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. 2023 Mar 30;11:e15121. doi: 10.7717/peerj.15121

The prevalence of clarithromycin-resistant Helicobacter pylori isolates: a systematic review and meta-analysis

Mohammad Sholeh 1, Saeed Khoshnood 2,3, Taher Azimi 4, Jasem Mohamadi 5, Vahab Hassan Kaviar 2, Marzieh Hashemian 6, Somayeh Karamollahi 6, Nourkhoda Sadeghifard 2, Hedayat Heidarizadeh 5, Mohsen Heidary 7,8,✉, Morteza Saki 9,✉
Editor: Bernardo Franco
PMCID: PMC10066884  PMID: 37016679

Abstract

Background

Knowledge of global clarithromycin (CLA)-resistant rates of Helicobacter pylori (H. pylori) is crucial for decision of the most appropriate eradication therapies with good clinical outcomes. Therefore, this review and meta-analysis aimed to evaluate the global prevalence of the CLA resistance in H. pylori to provide some guidance for selecting the first-line antibiotics.

Method

A comprehensive search was performed for relevant literature until April 2021 in PubMed, Embase, and Web of Science databases. Freeman-Tukey double arcsine transformation was performed to estimate the weighted pooled prevalence of resistance.

Results

The meta-analysis included 248 articles. The prevalence of CLA-resistant H. pylori was 27.53% (95% CI [25.41–29.69]). The heterogeneity between reports was significant (I2 = 97.80%, P < 0.01). The resistance rate increased from 24.28% in 2010–2017 to 32.14% in 2018–2021 (P < 0.01). Iran, with 38 articles, has the most report. Nevertheless, Switzerland, Portugal, and Israel had the highest resistance rates (67.16%, 48.11%, and 46.12%, respectively). The heterogeneity between the continents and the antimicrobial susceptibility methods also interpreted standard guidelines and breakpoints was insignificant (P > 0.05).

Conclusion

Overall CLA resistance rate was 27.53%, worldwide. The difference in CLA resistance rate among the included studies can be due to several reasons such as differences in antibiotic prescription rates in various geographic areas, use of different breakpoints or inaccurate criteria in performed studies, and the emergence of multidrug-resistant (MDR) strains.

Keywords: Clarithromycin, Meta-analysis, Antibiotic resistance, Helicobacter pylori

Introduction

Helicobacter pylori is one of the most successful human pathogens that affects approximately 50% of the population worldwide. In developing countries 70% to 90% of the population are infected by this bacterium (Arenas et al., 2019; Kocsmár et al., 2021). H. pylori infection is related to many gastric diseases, such as peptic ulcers, chronic gastritis, uninvestigated and functional dyspepsia and mucosa-associated lymphoid tissue lymphoma, and even increases the risk of gastric cancer (Savoldi et al., 2018). As for the high prevalence of the bacterium and its related diseases, proper treatment is very important. Today, standard treatment is a three-stage drug that consists of an acid neutralizer and two antibiotics, clarithromycin (CLA), and amoxicillin or metronidazole for 14 days (Hosseini et al., 2021).

However, treatment is difficult because the bacterium quickly develops resistance to the few antibiotics known to be effective (Park et al., 2016). The World Health Organization (WHO) has classified it among the 12 most resistant bacteria in the world (Essaidi et al., 2022). The increasing failure rate of eradication treatment due to the appearance of resistant H. pylori strains contributes to the worldwide prevalence of this infection and subsequent inflammatory and neoplastic disorders. Unfortunately, nowadays, the success of this treatment is less than 80% worldwide (Kocsmár et al., 2021; Hussein, Al-Ouqaili & Majeed, 2022).

CLA has been emerged as the basis for H. pylori treatment in combined therapy because of small effect on gastric acidity, its low minimal inhibitory concentration, and relatively good mucosal diffusion (Marques et al., 2020; Nishizawa & Suzuki, 2014). Due to extensive usage of CLA in some geographical regions, global prevalence rate of CLA resistance is increasing (Zou et al., 2020). In developing countries, CLA resistance and frequency of re-infection are factors that contribute to high worldwide prevalence of H. pylori infection and subsequent inflammatory and neoplastic disorders (Alarcón-Millán et al., 2016). In most European countries, as well as the rest of the world, the prevalence of CLA resistance has reached 20%. With rare exceptions, it is no longer recommended to include CLA in empirical treatment in regions where primary resistance to this antibiotic is 20% (Alarcón-Millán et al., 2016; Morilla et al., 2019).

Knowledge of global CLA-resistant rates of H. pylori is crucial for decision of the most appropriate eradication therapies with good clinical outcomes. Therefore, the aim of current review and meta-analysis is to evaluation of the global prevalence of the CLA resistance in H. pylori.

Method

Search strategy

A comprehensive search was conducted by two researchers in the online databases PubMed, Embase, and Web of Science until April 2021, using relevant keywords such as clarithromycin, antibiotic resistance, and H. pylori, as well as related MeSH terms (see Supplemental File 1 for the search syntax). The search syntax is available in Table 1.

Table 1. A systematic search including PubMed, Embase, and Web of Science with relevant keywords such as clarithromycin, antibiotic resistance, and Helicobacter pylori.

First author (Reference) Country Enrollment time Published year Type of study N. patients Mean age N. HP N. Clarithromycin-resistant AST method Breakpoint
Horie et al. (2020) Japan 2005–2018 2020 RET 5,249 58.3 1300 426 MIC 1
Haddadi et al. (2020) Iran 2020 CS 280 46 128 3 DD CLSI 2015
21
Eisig et al. (2011) Brazil 2011 PCS 54 46.6 39 3 MIC 1
Aftab et al. (2016) Bangladesh 2014–2014 2015 CS 133 35.2 56 22 MIC 0.25
Ortiz et al. (2019) Honduras 2013–2013 2019 CS 189 54 116 13 MIC 0.5
Silva et al. (2018) Portugal 2013–2017 2018 PCS 74 14 58 7 MIC 1
Almeida et al. (2014) Portugal 2009–2013 2014 PCS 180 43.4 180 90 MIC 1
Ilie et al. (2011) Romania 2011 CS 100 Range: 19–80 70 22 DD >20
CLSI 2010
Vécsei et al. (2011) Austria 2007–2009 2011 RET 96 10.8 96 16 MIC 1
Ranjbar & Chehelgerdi (2018) Iran 2016–2017 2018 CS 700 Range: 3–72 526 335 DD 21
Hamza et al. (2018) Egypt 2018 CS 150 20 12 DD 21
Gong et al. (2020) South Korea 2017–2018 2020 RET 13 46 38 MIC 0.5
Wang et al. (2020) China 2018–2019 2020 CS 124 124 44 MIC 0.5
Su et al. (2022) Taiwan 2009–2019 2021 RET 87 13.5 65 15 MIC 1
Sugimoto et al. (2017) Japan 2011–2015 2016 RET 111 55.2 111 90 MIC 1
Abadi et al. (2011) Iran 2009–2009 2011 CS 210 40.7 197 89 DD 30
Teh et al. (2014) Malaysia 2014 CS 110 102 7 MIC 1
Peng et al. (2017) China 2013–2014 2017 CS 178 41.6 78 38 MIC 1
Hashemi et al. (2019) Iran 2015–2016 2019 CS 150 157 38 MIC 1
Lauener et al. (2019) Switzerland 2013–2017 2019 CS 140 140 96 MIC 1
Domanovich-Asor et al. (2020) Israel 2015–2019 2020 CS 48 48 26 MIC 1
Wu et al. (2015) Taiwan 2010–2014 2015 RET 137 137 95 MIC 1
Vala et al. (2016) Iran 2011–2012 2016 CS 80 20 4 MIC 0.5
Omar et al. (2014) Australia 2014 CS 11 46.8 11 8 MIC 1
Vilaichone et al. (2016) Thailand 2013–2013 2016 CS 291 46.6 124 7 MIC 0.5
Lee et al. (2014) South Korea 2003–2013 2014 PCS 2,202 52.9 475 147 MIC 1
Lee et al. (2019) South Korea 2014–2018 2018 PCS 85 55.2 74 24 MIC 1
Goudarzi et al. (2016) Iran 2014–2014 2016 CS 65 42 65 28 MIC 1
Karpinski et al. (2015) Poland 1998–1999
2013–2014		 2015 CS 108 108 9 MIC 1
Miyata et al. (2021) Japan 2007–2018 2020 CS 119 12 45 26 MIC 1
Palmitessa et al. (2020) Italy 2017–2018 2020 CS 224 48.6 92 49 MIC 0.5
Hung et al. (2021) Taiwan 2016–2019 2021 RET 197 54.8 62 9 MIC 1
Miftahussurur et al. (2016) Japan 2012–2012 2016 CS 146 42.2 42 9 MIC 0.25
Siddiqui et al. (2016) Pakistan 2008–2013 2016 CS 889 35.6 92 5 MIC 0.5
Sugimoto et al. (2014) Japan 2009–2013 2014 CS 153 153 64 MIC 1
Jolaiya et al. (2020) Nigeria 2020 CS 492 104 41 MIC 0.5
Pandya et al. (2014) India 2008–2011 2014 CS 125 80 47 DD 30
Lehours, Siffré & Mégraud (2011) France 2009–2009 2011 CS 127 43 26 MIC 0.5
Sun et al. (2018) China 2018 CS 49 Range: 27–76 43 9 MIC 0.75
Dekhnich et al. (2018) Russia 2009–2017 2018 CS 783 51.8 276 16 MIC 0.5
Sugimoto et al. (2020) Japan 2015–2019 2020 RET 307 62.3 307 102 MIC 1
Siavoshi, Saniee & Malekzadeh (2018) Iran 2018 CS 450 44.1 104 37 MIC 2
Szadkowski, Zemlak & Muszynski (2018) Poland 2005–2015 2018 CS 154 55 15 DD 21
Costa, Soares & Goncalves (2017) Portugal 2012–2016 2017 RET 42 48.9 42 36 DD 17
Aguilera-Correa et al. (2017) Spain 2016 CS 136 84 48 MIC 0.5
Akar et al. (2021) Turkey 2018–2019 2021 CS 422 50 133 25 MIC 0.5
Yula et al. (2013) Turkey 2010–2011 2012 CS 110 41.4 79 7 MIC 1
Zhang et al. (2019) China 2015–2016 2018 CS 150 149 104 MIC 1
Macin et al. (2015) Turkey 2006–2012 2015 CS 311 Range: 5–19 93 28 MIC 1
Auttajaroon et al. (2019) Thailand 2017–2017 2019 CS 93 54.5 70 9 MIC 0.5
Eghbali et al. (2016) Iran 2012–2013 2016 CS 89 53.6 89 5 MIC 1
Wu et al. (2014) Taiwan 2014 CS 231 43 5 MIC 1
Kocazeybek et al. (2019) Turkey 2014–2017 2019 CS 63 47.08 63 24 MIC 1
Egli et al. (2020) Switzerland 2013–2017 2020 CS 76 76 49 MIC 1
Khani, Talebi Bezmin Abadi & Mohabati Mobarez (2019) Iran 2017–2018 2019 CS 81 56.8 61 13 MIC 0.5
Morimoto et al. (2015) Japan 2014 RET 135 62.3 135 35 MIC 1
Alarcón-Millán et al. (2016) Mexico 2016 CS 144 48.3 45 8 DD 18
Tamayo et al. (2017) Spain 2013–2015 2017 CS 6,228 1986 349 MIC 1
Yoon et al. (2014) South Korea 2005–2010 2014 RET 204 52.5 212 18 MIC 1
Miftahussurur et al. (2017) Dominican 2017 CS 158 47.1 64 2 MIC 8
Mohammad et al. (2011) Iran 2007–2007 2011 CS 263 84 19 MIC 1
Ha et al. (2019) Vietnam 2012–2017 2018 CS 185 42.3 104 56 MIC 1
Tanih, Ndip & Ndip (2011) South Africa 2011 CS 254 44.5 200 40 MIC 1
Yeganeh et al. (2019) Israel 2016–2016 2019 PCS 218 42 218 96 MIC 1
Liu et al. (2019) China 2010–2017 2019 RET 1,463 1463 296 MIC 0.5
Zhu et al. (2013) China 2002–2006 2012 CS 365 365 42 MIC 1
Farzi et al. (2019) Iran 2014–2015 2018 CS 97 Ranging 10–70 40 14 MIC 0.25
Abdollahi et al. (2019) Iran 2017–2018 2019 CS 191 38.2 63 20 DD 21
Lee et al. (2019) South Korea 2015–2018 2018 CS 1,422 140 43 MIC 0.5
De Francesco et al. (2014) Italy 2011–2012 2014 CS 82 82 42 MIC 0.5
Seo et al. (2013) South Korea 1990–1994
2005–2009		 2013 CS 91 11.8 91 10 MIC 1
Kouitcheu Mabeku et al. (2019) Cameroon 2013–2015 2019 CS 140 140 19 DD 21
Yin et al. (2020) China 2016–2016 2016 CS 267 9.4 169 57 MIC 1
Chen et al. (2018) China 2018 CS 12 12 6 MIC 1
Kakiuchi et al. (2020) Japan 2018–2018 2020 CS 71 14.7 years 23 7 MIC 0.5
Cuadrado-Lavín et al. (2012) Spain 2010–2010 2011 CS 76 68 10 MIC 2
Gehlot et al. (2016) India 2011–2014 2015 CS 68 Range: 18–86 68 8 MIC 0.5
Ogata, Gales & Kawakami (2014) Brazil 2008–2009 2014 CS 77 11.1 77 16 MIC 1
Eng et al. (2015) Canada 2012–2013 2015 CS 301 20 8 MIC 0.5
Alarcón et al. (2017) Spain 2007–2014 2017 CS 824 26 824 422 MIC 0.5
Akhtereeva et al. (2018) Russia 2011–2013 2018 CS 76 13.6 30 9 DD 30
Selgrad et al. (2013) Germany 2005–2012 2013 RET 436 51.7 159 12 MIC 1
Gunnarsdottir et al. (2017) Iceland 2012–2013 2017 PRO 613 57 105 9 MIC 1
Mahmoudi et al. (2017) Iran 2014–2015 2017 CS 90 9.4 32 7 MIC 1
Shokrzadeh et al. (2011) Iran 2007–2008 2010 CS 92 45 ± 18 M 38 ± 14 F 42 6 MIC 1
Savari et al. (2010) Iran 2009–2009 2010 CS 191 Range: 14–84 63 19 DD 21
Shu et al. (2018) China 2012–2014 2018 CS 1,390 9.5 545 112 MIC 8
Mosites et al. (2018) USA 2000–2016 2018 CS 763 52 800 238 MIC 1
Parra-Sepúlveda et al. (2019) Chile 2005–2007
2015–2017		 2019 CS 1,655 48.8 405 96 DD 21
Fiorini et al. (2018) Italy 2010–2016 2018 CS 1,730 51.1 1424 114 MIC 0.5
Shao et al. (2018) China 2013–2016 2017 CS 2,283 2283 519 MIC 1
Li et al. (2020) China 2019–2019 2021 CS 157 10.9 87 48 MIC 0.5
Su et al. (2013) China 2010–2012 2013 CS 51,891 17731 3810 MIC 1
Hojsak et al. (2012) Croatia 2001–2010 2012 RET 2,313 12.9 168 20 MIC 1
Hamidi et al. (2020) Iran 2017–2018 2020 CS 80 50.2 50 11 MIC 0.5
An et al. (2013) Korea 2009–2012 2013 RET 165 165 20 MIC 1
Shiota et al. (2015) USA 2009–2013 2015 CS 656 128 6 MIC 1
Li et al. (2017) China 2009–2015 2017 RET 5,610 14 1746 286 MIC 1
Bolor-Erdene et al. (2017) Mongolia 2011–2014 2017 CS 320 43.7 152 54 MIC 1
Boehnke et al. (2017) Peru 2011–2013 2017 CS 109 76 27 MIC 0.5
Ahmad, Zakaria & Mohamed (2011) Malaysia 2004–2007 2011 CS 777 187 4 MIC 1
Rasheed et al. (2014) USA 2011–2012 2014 CS 93 47.4 46 22 MIC 1
Guo et al. (2019) China 2016–2017 2018 CS 346 Range: 1–15 22 8 MIC 1
Jiang et al. (2021) China 2017–2019 2021 CS 1,533 1533 721 MIC 0.5
Butenko et al. (2017) Slovenia 2011–2014 2017 RET 107 12 104 25 MIC 8
Tveit et al. (2011) Alaska 2000–2008 2011 CS 1,181 51 531 159 MIC 1
Tuan et al. (2019) Vietnam 2019 CS 206 45.3 55 14 MIC 8
Maev et al. (2020) Russia 2015–2018 2020 CS 27 27 3 MIC 0.5
Figueroa et al. (2012) Colombia 2012 CS 203 40 146 29 MIC 1
Kim et al. (2011) Korea 2008–2008 2011 CS 99 54.6 99 26 MIC 1
Adeniyi et al. (2012) Nigeria 2012 CS 52 Range: 10–90 43 3 DD 30
Yao et al. (2019) Taiwan 2013–2014 2019 RET 719 61.2 41 14 MIC 1
Honma et al. (2019) Japan 2012–2015 2018 CS 1,298 14 13 5 MIC 1
Bayati et al. (2019) Iran 2014–2015 2019 CS 170 Range: 30–75 55 27 MIC 0.5
Pichon et al. (2020) France 2012–2014 2020 CS 3 33.3 189 1 MIC 0.5
Tanabe et al. (2018) Japan 2013–2016 2018 RET 1,355 212 50 MIC 1
Karabiber et al. (2014) Turkey. 2014 CS 159 98 23 DD 30
Saracino et al. (2020) Italy 2009–2019 2020 NA 3,178 52.3 1646 553 MIC 0.5
Liang et al. (2020) Taiwan 2013–2019 2020 RET 1,369 54.0 ± 11.9 1369 226 MIC 1
Khademi et al. (2014) Iran 2011–2012 2014 CS 130 30 4 MIC 1
Milani et al. (2012) Iran 2010–2011 2012 CS 395 35 ± 19 112 16 MIC 1
Famouri et al. (2018) Iran 2015–2018 2018 CS 102 8.65 ± 3.88 48 17 MIC 2
Bruce et al. (2019) Alaska 1998–2006 2019 PRO 362 260 74 MIC 1
Park et al. (2020) Korea 2017–2019 2020 PRO 174 70 20 MIC 0.5
Binh et al. (2013) Vietnam 2008–2008 2013 CS 103 44.8 103 34 MIC 1
Keshavarz Azizi Raftar et al. (2015) Iran 2013 CS 246 45.78 ± 16.23 95 32 MIC 1
Ang et al. (2016) Singapore 2000–2014 2016 RET 708 708 97 MIC 1
Gościniak et al. (2014) Poland 2008–2011 2014 CS 165 165 50 MIC 1
Wang et al. (2019) China 1998–2017 2019 CS 454 50.74 ± 10.942 100 31 MIC 1
Bai et al. (2015) China 2013–2013 2015 CS 181 44.9 181 56 MIC 0.5
Mégraud et al. (2021) France 2014–2018 2020 CS 951 52.4 ± 15.7 741 157 MIC 0.5
Sadeghifard et al. (2013) Iran 2009–2010 2013 CS 50 50 16 DD 20
Bedoya-Gómez et al. (2020) Colombia 2019 PRO 115 41.8 61 5 MIC 0.5
Miftahussurur et al. (2016) Japan 2012–2015 2016 PRO 849 49.25 77 7 MIC 0.25
Erkut et al. (2020) Turkey 2010–2011 2020 PRO 344 39.3 104 29 MIC 1
Zhang et al. (2018) China 2013 2018 CS 394 136 10 MIC 1
Tsay et al. (2012) Taiwan 2005–2009 2011 RET 233 55.7 32 2 MIC 1
Mascellino et al. (2018) Italy 2017 2020 RET 80 59 80 28 MIC 0.5
Khoury et al. (2017) Israel 2012–2015 2017 RET 107 64 26 MIC 0.5
Saracino et al. (2020) Italy 2016–2019 2020 RET 270 51.4 221 202 MIC 0.5
Lin et al. (2020) Taiwan 2008–2017 2019 RET 490 54.5 228 33 MIC 1
Alfizah et al. (2014) Malaysia 2004–2007 2014 CS 99 161 2 MIC 1
Fasciana et al. (2015) Italy 2015 CS 100 100 25 MIC 0.5
Ayala et al. (2011) Mexico 2002–2004 2011 CS/PRO 460 90 9 MIC 2
Picoli et al. (2014) Brazil 2011–2012 2014 CS 342 54 6 MIC 1
Larsen et al. (2013) Norway 2008–2009 2012 CS NA 102 6 MIC 0.5
Kumar et al. (2020) USA 2009–2019 2019 RET 109 65 39 MIC 0.5
Khademi et al. (2013) Iran 2011–2012 2013 CS 260 45.8 ± 17.8 78 12 MIC 1
Peretz et al. (2014) Israel 2011– 2012 2014 CS 176 85 20 MIC 1
Chung et al. (2012) Korea 2004–2007 2011 CS 185 50.7 ± 14.4 185 20 MIC 1
Ghotaslou et al. (2013) Iran 2013 CS 123 35 ± 18 123 21 DD 30
Kostamo et al. (2011) Finland 2000–2008 2010 RET 3,045 62 1037 83 MIC 1
Demiray-Gürbüz et al. (2017) Turkey 2006–2011 2016 CS 234 43.8 ± 14.0 114 32 MIC 1
Agudo et al. (2011) USA 2008 2011 CS 118 118 42 MIC 1
Matta, Zambrano & Pazos (2018) Colombia 2018 CS 409 74 34 MIC 1
Song et al. (2014) China 2008–2012 2014 PRO/CS 600 42.5 ± 13.2 600 225 MIC 0.5
Wüppenhorst et al. (2014) Germany 2001–2012 2014 PRO 1,651 1523 475 MIC 1
Shi, Jiang & Zhao (2016) China 2016 CS 328 328 78 MIC 1
Talebi Bezmin Abadi et al. (2012) Iran 2009–2010 2011 CS 170 38.6 150 51 MIC 1
Boyanova et al. (2017) Bulgaria 2011–2016 2017 CS 233 59.1 233 60 MIC 0.5
Manfredi et al. (2015) Italy 2011–2012 2015 CS 66 9.8 46 12 MIC 4
Morilla et al. (2019) Spain 2004–2016 2019 RET 3,426 55.7 ± 16.9 1439 278 MIC 0.5
Vekens et al. (2013) Belgium 2009–2010 2013 PRO 507 48.8 180 24 MIC 1
Maleknejad et al. (2015) Iran 2012–2014 2015 CS 169 7.30 ± 3.12 21 1 DD 30
Oleastro et al. (2011) Portugal 2000–2009 2011 PRO 1,115 10.17 ± 4.03 1115 387 MIC 1
Zhang et al. (2015) China 2009–2010
2013–2014		 2015 PRO/CS 1,555 42.4 1321 648 MIC 0.5
Dargiene et al. (2018) Lithuania 2013–2015 2017 CS 297 32.85 79 2 MIC 0.5
Liu et al. (2011) China 2009–2010 2011 CS 120 10.0 ± 5.8 73 62 MIC 1
Liu et al. (2018) China 2010–2016 2017 PRO 1,117 960 247 MIC 1
Tang et al. (2020) China 2017–2019 2020 CS 400 44.7 117 52 MIC 0.5
Bachir et al. (2018) Algeria 2012–2015 2017 CS 200 151 38 MIC 0.5
Seck et al. (2013) Senegal 2007–2009 2013 CS 108 45.3 108 1 MIC 1
Karczewska et al. (2011) Poland 2006–2008 2011 CS 115 115 39 MIC 1
Lee et al. (2019) South Korea 2003–2018 2019 PRO 740 56.3 740 280 MIC 1
Raaf et al. (2017) Algeria 2015–2016 2017 PRO 147 43 16 DD 17
Hansomburana et al. (2012) Thailand 2006–2008 2012 PRO 200 52.8 82 11 MIC 1
Mirzaei et al. (2013) Iran 2011–2011 2013 CS 110 34 48 7 MIC 1
Lee et al. (2013) Korea 2003–2012 2013 PRO 433 55.53 433 127 MIC 1
Shokrzadeh et al. (2015) Iran 2010–2011 2014 CS 197 46 111 29 MIC 1
Oporto et al. (2019) Chile 2018 2019 CS 229 50.68 44 18 MIC 0.5
Aumpan et al. (2020) Thailand 2019 2020 CS 58 43.8 14 4 MIC 0.5
Vilaichone et al. (2020) Thailand 2010–2015 2020 CS 1,178 41.5 357 7 MIC 0.5
Cerqueira et al. (2011) Portugal 2011 CS NA 33 21 MIC 1
Binyamin et al. (2017) Israel 2015–2016 2017 CS 85 54 34 MIC 1
Camorlinga-Ponce et al. (2021) Chile 1997–2017 2021 CS 167 50.72 167 15 MIC 0.5
Biernat et al. (2020) Poland 2016–2019 2020 RET 108 12.5 91 28 MIC 0.5
Trespalacios et al. (2013) Colombia 2009–2011 2013 CS 256 276 42 MIC 1
Lok et al. (2020) China 2018–2019 2020 CS 176 48.4. 65 34 MIC 0.5
Bahmaninejad et al. (2021) Iran 2020–2020 2021 CS 100 50 33 MIC 1
Draeger et al. (2015) Germany 2004–2013 2015 RET 481 481 409 MIC 1
Zerbetto De Palma et al. (2017) Argentina 2011–2013 2015 CS 52 52 14 MIC 0.5
Boyanova et al. (2012) Bulgaria 2004–2010 2012 CS 519 52.16 519 93 MIC 1
Tshibangu-Kabamba et al. (2020) Congo 2017–2018 2020 CS 220 45.3 ± 15.3 102 24 MIC 0.5
Okuda et al. (2017) Japan 1997–2013 2016 RET 332 11.6 ± 3.4 76 33 MIC 1
Vilaichone et al. (2013) Thailand 2004–2012 2013 CS 3,964 53.3 400 15 MIC 0.5
Zhang et al. (2020) China 2017–2019 2020 CS 238 238 84 MIC 0.5
Zhang et al. (2020) China 2012–2014 2020 CS 79 9.7 ± 2.8 79 29 MIC 1
Mansour et al. (2016) France 2009–2009 2015 PRO 149 53.65 42 12 MIC 1
Kuo et al. (2021) Taiwan 2017–2020 2021 CS 64 53.8 41 38 MIC 0.5
Miendje Deyi et al. (2011) Belgium 1990–2009 2011 CS 9,430 29.3 9430 524 MIC 1
Han et al. (2016) China 2015–2015 2016 CS 325 47.2 325 65 MIC 1
Bińkowska et al. (2018) Italy 2008–2016 2018 CS 170 170 29 MIC 1
Bachir et al. (2018) Algeria 2014–2016 2018 PRO 270 212 53 MIC 0.5
Hanafiah et al. (2019) Malaysia 2014–2015 2019 CS 288 52.41 ± 16.44 59 21 MIC 1
Vazirzadeh et al. (2020) Iran 2018–2018 2020 CS 165 50:3 ± 15:5 83 21 MIC 0.5
Rezaei, Abadi & Mobarez (2020) Iran 2015–2018 2019 CS 200 54 73 17 MIC 0.5
Yakoob et al. (2013) Pakistan 2008– 2010 2013 CS 120 41 ± 13 47 17 MIC 1
Gehlot et al. (2016) India 2011–2013 2016 CS 483 43 68 8 MIC 0.5
Boyanova et al. (2013) Bulgaria 2007– 2012 2013 RET 588 588 118 MIC 1
Boyanova et al. (2015) Bulgaria 2012–2014 2015 CS 53 50.7 53 9 MIC 0.5
Otth et al. (2011) Chile 2010 CS 240 54.5 ± 15.7 88 8 MIC 2
McNulty et al. (2012) Uk 2009–2010 2012 CS 2,063 241 86 MIC 1
Wang et al. (2018) China 2013–2014 2018 CS NA 100 13 MIC 0.5
Alavifard et al. (2021) Iran 2017–2019 2020 CS 82 49.7 ± 3.33 82 36 MIC 0.5
Regnath et al. (2017) Germany 2002–2015 2016 RET 582 12 years 608 75 MIC 0.5
Lu et al. (2019) Taiwan 1998–2018 2019 RET 70 13.2 ± 3.2 70 16 MIC 1
Di Giulio et al. (2016) Italy 2010–2014 2015 CS 115 181 131 MIC 0.5
Enany & Abdalla (2015) Egypt 2015 CS 150 107 6 DD 40
Trespalacios et al. (2015) Colombia 2014 CS 127 107 42 MIC 1
Gatta et al. (2018) Italy 2010–2015 2018 RET 1,682 1325 478 MIC 0.5
Goudarzi et al. (2016) Iran 2015–2015 2016 CS 154 110 28 MIC 1
Bayati et al. (2020) Iran 2019 CS 170 30 ± 75. 55 27 MIC 0.5
Dang et al. (2020) Vietnam 2014–2016 2020 CS 153 38.3 ± 10.7 153 111 MIC 1
Phan et al. (2015) Vietnam 2012–2014 2014 CS 92 44.1 ± 13.4 92 39 MIC 1
Khashei et al. (2016) Iran 2014–2014 2016 CS 318 41.5 100 20 MIC 1
Shetty et al. (2019) Australia 2014–2017 2019 CS 180 46.2 ± 14 113 23 MIC 0.5
Macías-García et al. (2017) Spain 2014–2016 2017 PROCS 217 64 76 17 MIC 1
Farzi et al. (2019) Iran 2016–2017 2019 CS 160 46.5 ± 8.3 68 23 MIC 1
Lyu et al. (2020) China 2016–2018 2020 PRO 1,113 43 791 271 MIC 0.5
Shmuely et al. (2020) Israel 2013–2017 2020 RET/CS 128 45 128 70 MIC 256
Ogata et al. (2013) Brazil 2008–2009 2013 CS 77 11.1 ± 3.9 77 15 MIC 2
Abadi et al. (2011) Iran 2008–2010 2011 CS 147 34.5 147 32 MIC 1
korn Vilaichone et al. (2017) Thailand 2016–2016 2017 CS 148 56.3 ± 13.3 50 1 MIC 0.5
Ferenc et al. (2017) Poland 2011 and 2013 2016 CS 185 49 ± 16.8 67 37 MIC 1
Azzaya et al. (2020) Mongolia 2014–2016 2020 CS 361 44.3 ± 13.4 361 108 MIC 0.5
Mi et al. (2021) China 2018–2018 2021 CS 48 65 21 MIC 0.5
Boyanova et al. (2014) Bulgaria 2012–2013 2014 CS 50 50.5 50 11 MIC 0.5
Boyanova et al. (2016) Bulgaria 2010–2015 2015 CS 299 47.3 299 84 MIC 0.5
Megraud et al. (2021) France 2018–2019 2021 PRO 1,211 51.2 1211 259 MIC 0.5
Megraud et al. (2013) France 2008–2009 2013 PRO 2,204 2204 431 MIC 1
Ducournau et al. (2016) France 2014–2015 2016 CS 984 51.5 ± 15.9 266 59 MIC 1
Bouihat et al. (2017) France 2015–2016 2016 PRO 255 47.5 177 45 MIC 0.5
Fernández-Reyes et al. (2019) Spain 2014–2017 2019 PRO 112 99 12 MIC 0.5
Saniee et al. (2018) Iran 2010–2017 2018 CS 985 218 75 DD 2
Mokhtar et al. (2019) Malaysia 2015–2016 2019 CS 352 52 13 4 MIC 0.5
Montes et al. (2015) Spain 2008–2012 2014 RET 143 74 25 MIC 1
Deyi et al. (2019) Belgium 2015–2016 2019 CS 846 846 141 MIC 0.5
Tang et al. (2020) China 2016–2019 2020 CS NA 301 201 MIC 0.5
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Study selection

All records obtained from online databases were imported into EndNote (Version 20), and duplicates were eliminated. M-H and S-K independently assessed the titles and abstracts; V-H-K resolved discrepancies. Studies were considered to be appropriate for the analysis if they presented data concerning the prevalence of H. pylori resistant to CLA. An English language restriction was imposed, while abstracts, conferences, case reports, case series, reviews, studies with unclear results, and duplicate articles were excluded from the analysis.

Data extraction

Our study included studies based on pre-defined criteria and evaluated as full-text articles. Two reviewers conducted the data extraction process independently (M-H, S-K). Any discrepancies were discussed and resolved by consensus of the two reviewers. The primary outcome of focus was the prevalence of clarithromycin-resistant Helicobacter pylori. Information extracted from each study included the first author’s name, year of publication, geographical location, antimicrobial susceptibility testing method, breakpoints for interpretation of the test results, sample size, and the number of clarithromycin-resistant H. pylori. All extracted data are available in an accompanying Supplemental File.

Quality assessment

Two reviewers (S-K and M-H) evaluated the quality of the studies using the Newcastle Ottawa Scale (NOS). In cases of disagreement, a third author (M-SH) was consulted to determine a consensus. The assessment of the studies was based on three criteria: selection, comparability, and exposure/outcome assessments.

Statistical analysis

For the present study, the sample size of isolates for antimicrobial susceptibility testing (AST) and the number of resistances to each antibiotic were used to calculate a weighted pooled resistance and their 95% confidence intervals. In order to prevent the exclusion of studies from the meta-analysis due to 0 or 100 resistance prevalence, the Inverse of Freeman-Tukey double arcsine transformation was conducted using Metaprop command in STATA software (version 17.1). A random-effects model was implemented to estimate pool proportions (Egger et al., 1997; Harbord et al., 2010). The I2 with a P ≤ 0.05 was used to identify significant heterogeneity. The presence of a small-study effect or publication bias was assessed using Egger’s linear regression test and Begg’s test (Harbord, Harris & Sterne, 2009). Subgroup analyses were conducted to determine the impact of the country, continent, publication year (2010–2017, 2018–2021), (AST) (Disc diffusion, Gradient methods), and breakpoints for interpretation of AST results on the variation.

Results

Descriptive statistics

In this research, 19,169 records were acquired in EndNote version 20, a reference manager software. A total of 8,689 duplicated articles were then removed, leaving a total of 247 eligible studies that were included in the systematic review and meta-analysis. The screening and selection presage were summarized in the PRISMA flow chart (Fig. 1). Overly 20,936 H. pylori isolates have been investigated in included articles. More than half of the isolates were investigated in Asia (55.10% Isolated). Although most pieces were from Iran (38 articles), the highest number of isolates among the countries was that investigated from China (32,130 Isolates, 36.52% of total isolates). Description data are summarized in Table 2.

Figure 1. The study PRISMA flow diagram.

Figure 1

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Table 2. Clarithromycin-resistant Helicobacter pylori prevalence. 95% Confidence Intervals (CI) were used. P ≤ 0.05 was considered statistically significant.

No of article Clar-resistant, Total isolates Proportion (LCI, HCI) Weight I2 (P)
Overall 248 8736, 87991 27.53 (25.41, 29.69) 100.00 97.80% (P = 0.00)
2010–2017 143 12891, 60452 24.28 (21.7, 26.96) 57.68 97.91% (P = 0.00)
2018–2021 105 8045, 27476 32.14 (28.69, 35.69) 42.32 97.24% (P = 0.00)
Iran 38 1193, 3628 27.24 (21.68, 33.18) 14.91 93.14% (P = 0.00)
Finland 1 83, 1037 8.00 (6.43, 9.83) 0.43 NA
Chile 4 137, 704 18.56 (8.47, 31.34) 1.62 91.76% (P = 0.00)
Brazil 4 40, 247 15.29 (9.79, 21.7) 1.55 38.94% (P = 0.18)
Romania 1 22, 70 31.43 (20.85, 43.63) 0.40 NA
Austria 1 16, 96 16.67 (9.84, 25.65) 0.41 NA
France 8 990, 4873 21.13 (15.26, 27.66) 3.31 95.23% (P = 0.00)
Eastern Cape 1 40, 200 20 (14.69, 26.22) 0.42 NA
Spain 8 1161, 4650 27.41 (17.03, 39.18) 3.30 98.22% (P = 0.00)
Malaysia 5 38, 522 10.2 (1.59, 23.94) 1.91 93.33% (P = 0.00)
Alaska 2 233, 791 29.45 (26.31, 32.68) 0.86 NA
Korea 5 213, 952 20.59 (12.26, 30.37) 2.07 90.69% (P = 0.00)
Taiwan 10 453, 2088 29.16 (15.9, 44.45) 3.92 96.85% (P = 0.00)
Mexico 2 17, 135 12.3 (7.14, 18.53) 0.78 NA
USA 5 347, 1157 32.98 (17.21, 50.95) 2.03 95.84% (P = 0.00)
Portugal 5 541, 1428 48.11 (30.07, 66.41) 1.97 95.52% (P = 0.00)
China 32 8227, 32130 34.05 (29.33, 38.92) 13.14 98.16% (P = 0.00)
Poland 6 178, 601 29.77 (18.41, 42.52) 2.42 90.49% (P = 0.00)
Belgium 3 689, 10456 11.28 (3.95, 21.67) 1.29 NA
Turkey 7 170, 684 25.78 (19.44, 32.67) 3.22 76.74% (P = 0.00)
Croatia 1 20, 168 11.9 (7.43, 17.79) 0.42 #VALUE!
Colombia 5 152, 664 24.26 (12.96, 37.68) 2.04 92.33% (P = 0.00)
Nigeria 2 44, 147 28.22 (21.13, 35.86) 0.78 NA
Norway 1 6, 102 5.88 (2.19, 12.36) 0.41 NA
Thailand 7 54, 1097 6.24 (2.73, 10.86) 2.73 81.45% (P = 0.00)
Bulgaria 6 375, 1742 21.89 (18.2, 25.81) 2.48 66.49% (P = 0.01)
UK 1 86, 241 35.68 (29.64, 42.09) 0.42 NA
South Korea 7 560, 1778 31.4 (19.68, 44.43) 2.88 96.35% (P = 0.00)
Germany 4 971, 2771 32.08 (6.55, 65.66) 1.71 99.64% (P = 0.00)
Vietnam 5 254, 507 45.72 (28.85, 63.11) 2.02 93.56% (P = 0.00)
Senegal 1 1, 108 0.93 (0.02, 5.05) 0.41 NA
Pakistan 2 22, 139 13.33 (8.04, 19.63) 0.78 NA
Australia 2 31, 124 23.47 (16.01, 31.75) 0.67 NA
Japan 12 854, 2494 35.89 (27.02, 45.26) 4.68 93.72% (P = 0.00)
India 3 63, 216 25.25 (2.81, 59.01) 1.19 NA
Italy 11 1663, 5367 40.38 (25.65, 56.04) 4.55 99.12% (P = 0.00)
Israel 6 272, 597 46.12 (35.66, 56.75) 2.39 84.00% (P = 0.00)
Bangladesh 1 22, 56 39.29 (26.5, 53.25) 0.39 NA
Canada 1 8, 20 40.00 (19.12, 63.95) 0.32 NA
Argentina 1 14, 52 26.92 (15.57, 41.02) 0.38 NA
Egypt 2 18, 127 10.61 (5.53, 16.89) 0.73 NA
Singapore 1 97, 708 13.70 (11.25, 16.46) 0.43 NA
Dominican 1 2, 64 3.13 (0.38, 10.84) 0.39 NA
Iceland 1 9, 105 8.57 (3.99, 15.65) 0.41 NA
Mongolia 2 162, 513 31.54 (27.57, 35.64) 0.84 NA
Peru 1 27, 76 35.53 (24.88, 47.34) 0.40 NA
Slovenia 1 25, 104 24.04 (16.2, 33.41) 0.41 NA
Lithuania 1 2, 79 2.53 (0.31, 8.85) 0.40 NA
Algeria 3 107, 406 26.62 (21.42, 32.15) 1.21 NA
Russia 3 28, 333 13.34 (2.11, 30.9) 1.12 NA
Honduras 1 13, 116 11.21 (6.1, 18.4) 0.41 NA
Switzerland 2 145, 216 67.16 (60.71, 73.31) 0.81 NA
Cameroon 1 19, 140 13.57 (8.37, 20.38) 0.41 NA
Congo 1 24, 102 23.53 (15.69, 32.96) 0.41 NA
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Note:

High confidence interval, HCI; low confidence interval, LCI; I-squared, I2; Degrees of freedom, DF.

Publication bias

The publication bias was significant by the regression-based Egger test for small-study effects (P = 0.04), but Begg’s test for small-study effects was insignificant (P = 0.09). The Nonparametric trim-and-fill analysis of publication bias also did not change the effect size. The funnel plot also did not have significant evidence of publication bias (Fig. 2A). The sensitivity analysis or one leave-out method also had no significant bias.

Figure 2. Meta-analysis charts.

Figure 2

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(A) The funnel plot of clarithromycin-resistant Helicobacter pylori prevalence did not have significant evidence of publication bias; (B) the subgroup analysis forest plot of clarithromycin-resistant Helicobacter pylori prevalence in different continents; (C) the subgroup analysis forest plot of clarithromycin-resistant Helicobacter pylori prevalence using different AST methods; (D) subgroup analysis forest plot of clarithromycin-resistant Helicobacter pylori prevalence in different breakpoints to interpret antimicrobial susceptibility test data; (E) subgroup analysis forest plot of clarithromycin-resistant Helicobacter pylori prevalence in years; (F) the regression analysis of clarithromycin-resistant Helicobacter pylori prevalence over years with 95% Confidence interval had a significant correlation 0.013 (95% CI [0.01–0.02]) (P < 0.001).

Meta-analysis

In 248 included studies, 20,936 isolates have been investigated, and 8,736 isolates have been reported as resistant. The pooled prevalence of CLA-resistance H. pylori was 27.53 (95% CI [25.41–29.6]). Heterogeneity between reports was significant (I2 = 97.80, P < 0.01). The heterogeneity between countries was substantial (P < 0.001). Switzerland, Portugal, and Israel had the highest resistance rates (67.16%, 48.11%, and 46.12%, respectively), and Senegal, Lithuania, and the Dominican Republic had the lowest resistance prevalence, 0.93%, 2.53%, and 3.13%, respectively) (Table 2). The heterogeneity between the continent subgroups was insignificant (P > 0.05) (Fig. 2B). The heterogeneity between the AST methods subgroup was insignificant (Fig. 2C). The breakpoints for the interpretation AST subgroup were insignificant (P > 0.05) (Fig. 2D). The CLA-resistant H. pylori prevalence increased from 24.28% in 2010–2017 to 32.14% in the 2018–2021 years period (P < 0.01) (Fig. 2E). All statistics are summarized in Table 2. The regression meta-analysis for resistance rate over the publication year had a significant correlation of 0.013 (95% CI [0.01–0.02]) (P < 0.001) (Fig. 2F).

Discussion

Over the past years, the treatment of H. pylori infections has been performed using the standard triple therapy regimen, including CLA, a proton pump inhibitor, with either metronidazole or amoxicillin (Gong et al., 2020). However, in recent years, it is revealed that some H. pylori isolates have developed resistance to CLA (Sanches et al., 2016). Therefore, the efficacy of the standard triple therapy regimen is in decline. In 2017, WHO listed the CLA-resistant H. pylori among antibiotic-resistant priority pathogens that need research and development of new antibiotics (Khani, Abadi & Mobarez, 2019). Globally, surveillance and being aware of the frequency of resistance to antibiotics among pathogens is critical, and obtained results can be helpful in different sections such as the design of screening or follow-up programs, and the development of antimicrobial stewardship programs (Azimi et al., 2019; Pormohammad, Nasiri & Azimi, 2019).

In the present systematic review and meta-analysis study, we surveyed and analyzed the worldwide prevalence of CLA resistance among H. pylori isolates from 2010 to 2021. The awareness of CLA resistance among different countries of the world and effective treatment of H. pylori infections are the main goal of the current study. The present systematic review and meta-analysis study included 247 eligible studies from 54 different countries. Our analyses revealed that the overall prevalence of clarithromycin-resistance H. pylori was 27.53%, worldwide.

Resistance to CLA among H. pylori is occur in two different levels including (1) a high level of resistance (MIC more than 64 mg l−1) and (2) a low level of resistance (0.5 ≤ MIC ≤ 1 mg l−1) (He et al., 2021). Point mutations, multidrug efflux pump systems, and synergistic effect of mutations in genes rpl22 (ribosomal protein L22) and infB (translation initiation factor IF-2) with 23S rRNA point mutations are the main CLA resistance mechanisms among H. pylori isolates (Marques et al., 2020; Li et al., 2021). Moreover, it is presumed that some outer-membrane proteins have a role in CLA resistance in H. pylori isolates (Marques et al., 2020). In the Western world and among developed countries, more than 90% of CLA resistance is related to point mutations in the peptidyl transferase region of the V domain of 23S rRNA gene (Mégraud, 2004). The main point mutations related to CLA resistance are A2142G, A2143G (adenine-to-guanine transition at either position 2142 or 2143), A2142C (adenine-to-cytosine transversion at position 2142), A2115G, A2144T, G2141A, G2144T, T2289C, T2717C, and C2694A (Gong et al., 2020; Marques et al., 2020; Li et al., 2021). Moreover, hp1181 and hp1184 mutations are associated with CLA resistance (Li et al., 2021). Mutation in the 2142 and 2143 positions leads to restricted resistance and different levels of resistance, respectively (Kim et al., 2020).

In the present research, more than half of the included studies were performed in Asia. These results demonstrated that CLA resistance is a main public health issue in most Asian countries. Among studies surveyed CLA resistance rates in 54 different countries, Switzerland (67.16%) and Senegal (0.93%) had the highest and lowest resistance rates, respectively. The high level of CLA resistance can be due to the following reasons: (1) inappropriate prescription and unregulated or widespread use of CLA, and (2) the use of CLA in other infections such as respiratory tract infections or intestinal parasites infections (Chen et al., 2017). Time trend analyses revealed that the CLA-resistant rates among H. pylori isolate increased from 24.28% in 2010–2017 to 32.14% in the 2018–2021 years’ period. An increase in CLA resistance rates is an alarming finding. In areas where CLA-resistance is more than 15%, it is recommended to perform susceptibility testing before prescribing the standard triple therapy regimen (Sanches et al., 2016; Abadi, 2017). Combination therapy with other drugs such as tinidazole can be helpful in the treatment of H. pylori infections. It is revealed that CLA combined with tinidazole can reduce the CLA resistance rate, decrease inflammatory reactions, and can effectively eliminate H. pylori infections (He et al., 2021). One of the limitations of this study was that we evaluated the CLA resistance rate only and the other antibiotics were not considered.

Conclusion

Our analysis revealed that CLA resistance rates varied among studies performed in different 54 countries. Altogether, results showed that the overall CLA resistance rate is 27.53%, worldwide. The difference in CLA resistance rate among the included studies can be due to several reasons such as differences in antibiotic prescription rates in various geographic areas, use of different MIC breakpoints or inaccurate criteria in performed studies, and the emergence of multidrug-resistant (MDR) strains. We performed a time trend analysis and the results revealed that the clarithromycin-resistance rates in increasing in recent years. Based on our findings, systematic surveillance, and proper monitoring of CLA resistance rates, as well as monitoring the use of CLA in patients, and performing the CLA susceptibility test before prescription may be critical actions for the inhibition and control of H. pylori infections.

Supplemental Information

Supplemental Information 1. Prisma 2009 checklist.
Click here for additional data file. (66.5KB, doc)
DOI: 10.7717/peerj.15121/supp-1
Supplemental Information 2. Characteristics of studies included in the meta-analysis.
Click here for additional data file. (29.5KB, xlsx)
DOI: 10.7717/peerj.15121/supp-2

Funding Statement

The authors received no funding for this work.

Contributor Information

Mohsen Heidary, Email: mohsenheidary40@gmail.com.

Morteza Saki, Email: mortezasaki1981@gmail.com.

Additional Information and Declarations

Competing Interests

Morteza Saki is a PeerJ Academic Editor. The authors declare that they have no competing interests.

Author Contributions

Mohammad Sholeh conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Saeed Khoshnood conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Taher Azimi conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Jasem Mohamadi performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Vahab Hassan Kaviar conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Marzieh Hashemian conceived and designed the experiments, prepared figures and/or tables, and approved the final draft.

Somayeh Karamollahi conceived and designed the experiments, prepared figures and/or tables, and approved the final draft.

Nourkhoda Sadeghifard conceived and designed the experiments, performed the experiments, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Hedayat Heidarizadeh conceived and designed the experiments, performed the experiments, prepared figures and/or tables, and approved the final draft.

Mohsen Heidary conceived and designed the experiments, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Morteza Saki conceived and designed the experiments, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Data Availability

The following information was supplied regarding data availability:

The raw measurements are available in the Supplemental File.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental Information 1. Prisma 2009 checklist.
Click here for additional data file. (66.5KB, doc)
DOI: 10.7717/peerj.15121/supp-1
Supplemental Information 2. Characteristics of studies included in the meta-analysis.
Click here for additional data file. (29.5KB, xlsx)
DOI: 10.7717/peerj.15121/supp-2

Data Availability Statement

The following information was supplied regarding data availability:

The raw measurements are available in the Supplemental File.

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