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<article article-type="letter" dtd-version="2.0" xmlns:xlink="http://www.w3.org/1999/xlink">
  <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">v10i1e55418</article-id>
      <article-id pub-id-type="pmid">38865169</article-id>
      <article-id pub-id-type="doi">10.2196/55418</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Letter</subject>
        </subj-group>
        <subj-group subj-group-type="article-type">
          <subject>Research Letter</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Geographic Disparities in Access to Assisted Reproductive Technology Centers in China: Spatial-Statistical Study</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="editor">
          <name>
            <surname>Mavragani</surname>
            <given-names>Amaryllis</given-names>
          </name>
        </contrib>
        <contrib contrib-type="editor">
          <name>
            <surname>Sanchez</surname>
            <given-names>Travis</given-names>
          </name>
        </contrib>
      </contrib-group>
      <contrib-group>
        <contrib contrib-type="reviewer">
          <name>
            <surname>Song</surname>
            <given-names>Chao</given-names>
          </name>
        </contrib>
        <contrib contrib-type="reviewer">
          <name>
            <surname>Soerensen</surname>
            <given-names>Simon John Christoph</given-names>
          </name>
        </contrib>
      </contrib-group>
      <contrib-group>
        <contrib id="contrib1" contrib-type="author">
          <name name-style="western">
            <surname>Zhou</surname>
            <given-names>Qingqing</given-names>
          </name>
          <degrees>MSc</degrees>
          <xref rid="aff1" ref-type="aff">1</xref>
          <ext-link ext-link-type="orcid">https://orcid.org/0009-0007-5248-3111</ext-link>
        </contrib>
        <contrib id="contrib2" contrib-type="author">
          <name name-style="western">
            <surname>Zeng</surname>
            <given-names>Huatang</given-names>
          </name>
          <degrees>MSc</degrees>
          <xref rid="aff2" ref-type="aff">2</xref>
          <ext-link ext-link-type="orcid">https://orcid.org/0009-0008-2771-7382</ext-link>
        </contrib>
        <contrib id="contrib3" contrib-type="author">
          <name name-style="western">
            <surname>Wu</surname>
            <given-names>Liqun</given-names>
          </name>
          <degrees>MSc</degrees>
          <xref rid="aff2" ref-type="aff">2</xref>
          <ext-link ext-link-type="orcid">https://orcid.org/0009-0007-4320-8628</ext-link>
        </contrib>
        <contrib id="contrib4" contrib-type="author">
          <name name-style="western">
            <surname>Diao</surname>
            <given-names>Kaichuan</given-names>
          </name>
          <degrees>MSc</degrees>
          <xref rid="aff2" ref-type="aff">2</xref>
          <ext-link ext-link-type="orcid">https://orcid.org/0000-0002-7808-1571</ext-link>
        </contrib>
        <contrib id="contrib5" contrib-type="author">
          <name name-style="western">
            <surname>He</surname>
            <given-names>Rongxin</given-names>
          </name>
          <degrees>PhD</degrees>
          <xref rid="aff3" ref-type="aff">3</xref>
          <ext-link ext-link-type="orcid">https://orcid.org/0000-0002-9036-3416</ext-link>
        </contrib>
        <contrib id="contrib6" contrib-type="author" corresp="yes">
          <name name-style="western">
            <surname>Zhu</surname>
            <given-names>Bin</given-names>
          </name>
          <degrees>PhD</degrees>
          <xref rid="aff1" ref-type="aff">1</xref>
          <address>
            <institution>School of Public Health and Emergency Management</institution>
            <institution>Southern University of Science and Technology</institution>
            <addr-line>No 1088 Xueyuan Avenue, Nanshan District</addr-line>
            <addr-line>Shenzhen, 518055</addr-line>
            <country>China</country>
            <phone>86 0755 8801 5610</phone>
            <email>zhub6@sustech.edu.cn</email>
          </address>
          <ext-link ext-link-type="orcid">https://orcid.org/0000-0002-0091-6356</ext-link>
        </contrib>
      </contrib-group>
      <aff id="aff1">
        <label>1</label>
        <institution>School of Public Health and Emergency Management</institution>
        <institution>Southern University of Science and Technology</institution>
        <addr-line>Shenzhen</addr-line>
        <country>China</country>
      </aff>
      <aff id="aff2">
        <label>2</label>
        <institution>Shenzhen Health Development Research and Data Management Center</institution>
        <addr-line>Shenzhen</addr-line>
        <country>China</country>
      </aff>
      <aff id="aff3">
        <label>3</label>
        <institution>Vanke School of Public Health</institution>
        <institution>Tsinghua University</institution>
        <addr-line>Beijing</addr-line>
        <country>China</country>
      </aff>
      <author-notes>
        <corresp>Corresponding Author: Bin Zhu <email>zhub6@sustech.edu.cn</email></corresp>
      </author-notes>
      <pub-date pub-type="collection">
        <year>2024</year>
      </pub-date>
      <pub-date pub-type="epub">
        <day>12</day>
        <month>6</month>
        <year>2024</year>
      </pub-date>
      <volume>10</volume>
      <elocation-id>e55418</elocation-id>
      <history>
        <date date-type="received">
          <day>12</day>
          <month>12</month>
          <year>2023</year>
        </date>
        <date date-type="rev-request">
          <day>16</day>
          <month>1</month>
          <year>2024</year>
        </date>
        <date date-type="rev-recd">
          <day>4</day>
          <month>2</month>
          <year>2024</year>
        </date>
        <date date-type="accepted">
          <day>8</day>
          <month>5</month>
          <year>2024</year>
        </date>
      </history>
      <copyright-statement>©Qingqing Zhou, Huatang Zeng, Liqun Wu, Kaichuan Diao, Rongxin He, Bin Zhu. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 12.06.2024.</copyright-statement>
      <copyright-year>2024</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/2024/1/e55418" xlink:type="simple"/>
      <abstract>
        <p>A study on infertility in China found that while 543 health care institutions are approved for assisted reproductive technology (ART), only 10.1% offer all ART services, with a significant skew toward the eastern regions, highlighting the accessibility challenges faced by rural and remote populations; this study recommends government measures including travel subsidies and education initiatives to improve ART access for economically disadvantaged individuals.</p>
      </abstract>
      <kwd-group>
        <kwd>assisted reproductive technology</kwd>
        <kwd>spatial accessibility</kwd>
        <kwd>travel time</kwd>
        <kwd>travel cost</kwd>
        <kwd>China</kwd>
      </kwd-group>
    </article-meta>
  </front>
  <body>
    <sec sec-type="introduction">
      <title>Introduction</title>
      <p>Infertility is a growing, serious public health concern [<xref ref-type="bibr" rid="ref1">1</xref>]. In 2023, the World Health Organization reported that infertility affects about 17.5% of the adult population globally [<xref ref-type="bibr" rid="ref2">2</xref>]. Infertility is also prominent in China, affecting nearly 50 million people [<xref ref-type="bibr" rid="ref3">3</xref>]. However, the travel time and costs are often a significant burden faced by patients with infertility [<xref ref-type="bibr" rid="ref4">4</xref>,<xref ref-type="bibr" rid="ref5">5</xref>]. Here we used a web path planning engine [<xref ref-type="bibr" rid="ref6">6</xref>] to explore the spatial-economic disparities in access to assistive reproductive technology (ART) centers in mainland China.</p>
    </sec>
    <sec sec-type="methods">
      <title>Methods</title>
      <sec>
        <title>Study Design</title>
        <p>The National Health Commission of the People’s Republic of China supplied a list of ART centers, while population data were sourced from the 2020 Worldpop data set [<xref ref-type="bibr" rid="ref7">7</xref>]. Residential point locations were gathered from the Gaode Maps open platform. After retaining one residential point within a 1-km radius, 57,469 residential points were acquired. We opted for a contemporary approach by leveraging real-time traffic data from web map navigation services—a departure from traditional methods. Using the Path Planning 2.0 algorithm, we determined optimal paths among residential points and the nearest ART center, predicting detailed travel times and corresponding costs [<xref ref-type="bibr" rid="ref8">8</xref>,<xref ref-type="bibr" rid="ref9">9</xref>]. We averaged the travel times and costs of residential points within the county, thus representing its overall level. Subsequently, isochronous maps depicting 1-hour and 2-hour travel times for ART services were generated.</p>
      </sec>
      <sec>
        <title>Ethical Considerations</title>
        <p>This study used deidentified publicly available data sets, all aggregated at the county level. Per Article 3 of the Southern University of Science and Technology’s institutional review board guidelines, we did not require peer review as this is a secondary analysis using publicly accessible locations of ART centers from the National Health Commission’s website and publicly accessible locations of residential points. These data sets contain no personally identifiable information and pose no risk of ethical violation.</p>
      </sec>
    </sec>
    <sec sec-type="results">
      <title>Results</title>
      <p>As of June 2022, in total, 543 health care institutions in Mainland China have received approved to conduct ART. However, only 55 (10.1%) institutions offer all 5 types of ART services. The distribution of ART centers in China predominantly favors the eastern plains and coastal regions (<xref rid="figure1" ref-type="fig">Figure 1</xref>A). Among 7 geographical subregions of China, East China boasts the highest number of ART centers (n=162, 29.8%). Specifically, Guangdong province leads with the greatest number of ART centers (n=56, 10.3%), with 83.9% (n=104) of the province’s counties accessible to ART centers within 1.5 hours and an average taxi cost of merely 66.7 CNY (US $9.21) [<xref ref-type="bibr" rid="ref10">10</xref>]. In contrast, the Northwest region has the lowest number of ART centers (n=27, 5.0%). Tibet, in particular, has only 1 ART center, and a mere 12.2% (n=9) of Tibetan counties have access to it within 1.5 hours, with taxi costs soaring as high as 1485.2 CNY (US $205.08).</p>
      <fig id="figure1" position="float">
        <label>Figure 1</label>
        <caption>
          <p>Travel time to assisted reproductive technology (ART) centers in China. (A) A map of travel times to ART centers at the county level. (B) The population density of China's counties covering 1-hour and 2-hour travel time isochrones. Counties with higher population densities are shaded in dark gray. Areas shaded in red are located within the 1-hour and 2-hour travel time isochrones of the ART centers.</p>
        </caption>
        <graphic xlink:href="publichealth_v10i1e55418_fig1.png" alt-version="no" mimetype="image" position="float" xlink:type="simple"/>
      </fig>
      <p>Travel time and costs to ART centers by province are presented in <xref ref-type="table" rid="table1">Table 1</xref>. Detailed information on travel time and costs is visualized in <xref ref-type="supplementary-material" rid="app1">Multimedia Appendix 1</xref>. In China, a substantial portion of the population faces challenges in accessing ART facilities within a short time frame. Specifically, 76.8% (n=180,330,033) of the total population and 63.5% (n=36,390) of residential points are not reachable to an ART center within an hour.</p>
      <table-wrap position="float" id="table1">
        <label>Table 1</label>
        <caption>
          <p>Travel time and travel costs to assistive reproductive technology (ART) centers by province and proportion of population access to ART services at different time thresholds.</p>
        </caption>
        <table width="1000" cellpadding="5" cellspacing="0" border="1" rules="groups" frame="hsides">
          <col width="30"/>
          <col width="180"/>
          <col width="180"/>
          <col width="170"/>
          <col width="110"/>
          <col width="110"/>
          <col width="110"/>
          <col width="110"/>
          <thead>
            <tr valign="top">
              <td colspan="2">Province</td>
              <td>ART centers, n</td>
              <td>Travel time (minutes)</td>
              <td>Cost (CNY<sup>a</sup>)</td>
              <td colspan="3">Population (%)</td>
            </tr>
            <tr valign="top">
              <td colspan="2">
                <break/>
              </td>
              <td>
                <break/>
              </td>
              <td>
                <break/>
              </td>
              <td>
                <break/>
              </td>
              <td>≤30 minutes</td>
              <td>≤60 minutes</td>
              <td>≤120 minutes</td>
            </tr>
          </thead>
          <tbody>
            <tr valign="top">
              <td colspan="8">
                <bold>North</bold>
              </td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Beijing</td>
              <td>18</td>
              <td>20.8</td>
              <td>52.2</td>
              <td>80.7</td>
              <td>97.7</td>
              <td>100</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Tianjin</td>
              <td>12</td>
              <td>22.5</td>
              <td>38.5</td>
              <td>78.7</td>
              <td>93.9</td>
              <td>99.9</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Hebei</td>
              <td>31</td>
              <td>43.0</td>
              <td>93.1</td>
              <td>44.3</td>
              <td>75.5</td>
              <td>94.1</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Shanxi</td>
              <td>12</td>
              <td>45.0</td>
              <td>112.1</td>
              <td>49.2</td>
              <td>65.1</td>
              <td>96.7</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Inner Mongolia</td>
              <td>8</td>
              <td>89.3</td>
              <td>237.3</td>
              <td>44.0</td>
              <td>55.9</td>
              <td>70.5</td>
            </tr>
            <tr valign="top">
              <td colspan="8">
                <bold>Northeast</bold>
              </td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Heilongjiang</td>
              <td>11</td>
              <td>63.0</td>
              <td>151.3</td>
              <td>52.9</td>
              <td>64.0</td>
              <td>79.3</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Jilin</td>
              <td>9</td>
              <td>72.4</td>
              <td>153.3</td>
              <td>40.0</td>
              <td>52.3</td>
              <td>77.3</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Liaoning</td>
              <td>19</td>
              <td>47.5</td>
              <td>90.3</td>
              <td>44.1</td>
              <td>69.4</td>
              <td>94.9</td>
            </tr>
            <tr valign="top">
              <td colspan="8">
                <bold>East</bold>
              </td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Shanghai</td>
              <td>20</td>
              <td>17.0</td>
              <td>49.9</td>
              <td>86.9</td>
              <td>99.5</td>
              <td>100</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Jiangsu</td>
              <td>33</td>
              <td>38.0</td>
              <td>71.6</td>
              <td>42.9</td>
              <td>83.8</td>
              <td>100</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Zhejiang</td>
              <td>27</td>
              <td>40.0</td>
              <td>90.1</td>
              <td>35.5</td>
              <td>81.6</td>
              <td>99.8</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Anhui</td>
              <td>16</td>
              <td>44.8</td>
              <td>101.8</td>
              <td>46.2</td>
              <td>63.2</td>
              <td>97.8</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Jiangxi</td>
              <td>18</td>
              <td>50.4</td>
              <td>126.9</td>
              <td>41.7</td>
              <td>59.0</td>
              <td>96.1</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Shandong</td>
              <td>32</td>
              <td>38.1</td>
              <td>90.0</td>
              <td>44.1</td>
              <td>79.5</td>
              <td>100</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Fujian</td>
              <td>16</td>
              <td>36.2</td>
              <td>108.2</td>
              <td>52.2</td>
              <td>83.7</td>
              <td>97.6</td>
            </tr>
            <tr valign="top">
              <td colspan="8">
                <bold>Central</bold>
              </td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Henan</td>
              <td>33</td>
              <td>38.8</td>
              <td>66.6</td>
              <td>45.5</td>
              <td>97.3</td>
              <td>99.5</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Hunan</td>
              <td>24</td>
              <td>52.0</td>
              <td>121.0</td>
              <td>39.2</td>
              <td>59.8</td>
              <td>95.6</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Hubei</td>
              <td>32</td>
              <td>39.5</td>
              <td>68.2</td>
              <td>49.5</td>
              <td>72.1</td>
              <td>98.6</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Southern</td>
              <td>
                <break/>
              </td>
              <td>
                <break/>
              </td>
              <td>
                <break/>
              </td>
              <td>
                <break/>
              </td>
              <td>
                <break/>
              </td>
              <td>
                <break/>
              </td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Guangdong</td>
              <td>56</td>
              <td>27.7</td>
              <td>66.7</td>
              <td>67.6</td>
              <td>91.2</td>
              <td>99.5</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Guangxi</td>
              <td>21</td>
              <td>42.3</td>
              <td>102.0</td>
              <td>53.0</td>
              <td>65.1</td>
              <td>98.0</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Hainan</td>
              <td>10</td>
              <td>58.3</td>
              <td>246.1</td>
              <td>50.9</td>
              <td>60.0</td>
              <td>75.1</td>
            </tr>
            <tr valign="top">
              <td colspan="8">
                <bold>Southwest</bold>
              </td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Chongqing</td>
              <td>12</td>
              <td>44.9</td>
              <td>153.0</td>
              <td>44.9</td>
              <td>68.4</td>
              <td>95.6</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Guizhou</td>
              <td>13</td>
              <td>46.2</td>
              <td>99.9</td>
              <td>49.1</td>
              <td>66.1</td>
              <td>94.4</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Sichuan</td>
              <td>14</td>
              <td>50.5</td>
              <td>140.2</td>
              <td>44.6</td>
              <td>70.5</td>
              <td>91.7</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Yunnan</td>
              <td>18</td>
              <td>69.8</td>
              <td>199.2</td>
              <td>44.4</td>
              <td>59.1</td>
              <td>77.3</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Tibet</td>
              <td>1</td>
              <td>267.9</td>
              <td>1485.2</td>
              <td>47.3</td>
              <td>53.0</td>
              <td>58.2</td>
            </tr>
            <tr valign="top">
              <td colspan="8">
                <bold>Northwest</bold>
              </td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Ningxia</td>
              <td>2</td>
              <td>63.2</td>
              <td>119.3</td>
              <td>41.9</td>
              <td>61.1</td>
              <td>81.6</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Qinghai</td>
              <td>2</td>
              <td>82.8</td>
              <td>190.8</td>
              <td>60.2</td>
              <td>77.7</td>
              <td>85.9</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Gansu</td>
              <td>4</td>
              <td>131.9</td>
              <td>157.1</td>
              <td>29.5</td>
              <td>32.6</td>
              <td>49.3</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Shaanxi</td>
              <td>10</td>
              <td>64.6</td>
              <td>179.2</td>
              <td>38.5</td>
              <td>58.4</td>
              <td>79.3</td>
            </tr>
            <tr valign="top">
              <td>
                <break/>
              </td>
              <td>Xinjiang</td>
              <td>9</td>
              <td>108.4</td>
              <td>275.5</td>
              <td>56.3</td>
              <td>61.5</td>
              <td>67.1</td>
            </tr>
          </tbody>
        </table>
        <table-wrap-foot>
          <fn id="table1fn1">
            <p><sup>a</sup>1CNY=US $0.14. Travel cost was determined by the local taxi fare, which varies depending on the location. The first price covers a specified distance, often 2.5-3 km, and every additional kilometer is then calculated at a certain price.</p>
          </fn>
        </table-wrap-foot>
      </table-wrap>
    </sec>
    <sec sec-type="discussion">
      <title>Discussion</title>
      <p>The distribution of ART centers in China exhibits significant disparities. A higher concentration of ART centers is observed in urban and eastern regions, while individuals in northwestern and rural areas encounter prolonged travel times and elevated transportation costs when seeking ART treatment. The map serves the dual purpose of estimating the likelihood of individuals seeking ART treatment when needed and providing an evidence-based foundation for efficient allocation of limited ART resources to underserved populations (present and future). The government is suggested to implement a series of measures, including counterpart aid and effective initiatives to educate and recruit ART doctors in disadvantaged units. Residents in rural and remote areas contend with extended travel times and substantial travel costs when accessing ART services, which should be covered fully or partially through travel subsidies or paid leaves. The introduction of telehealth services is a viable solution to surmount these barriers for patients residing in remote areas, effectively reducing in-person office visits. Notably, ensuring privacy is paramount when helping patients seeking ART treatments. This study bears limitations. While we used the best available data, the residential point data sets remain susceptible to omission errors, which confines the algorithm’s usage to only 1 mode of transportation—a limitation not potentially aligning with real-world scenarios. Additionally, individuals may not necessarily receive ART treatment at the nearest facility or may opt for alternative transportation means.</p>
    </sec>
  </body>
  <back>
    <app-group>
      <supplementary-material id="app1">
        <label>Multimedia Appendix 1</label>
        <p>Travel time and cost analyses.</p>
        <media xlink:href="publichealth_v10i1e55418_app1.docx" xlink:title="DOCX File , 12259 KB"/>
      </supplementary-material>
    </app-group>
    <glossary>
      <title>Abbreviations</title>
      <def-list>
        <def-item>
          <term id="abb1">ART</term>
          <def>
            <p>assisted reproductive technology</p>
          </def>
        </def-item>
      </def-list>
    </glossary>
    <ack>
      <p>This study is funded by the Guangdong Philosophy and Social Sciences Planning Project (GD22YGL10), Shenzhen Philosophy and Social Sciences Planning Project (SZ2023C007), and Shenzhen Science and Technology Program (JCYJ20220530113208019). The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.</p>
    </ack>
    <fn-group>
      <fn fn-type="con">
        <p>BZ had full access to all of the study data and takes responsibility for the integrity of the data and the accuracy of the data analysis. BZ conceptualized and designed the study; acquired the data; obtained the funding; provided administrative, technical, or material support; and supervised the study. QZ carried out the statistical analysis and drafted the manuscript. QZ and BZ interpreted the data. HZ, LW, KD, RH, and BZ critically revised the manuscript for important intellectual content.</p>
      </fn>
      <fn fn-type="conflict">
        <p>None declared.</p>
      </fn>
    </fn-group>
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