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<?covid-19-tdm?>
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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">JPH</journal-id>
      <journal-id journal-id-type="nlm-ta">JMIR Public Health Surveill</journal-id>
      <journal-title>JMIR Public Health and Surveillance</journal-title>
      <issn pub-type="epub">2369-2960</issn>
      <publisher>
        <publisher-name>JMIR Publications</publisher-name>
        <publisher-loc>Toronto, Canada</publisher-loc>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="publisher-id">v9i1e34386</article-id>
      <article-id pub-id-type="pmid">38090794</article-id>
      <article-id pub-id-type="doi">10.2196/34386</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Original Paper</subject>
        </subj-group>
        <subj-group subj-group-type="article-type">
          <subject>Original Paper</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Manifestations and Outcomes of Intracerebral Hemorrhage During the COVID-19 Pandemic in China: Multicenter, Longitudinal Cohort Study</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="editor">
          <name>
            <surname>Sanchez</surname>
            <given-names>Travis</given-names>
          </name>
        </contrib>
        <contrib contrib-type="editor">
          <name>
            <surname>Mavragani</surname>
            <given-names>Amaryllis</given-names>
          </name>
        </contrib>
      </contrib-group>
      <contrib-group>
        <contrib contrib-type="reviewer">
          <name>
            <surname>Sotiropoulos</surname>
            <given-names>Marinos</given-names>
          </name>
        </contrib>
        <contrib contrib-type="reviewer">
          <name>
            <surname>Lami</surname>
            <given-names>Faris</given-names>
          </name>
        </contrib>
        <contrib contrib-type="reviewer">
          <name>
            <surname>Hertling</surname>
            <given-names>Stefan</given-names>
          </name>
        </contrib>
      </contrib-group>
      <contrib-group>
        <contrib id="contrib1" contrib-type="author" equal-contrib="yes">
          <name name-style="western">
            <surname>Wan</surname>
            <given-names>Yan</given-names>
          </name>
          <degrees>PhD</degrees>
          <xref rid="aff1" ref-type="aff">1</xref>
          <ext-link ext-link-type="orcid">https://orcid.org/0000-0002-1864-0714</ext-link>
        </contrib>
        <contrib id="contrib2" contrib-type="author" equal-contrib="yes">
          <name name-style="western">
            <surname>He</surname>
            <given-names>Quan Wei</given-names>
          </name>
          <degrees>PhD</degrees>
          <xref rid="aff1" ref-type="aff">1</xref>
          <ext-link ext-link-type="orcid">https://orcid.org/0000-0002-8318-6976</ext-link>
        </contrib>
        <contrib id="contrib3" contrib-type="author" equal-contrib="yes">
          <name name-style="western">
            <surname>Chen</surname>
            <given-names>Shaoli</given-names>
          </name>
          <degrees>MD</degrees>
          <xref rid="aff1" ref-type="aff">1</xref>
          <ext-link ext-link-type="orcid">https://orcid.org/0000-0002-8986-8775</ext-link>
        </contrib>
        <contrib id="contrib4" contrib-type="author">
          <name name-style="western">
            <surname>Li</surname>
            <given-names>Man</given-names>
          </name>
          <degrees>PhD</degrees>
          <xref rid="aff1" ref-type="aff">1</xref>
          <ext-link ext-link-type="orcid">https://orcid.org/0009-0007-0623-9476</ext-link>
        </contrib>
        <contrib id="contrib5" contrib-type="author">
          <name name-style="western">
            <surname>Xia</surname>
            <given-names>Yuanpeng</given-names>
          </name>
          <degrees>PhD</degrees>
          <xref rid="aff1" ref-type="aff">1</xref>
          <ext-link ext-link-type="orcid">https://orcid.org/0009-0003-1483-3724</ext-link>
        </contrib>
        <contrib id="contrib6" contrib-type="author">
          <name name-style="western">
            <surname>Zhang</surname>
            <given-names>Lei</given-names>
          </name>
          <degrees>MD</degrees>
          <xref rid="aff1" ref-type="aff">1</xref>
          <ext-link ext-link-type="orcid">https://orcid.org/0000-0003-2837-0890</ext-link>
        </contrib>
        <contrib id="contrib7" contrib-type="author">
          <name name-style="western">
            <surname>Sun</surname>
            <given-names>Zhou</given-names>
          </name>
          <degrees>MD</degrees>
          <xref rid="aff1" ref-type="aff">1</xref>
          <ext-link ext-link-type="orcid">https://orcid.org/0009-0002-2514-4177</ext-link>
        </contrib>
        <contrib id="contrib8" contrib-type="author">
          <name name-style="western">
            <surname>Chen</surname>
            <given-names>Xiaolu</given-names>
          </name>
          <degrees>MD</degrees>
          <xref rid="aff1" ref-type="aff">1</xref>
          <ext-link ext-link-type="orcid">https://orcid.org/0009-0009-9206-7205</ext-link>
        </contrib>
        <contrib id="contrib9" contrib-type="author">
          <name name-style="western">
            <surname>Wang</surname>
            <given-names>David</given-names>
          </name>
          <degrees>DO</degrees>
          <xref rid="aff2" ref-type="aff">2</xref>
          <ext-link ext-link-type="orcid">https://orcid.org/0000-0003-2277-4608</ext-link>
        </contrib>
        <contrib id="contrib10" contrib-type="author">
          <name name-style="western">
            <surname>Chang</surname>
            <given-names>Jiang</given-names>
          </name>
          <degrees>PhD</degrees>
          <xref rid="aff3" ref-type="aff">3</xref>
          <ext-link ext-link-type="orcid">https://orcid.org/0000-0001-8551-9458</ext-link>
        </contrib>
        <contrib id="contrib11" contrib-type="author" corresp="yes">
          <name name-style="western">
            <surname>Hu</surname>
            <given-names>Bo</given-names>
          </name>
          <degrees>PhD</degrees>
          <xref rid="aff1" ref-type="aff">1</xref>
          <address>
            <institution>Department of Neurology, Union Hospital</institution>
            <institution>Tongji Medical College</institution>
            <institution>Huazhong University of Science and Technology</institution>
            <addr-line>NO.1277, Jiefang Road</addr-line>
            <addr-line>Wuhan, 430022</addr-line>
            <country>China</country>
            <phone>86 13707114863</phone>
            <email>hubo@mail.hust.edu.cn</email>
          </address>
          <ext-link ext-link-type="orcid">https://orcid.org/0000-0003-1462-8854</ext-link>
        </contrib>
      </contrib-group>
      <aff id="aff1">
        <label>1</label>
        <institution>Department of Neurology, Union Hospital</institution>
        <institution>Tongji Medical College</institution>
        <institution>Huazhong University of Science and Technology</institution>
        <addr-line>Wuhan</addr-line>
        <country>China</country>
      </aff>
      <aff id="aff2">
        <label>2</label>
        <institution>Barrow Neurological Institute</institution>
        <institution>St. Joseph's Hospital and Medical Center</institution>
        <addr-line>Phoenix, AZ</addr-line>
        <country>United States</country>
      </aff>
      <aff id="aff3">
        <label>3</label>
        <institution>School of Public Health, Tongji Medical College</institution>
        <institution>Huazhong University of Science and Technology</institution>
        <addr-line>Wuhan</addr-line>
        <country>China</country>
      </aff>
      <author-notes>
        <corresp>Corresponding Author: Bo Hu <email>hubo@mail.hust.edu.cn</email></corresp>
      </author-notes>
      <pub-date pub-type="collection">
        <year>2023</year>
      </pub-date>
      <pub-date pub-type="epub">
        <day>13</day>
        <month>12</month>
        <year>2023</year>
      </pub-date>
      <volume>9</volume>
      <elocation-id>e34386</elocation-id>
      <history>
        <date date-type="received">
          <day>22</day>
          <month>10</month>
          <year>2021</year>
        </date>
        <date date-type="rev-request">
          <day>29</day>
          <month>11</month>
          <year>2022</year>
        </date>
        <date date-type="rev-recd">
          <day>19</day>
          <month>3</month>
          <year>2023</year>
        </date>
        <date date-type="accepted">
          <day>5</day>
          <month>9</month>
          <year>2023</year>
        </date>
      </history>
      <copyright-statement>©Yan Wan, Quan Wei He, Shaoli Chen, Man Li, Yuanpeng Xia, Lei Zhang, Zhou Sun, Xiaolu Chen, David Wang, Jiang Chang, Bo Hu. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 13.12.2023.</copyright-statement>
      <copyright-year>2023</copyright-year>
      <license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/">
        <p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on https://publichealth.jmir.org, as well as this copyright and license information must be included.</p>
      </license>
      <self-uri xlink:href="https://publichealth.jmir.org/2023/1/e34386" xlink:type="simple"/>
      <abstract>
        <sec sec-type="background">
          <title>Background</title>
          <p>The COVID-19 pandemic has inevitably affected the distribution of medical resources, and epidemic lockdowns have had a significant impact on the nursing and treatment of patients with other acute diseases, including intracerebral hemorrhage (ICH).</p>
        </sec>
        <sec sec-type="objective">
          <title>Objective</title>
          <p>This study aimed to investigate how the COVID-19 pandemic affected the manifestations and outcomes of patients with ICH.</p>
        </sec>
        <sec sec-type="methods">
          <title>Methods</title>
          <p>Patients with acute ICH before (December 1, 2018-November 30, 2019) and during (December 1, 2019-November 30, 2020) the COVID-19 pandemic at 31 centers in China from the Chinese Cerebral Hemorrhage: Mechanism and Intervention (CHEERY) study were entered into the analysis. Demographic information and clinical manifestations and outcomes were collected and compared between the 2 groups.</p>
        </sec>
        <sec sec-type="results">
          <title>Results</title>
          <p>From December 1, 2018, to November 30, 2020, a total of 3460 patients with ICH from the CHEERY study were enrolled and eventually analyzed. The results showed that during the COVID-19 pandemic, patients with ICH were more likely to be older (<italic>P</italic>&#60;.001) with a history of ischemic stroke (<italic>P</italic>=.04), shorter time from onset to admission (<italic>P</italic>&#60;.001), higher blood pressure (<italic>P</italic>&#60;.001), higher fasting blood glucose (<italic>P</italic>=.003), larger hematoma volume (<italic>P</italic>&#60;.001), and more common deep ICH (<italic>P</italic>=.01) and intraventricular hemorrhage (<italic>P</italic>=.02). These patients required more intensive care unit treatment (<italic>P</italic>&#60;.001) and preferred to go to the hospital directly rather than call an ambulance (<italic>P</italic>&#60;.001). In addition, the COVID-19 pandemic was associated with an increased risk of pulmonary infection during hospitalization (adjusted risk ratio [RR<sub>adjusted</sub>] 1.267, 95% CI 1.065-1.509), lower probability of a 3-month good outcome (RR<sub>adjusted</sub> 0.975, 95% CI 0.956-0.995), and a higher probability of in-hospital (RR<sub>adjusted</sub> 3.103, 95% CI 2.156-4.465), 1-month (RR<sub>adjusted</sub> 1.064, 95% CI 1.042-1.087), and 3-month (RR<sub>adjusted</sub> 1.069, 95% CI 1.045-1.093) mortality.</p>
        </sec>
        <sec sec-type="conclusions">
          <title>Conclusions</title>
          <p>Our study indicated that the cloud of COVID-19 has adversely impacted the presentation and outcomes of ICH. Medical workers may pay more attention to patients with ICH, while the public should pay more attention to hypertension control and ICH prevention.</p>
        </sec>
        <sec sec-type="trial registration">
          <title>Trial Registration</title>
          <p>Chinese Clinical Trial Registry ChiCTR1900020872; https://www.chictr.org.cn/showprojEN.html?proj=33817</p>
        </sec>
      </abstract>
      <kwd-group>
        <kwd>COVID-19</kwd>
        <kwd>intracerebral hemorrhage</kwd>
        <kwd>manifestation</kwd>
        <kwd>outcome</kwd>
        <kwd>cohort study</kwd>
      </kwd-group>
    </article-meta>
  </front>
  <body>
    <sec sec-type="introduction">
      <title>Introduction</title>
      <p>COVID-19, the pneumonia caused by SARS-CoV-2 infection, emerged in Wuhan City, Hubei Province, China in December 2019 [<xref ref-type="bibr" rid="ref1">1</xref>]. The World Health Organization (WHO) declared the COVID-19 outbreak a public health emergency of international concern on January 30, 2020, and declared a pandemic on March 11, 2020 [<xref ref-type="bibr" rid="ref2">2</xref>]. In China, from January 3, 2020, to March 16, 2023, there have been 99,185,059 confirmed cases of COVID-19 with 120,576 deaths reported to the WHO [<xref ref-type="bibr" rid="ref3">3</xref>]. Wuhan was the core area of the COVID-19 storm in China, and this city was locked down from January 23, 2020, to April 8, 2020 [<xref ref-type="bibr" rid="ref4">4</xref>]. Various public health interventions were implemented by the Chinese government to suppress the big pandemic, including but not limited to intensive intracity and intercity traffic restrictions, social distancing measures, home isolation and centralized quarantine, and improvement of medical resources [<xref ref-type="bibr" rid="ref5">5</xref>,<xref ref-type="bibr" rid="ref6">6</xref>].</p>
      <p>In addition, as a global health crisis, the COVID-19 pandemic has been the most serious challenge to delivering timely care to patients with other conditions [<xref ref-type="bibr" rid="ref7">7</xref>]. Its outbreak proved to have a major impact on patients with cerebrovascular diseases [<xref ref-type="bibr" rid="ref8">8</xref>,<xref ref-type="bibr" rid="ref9">9</xref>], for example, significantly decreasing the rate of stroke admissions [<xref ref-type="bibr" rid="ref10">10</xref>,<xref ref-type="bibr" rid="ref11">11</xref>], intravenous thrombolysis and mechanical thrombectomy [<xref ref-type="bibr" rid="ref12">12</xref>,<xref ref-type="bibr" rid="ref13">13</xref>], and prolonging the time from symptom onset to treatment of ischemic stroke [<xref ref-type="bibr" rid="ref14">14</xref>]. Intracerebral hemorrhage (ICH), one of the most devastating diseases worldwide with a mortality rate of up to twice that of ischemic stroke [<xref ref-type="bibr" rid="ref15">15</xref>], requires emergency medical care in specialized neurological intensive care units [<xref ref-type="bibr" rid="ref16">16</xref>]. Under the condition of tight medical resources, it was difficult to balance stroke treatment and COVID-19 prevention during the COVID-19 pandemic [<xref ref-type="bibr" rid="ref8">8</xref>-<xref ref-type="bibr" rid="ref10">10</xref>,<xref ref-type="bibr" rid="ref12">12</xref>], and it is not clear whether the COVID-19 pandemic affected the manifestations and outcomes of patients with ICH.</p>
      <p>Moreover, hypertension management during the COVID-19 pandemic may be a challenge, which is an important therapy for ICH [<xref ref-type="bibr" rid="ref17">17</xref>,<xref ref-type="bibr" rid="ref18">18</xref>]. There is an unproven notion that angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) might theoretically increase the risk of COVID-19 infection because increased angiotensin-converting enzyme-2 activity has been shown to lead to practical medicine restriction. Recently, there have been reports that discontinuation of ACEI/ARB in patients with COVID-19 is not related to the severity of COVID-19 [<xref ref-type="bibr" rid="ref19">19</xref>,<xref ref-type="bibr" rid="ref20">20</xref>]. However, discontinuation of ACEI/ARB was once not an uncommon phenomenon. Besides, during the COVID-19 pandemic, the public may have also suffered from social pressure, anxiety, the depressed economy, a lack of public health resources, and so on [<xref ref-type="bibr" rid="ref21">21</xref>,<xref ref-type="bibr" rid="ref22">22</xref>]. All of these factors may disturb the control of risk factors of ICH, resulting in increased occurrence and poor outcomes.</p>
      <p>The Chinese Cerebral Hemorrhage: Mechanism and Intervention (CHEERY) study is a prospective multicenter cohort study that began on December 1, 2018, and recruits patients with spontaneous ICH within 7 days of onset in China [<xref ref-type="bibr" rid="ref18">18</xref>]. This cohort covered periods before and after the COVID-19 outbreak. In this study, in order to investigate how the COVID-19 pandemic affected the manifestations and outcomes of patients with ICH, we retrospectively analyzed the data of patients with ICH in 1-year periods before (December 1, 2018-November 30, 2019) and during (December 1, 2019-November 30, 2020) the COVID-19 pandemic in the CHEERY study.</p>
    </sec>
    <sec sec-type="introduction">
      <title>Methods</title>
      <sec>
        <title>Study Design</title>
        <p>The CHEERY study is a multicenter Chinese program that includes consecutive patients with ICH admitted to 31 stroke centers beginning in December 2018. It recruits patients with spontaneous ICH within 7 days of onset and aims to identify any major risk factors affecting the prognosis of ICH [<xref ref-type="bibr" rid="ref18">18</xref>]. To explore the effects of the COVID-19 pandemic on the manifestations and outcomes of patients with ICH, we selected and analyzed data from December 1, 2018, to November 30, 2020, which happens to span 1 year before (December 1, 2018-November 30, 2019) and during (December 1, 2019-November 30, 2020) the epidemic. We followed the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) reporting guidelines.</p>
      </sec>
      <sec>
        <title>Participants</title>
        <p>All patients with ICH enrolled in the study must meet the following criteria: (1) older than 18 years; (2) have acute ICH with ≤7 days duration from onset to admission; and (3) have received either computerized tomography or magnetic resonance imaging of the brain to confirm the diagnosis of ICH. Due to the centralized isolation of patients with COVID-19, this study did not include patients with ICH complicated with COVID-19 infection.</p>
      </sec>
      <sec>
        <title>Data Collection and Analysis</title>
        <p>Demographic information was collected, including age, gender, residence, smoking and alcohol history, and medical history from the medical record. Gender was recorded from the medical record, which was copied from the patient’s resident ID card. The mode of transportation to the hospital and duration from onset to admission were collected shortly after admission from patients or escorts. Systolic blood pressure, diastolic blood pressure, hematoma location, hematoma volume, and laboratory data were copied from the medical record. The baseline National Institute of Health Stroke Scale and Glasgow coma scale were assessed by 2 trained neurologists. After admission, the first head computerized tomography scan was acquired, and 2 imageologists calculated the volume of bleeding with the ABC/2 formula and recorded the location of bleeding and intraventricular hemorrhage (IVH). The SMASH-U (structural vascular lesions, medication, amyloid angiopathy, systemic disease, hypertension, and undetermined) flowchart was adopted to classify the most likely cause for each ICH event [<xref ref-type="bibr" rid="ref23">23</xref>]. Structural vascular lesions were defined as vascular structural abnormalities at the bleeding sites verified by imaging or pathological findings. Medication was defined as warfarin use with an international normalized ratio ≥2, novel oral anticoagulant use within 3 days, full-dose heparin, or thrombolytic agent use. Amyloid angiopathy was defined as lobar, cortical, or subcortical hemorrhage among patients aged ≥55 years, according to the Boston criteria. Systemic disease was conventionally defined by the presence of thrombocytopenia or liver cirrhosis or alternatively by the presence of non–drug-induced coagulopathy or renal failure. Hypertension was defined as deep or infratentorial hemorrhage with pre-existing hypertension history. All other causes and unknown causes were considered undetermined. Pulmonary infection was diagnosed by 2 well-trained and experienced neurologists according to the modified Centers for Disease Control and Prevention criteria in combination with the patient’s clinical symptoms, laboratory results, and radiological examinations within 1 week after ICH. A modified ranking scale score was assessed centrally by face-to-face, telephone, or WeChat interview.</p>
      </sec>
      <sec>
        <title>Definitions</title>
        <p>ICH was defined as hemorrhage into the cerebral parenchyma that may also extend into ventricles, and rarely, subarachnoid spaces. A modified ranking scale score of 0-3 points was defined as a good outcome.</p>
      </sec>
      <sec>
        <title>Statistical Analysis</title>
        <p>Data were presented as the mean (SD), number (%), and median (IQR). The Kolmogorov-Smirnov test for normality was used to assess the data distribution of continuous variables. Continuous variables were tested with a <italic>t</italic> test or Mann-Whitney U test. Categorical variables were tested with the χ<sup>2</sup> test or Fisher exact test. The associations of the outcomes of interest between the 2 groups were analyzed using the modified Poisson regression model, and risk ratios (RRs) were reported along with 95% CIs. Age and gender were used as covariables. SPSS statistical software (version 26.0, SPSS Corp) was used to analyze the data, and the statistical significance was set at <italic>P</italic>&#60;.05.</p>
      </sec>
      <sec>
        <title>Ethical Considerations</title>
        <p>The study protocol and data collection were conducted strictly following the Declaration of Helsinki and approved by the Research Ethics Committee of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (approval no. 2018-S485) and registered in the Chinese Clinical Trial Registry (ChiCTR1900020872) [<xref ref-type="bibr" rid="ref24">24</xref>]. Protocol training was conducted for all researchers prior to the implementation of the project. All participants signed a written informed consent form prior to enrollment. The informed consent should be signed by the participant, but for those participants who could not sign the informed consent form by themselves for any reason, their parents, legal guardians, or protectors signed the informed consent form.</p>
      </sec>
    </sec>
    <sec sec-type="results">
      <title>Results</title>
      <p>From December 1, 2018, to November 30, 2020, there were 3460 patients with acute ICH enrolled in the CHEERY study, and 3260 patients were included in the final analysis (<xref rid="figure1" ref-type="fig">Figure 1</xref>). The demographic data are listed in <xref ref-type="table" rid="table1">Table 1</xref>.</p>
      <fig id="figure1" position="float">
        <label>Figure 1</label>
        <caption>
          <p>Flowchart of patient enrollment in the Chinese Cerebral Hemorrhage: Mechanism and Intervention study. ICH: intracerebral hemorrhage; mRS: modified ranking scale; SMASH-U: structural vascular lesions, medication, amyloid angiopathy, systemic disease, hypertension, and undetermined.</p>
        </caption>
        <graphic xlink:href="publichealth_v9i1e34386_fig1.png" alt-version="no" mimetype="image" position="float" xlink:type="simple"/>
      </fig>
      <table-wrap position="float" id="table1">
        <label>Table 1</label>
        <caption>
          <p>Baseline and admission characteristics of patients admitted to hospitals in the year during COVID-19 pandemic (December 1, 2019-November 30, 2020) and the year before the COVID-19 outbreak (December 1, 2018-Novemver 30, 2019).</p>
        </caption>
        <table width="1000" cellpadding="5" cellspacing="0" border="1" rules="groups" frame="hsides">
          <col width="30"/>
          <col width="200"/>
          <col width="300"/>
          <col width="300"/>
          <col width="170"/>
          <thead>
            <tr valign="top">
              <td colspan="2">Characteristic</td>
              <td colspan="2">Time period</td>
              <td><italic>P</italic> value</td>
            </tr>
            <tr valign="top">
              <td colspan="2"/>
              <td>During the COVID-19 pandemic (n=1510)</td>
              <td>Before the COVID-19 pandemic (n=1750)</td>
              <td/>
            </tr>
          </thead>
          <tbody>
            <tr valign="top">
              <td colspan="2">Age (years), median (IQR)</td>
              <td>63 (54-71)</td>
              <td>61 (52-70)</td>
              <td>&#60;.001</td>
            </tr>
            <tr valign="top">
              <td colspan="4">
                <bold>Age (years), n (%)</bold>
              </td>
              <td>.001</td>
            </tr>
            <tr valign="top">
              <td/>
              <td>&#60;60</td>
              <td>625 (41.4)</td>
              <td>828 (47.3)</td>
              <td/>
            </tr>
            <tr valign="top">
              <td/>
              <td>≥60</td>
              <td>885 (58.6)</td>
              <td>922 (52.7)</td>
              <td/>
            </tr>
            <tr valign="top">
              <td colspan="4">
                <bold>Gender, n (%)</bold>
              </td>
              <td>.009</td>
            </tr>
            <tr valign="top">
              <td/>
              <td>Men</td>
              <td>1045 (69.2)</td>
              <td>1136 (64.9)</td>
              <td/>
            </tr>
            <tr valign="top">
              <td/>
              <td>Women</td>
              <td>465 (30.8)</td>
              <td>614 (35.1)</td>
              <td/>
            </tr>
            <tr valign="top">
              <td colspan="4">
                <bold>Residence, n (%)</bold>
              </td>
              <td/>
            </tr>
            <tr valign="top">
              <td/>
              <td>Rural</td>
              <td>850 (56.3)</td>
              <td>930 (53.1)</td>
              <td>.18</td>
            </tr>
            <tr valign="top">
              <td/>
              <td>Urban</td>
              <td>639 (42.3)</td>
              <td>797 (45.5)</td>
              <td/>
            </tr>
            <tr valign="top">
              <td/>
              <td>Unknown</td>
              <td>21 (1.4)</td>
              <td>23 (1.3)</td>
              <td/>
            </tr>
            <tr valign="top">
              <td colspan="4">
                <bold>Smoking history, n (%)</bold>
              </td>
              <td>.001</td>
            </tr>
            <tr valign="top">
              <td/>
              <td>Yes</td>
              <td>424 (28.1)</td>
              <td>584 (33.4)</td>
              <td/>
            </tr>
            <tr valign="top">
              <td/>
              <td>No</td>
              <td>1081 (71.6)</td>
              <td>1159 (66.2)</td>
              <td/>
            </tr>
            <tr valign="top">
              <td/>
              <td>Unknown</td>
              <td>5 (0)</td>
              <td>7 (0)</td>
              <td/>
            </tr>
            <tr valign="top">
              <td colspan="4">
                <bold>Alcohol history, n (%)</bold>
              </td>
              <td>.02</td>
            </tr>
            <tr valign="top">
              <td/>
              <td>Yes</td>
              <td>346 (22.9)</td>
              <td>464 (26.5)</td>
              <td/>
            </tr>
            <tr valign="top">
              <td/>
              <td>No</td>
              <td>1159 (76.8)</td>
              <td>1279 (73.1)</td>
              <td/>
            </tr>
            <tr valign="top">
              <td/>
              <td>Unknown</td>
              <td>5 (0)</td>
              <td>7 (0)</td>
              <td/>
            </tr>
            <tr valign="top">
              <td colspan="4">
                <bold>Comorbidities, n (%)</bold>
              </td>
              <td/>
            </tr>
            <tr valign="top">
              <td/>
              <td>Ischemic heart disease</td>
              <td>62 (4.1)</td>
              <td>53 (3)</td>
              <td>.25</td>
            </tr>
            <tr valign="top">
              <td/>
              <td>Ischemic stroke</td>
              <td>156 (10.4)</td>
              <td>150 (8.6)</td>
              <td>.04</td>
            </tr>
            <tr valign="top">
              <td/>
              <td>Hypertension</td>
              <td>970 (64.7)</td>
              <td>1141 (66.1)</td>
              <td>.40</td>
            </tr>
            <tr valign="top">
              <td/>
              <td>Diabetes</td>
              <td>137 (9.1)</td>
              <td>143 (8.2)</td>
              <td>.06</td>
            </tr>
            <tr valign="top">
              <td colspan="2">Antihypertension<sup>a</sup></td>
              <td>281/970 (29)</td>
              <td>299/1141 (26.2)</td>
              <td>.16</td>
            </tr>
            <tr valign="top">
              <td colspan="2">Pre-ACEI/ARB<sup>b</sup> usage</td>
              <td>49/281 (17.4)</td>
              <td>45/299 (15.1)</td>
              <td>.44</td>
            </tr>
            <tr valign="top">
              <td colspan="4">
                <bold>Transportation to hospital, n (%)</bold>
              </td>
              <td>&#60;.001</td>
            </tr>
            <tr valign="top">
              <td/>
              <td>Ambulance</td>
              <td>660 (43.7)</td>
              <td>1012 (57.8)</td>
              <td/>
            </tr>
            <tr valign="top">
              <td/>
              <td>Self-admission</td>
              <td>838 (55.5)</td>
              <td>686 (39.2)</td>
              <td/>
            </tr>
            <tr valign="top">
              <td/>
              <td>Unknown</td>
              <td>12 (0.8)</td>
              <td>52 (3)</td>
              <td/>
            </tr>
            <tr valign="top">
              <td colspan="2">Duration from onset to admission (hours), median (IQR)</td>
              <td>4 (2-12)</td>
              <td>6 (2-24)</td>
              <td>&#60;.001</td>
            </tr>
            <tr valign="top">
              <td colspan="2">SBP<sup>c</sup> on admission (mm Hg), median (IQR)</td>
              <td>170 (150-190)</td>
              <td>162 (145-180)</td>
              <td>&#60;.001</td>
            </tr>
            <tr valign="top">
              <td colspan="2">DBP<sup>d</sup> on admission (mm Hg), median (IQR)</td>
              <td>97 (87-108)</td>
              <td>94 (84-106)</td>
              <td>&#60;.001</td>
            </tr>
            <tr valign="top">
              <td colspan="2">Fasting blood glucose (mmol/L), median (IQR)</td>
              <td>6.3 (5.3-7.9)</td>
              <td>6.2 (5.3-7.4)</td>
              <td>.003</td>
            </tr>
            <tr valign="top">
              <td colspan="2">NIHSS<sup>e</sup> on admission, median (IQR)</td>
              <td>9 (4-16)</td>
              <td>8 (3-15)</td>
              <td>.05</td>
            </tr>
            <tr valign="top">
              <td colspan="2">GCS<sup>f</sup> on admission, median (IQR)</td>
              <td>14 (10-15)</td>
              <td>14 (11-15)</td>
              <td>.009</td>
            </tr>
            <tr valign="top">
              <td colspan="4">
                <bold>Location of hematoma, n (%)</bold>
              </td>
              <td>.01</td>
            </tr>
            <tr valign="top">
              <td/>
              <td>Nondeep</td>
              <td>292 (19.3)</td>
              <td>403 (23)</td>
              <td/>
            </tr>
            <tr valign="top">
              <td/>
              <td>Deep</td>
              <td>1218 (80.7)</td>
              <td>1347 (77)</td>
              <td/>
            </tr>
            <tr valign="top">
              <td colspan="2">Hematoma volume (ml), median (IQR)</td>
              <td>12.0 (5.0-28.0)</td>
              <td>10.7 (4.9-22.0)</td>
              <td>&#60;.001</td>
            </tr>
            <tr valign="top">
              <td colspan="2">IVH<sup>g</sup>, n (%)</td>
              <td>249 (16.5)</td>
              <td>238 (13.6)</td>
              <td>.02</td>
            </tr>
            <tr valign="top">
              <td colspan="2">ICU<sup>h</sup> treatment, n (%)</td>
              <td>960 (63.7)</td>
              <td>938 (55.2)</td>
              <td>&#60;.001</td>
            </tr>
            <tr valign="top">
              <td colspan="2">Surgical intervention, n (%)</td>
              <td>271 (17.9)</td>
              <td>358 (20.5)</td>
              <td>.07</td>
            </tr>
            <tr valign="top">
              <td colspan="4">
                <bold>SMASH-U<sup>i</sup> classification, n (%)</bold>
              </td>
              <td/>
            </tr>
            <tr valign="top">
              <td/>
              <td>Structural lesion</td>
              <td>26 (1.9)</td>
              <td>31 (1.8)</td>
              <td>.83</td>
            </tr>
            <tr valign="top">
              <td/>
              <td>Medication</td>
              <td>7 (0.5)</td>
              <td>4 (0.2)</td>
              <td>.19</td>
            </tr>
            <tr valign="top">
              <td/>
              <td>Amyloid angiopathy</td>
              <td>196 (14.7)</td>
              <td>235 (13.9)</td>
              <td>.58</td>
            </tr>
            <tr valign="top">
              <td/>
              <td>Systemic diseases</td>
              <td>16 (1.2)</td>
              <td>18 (1.1)</td>
              <td>.74</td>
            </tr>
            <tr valign="top">
              <td/>
              <td>Hypertension</td>
              <td>725 (54.2)</td>
              <td>919 (54.5)</td>
              <td>.86</td>
            </tr>
            <tr valign="top">
              <td/>
              <td>Undetermined</td>
              <td>361 (27)</td>
              <td>478 (28.4)</td>
              <td>.41</td>
            </tr>
            <tr valign="top">
              <td colspan="2">Hospitalization length (days), median (IQR)</td>
              <td>15 (8-21)</td>
              <td>15 (10-21)</td>
              <td>.02</td>
            </tr>
          </tbody>
        </table>
        <table-wrap-foot>
          <fn id="table1fn1">
            <p><sup>a</sup>Percentage of pre-ICH antihypertension treatments in patients with hypertension.</p>
          </fn>
          <fn id="table1fn2">
            <p><sup>b</sup>ACEI/ARB: angiotensin-converting enzyme inhibitor/angiotensin receptor blocker.</p>
          </fn>
          <fn id="table1fn3">
            <p><sup>c</sup>SBP: systolic blood pressure.</p>
          </fn>
          <fn id="table1fn4">
            <p><sup>d</sup>DBP: diastolic blood pressure.</p>
          </fn>
          <fn id="table1fn5">
            <p><sup>e</sup>NIHSS: National Institute of Health Stroke Scale.</p>
          </fn>
          <fn id="table1fn6">
            <p><sup>f</sup>GCS: Glasgow coma scale.</p>
          </fn>
          <fn id="table1fn7">
            <p><sup>g</sup>IVH: intraventricular hemorrhage.</p>
          </fn>
          <fn id="table1fn8">
            <p><sup>h</sup>ICU: intensive care unit.</p>
          </fn>
          <fn id="table1fn9">
            <p><sup>i</sup>SMASH-U: structural vascular lesions, medication, amyloid angiopathy, systemic disease, hypertension, and undetermined.</p>
          </fn>
        </table-wrap-foot>
      </table-wrap>
      <p>The patients’ median age was 63 (IQR 54-71) years during the COVID-19 pandemic and 61 (IQR 52-70) years before the COVID-19 breakout (<italic>P</italic>&#60;.001). The COVID-19 pandemic was associated with a higher percentage of male patients (<italic>P</italic>=.009) and patients with a history of ischemic stroke (<italic>P</italic>=.04). Fewer patients had a history of smoking (<italic>P</italic>=.001) and drinking (<italic>P</italic>=.02) during the pandemic (<xref ref-type="table" rid="table1">Table 1</xref>).</p>
      <p>During the COVID-19 pandemic, patients were more likely to go to the hospital directly by themselves (during: n=838, 55.5%; before: n=686, 39.2%; <italic>P</italic>&#60;.001) and took less time from onset to admission (during: median 4, IQR 2-12 hours; before: median 6, IQR 2-24 hours; <italic>P</italic>&#60;.001) compared with before the pandemic. The COVID-19 pandemic was also associated with an increase in systolic blood pressure (during: median 170, IQR 150-190 mm HG; before: median 162, IQR 145-180 mm Hg; <italic>P</italic>&#60;.001), diastolic blood pressure (during: median 97, IQR 87-108 mm HG; before: median 94, IQR 84-106 mm Hg; <italic>P</italic>&#60;.001), fasting blood glucose (during: median 6.3, IQR 5.3-7.9 mmol/L; before: median 6.2, IQR 5.3-7.4 mmol/L; <italic>P</italic>=.003), deep ICH (during: n=1218, 80.7%; before: n=1347, 77%; <italic>P</italic>=.01), hematoma volume (during: median 12.0, IQR 5.0-28.0 mL; before: median 10.7, IQR 4.9-22.0 mL; <italic>P</italic>&#60;.001), intraventricular hemorrhage (during: n=249, 16.5%; before: n=238, 13.6%; <italic>P</italic>=.02), and ICU treatment (during: n=960, 63.7%; before: n=938, 55.2%; <italic>P</italic>&#60;.001) (<xref ref-type="table" rid="table1">Table 1</xref>).</p>
      <p>We then comparatively evaluated the outcomes of ICH. <xref ref-type="table" rid="table2">Table 2</xref> shows that the COVID-19 pandemic was associated with an increased risk of pulmonary infection during hospitalization (RR<sub>unadjusted</sub> 1.299, 95% CI 1.091-1.546; RR<sub>adjusted</sub> 1.267, 95% CI 1.065-1.509), lower probability of 3-month good outcome (RR<sub>unadjusted</sub> 0.970, 95% CI 0.950-0.989; RR<sub>adjusted</sub> 0.975, 95% CI 0.956-0.995), and higher in-hospital mortality (RR<sub>unadjusted</sub> 3.233, 95% CI 2.246-4.653; RR<sub>adjusted</sub> 3.103, 95% CI 2.156-4.465), 1-month mortality (RR<sub>unadjusted</sub> 1.070, 95% CI 1.048-1.093; RR<sub>adjusted</sub> 1.064, 95% CI 1.042-1.087), and 3-month mortality (RR<sub>unadjusted</sub> 1.075, 95% CI 1.051-1.099; RR<sub>adjusted</sub> 1.069, 95% CI 1.045-1.093).</p>
      <table-wrap position="float" id="table2">
        <label>Table 2</label>
        <caption>
          <p>Outcomes in patients admitted to hospitals during (December 1, 2019-November 30, 2020) and before (December 1, 2018-Novemver 30, 2019) the COVID-19 pandemic.</p>
        </caption>
        <table width="1000" cellpadding="5" cellspacing="0" border="1" rules="groups" frame="hsides">
          <col width="160"/>
          <col width="200"/>
          <col width="200"/>
          <col width="150"/>
          <col width="70"/>
          <col width="150"/>
          <col width="70"/>
          <thead>
            <tr valign="top">
              <td>Characteristics</td>
              <td colspan="2">Time period</td>
              <td colspan="2">Unadjusted analysis</td>
              <td colspan="2">Adjusted analysis<sup>a</sup></td>
            </tr>
            <tr valign="top">
              <td/>
              <td>During the COVID-19 pandemic (n=1510), n (%)</td>
              <td>Before the COVID-19 pandemic (n=1750), n (%)</td>
              <td>Risk ratio (95% CI)</td>
              <td><italic>P</italic> value</td>
              <td>Risk ratio (95% CI)</td>
              <td><italic>P</italic> value</td>
            </tr>
          </thead>
          <tbody>
            <tr valign="top">
              <td>Pulmonary infection</td>
              <td>232 (15.4)</td>
              <td>207 (11.8)</td>
              <td>1.299 (1.091-1.546)</td>
              <td>.003</td>
              <td>1.267 (1.065-1.509)</td>
              <td>.008</td>
            </tr>
            <tr valign="top">
              <td>1-month mRS<sup>b</sup> 0-3</td>
              <td>797 (52.8)</td>
              <td>977 (55.8)</td>
              <td>0.980 (0.959-1.002)</td>
              <td>.08</td>
              <td>0.985 (0.963-1.007)</td>
              <td>.17</td>
            </tr>
            <tr valign="top">
              <td>3-month mRS 0-3</td>
              <td>923 (61.1)</td>
              <td>1158 (66.2)</td>
              <td>0.970 (0.950-0.989)</td>
              <td>.003</td>
              <td>0.975 (0.956-0.995)</td>
              <td>.01</td>
            </tr>
            <tr valign="top">
              <td>In-hospital mortality</td>
              <td>106 (7)</td>
              <td>38 (2.2)</td>
              <td>3.233 (2.246-4.653)</td>
              <td>&#60;.001</td>
              <td>3.103 (2.156-4.465)</td>
              <td>&#60;.001</td>
            </tr>
            <tr valign="top">
              <td>1-month mortality</td>
              <td>282 (18.7)</td>
              <td>190 (10.9)</td>
              <td>1.070 (1.048-1.093)</td>
              <td>&#60;.001</td>
              <td>1.064 (1.042-1.087)</td>
              <td>&#60;.001</td>
            </tr>
            <tr valign="top">
              <td>3-month mortality</td>
              <td>348 (23)</td>
              <td>253 (14.5)</td>
              <td>1.075 (1.051-1.099)</td>
              <td>&#60;.001</td>
              <td>1.069 (1.045-1.093)</td>
              <td>&#60;.001</td>
            </tr>
          </tbody>
        </table>
        <table-wrap-foot>
          <fn id="table2fn1">
            <p><sup>a</sup>Adjusted for age and gender.</p>
          </fn>
          <fn id="table2fn2">
            <p><sup>b</sup>mRS: modified ranking scale.</p>
          </fn>
        </table-wrap-foot>
      </table-wrap>
    </sec>
    <sec sec-type="discussion">
      <title>Discussion</title>
      <p>Since December 2019, the outbreak of COVID-19 infections has had a major impact on the occurrence, development, and treatment of cerebrovascular diseases [<xref ref-type="bibr" rid="ref7">7</xref>,<xref ref-type="bibr" rid="ref8">8</xref>]. Currently, despite the international open policy, the epidemic still occasionally erupts in small areas. In this study, in order to investigate how the COVID-19 pandemic affected the manifestations and outcomes of patients with ICH, we retrospectively analyzed the data of patients with ICH in 1-year periods before (December 1, 2018-November 30, 2019) and during (December 1, 2019-November 30, 2020) the COVID-19 pandemic from the CHEERY study, a prospective multicenter cohort study. Our study found that during the COVID-19 pandemic, patients with ICH were more likely to be older with a history of ischemic stroke, higher blood pressure and fasting blood glucose, larger hematoma volume, shorter time from onset to admission, more common deep ICH and IVH, more ICU treatment, and preferred to go to hospital directly rather than call an ambulance. In addition, during the COVID-19 pandemic, patients with ICH had a higher risk of pulmonary infection, lower probability of 3-month good outcomes, and higher in-hospital, 1-month, and 3-month mortality.</p>
      <p>ICH is among the diseases with the highest mortality; in low-income countries, its 30-day mortality may be as high as 40% [<xref ref-type="bibr" rid="ref25">25</xref>]. Early identification and timely treatment are crucial to reducing patient mortality [<xref ref-type="bibr" rid="ref26">26</xref>]. Research has found that more than one-third of patients have hematoma enlargement within 24 hours of onset, and approximately 38%-76% experience hematoma enlargement within 3 hours of onset. Every 10% increase in hematoma volume increases the risk of mortality by 5%, and every 1 mL increase in hematoma volume increases the risk of poor prognosis by 7% [<xref ref-type="bibr" rid="ref27">27</xref>,<xref ref-type="bibr" rid="ref28">28</xref>]. On the other hand, brain edema that occurs within hours of a patient’s onset and persists for days to weeks is also closely related to patient outcomes [<xref ref-type="bibr" rid="ref29">29</xref>,<xref ref-type="bibr" rid="ref30">30</xref>], with an average increase of 75% in perihematomal edema within the first 24 hours after ICH, which is also closely related to patient mortality [<xref ref-type="bibr" rid="ref31">31</xref>,<xref ref-type="bibr" rid="ref32">32</xref>]. In addition, persistent high systolic blood pressure in the early stages of ICH can lead to an increased risk of hematoma expansion, neurological deterioration, and even brain hernia formation, leading to patient death [<xref ref-type="bibr" rid="ref33">33</xref>]. Therefore, early blood pressure reduction and intensive care for patients with ICH are crucial to reducing the mortality rate [<xref ref-type="bibr" rid="ref34">34</xref>,<xref ref-type="bibr" rid="ref35">35</xref>]. Unfortunately, such actions may have been difficult to implement during the pandemic.</p>
      <p>During the pandemic, many studies reported a significant increase in the number of deaths from diseases other than COVID-19 [<xref ref-type="bibr" rid="ref36">36</xref>,<xref ref-type="bibr" rid="ref37">37</xref>]. Researchers found that in the early stages of the pandemic (March-July 2020), there was a marked increase in the number of patients dying from cerebrovascular disease [<xref ref-type="bibr" rid="ref38">38</xref>]; this may have resulted from public health interventions [<xref ref-type="bibr" rid="ref39">39</xref>] and limited medical care [<xref ref-type="bibr" rid="ref40">40</xref>,<xref ref-type="bibr" rid="ref41">41</xref>], patients refusing to enter the hospital for fear of infection [<xref ref-type="bibr" rid="ref42">42</xref>,<xref ref-type="bibr" rid="ref43">43</xref>], a decline in hospitals’ emergency care capacity [<xref ref-type="bibr" rid="ref44">44</xref>,<xref ref-type="bibr" rid="ref45">45</xref>], and insufficient poststroke intensive care [<xref ref-type="bibr" rid="ref46">46</xref>] and rehabilitation care [<xref ref-type="bibr" rid="ref10">10</xref>,<xref ref-type="bibr" rid="ref47">47</xref>].</p>
      <p>The global burden of ICH is related to the inadequate management of chronic hypertension and other modifiable risk factors [<xref ref-type="bibr" rid="ref48">48</xref>]. In China, nearly half of adults aged 35-75 years have hypertension, but only 30.1% of patients with hypertension are being treated, and only about 7.2% are under control [<xref ref-type="bibr" rid="ref49">49</xref>]. During the COVID-19 pandemic, the situation was worse, and the higher blood pressure in patients with ICH could be attributed to social pressure, anxiety, the depressed economy, the lack of public health resources, inadequate control of risk factors, and unwillingness to seek medical treatment during this special period [<xref ref-type="bibr" rid="ref40">40</xref>-<xref ref-type="bibr" rid="ref47">47</xref>]. Furthermore, the use of ACEI/ARBs might have theoretically increased the risk of COVID-19 infection, though recent reports did not find any correlation between the discontinuation of ACEI/ARB in patients with COVID-19 and the severity of COVID-19 [<xref ref-type="bibr" rid="ref19">19</xref>,<xref ref-type="bibr" rid="ref20">20</xref>]. For the fear of infection of COVID-19, patients may have discontinued the use of ACEI/ARB. A previous study in China also showed a significant increase in excess deaths due to cardiovascular disease and diabetes in the early lockdown period [<xref ref-type="bibr" rid="ref43">43</xref>], presumably due to poor hypertension control during lockdowns in populations due to COVID-19 infections conflicting with antihypertensive drugs, such as ACEI.</p>
      <p>Our previous studies have found that age, deep ICH, fasting blood glucose, and hematoma volume were predicters of in-hospital neurological deterioration and 3-month poor outcomes in the SIGNALS and ADVISING scores [<xref ref-type="bibr" rid="ref50">50</xref>,<xref ref-type="bibr" rid="ref51">51</xref>]. In this study, we found these factors were also associated with poor outcomes in patients with ICH during the COVID-19 pandemic. Older people had more risk factors, such as hypertension, diabetes mellitus, ischemic stroke, ischemic heart disease, and other diseases, which could induce and aggravate ICH. COVID-19 may have restricted the control of risk factors of ICH, resulting in more older patients with ICH.</p>
      <p>Our study found that more patients with ICH went to hospitals directly without calling an ambulance. We speculate that this phenomenon was related to limited public medical resources and anxiety.</p>
      <p>Most important of all, our study found that patients with ICH demonstrated higher NIHSS scores on admission and larger hemorrhage volumes. They also needed more ICU intervention and had poorer outcomes and mortality at 3 months. This finding indicated that ICH was more severe during the COVID-19 pandemic; this could be related to the patients’ higher blood pressure on admission, which may lead to a larger hematoma and poor prognosis. Our results were consistent with the report that older age and larger ICH volume are poor predictors of mortality [<xref ref-type="bibr" rid="ref52">52</xref>].</p>
      <p>One main limitation of our study was that we only included hospitalized patients. Those who were treated in an outpatient setting and died before reaching the hospital were not included. During the COVID-19 pandemic period corresponding to this study (December 1, 2019-November 30, 2020), more than 86,000 Chinese people were infected with COVID-19, as reported to the WHO. Vaccines, treatments, and minimally intrusive suppression measures are being developed for long-term prevention and control of COVID-19 in China [<xref ref-type="bibr" rid="ref53">53</xref>]. As per government policy, infected patients were transferred to designated hospitals, which were not included in our study centers. Therefore, this study did not contain patients with COVID-19.</p>
      <p>A major strength of this study is the use of the CHEERY study and its consecutive enrollment of patients within a defined study time, including the year before and after the COVID-19 outbreak. This excludes relevant selection bias and ensures that results from this cohort are fairly representative.</p>
      <p>Our study indicated that the cloud of COVID-19 has adversely impacted the presentation and outcomes of ICH. Medical workers should pay more attention to patients with ICH, while the public should pay more attention to hypertension control and ICH prevention.</p>
    </sec>
  </body>
  <back>
    <app-group>
      <supplementary-material id="app1">
        <label>Multimedia Appendix 1</label>
        <p>Data set.</p>
        <media xlink:href="publichealth_v9i1e34386_app1.xlsx" xlink:title="XLSX File  (Microsoft Excel File), 475 KB"/>
      </supplementary-material>
    </app-group>
    <glossary>
      <title>Abbreviations</title>
      <def-list>
        <def-item>
          <term id="abb1">ACEI</term>
          <def>
            <p>angiotensin-converting enzyme inhibitor</p>
          </def>
        </def-item>
        <def-item>
          <term id="abb2">ARB</term>
          <def>
            <p>angiotensin receptor blockers</p>
          </def>
        </def-item>
        <def-item>
          <term id="abb3">CHEERY</term>
          <def>
            <p>Chinese cerebral hemorrhage: mechanism and intervention</p>
          </def>
        </def-item>
        <def-item>
          <term id="abb4">ICH</term>
          <def>
            <p>intracerebral hemorrhage</p>
          </def>
        </def-item>
        <def-item>
          <term id="abb5">IVH</term>
          <def>
            <p>intraventricular hemorrhage</p>
          </def>
        </def-item>
        <def-item>
          <term id="abb6">RR</term>
          <def>
            <p>risk ratio</p>
          </def>
        </def-item>
        <def-item>
          <term id="abb7">SMASH-U</term>
          <def>
            <p>structural vascular lesions, medication, amyloid angiopathy, systemic disease, hypertension, and undetermined</p>
          </def>
        </def-item>
        <def-item>
          <term id="abb8">STROBE</term>
          <def>
            <p>Strengthening the Reporting of Observational Studies in Epidemiology</p>
          </def>
        </def-item>
        <def-item>
          <term id="abb9">WHO</term>
          <def>
            <p>World Health Organization</p>
          </def>
        </def-item>
      </def-list>
    </glossary>
    <ack>
      <p>The authors would like to express their gratitude to all the participants and investigators in the 31 centers of the CHEERY study. This work was supported by the National Natural Science Foundation of China (grants 81901214 to YW, 82071335 to QWH, and 81820108010 to BH) and the National Key Research and Development Program of China (grant 2018YFC1312200 to BH).</p>
    </ack>
    <notes>
      <sec>
        <title>Data Availability</title>
        <p>Some of the data of this study are provided in <xref ref-type="supplementary-material" rid="app1">Multimedia Appendix 1</xref>. Additional data are available upon reasonable request after approval by BH.</p>
      </sec>
    </notes>
    <fn-group>
      <fn fn-type="con">
        <p>BH was responsible for the concept and design of the study. YW and QWH did the literature search and wrote the manuscript. ML, YPX, LZ, ZS, and XLC acquired and interpreted the data. SLC and JC analyzed the data. DW and JC revised the manuscript. JC and YW provided administrative, technical, or statistical support.</p>
      </fn>
      <fn fn-type="conflict">
        <p>None declared.</p>
      </fn>
    </fn-group>
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            </name>
            <name name-style="western">
              <surname>Yu</surname>
              <given-names>H</given-names>
            </name>
            <name name-style="western">
              <surname>Zhang</surname>
              <given-names>R</given-names>
            </name>
            <name name-style="western">
              <surname>Yin</surname>
              <given-names>W</given-names>
            </name>
            <name name-style="western">
              <surname>Ni</surname>
              <given-names>D</given-names>
            </name>
            <name name-style="western">
              <surname>Qin</surname>
              <given-names>Y</given-names>
            </name>
            <name name-style="western">
              <surname>Zhang</surname>
              <given-names>T</given-names>
            </name>
            <name name-style="western">
              <surname>Cui</surname>
              <given-names>J</given-names>
            </name>
            <name name-style="western">
              <surname>Wang</surname>
              <given-names>Q</given-names>
            </name>
            <name name-style="western">
              <surname>Yang</surname>
              <given-names>X</given-names>
            </name>
            <name name-style="western">
              <surname>Zhang</surname>
              <given-names>M</given-names>
            </name>
            <name name-style="western">
              <surname>Chen</surname>
              <given-names>W</given-names>
            </name>
            <name name-style="western">
              <surname>Peng</surname>
              <given-names>Z</given-names>
            </name>
            <name name-style="western">
              <surname>Ren</surname>
              <given-names>X</given-names>
            </name>
            <name name-style="western">
              <surname>An</surname>
              <given-names>Z</given-names>
            </name>
            <name name-style="western">
              <surname>Wu</surname>
              <given-names>D</given-names>
            </name>
            <name name-style="western">
              <surname>Sun</surname>
              <given-names>X</given-names>
            </name>
            <name name-style="western">
              <surname>Li</surname>
              <given-names>Y</given-names>
            </name>
            <name name-style="western">
              <surname>Zhou</surname>
              <given-names>L</given-names>
            </name>
            <name name-style="western">
              <surname>Qi</surname>
              <given-names>X</given-names>
            </name>
            <name name-style="western">
              <surname>Gao</surname>
              <given-names>GF</given-names>
            </name>
            <name name-style="western">
              <surname>Feng</surname>
              <given-names>Z</given-names>
            </name>
            <name name-style="western">
              <surname>Song</surname>
              <given-names>T</given-names>
            </name>
            <name name-style="western">
              <surname>Luo</surname>
              <given-names>H</given-names>
            </name>
            <name name-style="western">
              <surname>Yin</surname>
              <given-names>Z</given-names>
            </name>
            <name name-style="western">
              <surname>Wang</surname>
              <given-names>L</given-names>
            </name>
            <name name-style="western">
              <surname>Ma</surname>
              <given-names>C</given-names>
            </name>
            <name name-style="western">
              <surname>Li</surname>
              <given-names>S</given-names>
            </name>
          </person-group>
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          <year>2020</year>
          <month>07</month>
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  </back>
</article>
