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World Journal of Gastroenterology logoLink to World Journal of Gastroenterology
. 2024 Nov 21;30(43):4636–4656. doi: 10.3748/wjg.v30.i43.4636

Prevalence of Helicobacter pylori infection in China from 2014-2023: A systematic review and meta-analysis

Lu Xie 1,2, Guang-Wei Liu 3, Ya-Nan Liu 4,5, Peng-Yu Li 6,7, Xin-Ning Hu 8,9, Xin-Yi He 10,11, Rui-Bo Huan 12,13, Tai-Long Zhao 14,15, Hui-Jun Guo 16,17
PMCID: PMC11572641  PMID: 39575409

Abstract

BACKGROUND

Helicobacter pylori (H. pylori) stands as the predominant infectious agent linked to the onset of gastritis, peptic ulcer diseases, and gastric cancer (GC). Identified as the exclusive bacterial factor associated with the onset of GC, it is classified as a group 1 carcinogen by the World Health Organization. The elimination of H. pylori plays a crucial role in the primary prevention of GC. While the prevalence has declined in recent decades, H. pylori infection is still highly prevalent in China, accounting for a significant part of the disease burden of GC. Therefore, updated prevalence information for H. pylori infection, especially regional and demographic variations in China, is an important basis for the design of targeted strategies that will be effective for the prevention of GC and application of policies for H. pylori control.

AIM

To methodically evaluate the occurrence of H. pylori infection throughout China and establish a reference point for subsequent investigations.

METHODS

A systematic review and meta-analysis was conducted following established guidelines, as detailed in our methodology section.

RESULTS

Our review synthesized data from 152 studies, covering a sample of 763827 individuals, 314423 of whom were infected with H. pylori. We evaluated infection rates in mainland China and the combined prevalence of H. pylori was 42.8% (95%CI: 40.7-44.9). Subgroup analysis indicated the highest prevalence in Northwest China at 51.3% (95%CI: 45.6-56.9), and in Qinghai Province, the prevalence reached 60.2% (95%CI: 46.5-73.9). The urea breath test, which recorded the highest infection rate, showed a prevalence of 43.7% (95%CI: 41.4-46.0). No notable differences in infection rates were observed between genders. Notably, the prevalence among the elderly was significantly higher at 44.5% (95%CI: 41.9-47.1), compared to children, who showed a prevalence of 27.5% (95%CI: 19.58-34.7).

CONCLUSION

Between 2014 and 2023, the prevalence of H. pylori infection in China decreased to 42.8%, down from the previous decade. However, the infection rates vary considerably across different geographical areas, among various populations, and by detection methods employed.

Keywords: Helicobacter pylori, Meta-analysis, Prevalence, Epidemiology, China

Core Tip: Globally, Helicobacter pylori infection continues to be the most prevalent infectious disease, posing substantial public health challenges. Despite a reduction in overall prevalence to 42.8% over the past decade, high rates persist in specific areas and demographics within China, necessitating continued vigilance and targeted interventions.

INTRODUCTION

The presence of Helicobacter pylori (H. pylori) infection is linked to various gastrointestinal conditions and serves as the primary etiological agent for chronic gastritis, peptic ulcers, and gastric cancer (GC). The condition impacts approximately 4.4 billion individuals globally, representing a prevalence rate of around 48.5%, thus posing a significant public health issue on a global scale[1]. In the year 2020, global cancer statistics indicated that there were approximately 1013103 new instances of GC, which corresponded to 768793 fatalities across the globe. Notably, China accounted for 478508 of these new cases and 373706 of the deaths recorded[2]. The occurrence of GC in China is 28.68 per 100000, placing it third among cancers, following lung and colon cancers[3], and it remains a significant contributor to cancer mortality. Despite advancements in sanitation and living conditions, China continues to face a significant prevalence of H. pylori infection and GC. The elimination of H. pylori has been shown to markedly decrease immune and inflammatory responses, promote ulcer healing, and diminish the likelihood of GC development. Long-term studies from Linqu County of Shandong Province and Matsu Island of Taiwan have demonstrated reductions in GC risk by 52% and 53%, respectively, after H. pylori eradication[4,5]. A vast country, China presents a huge variation in geographic, environmental, and socioeconomic aspects of the country; hence, its prevalence rates for diseases also vary significantly amongst its regions. The National Cancer Center, the Cancer Hospital of the Chinese Academy of Medical Sciences, and Peking Union Medical College of China have all documented regional differences in cancer incidence[6]. For example, Qinghai Province exhibits the highest incidence rate of GC in the nation, while Hubei Province is positioned sixth. It is crucial to tailor prevention and treatment strategies to China's unique national circumstances, including the specific epidemiological disease profiles and the diverse needs of various regions and demographics. This is vital for decelerating disease progression and minimizing premature mortality due to end-stage events[7]. Examining the impact of H. pylori infections on disease burden is crucial for formulating national policies[8]. In this research, a meta-analysis approach will be employed, utilizing evidence-based methods with individual rental rates to examine the infection rate within the general population of China. The analysis aids in reliable evidence that is needed at clinical prevention and intervention strategies. The results are as follows.

MATERIALS AND METHODS

This meta-analysis was registered on the PROSPERO database (No. CRD42024555621) and carried out in compliance with the Preferred Items for Systematic Reviews and Meta-Analysis standards.

Search strategy

A systematic review of literature was performed using six databases: (1) PubMed; (2) EMBASE; (3) Cochrane Library; (4) China National Knowledge Infrastructure; (5) Wanfang; and (6) Cqvip, covering the period from January 1, 2014, to January 1, 2024 (Supplementary Table 1).

Inclusion criteria and exclusion criteria

The inclusion criteria were as follows: (1) Patients diagnosed with H. pylori infection via urea breath test (UBT), serological antibody test, stool antigen detection, endoscopic examination, or histopathological evaluation of gastric mucosa biopsies; (2) Studies with clearly defined time and location, involving subjects confirmed to be Chinese; (3) Studies where the infection rate was explicitly stated or could be calculated; and (4) Research findings disseminated in either Chinese or English.

The criteria for exclusion encompassed: (1) Intervention trials, reviews, conference papers, case reports, and meta-analyses; (2) Studies with questionable or incorrect data; (3) Duplicate studies; and (4) Studies with sample sizes less than 50. Literature management was facilitated using Endnote 9.1 software. Two researchers worked separately to search for and screen literature; when they disagreed, they consulted an expert third party to settle the dispute.

Data extraction and quality assessment

Two researchers worked separately to retrieve the data using an Excel spreadsheet. The first author, publication year, study site, geographical area, sample size, and gender distribution of participants were among the data points retrieved. A third researcher resolved any disagreements, and the studies were assessed according to the Loney criterion[9]. Details of these quality assessments are presented in Supplementary Table 2[10-160].

Statistical analysis

The occurrence of H. pylori infection was assessed via meta-analysis. The variation among the studies was evaluated through the application of Cochran’s Q statistic and the I-square statistics (I2). The random-effects model was utilized to calculate the pooled prevalence along with its 95%CI. Sensitivity and subgroup analyses were conducted to further investigate heterogeneity and validate the robustness of the study findings. The subgroup analyses examined the prevalence of H. pylori by province, municipality, autonomous region, geographical area, gender, age, and detection method. Meta-analyses were conducted in RStudio using the "metaprop()" function. Funnel plots were created with the "metabias()" function, and asymmetry was evaluated using the AS-Thompson test[161]. All statistical tests were two-sided, with statistical significance set at P < 0.05.

RESULTS

Literature search and screening results

Figure 1 shows the steps involved in searching for and screening literature. The original list of books included 6430 items; 929 of those were later eliminated due to duplication. We removed 5042 results after going over their titles and abstracts. A detailed review of 459 publications was conducted, resulting in 151 studies being selected for inclusion.

Figure 1.

Figure 1

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Literature search and screening process.

Basic characteristics of included literature

This analysis involved 152 studies, with a cumulative sample size of 763827 participants. Zou et al[10] presented two studies meeting the inclusion criteria within a single publication. As a result, the table displaying the basic characteristics of the included literature (Table 1) lists 152 studies, whereas the referenced literature totals 151 articles. The study encompassed thirty provinces, municipalities, and autonomous regions across mainland China, with Zhejiang Province (n = 12), Guangdong Province (n = 10), and Hubei Province (n = 9) recording the highest number of studies. Heilongjiang province was excluded due to the lack of studies that met the inclusion criteria during the search period. East China recorded the most studies (n = 43) among the seven geographic regions, while Northeast China had the fewest (n = 6) (Table 1)[10-160].

Table 1.

Characteristics of the included studies

Ref.
Year
Type of study
Region
Data collection time
Detection methods
Age (range/mean ± SD)
Simple size
Male
Positive (male/female)
Prevalence
95%CI
North East
Zhang et al[11] 2016 Cross-sectional Jilin (Jilin Province) 2014.11-2024.12 2 21-60 1932 44.77 293/395 35.6 33.5-37.8
Wang et al[12] 2014 Cross-sectional Changchun (Jilin Province) 2012.05-2012.12 1 22-83 1059 65.06 324/138 43.6 40.6-46.7
Zhang et al[13] 2014 Cross-sectional Shenyang (Liaoning Province) 2012.04-2012.06 2 3-6 1150 52.00 79/72 13.1 11.2-15.2
Zhu et al[14] 2020 Cross-sectional Jinzhou (Liaoning Province) 2019.02-2019.04 1 21-61 2859 77.86 792/166 33.5 31.8-35.3
Jiang et al[15] 2015 Cross-sectional Dalian (Liaoning Province) 2012.12-2013.11 2 1-100 4127 53.62 664/591 30.4 29.0-31.8
Ji and Gu[16] 2016 Cross-sectional Dalian (Liaoning Province) 2013.09-2015.09 2 18-88 4214 47.51 480/437 21.8 20.5-23.0
North China
Zheng et al[17] 2022 Cross-sectional Tangshan (Hebei Province) 2019.07-2020.09 1 21-78 9944 67.09 3115/1395 45.4 44.4-46.3
Yang et al[18] 2020 Cross-sectional Beijing 2013.09-2019.06 1 NA 7260 36.54 1106/1726 39.0 37.9-40.1
Zhao et al[19] 2017 Cross-sectional Beijing 2009.09-2010.02 4 1 month–18 years 1196 50.67 65/62 10.6 8.9-12.5
Xi et al[20] 2018 Cross-sectional Beijing NA 4 6-13 291 55.33 47/20 23.0 18.3-28.3
Chen et al[21] 2016 Cross-sectional Beijing 2012.01-2016.12 1 14-80 11000 51.65 2826/2313 46.7 45.8-47.7
Wu et al[22] 2014 Cross-sectional Beijing 2010.01-2013.12 1 16-88 10331 69.29 3236/1353 44.4 43.5-45.4
Cui et al[23] 2020 Cross-sectional Beijing 2018.06-2019.05 1 18-81 1058 52.65 175/163 31.9 29.1-34.9
Zhang et al[24] 2014 Cross-sectional Beijing 2010 1 70.9 2006 50.10 851/822 83.4 81.7-85.0
Zhang et al[25] 2015 Cross-sectional Baoding (Hebei Province) 2015 2 20-61 379 49.34 86/89 46.2 41.1-51.3
Chen et al[26] 2019 Cross-sectional Hohhot (Inner Mongolia Autonomous Region) 2016-2019 1 18-81 13568 56.60 2480/1677 30.6 29.9-31.4
Wang et al[27] 2019 Cross-sectional Hailar District (Inner Mongolia Autonomous Region) 2017.01-2017.12 1 7-88 15293 56.14 4232/3031 47.5 46.7-48.3
Kan et al[28] 2015 Cross-sectional Chifeng (Inner Mongolia Autonomous Region) 2013.1.4-2013.5.31 2 17-91 3282 60.18 457/230 20.9 19.6-22.4
Yan et al[29] 2020 Cross-sectional Chengzhi (Shanxi Province) 2019.01-2019.12 2 16-96 1224 69.61 536/259 65.0 62.2-67.6
Li et al[30] 2022 Cross-sectional Shanxi Province 2019.01-2021.12 1 20-86 3365 75.99 474/197 19.9 18.6-21.3
Zhang et al[31] 2021 Cross-sectional Tianjin 2017.08-2018.08 1 16-90 10000 54.09 1978/1626 36.0 35.1-37.0
East China
Zhang et al[32] 2020 Cross-sectional She County Tongcheng (Anhui Province) NA 1 20-90 1536 39.84 389/581 63.2 60.7-65.6
Wang et al[33] 2022 Cross-sectional Suzhou (Anhui Province) 2017.01-2020.07 1 21-85 33634 57.73 7603/5647 39.4 38.9-39.9
Peng et al[34] 2021 Cross-sectional Anqing (Anhui Province) 2019.04-2019.08 2 18-92 2725 60.51 816/610 52.3 50.4-54.2
Han et al[35] 2021 Cohort study Anhui Province 2017.07-2020.11 1 ≥ 18 1094 67.92 329/153 44.1 41.1-47.1
Guo et al[36] 2019 Cross-sectional Jieshou (Anhui Province) 2017.1-2018.12 1 18-97 9684 45.34 1356/1676 31.3 30.4-32.2
Hu et al[37] 2015 Cross-sectional Fujian Province 2013.02-2013.11 2 18-76 2770 NA 1435 51.8 49.9-53.7
Xie et al[38] 2020 Cross-sectional Ningde (Fujian Province) 2019.10-2020.01 3 11-83 417 55.64 135/112 59.2 54.3-64.0
Li et al[39] 2016 Cross-sectional Jinjiang (Fujian Province) 2015.01-2015.12 1 17-86 8751 66.67 2640/1440 46.6 45.6-47.7
Liu et al[40] 2016 Cross-sectional Xiamen (Fujian Province) 2012-2014 1 44.8 ± 12.3 1444 66.90 475/216 47.9 45.2-50.5
Chen et al[41] 2022 Cross-sectional Zhangzhou (Fujian Province) 2019.09-2020.08 1 20-89 2608 54.79 655/594 47.9 46.0-49.8
Mao et al[42] 2017 Cross-sectional Suzhou (Jiangsu Province) 2015.01-2015.12 1 20-80 963 77.15 333/92 44.1 41.0-47.3
Zhang et al[43] 2019 Cross-sectional Nanjing (Jiangsu Province) 2017.05-2017.08 1 64.34 ± 8.32 935 34.33 126/239 35.9 32.9-39.1
Xie et al[44] 2017 Cross-sectional Suzhou (Jiangsu Province) 2015.10-2016.07 1 12-93 2664 52.74 769/753 57.1 55.2-59.0
Jiang et al[45] 2015 Cross-sectional Jiangsu Province 2013.01-2014.10 1 17-95 3480 62.44 949/615 44.9 43.3-46.6
Li et al[46] 2019 Cross-sectional Nanjing (Jiangsu Province) 2019 1 48.75 ± 5.97 700 57.14 132/124 36.6 33.0-40.3
Wang et al[47] 2023 Cross-sectional Nanjing (Jiangsu Province) 2022 1 NA 15160 54.47 2721/1906 30.5 29.8-31.3
Meng et al[48] 2015 Cross-sectional Dongtai (Jiangsu Province) 2012.06-2013.6 1 29-75 1598 52.00 403/307 44.4 42.0-46.9
Ji et al[49] 2023 Cross-sectional Suzhou (Jiangsu Province) 2018.1-2018.12 2 18-89 6588 64.31 2370/1339 56.3 55.1-57.5
Zhang et al[50] 2018 Cross-sectional Jiuzhou (Jiangxi Province) 2015.07-2016.10 1 18-90 1200 54.92 580 48.3 45.5-51.2
Ren et al[51] 2020 Cross-sectional Pingxiang (Jiangxi Province) 2016.01-2019.06 2 9-92 10487 58.81 3011/2010 47.9 46.9-48.8
Wang et al[52] 2017 Cross-sectional Jiuzhou (Jiangxi Province) 2016.01-2017.03 1 43.89 6165 74.16 1689/636 37.7 36.5-38.9
Fang et al[53] 2021 Cross-sectional Jingdezhen (Jiangxi Province) 2008.7-2019.12 1 6-95 48353 50.08 10462/10301 42.9 42.5-43.4
Xu et al[54] 2019 Cross-sectional Cao County (Shandong Province) 2018.04-2018.07 1 18-90 1182 49.49 198/175 31.6 28.9-34.3
Shi et al[55] 2022 Cross-sectional Xintai (Shandong Province) 2021.04-2022.04 1 18-84 400 52.50 106/46 38.0 33.2-43.0
Li et al[56] 2015 Cross-sectional Jining (Shandong Province) 2012.05-2013.05 1, 2 16-72 580 56.03 156/106 45.2 41.1-49.3
Liang et al[57] 2014 Cross-sectional Jining (Shandong Province) 2012.02-2012.12 1 16-74 4366 68.60 1690/750 55.9 54.4-57.4
Han et al[58] 2020 Cross-sectional Jining (Shandong Province) 2017.01-2017.12 2 19-91 2557 69.42 782/466 57.4 55.4-59.3
Kong et al[59] 2022 Cohort study Shandong Province 2021.07-2022.01 1 NA 1173 47.06 204/226 36.7 33.9-39.5
Zhang et al[60] 2019 Cross-sectional Shanghai 2017.01-2018.01 1 18-90 5164 52.05 1008/869 36.3 35.0-37.7
Sun et al[61] 2018 Cross-sectional Shanghai 2016.09-2016.12 2 40-93 3258 43.92 451/461 28.0 26.5-29.6
Jia et al[62] 2020 Cross-sectional Shanghai 2018.10-2019.9 1 25-70 29986 59.79 11547/6033 58.6 58.1-59.2
Qiu et al[63] 2022 Cross-sectional Hanghzou (Zhejiang Province) 2019.09-2019.11 1 21-69 225 54.67 31/39 31.1 25.1-37.6
Yu et al[64] 2018 Cross-sectional Jiaxing (Zhejiang Province) 2016.01-2017.06 1, 3 18-80 4220 47.44 900/709 38.1 36.7-39.6
Zhou et al[65] 2023 Cross-sectional Wenzhou (Zhejiang Province) 2020.01.1-2022.08.1 2 18-71 568 85.56 197/38 41.4 37.3-45.5
Zheng et al[66] 2015 Cross-sectional Hanghzou (Zhejiang Province) 2013.06-2014.05 1 12-76 2220 63.96 658/309 43.6 41.5-45.7
Yang et al[67] 2015 Cross-sectional Wenzhou (Zhejiang Province) 2013.10-2014.4 1 ≥ 20 15817 60.79 5078/3525 56.9 56.1-57.7
Lin et al[68] 2014 Cross-sectional Cangnan County (Zhejiang Province) 2007.06-2012.12 3 18-70 11986 58.06 3351 28.0 27.2-28.8
Yang et al[69] 2015 Cross-sectional Taizhou (Zhejiang Province) 2011.01-2013.12 1 20-70 2072 53.28 376/336 34.4 32.3-36.5
Wang et al[70] 2015 Cross-sectional Wenzhou (Zhejiang Province) 2010.04-2015.02 2 0-14 4520 NA 2118 46.9 45.4-48.3
Li et al[71] 2017 Cross-sectional Anji County (Zhejiang Province) 2015.01-2016.01 4 18-80 943 51.22 242/259 53.1 49.9-56.4
Shen et al[72] 2014 Cross-sectional Hanghzou (Zhejiang Province) 2012.1-2012.12 2 20-91 7911 54.25 2180/1850 50.9 49.8-52.0
He et al[73] 2016 Cross-sectional Jiangshan (Zhejiang Province) 2014.05-2015.08 2 3-5 3143 50.56 390/358 23.8 22.3-25.3
Fang et al[74] 2022 Cross-sectional Jinhua (Zhejiang Province) 2019.1-2021.12 2 NA 2060 50.58 542/523 51.7 49.5-53.9
Northwest China
Yu et al[75] 2016 Cross-sectional Lanzhou (Gansu Province) 2014.05-2015.07 1 16-96 3239 70.36 1197/482 51.8 50.1-53.6
Xie[76] 2021 Cross-sectional Yuan County (Gansu Province) 2016.01-2020.01 1 12-84 2369 43.31 694/927 68.4 66.5-70.3
Zou et al[77] 2018 Cross-sectional Gansu Province 2015.1-2015.12 2 27-87 1338 44.84 99/77 13.2 11.4-15.1
Qin et al[78] 2018 Cross-sectional Jingtai County (Gansu Province) 2013.05-2017.06 1 ≥ 14 7182 49.71 1667/1890 49.6 48.5-50.8
Wu et al[79] 2016 Cross-sectional Lanzhou (Gansu Province) 2013.07-2015.06 2 18-81 442 57.24 137/89 51.1 46.4-55.9
Li[80] 2016 Cross-sectional Zhangye County (Gansu Province) 2014.01-2015.12 1 18-83 1000 52.10 297/263 56.0 52.9-59.1
Ma et al[81] 2018 Cross-sectional Baiyin (Gansu Province) 2018 1 20-70 16722 52.76 4290/2802 42.4 41.7-43.2
Hou et al[82] 2020 Cross-sectional Qingyang (Gansu Province) 2016.1-2019.12 2 8-92 8321 60.06 2894/2206 61.3 60.2-62.3
Zhang et al[83] 2020 Cross-sectional Yinchuan (Ningxia Hui Autonomous Region) 2018.06-2018.09 1 ≥ 18 800 50.00 243/211 56.8 53.2-60.2
Hu et al[84] 2019 Cross-sectional Ningxia Hui Autonomous Region 2018.12-2019.12 1 14-88 710 33.10 146/257 56.8 53.0-60.4
Li et al[85] 2024 Cross-sectional Qinghai Province 2021-2022 1 3-85 1131 42.44 241/356 52.8 49.8-55.7
Li et al[86] 2022 Cross-sectional Qinghai Province 2021.05-2012.12 1 4-90 4724 42.65 1047/1484 53.6 52.1-55.0
Wang et al[87] 2019 Cross-sectional Qinghai Province 2017.08-2018.11 1 14-85 2103 48.64 775/784 74.1 72.2-76.0
Zhang et al[88] 2014 Cross-sectional Xian (Shaanxi Province) 2014.01-2014.02 1 21-82 548 74.09 191/72 48.0 43.7-52.3
Zhang et al[89] 2015 Cross-sectional Xian (Shaanxi Province) 2009-2013 2 18-70 16506 59.95 2983/1671 27.7 27.0-28.3
Tang et al[90] 2017 Cross-sectional Xian (Shaanxi Province) 2016 1 18-78 6085 46.05 1463/1585 50.1 48.8-51.4
Xiao et al[91] 2021 Cross-sectional Xian (Shaanxi Province) 2019.5-2020.05 1 NA 2100 60.81 600/383 46.8 44.7-49.0
Di et al[92] 2022 Cross-sectional Xian (Shaanxi Province) 2016.09-2020.12 1 5-96 10016 45.06 1753/1502 32.5 31.6-33.4
Zhu et al[93] 2017 Cross-sectional Hoboksar (Xinjiang Uygur Autonomous Region) NA 1, 2 20-78 1200 43.58 362/480 70.2 67.5-72.7
Yao and Wang[94] 2017 Cross-sectional Urumqi (Xinjiang Uygur Autonomous Region) 2016.05.10-2016.11.24 1 NA 2301 43.29 455/585 45.2 43.1-47.3
Li et al[95] 2023 Cross-sectional Tarbagatay Prefecture (Xinjiang Uygur Autonomous Region) 2019.01-2022.06 2 NA 2840 50.56 846/771 56.9 55.1-58.8
Wang et al[96] 2016 Cross-sectional Xinjiang Uygur Autonomous Region 2013.06-2014.06 1 17-87 4780 56.19 1264/1045 48.3 46.9-49.7
Fan et al[97] 2016 Cross-sectional Xinjiang Uygur Autonomous Region 2014.09-2015.09 1 16-75 4774 41.10 1243/1890 65.6 64.3-67.0
Southwest region
Shu[98] 2021 Cross-sectional Tongren County (Guizhou Province) 2018.04-2019.04 1 20-60 800 56.38 252/201 56.6 53.1-60.1
Yang et al[99] 2019 Cross-sectional Aba Tibetan and Qiang Autonomous Prefecture (Sichuan Province) 2015.05-2016.12 1 2-92 544 46.51 73/93 30.5 26.7-34.6
Zhou et al[100] 2022 Cross-sectional Guangyuan (Sichuan Province) 2020.3-2021.03 1 18-81 4296 44.41 1140/1128 52.8 51.3-54.3
Xu et al[101] 2019 Cross-sectional Luzhou (Sichuan Province) 2017.05-2018.05 1, 2 14-87 18684 63.35 3788/2086 31.4 30.8-32.1
Xiao et al[102] 2020 Cross-sectional Yibin (Sichuan Province) 2017.01-2018.12 4 2-6 622 54.98 71/60 21.1 17.9-24.5
Luo et al[103] 2022 Cross-sectional Nanchong (Sichuan Province) 2021.8-2022.5 1 ≥ 18 1478 50.47 375/327 47.5 44.9-50.1
Zou et al[10] 2023 Cross-sectional Chengdu (Sichuan Province) 2013-2014 1 NA 16914 52.49 3640/3341 41.3 40.5-42.0
Zou et al[10] 2023 Cross-sectional Chengdu (Sichuan Province) 2019-2021 1 NA 18281 46.74 2570/3035 30.7 30.0-31.3
Wu et al[104] 2017 Cross-sectional The Tibet Autonomous Region NA 1, 3 2-85 4332 53.46 1475/1208 61.9 60.5-63.4
Cai et al[105] 2018 Cross-sectional Lhasa (The Tibet Autonomous Region) 2015.11-2016.7 1 5-85 1000 44.30 245/331 57.6 54.5-60.7
Dawa et al[106] 2021 Cross-sectional The Tibet Autonomous Region 2018-2019 1 NA 717 52.44 196/192 54.1 50.4-57.8
Zhang et al[107] 2021 Cross-sectional Baishe (Yunnan Province) 2020.04-2020.08 4 3-6 321 50.47 159/40 25.9 21.2-31.0
Xie[108] 2017 Cross-sectional Qujing (Yunnang Province) 2016.1-2016.12 1 20-79 8790 58.07 1712/1172 32.8 31.8-33.8
Wang et al[109] 2015 Cross-sectional Yunang Province 2013.01-2014.08 1 ≥ 20 6680 72.54 1988/688 40.1 38.9-41.2
Li et al[110] 2018 Cross-sectional Kunming (Yunnan Province) 2015.01-2017.12 1 20-75 606 36.63 115/75 31.4 27.7-35.2
Jia et al[111] 2018 Cross-sectional Yunnan Province 2013.01-2015.02 1 21-85 1680 77.74 433/191 37.1 34.8-39.5
Fu et al[112] 2018 Cross-sectional Kunming (Yunnan Province) 2013.1-2016.12 1 3-18 12932 53.15 1706/1408 24.1 23.3-24.8
Ding et al[113] 2017 Cross-sectional Dali (Yunnan Province) 2014.06-2016.06 4 0-14 1127 51.46 75/53 11.4 9.6-13.4
Chen and Sun[114] 2019 Cross-sectional Puer (Yunnan Province) 2018.5.1-2019.4.30 1 18-92 15328 56.36 3793/2502 41.1 40.3-41.9
Liu et al[115] 2017 Cross-sectional Chongqing 2014.1-2014.12 1 44.1 ± 10.8 10912 52.91 1949/1801 34.4 33.5-35.3
Liu and Lei[116] 2020 Cross-sectional Chongqing 2017.01-2018.06 1 7-18 1982 50.15 109/121 11.6 10.2-13.1
Zhou[117] 2018 Cross-sectional Chongqing 2012.01-2016.08 1, 3 38.7 1000 54.60 324/203 52.7 49.6-55.8
Meng and Sun[118] 2018 Cross-sectional Chongqing 2017.1-2017.12 2 15-85 27662 59.16 4222/2576 24.6 24.1-25.1
Liu and Fan[119] 2016 Cross-sectional Chongqing 2014.01-2014.12 1 20-90 5788 65.74 1074/513 27.4 26.3-28.6
South China
Zhu et al[120] 2021 Cross-sectional Shenzhen (Guangdong Province) 2019.1-2019.12 1 20-69 985 55.53 222/164 39.2 36.1-42.3
Zhang et al[121] 2015 Cross-sectional Guangzhou (Guangdong Province) 2013.07-2014.06 1 8-68 440 45.45 120/132 57.3 52.5-61.9
Liang and Yang[122] 2018 Cross-sectional Yangjiang (Guangdong Province) 2017.09-2019.08 2 NA 6703 54.20 1741/1564 49.3 48.1-50.5
Yang[123] 2021 Cross-sectional Jiangmen (Guangdong Province) 2018.01-2019.12 2 28-88 100 52.00 17/7 24.0 16.0-33.6
Xie et al[124] 2014 Cross-sectional Zhuhai (Guangdong Province) 2013.01-2013.09 2 21-70 2963 63.75 1156/473 55.0 53.2-56.8
Xie et al[125] 2021 Cross-sectional Chaoshan (Guangdong Province) 2018.03-2020.06 1 16-88 3160 51.27 892/726 51.2 49.4-53.0
Tang and Zhang[126] 2021 Cross-sectional Shenzhen (Guangdong Province) 2018.06-2020.11 2 NA 3605 53.09 1154/996 59.6 58.0-61.2
Guan et al[127] 2022 Cross-sectional Guangzhou (Guangdong Province) 2020.1-2020.12 1 22-88 6436 54.93 1110/855 30.5 29.4-31.7
Dai et al[128] 2020 Cross-sectional Foshan (Guangdong Province) 2018.1-2018.12 1 28-78 15730 52.45 3855/3157 44.6 43.8-45.4
Li et al[129] 2022 Cross-sectional Shenzhen (Guangdong Province) 2020.09-2021.09 1 ≥ 20 5007 59.18 1052/700 35.0 33.7-36.3
Xu and Yan[130] 2018 Cohort study Nanning (Guangxi Zhuang Autonomous Region) 2014.03-2017.06 1 20-65 2956 58.93 787/552 45.3 43.5-47.1
Weng[131] 2017 Cross-sectional Laibin (Guangxi Zhuang Autonomous Region) 2014.01-2012.03 1 10-89 6328 59.07 2308/1054 53.1 51.9-54.4
Lin and Cheng[132] 2016 Cross-sectional Hechi (Guangxi Zhuang Autonomous Region) 2016.01-2016.03 1 20-75 1500 54.47 486/226 47.5 44.9-50.0
Wang[133] 2021 Cross-sectional Nanning (Guangxi Zhuang Autonomous Region) 2017.01-2019.12 1 21-75 1175 67.83 268/114 32.5 29.8-35.3
Chen et al[134] 2017 Cross-sectional Hezhou (Guangxi Zhuang Autonomous Region) 2015.05-2015.12 1 18-80 600 58.67 162/130 48.7 44.6-52.7
Chen[135] 2022 Cross-sectional Nanning (Guangxi Zhuang Autonomous Region) 2021.01-2021.12.31 2 19-89 1485 75.82 251/99 23.6 21.4-25.8
Cao et al[136] 2017 Cross-sectional Baise (Guangxi Zhuang Autonomous Region) 2012.1-2015.12 2 ≥ 7 3363 52.75 930/659 47.2 45.6-49.0
Zhang et al[137] 2023 Cross-sectional Qionghai (Hainan Province) NA 1 14-85 535 36.64 77/152 42.8 38.6-47.1
Qiu and Xu[138] 2017 Cross-sectional Chengmai County (Hainan Province) 2015.01-2016.12 2 22-85 1977 68.03 483/225 35.8 33.7-38.0
Ma et al[139] 2015 Cross-sectional Haikou (Hainan Province) 2013.07-2013.10 Interdental tartar 21-81 4122 51.55 550/507 25.6 24.3-27.0
Zeng et al[140] 2023 Cross-sectional Wuzhishan (Hainan Province) 2023.03 1 ≥ 18 528 26.70 61/162 42.2 38.0-46.6
Liu et al[141] 2023 Cross-sectional Hainan Province 2021.07-2022.04 1 NA 1355 44.72 269/360 46.4 43.7-49.1
Central China
Zhao et al[142] 2022 Cross-sectional Zhengzhou (Henan Province) 2020.01-2020.12 1 18-80 8312 61.81 2009/1217 38.8 37.8-39.9
Chai et al[143] 2021 Cross-sectional Zhengzhou (Henan Province) 2018-2019 1 18-87 2589 55.66 391/303 26.8 25.1-28.6
Wang et al[144] 2018 Cross-sectional Henan Province 2017.01-2017.06 1 18-67 2974 56.93 599/418 34.2 32.5-35.9
Liu et al[145] 2021 Cross-sectional Nanyang (Henan Province) 2019.05-2021.02 2 18-78 856 59.00 324/206 61.9 58.6-65.2
Gu et al[146] 2014 Cross-sectional Luohe (Henan Province) NA 2 13-77 1874 49.73 559/524 57.8 55.5-60.0
Fan et al[147] 2015 Cross-sectional Pingdingshan (Henan Province) 2012.04-2012.06 2 20-83 449 61.02 153/65 48.6 43.8-53.3
Yu et al[148] 2022 Cross-sectional Zhengzhou (Henan Province) 2020.09-2021.4 2 45.36 ± 19.38 772 42.75 173/246 54.3 50.7-57.8
Lei et al[149] 2023 Cross-sectional Zhengzhou (Henan Province) 2020.09-2021.03 2 3-90 731 54.31 164/233 54.3 50.6-58.0
Zhang et al[150] 2019 Cross-sectional Wuhan (Hubei Province) 2017.05-2017.10 1 20-60 11365 72.59 3449/1242 41.3 40.4-42.2
Yang[151] 2020 Cross-sectional Jingmen (Hubei Province) 2018.01-2018.12 1 15-81 984 57.62 243/125 37.4 34.4-40.5
Li[152] 2022 Cross-sectional Xianyang (Hubei Province) 2016.10-2017.07 1 15-87 1756 56.09 598/445 59.4 57.1-61.7
Xi[153] 2014 Cross-sectional Wuhan (Hubei Province) 2012.03-2013.02 1 16-80 3012 48.51 684/733 46.7 45.0-48.5
Liu et al[154] 2017 Cross-sectional Wuhan (Hubei Province) 2015.07-2015.08 1 20-91 2366 55.83 376/315 29.2 27.4-31.1
Zhou and Lin[155] 2018 Cross-sectional Huangshi (Hubei Province) 2013.10-2016.6 1 3-13 1240 51.94 596/284 46.0 43.2-48.9
Li et al[156] 2014 Cross-sectional Xiaogan (Hubei Province) 2012.6-2013.2 2 12-81 2005 55.16 899/486 50.7 48.5-52.9
Jia et al[157] 2016 Cross-sectional Wuhan (Hubei Province) 2015.03-2015.08 1 51.2 2180 52.02 549/446 47.0 44.9-49.1
Deng et al[158] 2020 Cohort study Wuhan (Hubei Province) 2018.1-2019.12 1 20-70 2619 63.54 778/331 42.3 40.4-44.3
Li et al[159] 2015 Cross-sectional Chenzhou (Hunan Province) 2013.08-2014.12 2 > 20 7015 34.23 663/1230 27.0 25.9-28.0
Peng et al[160] 2019 Cross-sectional Chenzhou (Hunan Province) 2016.01-2017.01 2 22-70 3123 63.24 680/382 34.0 32.3-35.7
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NA: Not available.

The pooled prevalence of H. pylori in Mainland China

The prevalence of H. pylori infection was analyzed in 152 studies. The heterogeneity test produced an I2 = 99.7%, P < 0.001, necessitating the use of a random-effects model. The combined findings suggest that the prevalence of H. pylori infection in China from 2014 to 2023 stood at 42.8% (95%CI: 40.7-44.9). The corresponding forest plot is shown in Supplementary Figure 1[10-160].

Subgroup analysis

Owing to substantial heterogeneity among the studies, prevalence rates were separately analyzed based on province, municipality, autonomous region, geographic region, gender, age, detection method, and publication year (Figure 2).

Figure 2.

Figure 2

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Subgroup analysis of Helicobacter pylori prevalence. N: Number of included studies; UBT: Urea breath test; RUT: Rapid urease test.

Among the 30 provinces, municipalities, and autonomous regions, the highest prevalence of H. pylori was observed in Qinghai Province at 60.2% (95%CI: 46.5-73.9), and the lowest in Liaoning Province at 24.7% (95%CI: 15.7-33.7). Forest plots detailing the prevalence across these areas are presented in Supplementary Figure 2. A bubble diagram illustrating the incidence of H. pylori across these administrative divisions and geographic areas is provided in Figure 3. Of the seven geographical regions, the Northwest exhibited the highest prevalence at 51.3% (95%CI: 45.6-56.9), while the Northeast had the lowest at 29.6% (95%CI: 21.0-38.2). These forest plots are available in Supplementary Figure 3.

Figure 3.

Figure 3

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Bubble diagram of Helicobacter pylori prevalence.

Regarding gender differences, the prevalence of H. pylori was slightly higher in males at 44.1% (95%CI: 41.9-46.4) than in females at 41.6% (95%CI: 39.3-44.0); these findings are depicted in Supplementary Figure 4. The meta-analysis revealed that the prevalence of H. pylori was 27.0% (95%CI: 19.9-34.0) in minors and 42.6% (95%CI: 39.9-45.2) in adults. Among the elderly (≥ 60 years), a higher prevalence of 44.5% (95%CI: 41.9-47.1) was noted (Supplementary Figures 5 and 6).

Concerning detection methods, 97 studies used the UBT and 2 studies employed the Rapid Urease Test (RUT). The infection rates were 43.7% (95%CI: 41.4-46.0) using the UBT, 42.5% (95%CI: 37.9-47.2) with the serum antibody test, 43.5% (95%CI: 12.9-74.2) using the RUT, and 24.1% (95%CI: 11.7-36.6) with the H. pylori stool antigen method. The highest infection rate was detected by the UBT method, while the lowest was by the H. pylori stool antigen method. Forest plots for these testing modalities are included in Supplementary Figure 7. From the literature spanning 2014-2018, the prevalence of H. pylori infection was 42.2% (95%CI: 39.2-45.3), and from 2019-2024, it was 43.3% (95%CI: 40.4-46.2). The slight increase in prevalence over these periods was not statistically significant (Supplementary Figures 8 and 9).

Sensitivity analysis

Sensitivity analysis of the combined results was performed using the "leave-one-out" method. The robustness of the study results was confirmed as there were no significant changes; this analysis is shown in Supplementary Figure 10.

Publication bias assessment

Publication bias was evaluated using a funnel plot and the AS-Thompson test, with arcsine transformation used in cases of significant heterogeneity. The analysis yielded a symmetrical funnel plot (t = 1.42, P = 0.157), indicating minimal publication bias. These results are visually displayed in Supplementary Figure 11 and 12.

DISCUSSION

H. pylori, a gram-negative microaerobic bacterium, forms colonies on the epithelial surfaces of the human gastric mucosa, leading to chronic infections[162]. This bacterium has a global prevalence, affecting approximately fifty percent of the world's population, thereby constituting a significant health issue. While a significant majority of infected individuals exhibit no symptoms, the infection carries considerable health implications, as 1%-3% of those affected may progress to GC, and 0.1% may develop mucosa-associated lymphoid tissue lymphoma[163]. The optimal initial therapeutic approach involves a combination of proton pump inhibitors (PPI), amoxicillin, and clarithromycin, commonly referred to as PPI, amoxicillin, and clarithromycin. In regions with elevated primary clarithromycin resistance, bismuth-based quadruple therapies are becoming a favored alternative[164]. Additionally, traditional Chinese medicine[165,166] and microecological agents[167,168] have become crucial in boosting eradication rates and combating drug resistance to H. pylori.

Given the demographic and regional disparities in China, understanding the prevalence of H. pylori is vital for informing future research and healthcare strategies.

This study revealed that the infection rate of H. pylori in China from 2014 to 2023 was 42.8% (95%CI: 40.7-44.9), a decrease from the rates reported in earlier periods by Ren et al[3] from 1990 to 2019 (44.2%, 95%CI: 43.0-45.5) and Li et al[169] from 1983 to 2020 (49.6%, 95%CI: 46.9-52.4). Research by Li et al[170] suggests that the global estimated prevalence of H. pylori decreased from 58.2% (95%CI: 50.7-65.8) between 1980 and 1990 to 43.1% (95%CI: 40.3-45.9) between 2011 and 2022. With the continuous improvement of living environment and hygiene level, and the increasing coverage of basic medical security, the prevalence of H. pylori in China has also shown a downward trend, similar to the global trend of H. pylori prevalence.

The ingestion of raw water and the consumption of vegetables and fruits that have been washed from contaminated water sources represent risk factors for H. pylori infection[171,172]. Nevertheless, the dissemination of H. pylori infections has been constrained alongside enhancements in living standards and hygiene, including augmented access to clean water and more effective sewage treatment[173]. Public awareness of H. pylori and its modes of transmission also resulted in reducing infection rates[174,175]. Studies[176,177] have shown that the public's knowledge about H. pylori and how it is spread has increased in the last decade. The detection of H. pylori infection has been included in the health examination items. The rapid, simple and reliable detection method has been preliminarily accessible. The awareness of active medical treatment among infected people was significantly enhanced[7]. The above reasons have all contributed to the decline of H. pylori infection rate in China in the past decade.

However, the diseases associated with them remain among the great challenges to public health. This report presents updated comprehensive trends in clinically significant and relevant H. pylori infection based on a critical analysis of 152 studies that have been published over the past decade. The results of this study is of a certain credibility and timeliness, and has a certain clinical reference value.

The highest prevalence rate of H. pylori infection was found in Northwest China, which showed a percentage of 51.3% (95%CI: 45.6-56.9). In the northwest region, it is highly prevalent in Qinghai Province, showing a prevalence of 60.2% (95%CI: 46.5-73.9). On the contrary, the lowest infection rates were observed in Northeast China, where the prevalence was 29.6% (95%CI: 21.0-38.2), and the minimum rate was in Liaoning Province at 24.7% (95%CI: 15.7-33.7).

H. pylori has co-evolved with the human host ever since its origin. Local transmission and genetic isolation have facilitated the development of different bacterial populations that are characteristic for geographical areas[178]. You et al[179] conducted a comparative genomic analysis of the genomic sequence features of Chinese H. pylori, and the results of Neighbor Joining analyses of 10 Chinese H. pylori strains showed geographic clustering of H. pylori strains in China. Geographical factors play a long-term and fundamental role in regional development. The expansive northwest region of China, distinguished by its extensive deserts and delicate ecosystems, also sustains robust animal husbandry and a considerable population of nomadic herders. Inhabitants predominantly consume a meat-rich diet[180], with vegetable intake falling below the levels suggested by the Chinese Balanced Dietary Pagoda[181]. The economic development in Northwest China is relatively underdeveloped, and compared with other regions, the allocation of health resources is insufficient[182]. Oral-oral and fecal-oral pathways stand as the primary mechanisms for the transmission of H. pylori, which tends to cluster within families[183]. The region of Northeast China is characterised by fertile land, abundant water and food resources, rich mineral resources and a relatively early economic start. The long winter season and low population density serve to restrict the dissemination of H. pylori infection. Although infection rates in the Northeast are comparably low, the prevalent antibiotic resistance remains a substantial issue[184]. Two provinces in the Southwest region, Qinghai-Tibet (58.1%; 95%CI: 53.6-62.6) and Guizhou Province (56.6%; 95%CI: 53.1-60.1), have particularly high infection rates.

Zhang et al[185] has shown that the high rates of H. pylori infection and GC among Tibetan residents of the plateau are not only related to the backward level of health and economy, but also to the environmental characteristics of the plateau. The frigid, oxygen-deprived conditions of the plateau can result in gastric mucosal injury and disruption to the intestinal barrier, thereby enhancing susceptibility[186]. The Guizhou Province region has a humid climate and the locals enjoy spicy diets and pickled foods. Both were found to be independent risk factors for H. pylori infection[187,188].

Consequently, elements such as environmental conditions, economic status, and sanitation practices collectively impact the prevalence of H. pylori to varying extents.

Addressing the pronounced prevalence of H. pylori infection in Northwest China, particularly given the high incidence and mortality rates of GC in Qinghai Province and Guizhou Province[6], we should launched health education initiatives in these high-incidence and high-risk areas to improve the population's awareness of the risks associated with H. pylori, alongside the available treatment options and their benefits.

So as to fostering changes in knowledge, attitudes, and behaviors, and enable general population actively participate in the work of prevention, screening and treatment of H. pylori. This approach additionally facilitates the primary prevention of GC[177,189]. In regions characterized by heightened antibiotic resistance, the use of antibiotics should be meticulously regulated. Where conditions permit, treatment programmes can be tailored to the antibiotic susceptibility results of individual patients. Compared to these high prevalence areas, infections in other areas are not as serious. However, the prevention and treatment of the disease should not be neglected. In these areas, the compliance of infected people with prescribed treatment and re examination should be improved in order to improve the eradication rate[190]. At the same time, family based management and treatment strategies should be implemented to avoid reinfection. Promote a healthy diet and reduce the intake of ultra-processed food; develop good living habits and actively prevent the occurrence of H. pylori infection[191].

In this study, the prevalence of infection was notably higher in adults compared to minors, with significant prominence in the elderly (> 60 years) (Supplementary Figure 5 and 6). The elderly are more susceptible due to distinctive gastric features, including delayed gastric emptying[192], diminished glandular function[193,194], and a less diverse microbial environment in the stomach[195,196]. People worldwide are living longer. The worldwide demographic is experiencing a significant increase in age, with forecasts from the World Health Organization suggesting that the population aged 60 years and above will double to 2.1 billion by the year 2050[197]. H. pylori is associated with gastrointestinal disturbances as well as the development of various systemic diseases. Studies suggest that H. pylori infection could increase the likelihood of developing neurodegenerative disorders, including Alzheimer's disease[198,199] and Parkinson's syndrome[200], in addition to cardiovascular diseases[201]. In treating the elderly, medical professionals must consider factors like antibiotic resistance, potential drug interactions, and adherence to medication, while carefully balancing the risks and benefits of eradication therapy[202,203].

The incidence of H. pylori infection in pediatric populations shows considerable variation internationally, with reported rates ranging from 3.2% to 84%, influenced by factors including geographical location, environmental conditions, diagnostic techniques, host specificity, and the timing of the study[204]. Factors such as early exposure to pre-chewed food, familial history of gastric disorders, the sharing of personal hygiene items like towels and mouthwash cups[205,206], and low socioeconomic status[207] are significant contributors to the risk of H. pylori infection in children. Recent updated guidelines do not recommend the "test-and-treat" approach for asymptomatic H. pylori infections in pediatric populations, referencing a lack of substantial evidence supporting its effectiveness in reducing the risk of future GC[208].

Regarding detection methods, the highest infection rate identified by the UBT was 43.7% (95%CI: 41.4-46.0), surpassing those detected by serological tests, stool antigen tests, and RUT. Ma et al[139] reported an H. pylori positivity rate of 26.85% utilizing an immunocolloidal gold assay for interdental tartar in subjects. The UBT, preferred for its non-invasive, painless, and repeatable nature, includes two variations: 13C-UBT and 14C-UBT, with the former being more sensitive, non-radioactive, and therefore safer[209]. Recent global guidelines endorse the UBT as the preferred diagnostic tool for H. pylori, recommending it for both initial diagnosis and post-eradication evaluation[210-212], with an emphasis on maintaining stringent quality controls to improve the test's precision. The monoclonal-based stool antigen test also remains a reliable, recommended method with comparable accuracy to the UBT, particularly beneficial for children under six who are less suited for UBT[213]. Serum H. pylori antibody tests are predominantly used in clinical epidemiological studies to ascertain both past and current infections[164,214], and their diagnostic accuracy is enhanced when used in conjunction with UBT[215]. However, the value of serological screening is limited in areas with low infection rates[216]. The identification of H. pylori subtypes aids in assessing infection status and virulence, and can forecast the development of GC[217]. Although RUTs are highly sensitive, their specificity is compromised by factors like sample size and the focal distribution of H. pylori in the stomach[218]. Selecting a diagnostic approach requires consideration of regional infection rates, clinical scenarios, patient age, among other factors, to ensure the selection of suitable and reliable tests for effective treatment planning.

This study's strengths are evident in: (1) Utilizing recent literature spanning the last ten years to mirror the current situation of H. pylori infection; (2) Conducting an exhaustive examination of H. pylori's current prevalence in China, pinpointing key demographics and areas where infection rates are heightened; (3) Incorporating a significant corpus of studies with ample sample sizes, excluding any with fewer than 50 subjects to reduce potential small-sample biases; and (4) Rigorous adherence to established protocols for selecting literature and extracting data, supplemented by cross-validation to enhance the reliability of findings.

The limitations of this study include the following: (1) Some provinces and diagnostic modalities in this study had a low number of included studies, which may have had some impact on the results; and (2) Although we performed subgroup analyses for the results of the study in terms of geographic region, testing modality, age, gender, and so on, there was still a large degree of heterogeneity in the study.

CONCLUSION

In summary, the prevalence of H. pylori infection in China from 2014-2023 is 42.8% (95%CI: 40.7-44.9), marking a decrease from the preceding decade. The rates of H. pylori infection vary depending on geographic location, detection methods, and population demographics. Given the substantial disease burden associated with H. pylori infections, further studies into the mechanisms behind the disparities in H. pylori prevalence, both in China and globally, is both significant and urgent.

ACKNOWLEDGEMENTS

We are very grateful to every participant for their support and contributions to this study.

Footnotes

Conflict-of-interest statement: The authors disclose that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.

Provenance and peer review: Unsolicited article; Externally peer reviewed.

Peer-review model: Single blind

Specialty type: Gastroenterology and hepatology

Country of origin: China

Peer-review report’s classification

Scientific Quality: Grade B, Grade C, Grade C

Novelty: Grade B, Grade B, Grade B

Creativity or Innovation: Grade B, Grade B, Grade B

Scientific Significance: Grade B, Grade B, Grade B

P-Reviewer: Zhang W; Zhang J S-Editor: Luo ML L-Editor: A P-Editor: Zheng XM

Contributor Information

Lu Xie, Department of Acquired Immune Deficiency Syndrome Treatment and Research Center, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou 450000, Henan Province, China; The First Clinical Medical School, Henan University of Chinese Medicine, Zhengzhou 450046, Henan Province, China.

Guang-Wei Liu, Department of Spleen, Stomach, Liver and Gallbladder, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou 450000, Henan Province, China.

Ya-Nan Liu, Department of Acquired Immune Deficiency Syndrome Treatment and Research Center, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou 450000, Henan Province, China; The First Clinical Medical School, Henan University of Chinese Medicine, Zhengzhou 450046, Henan Province, China.

Peng-Yu Li, Department of Acquired Immune Deficiency Syndrome Treatment and Research Center, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou 450000, Henan Province, China; Henan Key Laboratory of Viral Diseases Prevention and Treatment of Chinese Medicine, Henan University of Chinese Medicine, Zhengzhou 450000, Henan Province, China.

Xin-Ning Hu, Department of Acquired Immune Deficiency Syndrome Treatment and Research Center, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou 450000, Henan Province, China; The First Clinical Medical School, Henan University of Chinese Medicine, Zhengzhou 450046, Henan Province, China.

Xin-Yi He, Department of Acquired Immune Deficiency Syndrome Treatment and Research Center, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou 450000, Henan Province, China; The First Clinical Medical School, Henan University of Chinese Medicine, Zhengzhou 450046, Henan Province, China.

Rui-Bo Huan, Department of Acquired Immune Deficiency Syndrome Treatment and Research Center, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou 450000, Henan Province, China; The First Clinical Medical School, Henan University of Chinese Medicine, Zhengzhou 450046, Henan Province, China.

Tai-Long Zhao, Department of Acquired Immune Deficiency Syndrome Treatment and Research Center, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou 450000, Henan Province, China; The First Clinical Medical School, Henan University of Chinese Medicine, Zhengzhou 450046, Henan Province, China.

Hui-Jun Guo, Department of Acquired Immune Deficiency Syndrome Treatment and Research Center, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou 450000, Henan Province, China; Henan Key Laboratory of Viral Diseases Prevention and Treatment of Chinese Medicine, Henan University of Chinese Medicine, Zhengzhou 450000, Henan Province, China. guohuijun@hactcm.edu.cn.

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