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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Oncol.</journal-id>
<journal-title>Frontiers in Oncology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Oncol.</abbrev-journal-title>
<issn pub-type="epub">2234-943X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fonc.2022.850389</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Oncology</subject>
<subj-group>
<subject>Systematic Review</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Prevalence of <italic>Helicobacter pylori</italic> in Non-Cardia Gastric Cancer in China: A Systematic Review and Meta-Analysis</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Lu</surname><given-names>Yong</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="author-notes" rid="fn003"><sup>&#x2020;</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/1626799"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Xiao</surname><given-names>Fei</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="author-notes" rid="fn003"><sup>&#x2020;</sup></xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Wang</surname><given-names>Yuan</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="author-notes" rid="fn003"><sup>&#x2020;</sup></xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Wang</surname><given-names>Zhenyu</given-names>
</name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Liu</surname><given-names>Di</given-names>
</name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Hong</surname><given-names>Feng</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="author-notes" rid="fn001"><sup>*</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/1199202"/>
</contrib>
</contrib-group>
<aff id="aff1"><sup>1</sup><institution>School of Public Health, the Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University</institution>, <addr-line>Guiyang</addr-line>, <country>China</country></aff>
<aff id="aff2"><sup>2</sup><institution>State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University</institution>, <addr-line>Guangzhou</addr-line>, <country>China</country></aff>
<aff id="aff3"><sup>3</sup><institution>School of Public Health, Guangzhou Medical University</institution>, <addr-line>Guangzhou</addr-line>, <country>China</country></aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Maria Paula Curado, A.C. Camargo Cancer Center, Brazil</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Nosheen Masood, Fatima Jinnah Women University, Pakistan; Samantha Morais, McGill University, Canada</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Feng Hong, <email xlink:href="mailto:fhong@gmc.edu.cn">fhong@gmc.edu.cn</email>
</p>
</fn>
<fn fn-type="equal" id="fn003">
<p>&#x2020;These authors have contributed equally to this work and share first authorship</p>
</fn>
<fn fn-type="other" id="fn002">
<p>This article was submitted to Gastrointestinal Cancers: Gastric  Esophageal Cancers, a section of the journal Frontiers in Oncology</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>03</day>
<month>05</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="collection">
<year>2022</year>
</pub-date>
<volume>12</volume>
<elocation-id>850389</elocation-id>
<history>
<date date-type="received">
<day>07</day>
<month>01</month>
<year>2022</year>
</date>
<date date-type="accepted">
<day>30</day>
<month>03</month>
<year>2022</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2022 Lu, Xiao, Wang, Wang, Liu and Hong</copyright-statement>
<copyright-year>2022</copyright-year>
<copyright-holder>Lu, Xiao, Wang, Wang, Liu and Hong</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<p>Non-cardia gastric cancer was significantly associated with <italic>Helicobacter pylori</italic> (<italic>H. pylori</italic>) infection. Reducing <italic>H. pylori</italic> prevalence was an important prevention strategy for non-cardia gastric cancer. However, national-level data on the <italic>H. pylori</italic> prevalence in non-cardia gastric cancer were limited in China. Therefore, we conducted this study to estimate the pooled prevalence of <italic>H. pylori</italic> in non-cardia gastric cancer in China. We searched PubMed, Embase, the Cochrane Library, China National Knowledge Infrastructure (CNKI), Wan Fang, and VIP Database for Chinese Technical Periodicals for studies reporting <italic>H. pylori</italic> prevalence in non-cardia gastric cancer in China which were published before September 1, 2021. Pooled prevalence was calculated using a random-effect model. Subgroup analysis and meta-regression were used to explore the potential sources of heterogeneity. Egger&#x2019;s test and funnel plot were used to assess publication bias. A total number of 55 studies with 5324 cases of non-cardia&#xa0;gastric&#xa0;cancer were included in this study. The pooled prevalence of <italic>H. pylori</italic> in non-cardia&#xa0;gastric&#xa0;cancer in China was 66.5% (95%CI: 62%-71%, <italic>I</italic><sup>2</sup>=93.8%, <italic>P</italic>&lt;0.0001). In subgroup analysis, a significant difference in the prevalence of <italic>H. pylori</italic> in non-cardia&#xa0;gastric&#xa0;cancer was noted when stratified by geographic region of China (<italic>P</italic>=0.0112). The highest <italic>H. pylori</italic> prevalence (78.9%, 95%CI: 69.9%-87.8%) was noted in Northwest China and the lowest (53.1%, 95%CI: 38.9%-67.3%) was in North China. In meta-regression, a significant association between <italic>H. pylori</italic> prevalence and geographic region was found, while type of sample, <italic>H. pylori</italic> testing method, diagnosis period, detection timing, type of study design, quality grade, publication year, and sample size were not associated with the prevalence of <italic>H. pylori</italic> in non-cardia&#xa0;gastric&#xa0;cancer (<italic>P</italic>&gt;0.05). A large proportion of non-cardia&#xa0;gastric&#xa0;cancers were associated with <italic>H. pylori</italic> infection in China, emphasizing the possible benefits of <italic>H. pylori</italic> eradication for the prevention and control of non-cardia gastric cancer.</p>
</abstract>
<kwd-group>
<kwd>prevalence</kwd>
<kwd><italic>Helicobacter pylori</italic>
</kwd>
<kwd>non-cardia gastric cancer</kwd>
<kwd>meta-analysis</kwd>
<kwd>China</kwd>
</kwd-group>
<counts>
<fig-count count="3"/>
<table-count count="2"/>
<equation-count count="0"/>
<ref-count count="61"/>
<page-count count="10"/>
<word-count count="6106"/>
</counts>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<title>Introduction</title>
<p>Gastric cancer is one of the most common malignant tumors. In 2020, it was estimated that there were 1.09 million new gastric cancer cases and 0.77 million deaths from gastric cancer all over the world. Among all the new gastric cancer cases, more than 40% occurred in China. Meanwhile, gastric cancer caused about 12.4% of all cancer-related deaths, making it the third leading cause of cancer-related deaths in China (<xref ref-type="bibr" rid="B1">1</xref>).</p>
<p>Gastric cancer can be classified into two categories according to anatomical subsites: cardia gastric cancer and non-cardia gastric cancer (<xref ref-type="bibr" rid="B2">2</xref>&#x2013;<xref ref-type="bibr" rid="B4">4</xref>). Due to different epidemiological characteristics and distinct pathogeneses, cardia and non-cardia gastric cancer are treated as two different diseases. Non-cardia gastric cancer is more common than cardia gastric cancer. In 2018, non-cardia gastric cancer accounted for up to 82% (0.85/1.03 million) of all gastric cancer cases around the world (<xref ref-type="bibr" rid="B2">2</xref>, <xref ref-type="bibr" rid="B5">5</xref>). Regarding pathogeneses, <italic>Helicobacter pylori</italic> (<italic>H. pylori</italic>) has been proven to be one of the most important risk factors for non-cardia gastric cancer, with approximately 90% of non-cardia gastric cancer cases attributable to <italic>H. pylori</italic> infection worldwide in 2018 (<xref ref-type="bibr" rid="B5">5</xref>, <xref ref-type="bibr" rid="B6">6</xref>), whereas no association was found between cardia gastric cancer and <italic>H. pylori</italic> infection (<xref ref-type="bibr" rid="B7">7</xref>, <xref ref-type="bibr" rid="B8">8</xref>).</p>
<p>Given the strong association between non-cardia gastric cancer and <italic>H. pylori</italic> infection, reducing <italic>H. pylori</italic> prevalence has been listed as an important primary prevention strategy for gastric cancer prevention (<xref ref-type="bibr" rid="B9">9</xref>&#x2013;<xref ref-type="bibr" rid="B11">11</xref>). Reliable estimation of <italic>H. pylori</italic> prevalence in non-cardia gastric cancer may be essential to the control and prevention of non-cardia gastric cancer, policy-making, and health resource allocation. To date, lots of studies conducted in China have reported the prevalence of <italic>H. pylori</italic> in non-cardia gastric cancer. However, the prevalence varied greatly across studies (<xref ref-type="bibr" rid="B12">12</xref>, <xref ref-type="bibr" rid="B13">13</xref>). To the best of our knowledge, there is no study pooling the prevalence of <italic>H. pylori</italic> in non-cardia gastric cancer at the national level. Therefore, the primary objective of this meta-analysis was to estimate the pooled prevalence of <italic>H. pylori</italic> in non-cardia gastric cancer in China. Additionally, we also explored potential causes of heterogeneity in the reported prevalence.</p>
</sec>
<sec id="s2" sec-type="materials|methods">
<title>Materials and Methods</title>
<sec id="s2_1">
<title>Data Sources and Searches Strategy</title>
<p>We conducted a systematic literature review and meta-analysis according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to identify Chinese and English language studies published before September 1, 2021, which examined the prevalence of <italic>H. pylori</italic> in non-cardia gastric cancer in China. Two investigators (XF and WY) independently searched the literature in the following English databases: PubMed, Embase, the Cochrane Library, and the following Chinese databases: China National Knowledge Infrastructure (CNKI), Wan Fang, and VIP Database for Chinese Technical Periodicals. The search terms included (&#x201c;cardia&#x201d;), and (&#x201c;gastric&#x201d; or &#x201c;stomach&#x201d;), and (&#x201c;cancer&#x201d; or &#x201c;neoplasms&#x201d;), and (&#x201c;Helicobacter&#x201d; or &#x201c;pylori&#x201d;), and (&#x201c;China&#x201d; or &#x201c;Chinese&#x201d;). Authors (XF and WY) independently reviewed the studies to identify eligible studies. The three authors (LY, XF, and WY) discussed inconsistencies to reach consensus. We also reviewed the reference lists of included articles to identify additional eligible studies.</p>
</sec>
<sec id="s2_2">
<title>Eligibility&#xa0;Criteria</title>
<p>We set the inclusion criteria as follows (<xref ref-type="bibr" rid="B1">1</xref>): studies reporting the prevalence of <italic>H. pylori</italic> from at least 10 cases of non-cardia gastric cancer, regardless of study design (<xref ref-type="bibr" rid="B2">2</xref>). Study site(s) located in China and Chinese participants were required (<xref ref-type="bibr" rid="B3">3</xref>). Studies were original studies published in English or Chinese language in any journal (<xref ref-type="bibr" rid="B4">4</xref>). <italic>H. pylori</italic> testing methods were clearly mentioned. We excluded studies which involved populations with special characteristics (e.g., recurrent cases) and studies with purposively selected cases (e.g., only advanced stage cases or metastatic cases). If several articles were based on the same research population, the one with the largest sample size was kept.</p>
</sec>
<sec id="s2_3">
<title>Data Extraction and Quality Assessment</title>
<p>Two authors (XF and WY) extracted the data independently, and the inconsistencies between the two authors were managed through discussion with third author (LY). We made a standardized data extraction sheet in Microsoft Excel to extract the following variables: title, first author, journal of publication, publication year, geographic location of study, study period, sex distribution, age of diagnosis (mean/median/range), type of sample, <italic>H. pylori</italic> testing method, detection timing, type of study design, sample size, the number of <italic>H. pylori</italic>-positive cases, and prevalence of <italic>H. pylori</italic> in non-cardia gastric cancer. When the required information could not be extracted directly from the article, we contacted the authors for relevant information at least two times.</p>
<p>The 11-item Cross-Sectional/Prevalence Study Quality Assessment Forms recommended by the Agency for Healthcare Research and Quality (AHRQ) were used to assess the methodological quality of included cross-sectional studies (<xref ref-type="bibr" rid="B14">14</xref>) and the Newcastle-Ottawa Scale (NOS) was used to assess the methodological quality of included case-control and cohort studies (<xref ref-type="bibr" rid="B15">15</xref>). The studies would then be classified as low quality, moderate quality, and high quality, if they had scores of 0-3, 4-7, and 8-11 for cross-sectional studies, and 0-3, 4-6, and 7-9 for case-control and cohort studies.</p>
</sec>
<sec id="s2_4">
<title>Data Analysis</title>
<p>Cochran&#x2019;s <italic>Q</italic> test and <italic>I</italic><sup>2</sup> index were used to identify the heterogeneity across study. As the results showed significant heterogeneity, random-effect model was hence used to calculate the pooled prevalence of <italic>H. pylori</italic> in non-cardia&#xa0;gastric&#xa0;cancer and 95% confidence intervals (CI), weighted by DerSimonian-Laird model (<xref ref-type="bibr" rid="B16">16</xref>, <xref ref-type="bibr" rid="B17">17</xref>).</p>
<p>We conducted subgroup analysis and meta-regression to explore the potential sources of heterogeneity (<xref ref-type="bibr" rid="B18">18</xref>). Subgroup analyses were carried out by the geographic region of China (Northwest, Northeast, Southwest, South Central, East, North, and not specified [NS]) (<xref ref-type="bibr" rid="B19">19</xref>), province, type of sample (breath, tissue, blood, and other), <italic>H. pylori</italic> testing method (14C urea breath test, immunohistochemical staining, Giemsa stain, Polymerase Chain Reaction [PCR], rapid urease test, Enzyme linked immunosorbent assay [ELISA], and other), diagnosis period (before 1999, 2000-2004, 2005-2009, 2010-2014, 2015-2019, and other), detection timing (before treatment and NS), type of study design (cross-sectional study, case-control study, and cohort study), and quality grade. Univariate meta-regression was performed based on the following variables: geographic region of China, type of sample, <italic>H. pylori</italic> testing method, diagnosis period, detection timing, type of study design, quality grade, publication year, and sample size.</p>
<p>We used Egger&#x2019;s test and funnel plot to assess publication bias (<xref ref-type="bibr" rid="B20">20</xref>). Sensitivity analysis was conducted using leave-one-out method, which omitted one study at a time and re-conducted statistical analysis, in order to evaluate the influence of each omitted study on the pooled prevalence. All analyses were conducted using package &#x201c;meta&#x201d; in R 4.1.1, and statistical significance level was set as 0.05 for two-sided tests.</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<title>Results</title>
<sec id="s3_1">
<title>Studies&#xa0;Selection&#xa0;</title>
<p>A total number of 757 studies were initially identified through literature search. We excluded 132 studies because of duplication. After examination of titles and abstracts, 411 studies were excluded as they did not meet the eligibility criteria. After full-text review, 162 studies were excluded for the following reasons (<xref ref-type="bibr" rid="B1">1</xref>): 83 studies were not relevant to our study (<xref ref-type="bibr" rid="B2">2</xref>); 69 studies had no available data (authors could not provide original data or did not answer our requests) (<xref ref-type="bibr" rid="B3">3</xref>); eight studies reused data included in other studies (<xref ref-type="bibr" rid="B4">4</xref>); one study did not mention testing method of <italic>H. pylori</italic> (<xref ref-type="bibr" rid="B5">5</xref>); one study involved recurrent cases. Through checking reference lists, three additional relevant studies were added. Ultimately, 55 studies with 5324 cases of non-cardia&#xa0;gastric&#xa0;cancer were enrolled in the final meta-analysis. The detailed process of studies selection is shown in <xref ref-type="fig" rid="f1"><bold>Figure&#xa0;1</bold></xref>.</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>Flow diagram of studies selection.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-12-850389-g001.tif"/>
</fig>
</sec>
<sec id="s3_2">
<title>Characteristics of Included Studies</title>
<p>The detailed characteristics of the 55 included studies are summarized in <xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Table S1</bold></xref>. The included studies were published between 1996 and 2020, and the sample size ranged from 10 to 343. Moreover, the majority of the studies were published in Chinese (44/55, 80%), and others were published in English. Of all the 55 studies, 17 were conducted in East China, 16 in South Central China, 10 in Northwest China, six in North China, three in Northeast China, one in Southwest China, and the geographic region of three studies was not specified. Regarding the type of sample, tissue was tested in 27 studies, blood in 18 studies, and breath in four studies. The most common <italic>H. pylori</italic> testing method was ELISA (n=15), followed by immunohistochemical staining (n=6), PCR (n=4), 14C urea breath test (n=4), Giemsa stain (n=3), and rapid urease test (n=3). Regarding type of study design, more than half (34/55, 61.8%) of included studies were cross-sectional studies. Additionally, about half of the studies (n=27) were rated as moderate quality, and 18 studies were regarded as high quality and 10 as low quality.</p>
</sec>
<sec id="s3_3">
<title>Prevalence of <italic>H. pylori</italic>
</title>
<p>
<xref ref-type="fig" rid="f2"><bold>Figure&#xa0;2</bold></xref> shows that the pooled prevalence of <italic>H. pylori</italic> in non-cardia&#xa0;gastric&#xa0;cancer in China was 66.5% (95%CI: 62%-71%). Statistical heterogeneity was observed among the included studies (<italic>I</italic><sup>2</sup>=93.8%, <italic>P</italic>&lt;0.0001).</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>Forest plot for the pooled prevalence of <italic>Helicobacter pylori</italic> in non-cardia&#xa0;gastric&#xa0;cancer in China.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-12-850389-g002.tif"/>
</fig>
</sec>
<sec id="s3_4">
<title>Subgroup Analysis</title>
<p>
<xref ref-type="table" rid="T1"><bold>Table&#xa0;1</bold></xref> shows the results of subgroup analysis. Significant differences in the prevalence of <italic>H. pylori</italic> in non-cardia&#xa0;gastric&#xa0;cancer was noted when stratified by geographic region of China (<italic>P</italic>=0.0112) and type of sample (<italic>P</italic>=0.0326). The geographic region with the highest prevalence of <italic>H. pylori</italic> was Northwest China (78.9%, 95%CI: 69.9%-87.8%) followed by Northeast China (74.3%, 95%CI: 61.9%-86.7%), and Southwest China (72.8%, 95%CI: 63.7%-81.9%), while the geographic region with the lowest prevalence was North China (53.1%, 95%CI: 38.9%-67.3%). Additionally, when stratified by province, the prevalence of <italic>H. pylori</italic> in non-cardia&#xa0;gastric&#xa0;cancer was also statistically different (<italic>P</italic>&lt;0.0001). The top three provinces with the highest prevalence of <italic>H. pylori</italic> were Heilongjiang (90%, 95%CI: 71.4%-100%), Ningxia (89.2%, 95%CI: 79.5%-98.9%), and Shandong (84.2%, 95%CI: 72.6%-95.8%), whereas Beijing (43.1%, 95%CI: 29.5%-56.7%), Inner Mongolia (45.4%, 95%CI: 24.7%-66%), and Fujian (49.2%, 95%CI: 35.2%-63.3%) showed the lowest prevalence of <italic>H. pylori</italic>. The prevalence of <italic>H. pylori</italic> detected in breath (78.7%, 95%CI: 67.2%-90.2%) was significantly higher than that detected in tissue (67%, 95%CI: 60.7%-73.4%) and blood (64.1%, 95%CI: 54.8%-73.4%). The prevalence of <italic>H. pylori</italic> in non-cardia&#xa0;gastric&#xa0;cancer was generally similar for different <italic>H. pylori</italic> testing methods (<italic>P</italic>=0.4846), diagnosis periods (<italic>P</italic>=0.2623), detection timings (<italic>P</italic>=0.4719), types of study design, (<italic>P</italic>=0.6952), and quality grades (<italic>P</italic>=0.7712).</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Prevalence of <italic>Helicobacter pylori</italic> in non-cardia gastric cancer by stratification variables.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" rowspan="2" align="left"/>
<th valign="top" rowspan="2" align="center">Studies</th>
<th valign="top" rowspan="2" align="center">N</th>
<th valign="top" rowspan="2" align="center"><italic>H. pylori</italic> positive cases</th>
<th valign="top" rowspan="2" align="center">Pooled <italic>H. pylori</italic> prevalence(95% CI)</th>
<th valign="top" rowspan="2" align="center"><italic>P</italic> value</th>
<th valign="top" colspan="2" align="center">Heterogeneity</th>
</tr>
<tr>
<th valign="top" align="center"><italic>I</italic><sup>2</sup>(%)</th>
<th valign="top" align="center"><italic>P</italic> value</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left"><bold>Geographic region</bold>
</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">0.0112</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Northwest China</td>
<td valign="top" align="center">10</td>
<td valign="top" align="center">880</td>
<td valign="top" align="center">657</td>
<td valign="top" align="center">78.9 (69.9; 87.8)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">92.9</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Northeast China</td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">198</td>
<td valign="top" align="center">139</td>
<td valign="top" align="center">74.3 (61.9; 86.7)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">66.6</td>
<td valign="top" align="center">0.0502</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Southwest China</td>
<td valign="top" align="center">1</td>
<td valign="top" align="center">92</td>
<td valign="top" align="center">67</td>
<td valign="top" align="center">72.8 (63.7; 81.9)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;South Central China</td>
<td valign="top" align="center">16</td>
<td valign="top" align="center">1866</td>
<td valign="top" align="center">1307</td>
<td valign="top" align="center">69.9 (63.5; 76.4)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">90.2</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;East China</td>
<td valign="top" align="center">17</td>
<td valign="top" align="center">1543</td>
<td valign="top" align="center">911</td>
<td valign="top" align="center">62.2 (53.8; 70.6)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">93.4</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;North China</td>
<td valign="top" align="center">6</td>
<td valign="top" align="center">545</td>
<td valign="top" align="center">300</td>
<td valign="top" align="center">53.1 (38.9; 67.3)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">93.2</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;NS</td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">200</td>
<td valign="top" align="center">108</td>
<td valign="top" align="center">51.2 (31.2; 71.2)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">87.6</td>
<td valign="top" align="center">0.0003</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Province</bold>
</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">&lt;0.0001</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Heilongjiang</td>
<td valign="top" align="center">1</td>
<td valign="top" align="center">10</td>
<td valign="top" align="center">9</td>
<td valign="top" align="center">90.0 (71.4; 100.0)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Ningxia</td>
<td valign="top" align="center">2</td>
<td valign="top" align="center">211</td>
<td valign="top" align="center">188</td>
<td valign="top" align="center">89.2 (79.5; 98.9)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">80.7</td>
<td valign="top" align="center">0.0227</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Shandong</td>
<td valign="top" align="center">1</td>
<td valign="top" align="center">38</td>
<td valign="top" align="center">32</td>
<td valign="top" align="center">84.2 (72.6; 95.8)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Gansu</td>
<td valign="top" align="center">2</td>
<td valign="top" align="center">123</td>
<td valign="top" align="center">94</td>
<td valign="top" align="center">77.9 (64.8; 83.4)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">46.3</td>
<td valign="top" align="center">0.1723</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Shaanxi</td>
<td valign="top" align="center">6</td>
<td valign="top" align="center">546</td>
<td valign="top" align="center">375</td>
<td valign="top" align="center">75.5 (61.7; 89.2)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">94.3</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Henan</td>
<td valign="top" align="center">7</td>
<td valign="top" align="center">954</td>
<td valign="top" align="center">723</td>
<td valign="top" align="center">74.9 (70.3; 79.5)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">60.6</td>
<td valign="top" align="center">0.0186</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Liaoning</td>
<td valign="top" align="center">1</td>
<td valign="top" align="center">85</td>
<td valign="top" align="center">63</td>
<td valign="top" align="center">74.1 (64.8; 83.4)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Chongqing</td>
<td valign="top" align="center">1</td>
<td valign="top" align="center">92</td>
<td valign="top" align="center">67</td>
<td valign="top" align="center">72.8 (63.7; 81.9)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Hebei</td>
<td valign="top" align="center">2</td>
<td valign="top" align="center">176</td>
<td valign="top" align="center">126</td>
<td valign="top" align="center">71.5 (64.0; 79.0)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">13.8</td>
<td valign="top" align="center">0.2814</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Taiwan</td>
<td valign="top" align="center">1</td>
<td valign="top" align="center">167</td>
<td valign="top" align="center">119</td>
<td valign="top" align="center">71.3 (64.4; 78.1)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Jiangsu</td>
<td valign="top" align="center">7</td>
<td valign="top" align="center">397</td>
<td valign="top" align="center">294</td>
<td valign="top" align="center">70.7 (59.8; 81.6)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">77.2</td>
<td valign="top" align="center">0.0002</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Guangdong</td>
<td valign="top" align="center">5</td>
<td valign="top" align="center">453</td>
<td valign="top" align="center">304</td>
<td valign="top" align="center">67.5 (54.5; 80.4)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">90.5</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Hubei</td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">221</td>
<td valign="top" align="center">138</td>
<td valign="top" align="center">65.1 (35.8; 94.4)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">97.0</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Jilin</td>
<td valign="top" align="center">1</td>
<td valign="top" align="center">103</td>
<td valign="top" align="center">67</td>
<td valign="top" align="center">65.1 (55.8; 74.3)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Guangxi</td>
<td valign="top" align="center">1</td>
<td valign="top" align="center">238</td>
<td valign="top" align="center">142</td>
<td valign="top" align="center">59.7 (53.4; 65.9)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Shanghai</td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">365</td>
<td valign="top" align="center">189</td>
<td valign="top" align="center">57.5 (39.3; 75.7)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">93.1</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Fujian</td>
<td valign="top" align="center">5</td>
<td valign="top" align="center">576</td>
<td valign="top" align="center">277</td>
<td valign="top" align="center">49.2 (35.2; 63.3)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">92.4</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Inner Mongolia</td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">318</td>
<td valign="top" align="center">152</td>
<td valign="top" align="center">45.4 (24.7; 66.0)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">94.2</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Beijing</td>
<td valign="top" align="center">1</td>
<td valign="top" align="center">51</td>
<td valign="top" align="center">22</td>
<td valign="top" align="center">43.1 (29.5; 56.7)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">&#x2013;</td>
<td valign="top" align="center">&#x2013;</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;NS</td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">200</td>
<td valign="top" align="center">108</td>
<td valign="top" align="center">51.2 (31.2; 71.2)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">87.6</td>
<td valign="top" align="center">0.0003</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Type of sample</bold>
</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">0.0326</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Breath</td>
<td valign="top" align="center">4</td>
<td valign="top" align="center">278</td>
<td valign="top" align="center">204</td>
<td valign="top" align="center">78.7 (67.2; 90.2)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">83.2</td>
<td valign="top" align="center">0.0005</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Tissue</td>
<td valign="top" align="center">27</td>
<td valign="top" align="center">2452</td>
<td valign="top" align="center">1577</td>
<td valign="top" align="center">67.0 (60.7; 73.4)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">92.7</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Blood</td>
<td valign="top" align="center">18</td>
<td valign="top" align="center">2036</td>
<td valign="top" align="center">1367</td>
<td valign="top" align="center">64.1 (54.8; 73.4)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">96.2</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Other<xref ref-type="table-fn" rid="fnT1_1"><sup>a</sup></xref>
</td>
<td valign="top" align="center">6</td>
<td valign="top" align="center">558</td>
<td valign="top" align="center">341</td>
<td valign="top" align="center">61.4 (57.3; 65.4)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">0.0</td>
<td valign="top" align="center">0.5004</td>
</tr>
<tr>
<td valign="top" align="left"><bold><italic>H. pylori</italic> testing method</bold>
</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">0.4846</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">&#x2003;14C urea breath test</td>
<td valign="top" align="center">4</td>
<td valign="top" align="center">278</td>
<td valign="top" align="center">204</td>
<td valign="top" align="center">78.7 (67.2; 90.2)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">83.2</td>
<td valign="top" align="center">0.0005</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Immunohistochemical Staining</td>
<td valign="top" align="center">6</td>
<td valign="top" align="center">355</td>
<td valign="top" align="center">250</td>
<td valign="top" align="center">71.1 (63.9; 78.3)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">56.9</td>
<td valign="top" align="center">0.0408</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Giemsa stain</td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">412</td>
<td valign="top" align="center">242</td>
<td valign="top" align="center">66.4 (41.8; 91.0)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">97.4</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;PCR method</td>
<td valign="top" align="center">4</td>
<td valign="top" align="center">514</td>
<td valign="top" align="center">321</td>
<td valign="top" align="center">66.0 (50.5; 81.5)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">90.7</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Rapid urease test</td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">337</td>
<td valign="top" align="center">187</td>
<td valign="top" align="center">65.1 (32.8; 97.8)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">98.5</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;ELISA</td>
<td valign="top" align="center">15</td>
<td valign="top" align="center">1746</td>
<td valign="top" align="center">1138</td>
<td valign="top" align="center">62.4 (52.4; 72.4)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">96.0</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Other<xref ref-type="table-fn" rid="fnT1_2"><sup>b</sup></xref>
</td>
<td valign="top" align="center">20</td>
<td valign="top" align="center">1682</td>
<td valign="top" align="center">1147</td>
<td valign="top" align="center">66.4 (59.8; 73.1)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">91.0</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Diagnosis period</bold>
</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">0.2623</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Before1999</td>
<td valign="top" align="center">6</td>
<td valign="top" align="center">488</td>
<td valign="top" align="center">338</td>
<td valign="top" align="center">64.7 (52.2; 77.2)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">87.7</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;2000-2004</td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">299</td>
<td valign="top" align="center">154</td>
<td valign="top" align="center">52.7 (27.1; 78.3)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">95.9</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;2005-2009</td>
<td valign="top" align="center">3</td>
<td valign="top" align="center">390</td>
<td valign="top" align="center">236</td>
<td valign="top" align="center">60.6 (55.7; 65.4)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">0.0</td>
<td valign="top" align="center">0.7708</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;2010-2014</td>
<td valign="top" align="center">7</td>
<td valign="top" align="center">591</td>
<td valign="top" align="center">410</td>
<td valign="top" align="center">69.5 (62.5; 76.5)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">73.3</td>
<td valign="top" align="center">0.0010</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;2015-2019</td>
<td valign="top" align="center">4</td>
<td valign="top" align="center">188</td>
<td valign="top" align="center">127</td>
<td valign="top" align="center">69.8 (44.1; 95.5)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">95.4</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Other<xref ref-type="table-fn" rid="fnT1_3"><sup>c</sup></xref>
</td>
<td valign="top" align="center">32</td>
<td valign="top" align="center">3368</td>
<td valign="top" align="center">2224</td>
<td valign="top" align="center">67.6 (61.4; 73.8)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">94.8</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Detection timing</bold>
</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">0.4719</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Before treatment</td>
<td valign="top" align="center">19</td>
<td valign="top" align="center">1960</td>
<td valign="top" align="center">1192</td>
<td valign="top" align="center">64.1 (56.1; 72.2)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">93.9</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;NS</td>
<td valign="top" align="center">36</td>
<td valign="top" align="center">3364</td>
<td valign="top" align="center">2297</td>
<td valign="top" align="center">67.7 (62.2; 73.1)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">93.3</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Type of study design</bold>
</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">0.6952</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Cross-sectional study</td>
<td valign="top" align="center">34</td>
<td valign="top" align="center">2698</td>
<td valign="top" align="center">1784</td>
<td valign="top" align="center">67.9 (62.3; 73.4)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">92.2</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Case-control study</td>
<td valign="top" align="center">19</td>
<td valign="top" align="center">2231</td>
<td valign="top" align="center">1399</td>
<td valign="top" align="center">63.8 (55.5; 72.1)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">95.4</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Cohort study</td>
<td valign="top" align="center">2</td>
<td valign="top" align="center">395</td>
<td valign="top" align="center">306</td>
<td valign="top" align="center">70.0 (47.8; 92.3)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">90.1</td>
<td valign="top" align="center">0.0015</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Quality grade</bold>
</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center">0.7712</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">&#x2003;High</td>
<td valign="top" align="center">18</td>
<td valign="top" align="center">2278</td>
<td valign="top" align="center">1480</td>
<td valign="top" align="center">64.1 (55.5; 72.8)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">95.8</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Moderate</td>
<td valign="top" align="center">27</td>
<td valign="top" align="center">2222</td>
<td valign="top" align="center">1420</td>
<td valign="top" align="center">67.2 (61.2; 73.2)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">91.0</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Low</td>
<td valign="top" align="center">10</td>
<td valign="top" align="center">824</td>
<td valign="top" align="center">589</td>
<td valign="top" align="center">68.9 (58.0; 79.7)</td>
<td valign="top" align="center"/>
<td valign="top" align="center">93.3</td>
<td valign="top" align="center">&lt;0.0001</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>H. pylori, Helicobacter pylori; CI, confidence interval; NS, not specific; PCR, polymerase chain reaction; ELISA, enzyme linked immunosorbent assay.</p>
<fn id="fnT1_1"><label>a</label>
<p>using several types of samples.</p>
</fn>
<fn id="fnT1_2">
<label>b</label>
<p>using other testing method or several testing methods.</p>
</fn>
<fn id="fnT1_3">
<label>c</label>
<p>The diagnosis period could not be divided into corresponding group or the diagnosis period was not clear.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_5">
<title>Meta-Regression</title>
<p>The results of univariate meta-regression indicated that there was significant association between geographic region of China and the prevalence of <italic>H. pylori</italic> in non-cardia&#xa0;gastric&#xa0;cancer (<xref ref-type="table" rid="T2"><bold>Table&#xa0;2</bold></xref>). Compared with the prevalence in North China, the prevalence was higher in Northwest China (<italic>&#x3b2;</italic>=0.2570, 95%CI: 0.1002-0.4138, <italic>P</italic>=0.0013), Northeast China (<italic>&#x3b2;</italic>=0.2216, 95%CI: 0.0016-0.4417, <italic>P</italic>=0.0484), and South Central China (<italic>&#x3b2;</italic>=0.1663, 95%CI: 0.0202-0.3124, <italic>P</italic>=0.0257). The results of univariate meta-regression also revealed that type of sample, <italic>H. pylori</italic> testing method, diagnosis period, detection timing, type of study design, quality grade, publication year, and sample size were not significantly associated with the prevalence of <italic>H. pylori</italic> in non-cardia&#xa0;gastric&#xa0;cancer (<italic>P</italic>&gt;0.05).</p>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>Assessing the effect of study variables on the pooled prevalence of <italic>Helicobacter pylori</italic> in non-cardia gastric cancer in China using univariable meta-regression analysis.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left">Variable</th>
<th valign="top" align="center"><italic>&#x3b2;</italic> (95% CI)</th>
<th valign="top" align="center"><italic>SE</italic>
</th>
<th valign="top" align="center"><italic>P</italic> value</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left"><bold>Geographic region</bold>
</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Northwest China</td>
<td valign="top" align="center">0.2570 (0.1002; 0.4138)</td>
<td valign="top" align="center">0.0800</td>
<td valign="top" align="center">0.0013</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Southwest China</td>
<td valign="top" align="center">0.1969 (-0.1293; 0.5232)</td>
<td valign="top" align="center">0.1665</td>
<td valign="top" align="center">0.2368</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Northeast China</td>
<td valign="top" align="center">0.2216 (0.0016; 0.4417)</td>
<td valign="top" align="center">0.1123</td>
<td valign="top" align="center">0.0484</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;South Central China</td>
<td valign="top" align="center">0.1663 (0.0202; 0.3124)</td>
<td valign="top" align="center">0.0745</td>
<td valign="top" align="center">0.0257</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;East China</td>
<td valign="top" align="center">0.0907 (-0.0552; 0.2366)</td>
<td valign="top" align="center">0.0744</td>
<td valign="top" align="center">0.2233</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;NS</td>
<td valign="top" align="center">-0.0185 (-0.2370; 0.2000)</td>
<td valign="top" align="center">0.1115</td>
<td valign="top" align="center">0.8684</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;North China</td>
<td valign="top" align="center">Reference</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left"><bold>Type of sample</bold>
</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Breath</td>
<td valign="top" align="center">0.1674 (-0.0479; 0.3826)</td>
<td valign="top" align="center">0.1098</td>
<td valign="top" align="center">0.1275</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Tissue</td>
<td valign="top" align="center">0.0488 (-0.1023; 0.1999)</td>
<td valign="top" align="center">0.0771</td>
<td valign="top" align="center">0.5265</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Blood</td>
<td valign="top" align="center">0.0218 (-0.1358; 0.1794)</td>
<td valign="top" align="center">0.0804</td>
<td valign="top" align="center">0.7862</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Other<xref ref-type="table-fn" rid="fnT2_1"><sup>a</sup></xref>
</td>
<td valign="top" align="center">Reference</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left"><bold><italic>H. pylori</italic> testing method</bold>
</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">&#x2003;14C urea breath test</td>
<td valign="top" align="center">0.1268 (-0.0607; 0.3143)</td>
<td valign="top" align="center">0.0957</td>
<td valign="top" align="center">0.1851</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Immunohistochemical Staining</td>
<td valign="top" align="center">0.0415 (-0.1231; 0.2061)</td>
<td valign="top" align="center">0.0840</td>
<td valign="top" align="center">0.6213</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Rapid urease test</td>
<td valign="top" align="center">-0.0101 (-0.2187; 0.1984)</td>
<td valign="top" align="center">0.1064</td>
<td valign="top" align="center">0.9240</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Giemsa stain</td>
<td valign="top" align="center">0.0012 (-0.2077; 0.2101)</td>
<td valign="top" align="center">0.1066</td>
<td valign="top" align="center">0.9910</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;PCR method</td>
<td valign="top" align="center">-0.0014 (-0.1877; 0.1848)</td>
<td valign="top" align="center">0.0950</td>
<td valign="top" align="center">0.9879</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;ELISA</td>
<td valign="top" align="center">-0.0374 (-0.1544; 0.0797)</td>
<td valign="top" align="center">0.0597</td>
<td valign="top" align="center">0.5316</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Other<xref ref-type="table-fn" rid="fnT2_2"><sup>b</sup></xref>
</td>
<td valign="top" align="center">Reference</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left"><bold>Diagnosis period</bold>
</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Other<xref ref-type="table-fn" rid="fnT2_3"><sup>c</sup></xref>
</td>
<td valign="top" align="center">-0.0355 (-0.2223; 0.1514)</td>
<td valign="top" align="center">0.0954</td>
<td valign="top" align="center">0.7100</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Before1999</td>
<td valign="top" align="center">-0.0667 (-0.2920; 0.1586)</td>
<td valign="top" align="center">0.1150</td>
<td valign="top" align="center">0.5618</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;2000-2004</td>
<td valign="top" align="center">-0.1847 (-0.4471; 0.0777)</td>
<td valign="top" align="center">0.1339</td>
<td valign="top" align="center">0.1678</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;2005-2009</td>
<td valign="top" align="center">-0.0952 (-0.3588; 0.1684)</td>
<td valign="top" align="center">0.1345</td>
<td valign="top" align="center">0.4789</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;2010-2014</td>
<td valign="top" align="center">-0.0265 (-0.2461; 0.1931)</td>
<td valign="top" align="center">0.1120</td>
<td valign="top" align="center">0.8131</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;2015-2019</td>
<td valign="top" align="center">Reference</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left"><bold>Detection timing</bold>
</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Before treatment</td>
<td valign="top" align="center">-0.0352 (-0.1301; 0.0598)</td>
<td valign="top" align="center">0.0484</td>
<td valign="top" align="center">0.4678</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;NS</td>
<td valign="top" align="center">Reference</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left"><bold>Type of study design</bold>
</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Cross-sectional study</td>
<td valign="top" align="center">-0.0210 (-0.2646; 0.2226)</td>
<td valign="top" align="center">0.1243</td>
<td valign="top" align="center">0.8657</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Case-control study</td>
<td valign="top" align="center">-0.0609 (-0.3093; 0.1875)</td>
<td valign="top" align="center">0.1267</td>
<td valign="top" align="center">0.6308</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Cohort study</td>
<td valign="top" align="center">Reference</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left"><bold>Quality grade</bold>
</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left">&#x2003;High</td>
<td valign="top" align="center">-0.0471 (-0.1797; 0.0855)</td>
<td valign="top" align="center">0.0677</td>
<td valign="top" align="center">0.4865</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Moderate</td>
<td valign="top" align="center">-0.0173 (-0.1430; 0.1085)</td>
<td valign="top" align="center">0.0642</td>
<td valign="top" align="center">0.7875</td>
</tr>
<tr>
<td valign="top" align="left">&#x2003;Low</td>
<td valign="top" align="center">Reference</td>
<td valign="top" align="center"/>
<td valign="top" align="center"/>
</tr>
<tr>
<td valign="top" align="left"><bold>Publication year</bold>
</td>
<td valign="top" align="center">0.0008 (-0.0068; 0.0078)</td>
<td valign="top" align="center">0.0036</td>
<td valign="top" align="center">0.8243</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Sample size</bold>
</td>
<td valign="top" align="center">-0.0002 (-0.0010; 0.0006)</td>
<td valign="top" align="center">0.0004</td>
<td valign="top" align="center">0.6018</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>SE, standard error; CI, confidence interval; NS, not specific; PCR, polymerase chain reaction; ELISA, enzyme linked immunosorbent assay; H. pylori, Helicobacter pylori.</p>
<fn id="fnT2_1"><label>a</label>
<p>using several types of samples.</p>
</fn>
<fn id="fnT2_2">
<label>b</label>
<p>using other testing method or several testing methods.</p>
</fn>
<fn id="fnT2_3">
<label>c</label>
<p>The diagnosis period could not be divided into corresponding group or the diagnosis period was not clear.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_6">
<title>Publication Bias and Sensitivity Analysis</title>
<p>The Egger&#x2019;s test for funnel plot (<xref ref-type="fig" rid="f3"><bold>Figure&#xa0;3</bold></xref>) asymmetry was significant (<italic>t</italic>=-3.01, <italic>P</italic>=0.0040), indicating that there was obvious publication bias in all studies. The results of sensitivity analysis indicated that the lowest prevalence of <italic>H. pylori</italic> in non-cardia&#xa0;gastric&#xa0;cancer was 65.92% (95%CI: 0.6146-0.7038) when the Wang ML et&#xa0;al. study was omitted, and the highest prevalence was 67.25% (95%CI: 0.6292-0.7157) when the Zhang B study was omitted. The omission of studies did not significantly modify the pooled prevalence (<xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Figure S1</bold></xref>).</p>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>Funnel plot for the pooled prevalence of <italic>Helicobacter pylori</italic> in non-cardia&#xa0;gastric&#xa0;cancer in China.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-12-850389-g003.tif"/>
</fig>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<title>Discussion</title>
<p>Based on 5324 non-cardia gastric cancer patients from 55 studies, which covered more than half of provinces and autonomous regions in China, we conducted a meta-analysis to estimate the prevalence of <italic>H. pylori</italic> in non-cardia gastric cancer patients at the national level for the first time. We reported a comprehensive estimate of <italic>H. pylori</italic> prevalence in non-cardia gastric cancer patients in China at 66.5% (95%CI: 62%-71%). Moreover, substantial geographic variations were noted in the prevalence of <italic>H. pylori</italic>, which was the highest in Northwest China and the lowest in North China.</p>
<p>The results of our study indicated that the pooled prevalence of <italic>H. pylori</italic> in non-cardia gastric cancer was 66.5% (95%CI: 62%-71%) in China. The prevalence of <italic>H. pylori</italic> in non-cardia gastric cancer in different countries varied greatly, such as 38.5% in the United States, 57% in Spain, 79.5% in South Korea, and 91.8% in Japan (<xref ref-type="bibr" rid="B4">4</xref>, <xref ref-type="bibr" rid="B21">21</xref>&#x2013;<xref ref-type="bibr" rid="B23">23</xref>). This difference might be explained by the following reasons: socio-economic status, dietary habits, and racial disparities. Suerbaum S et&#xa0;al. have mentioned that there was a strong inverse correlation between <italic>H. pylori</italic> prevalence and socio-economic status. Populations with lower socio-economic status were more likely to be infected with <italic>H. pylori</italic> (<xref ref-type="bibr" rid="B24">24</xref>, <xref ref-type="bibr" rid="B25">25</xref>). Lower social status was commonly accompanied by crowded living conditions and poor hygienic conditions, which might increase the risk of acquisition and transmission of <italic>H. pylori</italic> (<xref ref-type="bibr" rid="B24">24</xref>, <xref ref-type="bibr" rid="B26">26</xref>). Several previous studies have revealed that the consumption of high-salt foods (such as pickles and preserved products) might be associated with an increased chance of <italic>H. pylori</italic> infection (<xref ref-type="bibr" rid="B27">27</xref>&#x2013;<xref ref-type="bibr" rid="B29">29</xref>). Tsugane S et&#xa0;al. speculated that a high-salt diet might induce gastric mucosa damage and destroy mucosal barrier. These changes in gastric mucosa might lead to an increase in <italic>H. pylori</italic> infection (<xref ref-type="bibr" rid="B29">29</xref>). Data based on National Health and Nutrition Examination Surveys of the United States have also shown that racial disparities played a certain role in the prevalence of <italic>H. pylori</italic>. Compared with <italic>H. pylori</italic> prevalence in whites, the prevalence in African Americans was higher (<xref ref-type="bibr" rid="B30">30</xref>).</p>
<p>Several previous studies, especially in Japan, reported that the <italic>H. pylori</italic> prevalence in gastric cancer was as high as 99% when strict criteria were used to diagnose <italic>H. pylori</italic> infection (<xref ref-type="bibr" rid="B31">31</xref>&#x2013;<xref ref-type="bibr" rid="B33">33</xref>). Some other studies in Japan and South Korea used multiple testing methods to diagnose <italic>H. pylori</italic> infection, which also showed that the <italic>H. pylori</italic> prevalence in gastric cancer patients was above 90% (<xref ref-type="bibr" rid="B34">34</xref>&#x2013;<xref ref-type="bibr" rid="B36">36</xref>). The <italic>H. pylori</italic> prevalence of these studies was much higher than the pooled <italic>H. pylori</italic> prevalence in our study. There were several possible reasons for this difference. First, these previous studies used strict criteria or multiple testing methods to diagnose <italic>H. pylori</italic> infection, which could greatly increase the sensitivity of detection. Moreover, most participants of these studies were cases of gastric cancer, while the participants of our study were non-cardia gastric cancer. Finally, most of these studies were conducted in Japan or South Korea. The <italic>H. pylori</italic> prevalence varied in different countries and regions.</p>
<p>The results of our study indicated a significant heterogeneity in the prevalence of <italic>H. pylori</italic> in non-cardia gastric cancer across different geographical regions in China. The highest <italic>H. pylori</italic> prevalence was noted in Northwest China at 78.9%, which was consistent with the highest incidence of gastric cancer in this region reported by the 2016 Chinese Cancer Registry Annual Report (<xref ref-type="bibr" rid="B37">37</xref>). Meanwhile, in line with our findings showing that the lowest <italic>H. pylori</italic> prevalence in non-cardia gastric cancer was noted in North China, Zhang WD et&#xa0;al. have also mentioned that the lowest <italic>H. pylori</italic> prevalence among the general population was in North China (<xref ref-type="bibr" rid="B38">38</xref>). The socio-economic status might be the main factor accounting for this geographic region difference. Unbalanced socio-economic development in different regions would lead to differences in living environment, education level, hygiene conditions, as well as water source (<xref ref-type="bibr" rid="B26">26</xref>, <xref ref-type="bibr" rid="B39">39</xref>&#x2013;<xref ref-type="bibr" rid="B41">41</xref>). The combined effect of the above factors may have resulted in the variation of <italic>H. pylori</italic> prevalence in different geographic regions.</p>
<p>The prevalence of <italic>H. pylori</italic> in non-cardia gastric cancer also showed significant variations across different provinces, and the highest <italic>H. pylori</italic> prevalence was noted in Heilongjiang Province at 90%. However, the estimate of Heilongjiang Province was based on only 10 cases from one study, leading to a wide range of its 95% CI. Furthermore, there were another seven provinces where the estimates of <italic>H. pylori</italic> prevalence were also based on only one study with the sample size ranging from 38 to 238. At the provincial level, part of the estimates in our study were estimated in the basis of a relatively small sample size, which might result in unstable results. Therefore, the results should be interpreted with caution, and additional studies with a larger sample size are needed to confirm our findings in the future.</p>
<p>The prevalence of <italic>H. pylori</italic> in the general population and non-cardia gastric cancer cases from different geographic regions was not completely consistent. From January 2002 to June 2004, Zhang WD et&#xa0;al. conducted an epidemiological survey on the <italic>H. pylori</italic> prevalence among 26,341 general people from 19 provinces and autonomous regions in China. The study showed that the <italic>H. pylori</italic> prevalence in the general population from different geographic regions ranged from high&#xa0;to&#xa0;low&#xa0;as: 66.26% in Central China, 59.16% in Eastern China, 58.27% in Western China, 50.08% in Southern China, and 46.84% in Northern China. In terms of provinces, the <italic>H. pylori</italic> prevalence among general population in Tibet was the highest at 84.62%, while the <italic>H. pylori</italic> prevalence in Guangdong was the lowest at 42.01% (<xref ref-type="bibr" rid="B38">38</xref>). Our results indicated that the geographic region with the lowest <italic>H. pylori</italic> prevalence in non-cardia gastric cancer cases was noted in North China, which was in line with the previous survey conducted by Zhang WD et&#xa0;al. The possible reason for the difference in the distribution of <italic>H. pylori</italic> between general population and cases of non-cardia gastric cancer was that non-cardia gastric cancer was caused by a combination of various risk factors including <italic>H. pylori</italic> infection, dietary habits, ethnicity, smoking, radiation exposure, family history, etc. <italic>H. pylori</italic> infection is one of the most important factors, but not the only one (<xref ref-type="bibr" rid="B42">42</xref>).</p>
<p>Type of sample might have an influence on the estimates of prevalence of <italic>H. pylori</italic> in non-cardia gastric cancer. The results of subgroup analysis revealed significant heterogeneity across different types of samples, with the highest prevalence of <italic>H. pylori</italic> detected in breath (78.7%). However, the results of meta-regression indicated that the differences in <italic>H. pylori</italic> prevalence among different sample types were not significant. The disparity in results might be attributed to the small number of the studies included. This meta-analysis only included four studies with 278 cases that detected the <italic>H. pylori</italic> prevalence in breath. Spineli LM et&#xa0;al. have mentioned that in order to obtain robust results, at least 10 studies should be included for each covariate in meta-regression implementation. In order to further confirm our finding, more studies detecting the <italic>H. pylori</italic> prevalence in breath will be needed in the future (<xref ref-type="bibr" rid="B43">43</xref>). In line with our findings, Liao YQ et&#xa0;al. also mentioned that the prevalence of <italic>H. pylori</italic> detected in breath would be higher than that in other samples. This might be partly because <italic>H. pylori</italic> infection detected in breath could reflect the <italic>H. pylori</italic> infection status in the whole gastrointestinal tract. Other pathogenic microorganisms in the gastrointestinal tract that could produce urease might cause false positive in the detection, which might increase the positive rate of <italic>H. pylori</italic> (<xref ref-type="bibr" rid="B44">44</xref>, <xref ref-type="bibr" rid="B45">45</xref>).</p>
<p>The results of subgroup analysis and meta-regression both showed that the prevalence of <italic>H. pylori</italic> in non-cardia&#xa0;gastric&#xa0;cancer was not significantly associated with different <italic>H. pylori</italic> testing methods. This finding was in agreement with a recent study estimating the prevalence of <italic>H. pylori</italic> in cases with gastrointestinal diseases other than gastric cancer, which showed that the <italic>H. pylori</italic> prevalence detected by different <italic>H. pylori</italic> testing methods were generally similar (<xref ref-type="bibr" rid="B46">46</xref>). In our study, all <italic>H. pylori</italic> detected in breath was tested using 14C urea breath test. However, type of sample was significantly associated with <italic>H. pylori</italic> prevalence, while the relationship between testing method and <italic>H. pylori</italic> prevalence was not significant. Spineli LM et&#xa0;al. have revealed that when the subgroups contained fewer studies, the subgroup analysis could be underpowered to test the relationship between variables (<xref ref-type="bibr" rid="B43">43</xref>). Borenstein M et&#xa0;al. also indicated that one of the key factors driving the precision of subgroup analysis was the number of studies (<xref ref-type="bibr" rid="B47">47</xref>). Compared with type of sample, <italic>H. pylori</italic> testing method contained more subgroups, resulting in less studies in each subgroup. This may be the possible reason that the results of the above two subgroup analyses were different.</p>
<p>The studies included in our meta-analysis used several single testing methods to detect <italic>H. pylori</italic> infection among non-cardia gastric cancer cases. However, several guidelines revealed that one single testing method could not be considered as the gold standard for <italic>H. pylori</italic> detection (<xref ref-type="bibr" rid="B48">48</xref>&#x2013;<xref ref-type="bibr" rid="B50">50</xref>). These commonly used testing methods all have some disadvantages. For example, IgG would remain in the blood for months or years even after <italic>H. pylori</italic> was eradicated. As such, antibody-based tests (e.g., ELISA) could not distinguish between current and past infections (<xref ref-type="bibr" rid="B51">51</xref>, <xref ref-type="bibr" rid="B52">52</xref>). Moreover, ELISA is less accurate than 14C urea breath test and the cut-off values need a local validation (<xref ref-type="bibr" rid="B48">48</xref>, <xref ref-type="bibr" rid="B53">53</xref>). As for 14C urea breath test, several factors such as atrophy, bismuth, proton pump inhibitor (PPI), and antibiotics may lead to false-negative, and it also should be validated locally (<xref ref-type="bibr" rid="B48">48</xref>, <xref ref-type="bibr" rid="B54">54</xref>). Several guidelines have recommended the combination of single testing methods (e.g., combination of a validated serology and urea breath test), which could improve the accuracy of the detection (<xref ref-type="bibr" rid="B48">48</xref>&#x2013;<xref ref-type="bibr" rid="B50">50</xref>). However, in our meta-analysis, most included studies (43/55, 78.2%) used a single testing method to detect <italic>H. pylori</italic>, which might lead to bias in the pooled <italic>H. pylori</italic> prevalence among non-cardia gastric cancer cases in China. Because <italic>H. pylori</italic> infection tends to clear as non-cardia gastric cancer progresses and the detection of past <italic>H. pylori</italic> infection is difficult, especially in retrospective studies (19/55, 34.5%), the pooled <italic>H. pylori</italic> prevalence in Chinese non-cardia gastric cancer cases may be underestimated (<xref ref-type="bibr" rid="B35">35</xref>, <xref ref-type="bibr" rid="B55">55</xref>, <xref ref-type="bibr" rid="B56">56</xref>).</p>
<p>The results of our study also suggested that there was no correlation between the diagnosis period and the prevalence of <italic>H. pylori</italic> in non-cardia gastric cancer. Previous meta-analysis conducted by Qiao J et&#xa0;al. showed that the prevalence of <italic>H. pylori</italic> in cases with gastric cancer in China declined from 1996 to 2015 (<xref ref-type="bibr" rid="B57">57</xref>). However, no decreasing trend of <italic>H. pylori</italic> prevalence in non-cardia gastric cancer was observed in our study. This difference might be attributed to the different study population. The previous study included cases with gastric cancer, while the included cases in our study were limited as cases with non-cardia gastric cancer.</p>
<p>The &#x201c;test and treat&#x201d; strategy for <italic>H. pylori</italic> infection has been shown to be cost-effective in some western countries (e.g., the United Kingdom and the United States) (<xref ref-type="bibr" rid="B58">58</xref>, <xref ref-type="bibr" rid="B59">59</xref>). Japan has included <italic>H. pylori</italic> eradication therapy into the coverage of national medical insurance, becoming the first country to implement universal <italic>H. pylori</italic> &#x201c;test and treat&#x201d; strategy worldwide (<xref ref-type="bibr" rid="B60">60</xref>). Even with high prevalence of <italic>H. pylori</italic> infection and gastric cancer, whether to implement the &#x201c;test and treat&#x201d; strategy for <italic>H. pylori</italic> infection among the general population in China was still controversial. Several factors such as cost-effectiveness, personal willingness, usage of antibiotic, gastric cancer incidence, and prevalence of <italic>H. pylori</italic> infection would affect the implementation of this strategy (<xref ref-type="bibr" rid="B59">59</xref>, <xref ref-type="bibr" rid="B61">61</xref>). Our study pooled the <italic>H. pylori</italic> prevalence in non-cardia gastric cancer and explored the influence factors of pooled prevalence, which might have certain public health significance in providing evidence for the control and prevention of non-cardia gastric cancer, as well as for policy making and health resource allocation.</p>
<p>The strengths of this meta-analysis included the following items. To the best of our knowledge, this was the first meta-analysis that estimated the pooled prevalence of <italic>H. pylori</italic> in non-cardia gastric cancer in China. An overview of current studies about <italic>H. pylori</italic> prevalence in non-cardia gastric cancer conducted in China was presented in our study. Moreover, our study has included a large number of studies. Based on a large sample size of 5324 cases from 55 studies, we were able to obtain a relatively robust estimate of the pooled <italic>H. pylori</italic> prevalence in non-cardia gastric cancer.</p>
<p>There were several limitations in our meta-analysis. Even though subgroup analysis and meta-regression were performed to minimize the heterogeneity across the included studies, significant heterogeneity still could be observed in subgroup analysis. The factors included in our study could not well explain the heterogeneity, which might affect the generalizability of our results. Moreover, some important factors (e.g., dietary habit, drinking, and gender) could not be extracted from the included studies, which might have potential influence on the heterogeneity. Another limitation was that some estimates in our study (e.g., <italic>H. pylori</italic> prevalence in different provinces) were calculated based on the small number of cases. Therefore, these results should be interpreted with caution, and more studies are needed to further confirm these results in the future. Another limitation of our study was that single testing methods used in the included studies for detecting <italic>H. pylori</italic> infection all had some limitations, and the results could not be completely accurate, which may lead to a certain bias in the pooled <italic>H. pylori</italic> prevalence. Finally, significant publication bias may result in an underestimation of pooled <italic>H. pylori</italic> prevalence.</p>
</sec>
<sec id="s5" sec-type="conclusions">
<title>Conclusions</title>
<p>This meta-analysis presented an overview of <italic>H. pylori</italic> prevalence in non-cardia gastric cancer in China. In conclusion, our study estimated the pooled prevalence of <italic>H. pylori</italic> in non-cardia gastric cancer was 66.5% (95%CI: 62%-71%) in China. Variation in <italic>H. pylori</italic> prevalence across different geographical regions was statistically significant, with the highest <italic>H. pylori</italic> prevalence (78.9%) in Northwest China and the lowest (53.1%) in North China. Type of sample might be associated with <italic>H. pylori</italic> prevalence, and further studies are needed to confirm this finding. A large proportion of non-cardia&#xa0;gastric&#xa0;cancers was associated with <italic>H. pylori</italic> infection, emphasizing the potential benefits of <italic>H. pylori</italic> eradication for reducing the disease burden of non-cardia gastric cancer. Our study might have certain public health significance in providing evidence for the control and prevention of non-cardia gastric cancer, as well as for policy making and health resource allocation.</p>
</sec>
<sec id="s6" sec-type="data-availability">
<title>Data Availability Statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Material</bold></xref>. Further inquiries can be directed to the corresponding author.</p>
</sec>
<sec id="s7" sec-type="author-contributions">
<title>Author Contributions</title>
<p>Study concepts: FH; Study design: FH; Data acquisition: FX and YW; Quality control of data and algorithms: YL and ZW; Data analysis and interpretation: YL and DL; Manuscript preparation: YL, FX, and YW; Manuscript editing: YL; Manuscript review: ZW, DL, and FH. All authors contributed to the article and approved the submitted version.</p>
</sec>
<sec id="s8" sec-type="funding-information">
<title>Funding</title>
<p>This study was supported by the Doctoral Start-up Foundation of Guizhou Medical University [No. J (2020)65], National Natural Science Foundation of China Incubation Program, Guizhou Medical University [No. 20NSP061], the First-Class Discipline Construction Project in Guizhou Province Public Health and Preventive Medicine [No.2017 (85)], Foundation for the Establishment of Postdoctoral Mobile Station in Public Health and Preventive Medicine, Guizhou Medical University [41202020204], and Guizhou Basic Research (Science and Technology Fund) Project [ZK (2022) General 373]. All funding parties did not have any role in the design of the study or in the explanation of the data.</p>
</sec>
<sec id="s9" sec-type="COI-statement">
<title>Conflict of Interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="s10" sec-type="disclaimer">
<title>Publisher&#x2019;s Note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
</body>
<back>
<sec id="s11" sec-type="supplementary-material">
<title>Supplementary Material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fonc.2022.850389/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fonc.2022.850389/full#supplementary-material</ext-link>
</p>
<supplementary-material xlink:href="DataSheet_1.docx" id="SM1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document"/>
</sec>
<sec id="s12">
<title>Abbreviations</title>
<p><italic>H. pylori</italic>, <italic>Helicobacter pylori</italic>; CNKI, China National Knowledge Infrastructure; PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses; AHRQ, Agency for Healthcare Research and Quality; NOS, Newcastle-Ottawa Scale; CI, confidence interval; PCR, Polymerase Chain Reaction; ELISA, Enzyme linked immunosorbent assay; SE, standard error; PPI, proton pump inhibitor.</p>
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