristics from 2014 to 2018

, figureFileSmall=null, figureFileBig=null, tableContent=
分类2014—2018年度变化百分比
AAPC（95%CI）t值P值
性别男5.71（-31.96 ～ 62.67）0.600.592
女31.97（18.04 ～ 55.52）4.910.016
年龄（岁）6～1428.33（9.95 ～ 57.43）3.520.039
15～1714.06（2.24 ～ 28.77）2.430.094
患病类型心境障碍34.39（25.81 ～ 46.87）11.700.001
其他6.22（-26.00 ～ 52.20）0.720.525
总体19.33（6.82 ～ 35.98）3.770.033
), ArticleFig(id=1240986266376925959, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972416202109237, language=CN, label=表1, caption=

2014—2018年不同特征儿童精神障碍新入院人群的Joinpoint回归结果

, figureFileSmall=null, figureFileBig=null, tableContent=
分类2014—2018年度变化百分比
AAPC（95%CI）t值P值
性别男5.71（-31.96 ～ 62.67）0.600.592
女31.97（18.04 ～ 55.52）4.910.016
年龄（岁）6～1428.33（9.95 ～ 57.43）3.520.039
15～1714.06（2.24 ～ 28.77）2.430.094
患病类型心境障碍34.39（25.81 ～ 46.87）11.700.001
其他6.22（-26.00 ～ 52.20）0.720.525
总体19.33（6.82 ～ 35.98）3.770.033
), ArticleFig(id=1240986266670527247, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972416202109237, language=EN, label=Table 2, caption=

Ranking of influencing factors of admissions for mental disorders in children settings by the coefficients of association derived from the grey correlation analysis

, figureFileSmall=null, figureFileBig=null, tableContent=
排序原序列名含义关联度
1X24人均可支配收入（元）0.841
2X22人均地区生产总值（元）0.837
3X18医院床位数（张/千人）0.825
4X15初中升高中升学率（%）0.814
4X11累积日照（h）0.814
4X19平均住院时长（d）0.814
7X13失业率（%）0.811
8X25基尼系数0.808
9X21城镇化水平（%）0.807
10X10相对湿度（%）0.806
10X20消费水平指数（%）0.806
12X17在校生人数0.805
13X6O3（μg/m3）0.802
14X8平均最高气温（℃）0.796
15X7平均气温（℃）0.795
16X16初中阶段升高中阶段升学率（%）0.793
17X9平均最低气温（℃）0.782
17X14小升初升学率（%）0.782
19X12离婚率（‰）0.780
20X23恩格尔系数（%）0.766
21X3NO2（μg/m3）0.758
22X4PM10（μg/m3）0.721
23X1PM2.5（μg/m3）0.702
24X5CO（mg/m3）0.635
25X2SO2（μg/m3）0.586
), ArticleFig(id=1240986266892825369, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972416202109237, language=CN, label=表2, caption=

儿童精神障碍新入院人数影响因素的灰色关联度及排序

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排序原序列名含义关联度
1X24人均可支配收入（元）0.841
2X22人均地区生产总值（元）0.837
3X18医院床位数（张/千人）0.825
4X15初中升高中升学率（%）0.814
4X11累积日照（h）0.814
4X19平均住院时长（d）0.814
7X13失业率（%）0.811
8X25基尼系数0.808
9X21城镇化水平（%）0.807
10X10相对湿度（%）0.806
10X20消费水平指数（%）0.806
12X17在校生人数0.805
13X6O3（μg/m3）0.802
14X8平均最高气温（℃）0.796
15X7平均气温（℃）0.795
16X16初中阶段升高中阶段升学率（%）0.793
17X9平均最低气温（℃）0.782
17X14小升初升学率（%）0.782
19X12离婚率（‰）0.780
20X23恩格尔系数（%）0.766
21X3NO2（μg/m3）0.758
22X4PM10（μg/m3）0.721
23X1PM2.5（μg/m3）0.702
24X5CO（mg/m3）0.635
25X2SO2（μg/m3）0.586
), ArticleFig(id=1240986267073180450, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972416202109237, language=EN, label=Table 3, caption=

Ranking of influencing factors of admissions for mental disorders children of different genders settings by the coefficients of association derived from the grey correlation analysis

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原序列含义男性女性
关联度排序关联度排序
X1PM2.50.724220.62123
X2SO20.614250.60224
X3NO20.769180.68921
X4PM100.752210.66322
X5CO0.688230.57525
X6O30.82730.71614
X7平均气温0.803100.72012
X8平均最高气温0.80590.72311
X9平均最低气温0.803100.71118
X10相对湿度0.83320.71515
X11累积日照0.792140.72410
X12离婚率0.89210.70319
X13失业率0.82340.72012
X14小升初升学率0.802130.71317
X15初中升高中升学率0.790160.7364
X16初中阶段升高中阶段升学率0.803100.71416
X17在校生人数0.82170.7258
X18医院床位数0.791150.7355
X19平均住院时长0.668240.7901
X20消费水平指数0.82250.7266
X21城镇化水平0.82250.7258
X22人均地区生产总值0.760200.7553
X23恩格尔系数0.788170.69220
X24人均可支配收入0.761190.7562
X25基尼系数0.81980.7266
), ArticleFig(id=1240986267316450092, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972416202109237, language=CN, label=表3, caption=

不同性别儿童精神障碍新入院人数影响因素的灰色关联度及排序

, figureFileSmall=null, figureFileBig=null, tableContent=
原序列含义男性女性
关联度排序关联度排序
X1PM2.50.724220.62123
X2SO20.614250.60224
X3NO20.769180.68921
X4PM100.752210.66322
X5CO0.688230.57525
X6O30.82730.71614
X7平均气温0.803100.72012
X8平均最高气温0.80590.72311
X9平均最低气温0.803100.71118
X10相对湿度0.83320.71515
X11累积日照0.792140.72410
X12离婚率0.89210.70319
X13失业率0.82340.72012
X14小升初升学率0.802130.71317
X15初中升高中升学率0.790160.7364
X16初中阶段升高中阶段升学率0.803100.71416
X17在校生人数0.82170.7258
X18医院床位数0.791150.7355
X19平均住院时长0.668240.7901
X20消费水平指数0.82250.7266
X21城镇化水平0.82250.7258
X22人均地区生产总值0.760200.7553
X23恩格尔系数0.788170.69220
X24人均可支配收入0.761190.7562
X25基尼系数0.81980.7266
), ArticleFig(id=1240986267391947573, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972416202109237, language=EN, label=Table 4, caption=

Ranking of influencing factors of admissions for mental disorders children of different types of disease settings by the coefficients of association derived from the grey correlation analysis

, figureFileSmall=null, figureFileBig=null, tableContent=
原序列含义心境障碍其他精神疾病
关联度排序关联度排序
X1PM2.50.604230.7694
X2SO20.587240.64625
X3NO20.663210.72723
X4PM100.642220.74118
X5CO0.561250.73422
X6O30.686160.7637
X7平均气温0.70490.73921
X8平均最高气温0.70880.74020
X9平均最低气温0.687150.74118
X10相对湿度0.684180.7685
X11累积日照0.72930.74816
X12离婚率0.666200.8081
X13失业率0.70870.7629
X14小升初升学率0.685170.74317
X15初中升高中升学率0.72450.75810
X16初中阶段升高中阶段升学率0.688140.7685
X17在校生人数0.695130.75810
X18医院床位数0.72540.7637
X19平均住院时长0.71460.72224
X20消费水平指数0.701100.75612
X21城镇化水平0.699120.75513
X22人均地区生产总值0.76520.7803
X23恩格尔系数0.668190.75015
X24人均可支配收入0.76810.7812
X25基尼系数0.701100.75214
), ArticleFig(id=1240986267496805181, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240972416202109237, language=CN, label=表4, caption=

不同患病类型儿童精神障碍新入院人数影响因素的灰色关联度及排序

, figureFileSmall=null, figureFileBig=null, tableContent=
原序列含义心境障碍其他精神疾病
关联度排序关联度排序
X1PM2.50.604230.7694
X2SO20.587240.64625
X3NO20.663210.72723
X4PM100.642220.74118
X5CO0.561250.73422
X6O30.686160.7637
X7平均气温0.70490.73921
X8平均最高气温0.70880.74020
X9平均最低气温0.687150.74118
X10相对湿度0.684180.7685
X11累积日照0.72930.74816
X12离婚率0.666200.8081
X13失业率0.70870.7629
X14小升初升学率0.685170.74317
X15初中升高中升学率0.72450.75810
X16初中阶段升高中阶段升学率0.688140.7685
X17在校生人数0.695130.75810
X18医院床位数0.72540.7637
X19平均住院时长0.71460.72224
X20消费水平指数0.701100.75612
X21城镇化水平0.699120.75513
X22人均地区生产总值0.76520.7803
X23恩格尔系数0.668190.75015
X24人均可支配收入0.76810.7812
X25基尼系数0.701100.75214
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2014—2018年北京市儿童精神障碍患者入院人数变化及影响因素研究
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姜震 1 , 成佩霞 1 , 徐枫 1 , 王茜 2 , 徐佳颖 2 , 高琦 1
现代预防医学 | 儿少卫生与妇幼保健 2024,51(9): 1609-1614
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现代预防医学 | 儿少卫生与妇幼保健 2024, 51(9): 1609-1614
2014—2018年北京市儿童精神障碍患者入院人数变化及影响因素研究
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姜震1, 成佩霞1, 徐枫1, 王茜2, 徐佳颖2, 高琦1
作者信息
  • 1.首都医科大学公共卫生学院流行病与卫生统计学系，北京 100069
  • 2.首都医科大学附属北京安定医院

    • 姜震（1998—），女，硕士在读，研究方向：流行病与卫生统计学

通讯作者:

高琦，E-mail:
Study on the changes and influencing factors of hospital admissions of children with mental disorder in Beijing from 2014 to 2018
Zhen JIANG1, Pei-xia CHENG1, Feng XU1, Qian WANG2, Jia-ying XU2, Qi GAO1
Affiliations
  • Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing 100069, China
出版时间: 2024-05-10 doi: 10.20043/j.cnki.MPM.202401329
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摘要
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目的

分析北京市儿童精神障碍患者入院人数的时间变化趋势及潜在影响因素。

方法

收集2014—2018年北京安定医院儿童住院精神障碍患者中居住地为北京的入院人数，采用Joinpoint回归模型、灰色关联分析探究其入院人数变化趋势和各影响因素的关联强度。

结果

2014—2018年儿童精神障碍入院人数整体以平均每年19.33%的年度变化百分率呈现上升趋势，在亚组中患心境障碍的入院儿童数量上升最为迅速［年平均变化百分率（average annual percentage rate change, AAPC）=34.39%］、其次是女性患儿（AAPC=31.97%）。灰色关联分析结果显示，人均可支配收入（关联度0.841）和人均地区生产总值（关联度0.837）与儿童精神障碍入院人数的关联最强。

结论

2014—2018年儿童精神障碍入院人数明显上升，不同特征精神障碍患儿入院人数趋势及影响因素关联度排序不同，应重点关注不同儿童精神障碍患者的较强关联影响因素，针对性地加强北京市儿童精神健康教育及防控工作，为相关政策的制定提供参考。

儿童精神障碍  /  入院人数  /  影响因素  /  Joinpoint回归  /  灰色关联分析
Objective

To analyze the time trend and potential influencing factors of hospitalization of children with mental disorders in Beijing.

Methods

The information on hospital admissions of children with mental disorders living in Beijing from 2014 to 2018 was collected. Joinpoint regression model and grey correlation analysis were used to explore the changing trend of hospital admissions and the correlation intensity of the influencing factors.

Results

From 2014 to 2018, the number of hospitalized children with mental disorders showed an upward trend with an average annual change rate of 19.33%. In the subgroup, the number of hospitalized children with mood disorders increased most rapidly (average annual percentage rate change (AAPC) =34.39%), followed by female children (AAPC=31.97%). The results of grey correlation analysis showed that per capita disposable income (correlation degree 0.841) and per capita GDP (correlation degree 0.837) had the strongest correlation with the number of hospitalized children with mental disorders.

Conclusion

The number of hospitalized children with mental disorders increased significantly from 2014 to 2018, and the trend of hospitalization of children with mental disorders with different characteristics and the correlation order of influencing factors were different. We should focus on the strongly related factors of different children with mental disorders and strengthen the mental health education and prevention and control of children in Beijing so as to provide reference for the formulation of relevant policies.

Children with mental disorders  /  Hospital admissions  /  Influencing factors  /  Joinpoint regression  /  Grey relational analysis
姜震, 成佩霞, 徐枫, 王茜, 徐佳颖, 高琦. 2014—2018年北京市儿童精神障碍患者入院人数变化及影响因素研究. 现代预防医学, 2024 , 51 (9) : 1609 -1614 . DOI: 10.20043/j.cnki.MPM.202401329
Zhen JIANG, Pei-xia CHENG, Feng XU, Qian WANG, Jia-ying XU, Qi GAO. Study on the changes and influencing factors of hospital admissions of children with mental disorder in Beijing from 2014 to 2018[J]. Modern Preventive Medicine, 2024 , 51 (9) : 1609 -1614 . DOI: 10.20043/j.cnki.MPM.202401329
正文
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精神障碍会对儿童生理及心理造成伤害，还会严重影响儿童学习、生活与社会功能和生活质量，也给个人乃至社会带来沉重负担[1-3]。而我国6～16岁在校学生的精神障碍总患病率已达到17.5%[4]。精神障碍患者住院情况很大程度上更能反映精神障碍的严重性[5]，但目前国内外缺少儿童精神障碍患者入院人数数据的时间趋势及影响因素研究，因此，本研究采用Joinpoint回归模型对2014—2018年北京安定医院儿童精神障碍患者入院人数的时间变化趋势进行分析，灰色关联分析评价各影响因素与儿童精神障碍入院人数的关联强度，为北京市制定有效的儿童精神障碍的针对性防治和医院相关措施配制等提供理论参考。
1　对象与方法
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1.1　数据来源
本研究的每年入院人数及住院时长来源于2014—2018年北京安定医院的电子病历系统，选取6～17岁新入院（不包括此前在安定医院有过住院经历）、居住地为北京的患儿进行分析。六大空气污染物：PM2.5、SO2、NO2、PM10、CO、O3数据摘自《北京市环境状况公报》；气象数据：平均气温、平均最高气温、平均最低气温、相对湿度源于美国国家海洋和大气管理局下设的国家环境信息中心，累积日照由中国地面气候资料日值数据集V3.0处理生成；教育方面指标：小升初升学率、初中升高中升学率、初中阶段升高中阶段升学率、在校生人数源于《北京教育事业发展统计概况》计算得出；社会及经济方面指标：离婚率、失业率、常住人口每千人拥有的医院床位数（简称医院床位数）、居民消费价格指数（简称消费水平指数）、城镇化水平、人均地区生产总值、恩格尔系数、人均可支配收入、基尼系数源自《中国统计年鉴》和《北京统计年鉴》。
其中《北京教育事业发展统计概况》详细数据从2014年开始，为加入教育方面进行影响因素分析，数据选取年份从2014年开始；北京安定医院2019年开始调整医院信息系统，数据与2018年及以前存在接口差异，故而数据选取年份仅到2018年。
本研究根据《赫尔辛基宣言》进行，并经首都医科大学伦理委员会（2018SY06）审查批准。
1.2　统计学方法
采用Excel 2019整理2014—2018年儿童新入院人数数据，并按照患儿性别、年龄段、患病类型进行分层，使用Joinpoint回归软件（5.0版本）对数据进行Joinpoint回归分析。在Joinpoint软件中，可通过模型计算年度变化百分率（annual percentage rate change, APC）与年平均变化百分率（average annual percentage rate change, AAPC），其中APC的置信区间基于t分布，AAPC的置信区间基于正态分布。目前本研究仅分析五年数据，无法进行转折点分析，此时AAPC等于APC，软件中AAPC的置信区间也使用t分布计算。通过计算AAPC评价2014—2018年儿童入院人数变化趋势，再通过“成对比较分析（pairwise comparison）”进行不同性别、年龄、患病类型分组的组间平行检验[6]。此后建立2014—2018年儿童入院人数及影响因素资料数据库，采用均值法对参考序列（X0）及各影响因素序列值进行无量纲化处理等，最后得出入院人数影响因素的关联度并排序。另外，还对不同性别、患病类型儿童的入院人数影响因素进行灰色关联的亚组分析。
2　结果
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2.1　儿童精神障碍新入院情况
2014—2018年北京儿童精神障碍新入院患者共557人，入院人数总体约呈现上升趋势。从性别角度看，除2015和2016年男性患者多于女性，其余年份女性患者的人数均多于男性患者。从变化趋势上看，女性患者人数逐年上升，而男性患者在2017年有过下降。见图1。
2014—2018年儿童各年龄段中新入院患者以15～17岁（即高中阶段）为主。2014—2018年儿童患病类型总人数中最多者为心境障碍，其余各类型人数均不多故而合为一类，即其他精神疾病。五年间，心境障碍患者新入院人数呈现出逐年增长趋势。见图2。
2.2　儿童精神障碍新入院人数的Joinpoint回归分析
Joinpoint回归分析结果显示，2014—2018年儿童新入院人数总体呈现上升趋势，平均每年上升值为19.33%，且趋势有统计学意义（t=3.77，P=0.033），见表1。
2.2.1　不同性别的儿童新入院人数
女性儿童在2014—2018年新入院人数总体均呈现上升趋势，平均每年上升值为31.97%，有统计学意义（t=4.91，P=0.016），而男性儿童新入院人数在2014—2018年的变化趋势无统计学意义（AAPC=5.71，t=0.60，P= 0.592），见表1。组间比较发现，男性与女性儿童精神障碍新入院人数的年平均变化速度差异有统计学意义（P<0.001）。
2.2.2　不同年龄段的儿童新入院人数
6～14岁儿童在2014—2018年新入院人数总体均呈现上升趋势，平均每年上升值为28.33%，有统计学意义（t=3.52，P=0.039），而15～17岁儿童新入院人数在2014—2018年的变化趋势无统计学意义（AAPC =14.06，t= 2.43，P=0.094），见表1。组间比较发现，6～14岁与15～17岁儿童精神障碍新入院人数的平均变化速度差异无统计学意义（P=0.313），说明两年龄组儿童新入院人数增长速度相同。
2.2.3　不同患病类型儿童的新入院儿童人数
患心境障碍的儿童在2014—2018年新入院人数总体均呈现上升趋势，平均每年上升值为34.39%，有统计学意义（t=11.70，P=0.001），而患其他精神疾病的儿童在2014—2018年新入院人数变化趋势无统计学意义（AAPC=6.22，t=0.72，P=0.525），见表1。组间比较发现，心境障碍儿童与患其他精神疾病的儿童新入院人数的平均变化速度差异有统计学意义（P<0.001）。
2.3　儿童精神障碍新入院人数影响因素
本研究以每年的儿童新入院人数（X0，人）为参考序列，以PM2.5浓度（X1，μg/m3）、SO2浓度（X2，μg/m3）、NO2浓度（X3，μg/m3）、PM10浓度（X4，μg/m3）、CO 24 h平均第95百分位浓度值浓度（X5，mg/m3）、O3日最大8 h滑动平均的第90百分位浓度（X6，μg/m3）、平均气温（X7，℃）、平均最高气温（X8，℃）、平均最低气温（X9，℃）、相对湿度（X10，%）、累积日照（X11，h）、离婚率（X12，‰）、失业率（X13，%）、小升初升学率（X14，%）、初中升高中升学率（X15，%）、初中阶段升高中阶段升学率（X16，%）、在校生人数（X17，人）、医院床位数（X18，张/千人）、平均住院时长（X19，d）、消费水平指数（X20，%）、城镇化水平（X21，%）、人均地区生产总值（X22，元）、恩格尔系数（X23，%）、人均可支配收入（X24，元）、基尼系数（X25）为比较序列。
运用均值法进行无量纲化处理后，求解各比较序列与参考序列同一年份数值的差值绝对值。本研究中，所有差值绝对值中最小值为0.004，所有差值绝对值中最大值为1.246。计算各比较序列与参考序列的灰色关联度，按关联度大小排序，排序越前越表明该影响因素与入院人数关联度越强，即儿童精神障碍新入院人数变化的主要影响因素有人均可支配收入（X24，元）、人均地区生产总值（X22，元）、医院床位数（X18，张/千人）、累积日照（X11，h）、初中升高中升学率（X15，%）、失业率（X13，%）、平均住院时长（X19，d）、基尼系数（X25）、城镇化水平（X21，%）、相对湿度（X10，%）等。见表2。
因在上一部分Joinpoint回归结果中发现不同性别、不同患病类型儿童入院人数的平均变化速度不同，进一步分别以不同性别、患病类型的每年儿童新入院人数为参考序列，分别计算其关联度并排序，结果见表3、表4。与总人群不同的是，离婚率（X12，‰）成为男性儿童和患有其他精神障碍儿童入院人数的首位影响因素，平均住院时长（X19，d）为女性儿童入院人数的首位影响因素。
3　讨论
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3.1　儿童精神障碍新入院情况不容乐观
本研究显示，2014—2018年，北京儿童精神障碍新入院患者共557人，新入院人数以平均每年19.33%的年度变化百分率相对平稳上升。在儿童精神障碍患者新入院患者中，以15～17岁高中阶段患者及心境障碍患者占比最高。
与十年前一项新疆精神卫生中心的研究一致的是儿童精神障碍住院人数缓慢增加，且随年龄增长住院人数递增[7]，但是患病类型占比次序发生改变，本研究中为首位的心境障碍在此研究中排第二位，提醒我们近年来心境障碍患者群体的增长不容小觑。在年龄方面，15～17岁人群内分泌和新陈代谢等生理状态变化明显，心理上也产生了个体与环境间的前所未有的更多的接触与联系，两者容易分别发生暂时的平衡失调，进而出现神经精神功能障碍或其他精神障碍[8]，使得此年龄段精神障碍患者入院人数较多。
3.2　儿童精神障碍新入院人数的影响因素分布广泛
从灰色关联分析的结果看，影响当前儿童精神障碍入院人数的因素主要包括医疗因素、教育因素、气候因素、社会及经济因素等方面。
本研究发现人均可支配收入（0.841）和人均地区生产总值（0.837）与儿童精神障碍入院人数具有较强关联。这考虑是由于人均可支配收入与人均地区生产总值的增长在一定程度上减轻了儿童的就医经济负担，使其有更多的钱愿意用于解决儿童精神健康问题上。
医疗因素方面，医院床位数（0.825）作为儿童精神障碍入院人数关联影响因素的第三位，与平均住院时长（0.814）均可通过影响医院载容量影响儿童精神障碍患者新入院人数。
儿童精神健康障碍与学业成绩不佳有关[9]。教育指标中初中升高中升学率（0.814）关联度最大，这反应出中考分流问题对于儿童精神健康的影响，越来越大的课业压力不利于儿童正常的心理发育。
累积日照（0.814）同样是儿童精神障碍入院人数关联影响因素的第四位，关联度高于其余气候影响因素（相对湿度0.806，平均最高气温0.796，平均气温0.795，平均最低气温0.782）；类似的，一项对中国七个地区精神分裂症流行病学调查资料的气候因素再分析研究显示，平均日照时数是研究中对4个气候变量（年平均气温、年平均降水量、年平均日照数及纬度）多元回归中唯一与精神分裂症患病率间存在显著关联的因素[10]。一项包含5 780例精神疾病的研究显示，气压、气温、空气湿度与精神类疾病发病和复发有关[11]。香港对2002—2011年每日精神障碍住院人数的研究发现[12]，温度与精神障碍入院率呈正相关，且在老年人中最为突出。
另外，经济指标失业率（0.811）、基尼系数（0.808）、城镇化水平（0.807）和消费水平指数（0.806）也对儿童精神障碍入院人数具有一定影响。其中失业率可能一部分影响家庭收入等影响儿童心理与就医选择，一部分会引发儿童对于当下社会发展状况的担忧。有研究显示贫穷和社会经济地位低下与精神障碍有很强的相关性[13]，无法继续上学又缺乏就业机会的地区儿童患精神障碍的危险性更高[14]。基尼系数是判断分配平等程度的指标，根据心理扭力理论，其可能通过与他人比较的扭力冲突对儿童精神健康产生影响[15]。城镇化水平是通过计算城市人口占总人口的比重来衡量城市化的指标，可很好的代表中国社会朝向现代化迈进的过程和整个社会转型的过程。居民消费价格指数代表的消费水平指数也可一定方面显示出居民的消费能力水平，一定程度上反应了儿童就医的经济负担。
空气污染物方面，有研究显示PM2.5、NO2、PM10、CO、O3等浓度可影响精神障碍发生或就医风险[16-17]，但本研究中2014—2018年北京空气质量稳步向好，与儿童精神障碍患者入院人数整体增加的趋势关联度不高。
3.3　不同性别或患病类型患儿的入院趋势和影响因素存在差异
儿童精神障碍患者入院人数及影响因素存在性别及患病类型差异。在本研究中，女性儿童入院人数平均变化速度高达31.97%，男性的变化趋势则无统计学意义；患心境障碍的儿童入院人数平均变化速度更是高达34.39%，而患其他精神障碍的儿童入院人数变化趋势亦无统计学意义。故而我们推测女性患儿、心境障碍患儿是这五年来入院人数增长的主要组成部分与因素，应着重关注。分别分析其入院人数的影响因素发现，与总体儿童入院人数的影响因素关联度最强为人均可支配收入这一经济指标不同的是，离婚率是男性和有其他精神障碍的患儿入院人数的关联最强的影响因素（0.892，0.808），平均住院时长为女性儿童入院人数的首位关联影响因素（0.790）。男性患者中入院人数影响因素中关联最强的离婚率，其关联度高达0.892，是本研究中唯一达到极强这一关联程度的影响因素，表明离婚率可通过家庭不和等因素对男性患儿的心理状况产生影响，且其影响程度较其他因素、其他人群更深。
本研究仍存在一定的局限，（1）本研究纳入的患者入院人数仅源自一所医院，年份、人数有限，尚未观察到儿童精神障碍患者入院人数的拐点；（2）儿童精神障碍患者入院人数的影响因素纷繁复杂，仍有一些因素未被纳入本次研究，如更多的个案信息（家族史等）或医院内部资料（门急诊人数、病床周转次数等）；（3）灰色关联分析实际上是动态过程中各序列数据发展态势的量化比较分析，未能对影响因素进行统计学检验。
综上，本研究从宏观角度出发，运用Joinpoint回归模型分析儿童精神障碍患者新入院人数的变化趋势，同时利用灰色关联分析法探索经济、社会、教育、医疗、气象、空气污染物等不同方面对于儿童精神障碍患者入院人数的主要影响因素，为北京市制定有效的针对性儿童精神障碍控制与防治政策和医院相关措施配制等提供理论参考，在研究视角的选取上具有一定的创新性。未来仍需更多针对不同来源、地区的儿童精神障碍患者住院数据及可能的影响因素进行长期收集，以扩展更多统计方法、提高模型分析的精确性，进行更为细致全面的探究。
基金
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  • 国家自然科学基金(81872688)
文献
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2024年第51卷第9期
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doi: 10.20043/j.cnki.MPM.202401329
  • 接收时间：2024-01-17
  • 首发时间：2026-03-18
  • 出版时间：2024-05-10
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  • 收稿日期：2024-01-17
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国家自然科学基金(81872688)
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    1.首都医科大学公共卫生学院流行病与卫生统计学系，北京 100069
    2.首都医科大学附属北京安定医院

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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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