Fei Zhao; Zinan Zhao; Di Chen; Bolin Zhu; Tianqi Zhang; Yuanchao Zhu; Xuelin Sun; Liang Liang; Nan Zheng; Lili Zou; Wenfeng Xu; Sirui Guo; Yue Wang; Ming Zhao; Xin Hu; Pengfei Jin

doi:10.3967/bes2025.169

Trends in Antibiotic Consumption and Antimicrobial Resistance in China: An Ecological Analysis from 2016 to 2022

Department of Pharmacy, Beijing Hospital; National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Science; Beijing Key Laboratory of Assessment of Clinical Drugs Risk and Individual Application (Beijing Hospital), Beijing 100730, China

More Information

Author Bio:
Fei Zhao, Associate Researcher, PhD, Research Direction: Pharmacoepidemiology, Tel: 86-10-85133648, E-mail: zhaofei21cn@126.com

Zinan Zhao, Chief Pharmacist, Master's Degree, Research Direction: Clinical Pharmacy, Tel: 86-10-85133648, E-mail: bellazhao2015@163.com

Corresponding author: Pengfei Jin, Prof, Tel: 86-10-85133620, E-mail: j790101@163.com

PFJ, XH, ZNZ, and FZ conceptualized the study. FZ and ZNZ performed the analyses. DC, BLZ, TQZ, YCZ, XLS, LL, NZ, LLZ, MZ, WFX, YW and SRG were involved in data curation and database management. FZ and TQZ designed this study. ZNZ, DC, YCZ, LL and BLZ accessed and verified the data. FZ, ZNZ, XH and PFJ interpreted the data. FZ wrote the first draft of the manuscript, which was edited and reviewed by all the authors. All the authors have read and approved the final version of the manuscript. All authors had full access to all data in the study and had the final responsibility for the decision to submit the manuscript for publication.

All authors declare no competing interests.

Not applicable.

^&These authors contributed equally to this work.

Abstract: Objective To examine national trends in antibiotic consumption and antimicrobial resistance (AMR) among six WHO-priority bacterial pathogens in China from 2016 to 2022. Methods This ecological study analyzed national and provincial data from the China Antibacterial Resistance Surveillance System (CARSS) and the National Hospital Information Network. Beta regression models assessed temporal trends, and hierarchical models evaluated associations between antibiotic use and resistance. Results From 2016 to 2022, carbapenem resistance in Acinetobacter baumannii and Pseudomonas aeruginosa, and vancomycin resistance in Enterococcus faecium and E. faecalis significantly declined (β < 0, P < 0.010), while carbapenem-resistant Klebsiella pneumoniae increased (β = 0.081, P < 0.001). Nationwide antibiotic consumption rose across 10 major classes. Positive associations were found between carbapenem use and resistance in A. baumannii (z = 2.719, P = 0.007) and P. aeruginosa (z = 3.241, P = 0.001), and between vancomycin use and resistance in E. faecium (z = 4.510, P = 0.001) and E. faecalis (z = 3.210, P = 0.001). Conclusion Carbapenem-resistant K. pneumoniae increased significantly in China, while other resistant pathogens declined. Resistance patterns were linked to the use of multiple antibiotic classes, underscoring the need for strengthened antibiotic stewardship and surveillance.

Key words:

注释:

1) Authors’ Contributions: 2) Competing Interests: 3) Ethic:

Variable

CRAB

CRPA

CRKP

CREC

β (95% CI)

Year

−0.04
(−0.06, −0.02)

<0.001

−0.05
(−0.06, −0.04)

<0.001

0.06
(0.03, 0.09)

<0.001

9.87×10⁻⁴
(−0.01, 0.02)

0.896

Population

6.96×10⁻⁵
(2.33×10⁻⁵, 11.59×10⁻⁵)

0.003

4.52×10⁻⁵
(1.05×10⁻⁶, 8.94×10⁻⁵)

0.045

1.55×10⁻⁴
(5.70×10⁻⁵, 2.53×10⁻⁴)

0.002

7.83×10⁻⁵
(2.64×10⁻⁵, 1.30×10⁻⁴)

0.003

Aminoglycosides

3.34×10⁻⁷
(−2.35×10⁻⁷, 9.03×10⁻⁷)

0.250

1.90×10⁻⁸
(−3.67×10⁻⁷, 4.05×10⁻⁷)

0.923

−6.71×10⁻⁷
(−1.29×10⁻⁶, −5.60×10⁻⁸)

0.033

1.54×10⁻⁷
(−3.98×10⁻⁷, 7.06×10⁻⁷)

0.584

Amphenicols

2.92×10⁻⁶
(−5.34×10⁻⁶, 1.12×10⁻⁵)

0.489

5.51×10⁻⁶
(4.51×10⁻⁷, 1.06×10⁻⁵)

0.033

−7.23×10⁻⁶
(−1.53×10⁻⁵, 8.70×10⁻⁷)

0.080

−6.51×10⁻⁶
(−1.31×10⁻⁵, 5.68×10⁻⁸)

0.052

β−lactamase inhibitors

2.63×10⁻⁸
(−4.59×10⁻⁸, 9.85×10⁻⁸)

0.476

−4.19×10⁻⁸
(⁻⁹.28×10⁻⁸, 8.97×10⁻⁹)

0.106

−8.63×10⁻⁸
(−1.66×10⁻⁷, −6.45×10⁻⁹)

0.034

4.15×10⁻⁹
(−6.94×10⁻⁸, 7.77×10⁻⁸)

0.912

Carbapenems

1.07×10⁻⁷
(−1.67×10⁻⁷, 3.82×10⁻⁷)

0.444

−2.42×10⁻⁸
(−2.32×10⁻⁷, 1.84×10⁻⁷)

0.819

−2.34×10⁻⁷
(−5.87×10⁻⁷, 1.18×10⁻⁷)

0.192

2.02×10⁻⁷
(−6.57×10⁻⁸, 4.69×10⁻⁷)

0.139

Cephalosporins

1.00×10⁻⁸
(−4.93×10⁻⁹, 2.50×10⁻⁸)

0.189

−1.37×10⁻⁹
(−1.47×10⁻⁸, 1.20×10⁻⁸)

0.841

−2.13×10⁻⁸
(−4.16×10⁻⁸, −1.04×10⁻⁹)

0.039

9.73×10⁻¹⁰
(−1.72×10⁻⁸, 1.92×10⁻⁸)

0.916

Vancomycin & norvancomycin

1.24×10⁻⁷
(−1.73×10⁻⁶, 1.92×10⁻⁶)

0.896

−4.58×10⁻⁷
(−1.92×10⁻⁶, 1.00×10⁻⁶)

0.538

−1.71×10⁻⁶
(−4.15×10⁻⁶, 7.42×10⁻⁷)

0.172

1.55×10⁻⁶
(⁻⁸.48×10⁻⁸, 3.18×10⁻⁶)

0.063

Teicoplanin

2.31×10⁻⁶
(−1.86×10⁻⁶, 6.48×10⁻⁶)

0.278

5.23×10⁻⁷
(−2.50×10⁻⁶, 3.54×10⁻⁶)

0.734

−2.21×10⁻⁶
(−7.11×10⁻⁶, 2.69×10⁻⁶)

0.376

3.21×10⁻⁶
(−1.33×10⁻⁶, 7.75×10⁻⁶)

0.166

Lincomycin−Clindamycin

2.07×10⁻⁸
(−1.43×10⁻⁷, 1.85×10⁻⁷)

0.804

−2.76×10⁻⁸
(−1.52×10⁻⁷, 9.63×10⁻⁸)

0.662

1.70×10⁻⁸
(−1.95×10⁻⁷, 2.29×10⁻⁷)

0.875

7.49×10⁻⁸
(−1.06×10⁻⁷, 2.55×10⁻⁷)

0.416

Macrolides

1.27×10⁻⁸
(−7.27×10⁻⁹, 3.26×10⁻⁸)

0.213

6.07×10⁻⁹
(⁻⁸.00×10⁻⁹, 2.01×10⁻⁸)

0.398

−3.07×10⁻⁸
(−5.28×10⁻⁸, −8.57×10⁻⁹)

0.007

−1.09×10⁻⁹
(−2.45×10⁻⁸, 2.23×10⁻⁸)

0.928

Oxazolidinones

−3.21×10⁻⁸
(⁻⁹.66×10⁻⁷, 9.01×10⁻⁷)

0.946

−7.48×10⁻⁷
(−1.61×10⁻⁶, 1.10×10⁻⁷)

0.088

6.44×10⁻⁸
(−1.29×10⁻⁶, 1.41×10⁻⁶)

0.925

2.09×10⁻⁷
(⁻⁵.26×10⁻⁷, 9.44×10⁻⁷)

0.577

Penicillin

2.74×10⁻⁹
(−2.17×10⁻⁸, 2.72×10⁻⁸)

0.826

−1.46×10⁻⁸
(−3.17×10⁻⁸, 2.60×10⁻⁹)

0.096

−4.56×10⁻⁸
(−6.96×10⁻⁸, −2.16×10⁻⁸)

<0.001

7.94×10−10
(−1.97×10⁻⁸, 2.13×10⁻⁸)

0.940

Polymyxin

−1.67×10⁻⁶
(−6.49×10⁻⁶, 3.15×10⁶)

0.498

−5.26×10⁻⁶
(−9.97×10⁻⁶, −5.47×10⁻⁷)

0.029

−6.42×10⁻⁶
(−1.54×10⁻⁵, 2.55×10⁻⁶)

0.162

−1.59×10⁻⁶
(−4.98×10⁻⁶, 1.80×10⁻⁶)

0.357

Quinolones

8.73×10⁻⁹
(−2.02×10⁻⁸, 3.77×10⁻⁸)

0.554

−7.44×10⁻⁹
(−2.87×10⁻⁸, 1.38×10⁻⁸)

0.492

−2.51×10⁻⁸
(−5.74×10⁻⁸, 7.26×10⁻⁹)

0.129

8.73×10⁻⁹
(−1.76×10⁻⁸, 3.51×10⁻⁸)

0.516

Sulfonamides

−2.00×10⁻⁸
(−4.31×10⁻⁷, 3.91×10⁻⁷)

0.924

−6.80×10⁻⁸
(−3.52×10⁻⁷, 2.16×10⁻⁷)

0.638

−3.24×10⁻⁷
(−8.61×10⁻⁷, 2.12×10⁻⁷)

0.236

2.08×10⁻⁷
(−2.26×10⁻⁷, 6.41×10⁻⁷)

0.348

Tetracyclines

−5.21×10⁻⁹
(−4.12×10⁻⁸, 3.08×10⁻⁸)

0.777

−2.68×10⁻⁸
(−6.28×10⁻⁸, 9.29×10⁻⁹)

0.146

3.06×10⁻⁸
(−1.53×10⁻⁸, 7.64×10⁻⁸)

0.191

1.73×10⁻⁸
(−1.22×10⁻⁸, 4.68×10⁻⁸)

0.251

　　Note. CRAB: Acinetobacter baumannii; CRPA: Pseudomonas aeruginosa; CRKP: Klebsiella pneumoniae; CREC: Escherichia coli. 95% CI: 95% confidence interval.

Variabl×10

CRAB

CRPA

CRKP

CR×10C

β (95% CI)

Year

−2.31×10⁻² (−4.28×10⁻², −3.32×10⁻³)

0.022

−5.70×10⁻² (−7.35×10⁻², −4.05×10⁻²)

<0.001

8.12×10⁻² (5.13×10⁻², 1.11×10⁻¹)

<0.001

−4.81×10⁻³ (−3.08×10⁻², 2.12×10⁻²)

0.717

Population

8.34×10⁻⁵ (2.41×10⁻⁵, 1.43×10⁻⁴)

0.006

5.03×10⁻⁵ (5.05×10⁻⁶, 9.55×10⁻⁵)

0.029

1.20×10⁻⁴ (3.63×10⁻⁶, 2.37×10⁻⁴)

0.043

6.54×10⁻⁵ (1.03×10⁻⁵, 1.21×10⁻⁴)

0.020

Aminoglycosides

−2.29×10⁻⁷ (−8.23×10⁻⁷, 3.65×10⁻⁷)

0.449

−1.82×10⁻⁷ (−6.41×10⁻⁷, 2.77×10⁻⁷)

0.437

−1.94×10⁻⁷ (−9.81×10⁻⁷, 5.92×10⁻⁷)

0.628

5.41×10⁻⁷ (−1.44×10⁻⁷, 1.23×10⁻⁶)

0.122

Amphenicols

−1.37×10⁻⁶ (−6.68×10⁻⁶, 3.94×10⁻⁶)

0.613

2.03×10⁻⁷ (−4.00×10⁻⁶, 4.41×10⁻⁶)

0.924

2.66×10⁻⁶ (−4.30×10⁻⁶, 9.61×10⁻⁶)

0.454

−4.40×10⁻⁶ (−1.09×10⁻⁵, 2.06×10⁻⁶)

0.182

β−lactamase inhibitors

−4.96×10⁻⁸ (−1.63×10⁻⁷, 6.38×10⁻⁸)

0.391

−1.26×10⁻⁷ (−2.20×10⁻⁷, −3.16×10⁻⁸)

0.009

−1.65×10⁻⁸ (−1.74×10⁻⁷, 1.41×10⁻⁷)

0.838

−1.14×10⁻⁷ (−2.45×10⁻⁷, 1.75×10⁻⁸)

0.089

Carbapenems

5.75×10⁻⁷ (1.60×10⁻⁷, 9.89×10⁻⁷)

0.007

5.26×10⁻⁷ (2.08×10⁻⁷, 8.45×10⁻⁷)

0.001

4.61×10⁻⁷ (−8.85×10⁻⁸, 1.01×10⁻⁶)

0.100

2.91×10⁻⁷ (−1.73×10⁻⁷, 7.55×10⁻⁷)

0.219

Cephalosporins

3.09×10⁻⁸ (−9.83×10⁻⁹, 7.16×10⁻⁸)

0.137

−4.29×10⁻⁹ (−3.66×10⁻⁸, 2.80×10⁻⁸)

0.794

2.12×10⁻⁸ (−3.32×10⁻⁸, 7.56×10⁻⁸)

0.445

−3.11×10⁻⁸ (−8.08×10⁻⁸, 1.87×10⁻⁸)

0.221

Vancomycin & norvancomycin

−2.21×10⁻⁶ (−5.56×10⁻⁶, 1.15×10⁻⁶)

0.197

−6.03×10⁻⁷ (−3.28×10⁻⁶, 2.08×10⁻⁶)

0.660

1.61×10⁻⁶ (−2.94×10⁻⁶, 6.16×10⁻⁶)

0.488

3.64×10⁻⁶ (−1.46×10⁻⁷, 7.43×10⁻⁶)

0.060

Teicoplanin

7.82×10−10 (−4.11×10⁻⁶, 4.11×10⁻⁶)

1.000

−1.34×10⁻⁶ (−4.56×10⁻⁶, 1.87×10⁻⁶)

0.413

1.80×10⁻⁷ (−5.21×10⁻⁶, 5.57×10⁻⁶)

0.948

−7.90×10⁻⁷ (−5.37×10⁻⁶, 3.79×10⁻⁶)

0.735

Lincomycin−Clindamycin

−7.22×10⁻⁸ (−2.89×10⁻⁷, 1.45×10⁻⁷)

0.514

−3.76×10⁻⁸ (−2.20×10⁻⁷, 1.45×10⁻⁷)

0.686

1.25×10⁻⁷ (−1.89×10⁻⁷, 4.39×10⁻⁷)

0.435

6.01×10⁻⁹ (−2.62×10⁻⁷, 2.74×10⁻⁷)

0.965

Macrolides

−1.90×10⁻⁸ (−6.32×10⁻⁸, 2.52×10⁻⁸)

0.400

1.15×10⁻⁸ (−2.41×10⁻⁸, 4.72×10⁻⁸)

0.527

−7.35×10⁻⁸ (−1.30×10⁻⁷, −1.68×10⁻⁸)

0.011

−1.68×10⁻⁸ (−7.07×10⁻⁸, 3.70×10⁻⁸)

0.540

Oxazolidinones

−1.28×10⁻⁶ (−3.40×10⁻⁶, 8.32×10⁻⁷)

0.234

−1.50×10⁻⁷ (−1.78×10⁻⁶, 1.48×10⁻⁶)

0.857

−4.01×10⁻⁷ (−2.83×10⁻⁶, 2.03×10⁻⁶)

0.747

7.93×10⁻⁷ (−1.56×10⁻⁶, 3.15×10⁻⁶)

0.509

Penicillin

−2.13×10⁻⁸ (−5.97×10⁻⁸, 1.72×10⁻⁸)

0.278

−3.57×10⁻⁸ (−6.75×10⁻⁸, −3.87×10⁻⁹)

0.028

−5.81×10⁻⁸ (−1.09×10⁻⁷, −6.90×10⁻⁹)

0.026

−2.50×10⁻⁸ (−7.17×10⁻⁸, 2.16×10⁻⁸)

0.293

Polymyxin

2.45×10⁻⁶ (−3.91×10⁻⁶, 8.80×10⁻⁶)

0.451

3.84×10⁻⁶ (−1.29×10⁻⁶, 8.97×10⁻⁶)

0.142

−1.93×10⁻⁵ (−2.78×10⁻⁵, −1.08×10⁻⁵)

<0.001

−1.13×10⁻⁵ (−1.92×10⁻⁵, −3.42×10⁻⁶)

0.005

Quinolones

3.79×10⁻⁸ (−4.18×10⁻⁸, 1.18×10⁻⁷)

0.351

3.85×10⁻⁸ (−2.38×10⁻⁸, 1.01×10⁻⁷)

0.226

5.86×10⁻⁸ (−4.04×10⁻⁸, 1.57×10⁻⁷)

0.246

5.70×10⁻⁸ (−3.32×10⁻⁸, 1.47×10⁻⁷)

0.215

Sulfonamides

1.76×10⁻⁸ (−3.67×10⁻⁷, 4.02×10⁻⁷)

0.929

2.51×10⁻⁷ (−8.00×10⁻⁸, 5.82×10⁻⁷)

0.137

−4.24×10⁻⁷ (−1.13×10⁻⁶, 2.86×10⁻⁷)

0.242

2.06×10⁻⁷ (−3.41×10⁻⁷, 7.52×10⁻⁷)

0.461

Tetracyclines

−4.39×10⁻⁸ (−1.03×10⁻⁷, 1.49×10⁻⁸)

0.143

−1.47×10⁻⁸ (−6.14×10⁻⁸, 3.21×10⁻⁸)

0.539

5.17×10⁻⁸ (−2.46×10⁻⁸, 1.28×10⁻⁷)

0.184

3.96×10⁻¹¹ (−6.79×10⁻⁸, 6.80×10⁻⁸)

0.999

　　Note. CRAB: Acinetobacter baumannii; CRPA: Pseudomonas aeruginosa; CRKP: Klebsiella pneumoniae; CREC: Escherichia coli. 95% CI: 95% confidence interval.

Variable

VR E. faecium

VR E. faecalis

β (95% CI)

Year

−0.13 (−0.19, −0.06)

< 0.001

−0.14 (−0.19, −0.10)

< 0.001

Population

1.47×10⁻⁵ (−7.00×10⁻⁵, 9.94×10⁻⁵)

0.733

−4.39×10⁻⁵ (−1.05×10⁻⁴, 1.73×10⁻⁵)

0.160

Aminoglycosides

2.18×10⁻⁶ (1.14×10⁻⁶, 3.23×10⁻⁶)

< 0.001

−3.34×10⁻⁸ (−1.03×10⁻⁶, 9.60×10⁻⁷)

0.947

Amphenicols

1.72×10⁻⁵ (1.05×10⁻⁶, 3.33×10⁻⁵)

0.037

1.65×10⁻⁵ (1.70×10⁻⁶, 3.14×10⁻⁵)

0.029

β−lactamase inhibitors

3.39×10⁻⁷ (1.96×10⁻⁷, 4.83×10⁻⁷)

< 0.001

−5.53×10⁻⁸ (−1.99×10⁻⁷, 8.82×10⁻⁸)

0.450

Carbapenems

1.42×10⁻⁶ (9.41×10⁻⁷, 1.90×10⁻⁶)

< 0.001

−1.81×10⁻⁷ (−7.24×10⁻⁷, 3.62×10⁻⁷)

0.514

Cephalosporins

7.50×10⁻⁸ (5.00×10⁻⁸, 9.99×10⁻⁸)

< 0.001

1.81×10⁻⁹ (−2.52×10⁻⁸, 2.88×10⁻⁸)

0.896

Vancomycin & norvancomycin

9.14×10⁻⁶ (6.38×10⁻⁶, 1.19×10⁻⁵)

< 0.001

8.72×10⁻⁷ (−2.58×10⁻⁶, 4.33×10⁻⁶)

0.621

Teicoplanin

3.94×10⁻⁷ (−7.47×10⁻⁶, 8.26×10⁻⁶)

0.922

−1.41×10⁻⁶ (−8.05×10⁻⁶, 5.24×10⁻⁶)

0.678

Lincomycin−Clindamycin

6.69×10⁻⁷ (3.79×10⁻⁷, 9.59×10⁻⁷)

< 0.001

−1.64×10⁻⁷ (−4.82×10⁻⁷, 1.55×10⁻⁷)

0.314

Macrolides

7.33×10⁻⁸ (4.07×10⁻⁸, 1.06×10⁻⁷)

< 0.001

−3.30×10⁻⁹ (−3.89×10⁻⁸, 3.23×10⁻⁸)

0.856

Oxazolidinones

7.42×10⁻⁶ (5.03×10⁻⁶, 9.81×10⁻⁶)

< 0.001

−2.06×10⁻⁶ (−5.66×10⁻⁶, 1.53×10⁻⁶)

0.261

Penicillin

1.53×10⁻⁷ (1.02×10⁻⁷, 2.03×10⁻⁷)

< 0.001

−2.10×10⁻⁸ (−8.23×10⁻⁸, 4.03×10⁻⁸)

0.503

Polymyxin

3.41×10⁻⁵ (1.59×10⁻⁵, 5.23×10⁻⁵)

< 0.001

−1.19×10⁻⁵ (−3.13×10⁻⁵, 7.59×10⁻⁶)

0.232

Quinolones

1.40×10⁻⁷ (9.07×10⁻⁸, 1.90×10⁻⁷)

< 0.001

−1.66×10⁻⁸ (−7.87×10⁻⁸, 4.55×10⁻⁸)

0.600

Sulfonamides

1.88×10⁻⁶ (1.09×10⁻⁶, 2.66×10⁻⁶)

< 0.001

1.61×10⁻⁷ (−6.58×10⁻⁷, 9.79×10⁻⁷)

0.701

Tetracyclines

2.48×10⁻⁷ (1.37×10⁻⁷, 3.59×10⁻⁷)

< 0.001

−6.69×10⁻⁸ (−1.65×10⁻⁷, 3.16×10⁻⁸)

0.183

　　Note. VR E. faecium: Enterococcus faecium; VR E. faecalis: Enterococcus faecalis. 95% CI: 95% confidence interval.

Variable

VR E. faecium

VR E. faecalis

β (95% CI)

Year

−0.31 (−0.37, −0.26)

< 0.001

−0.20 (−0.27, −0.14)

< 0.001

Population

−7.77×10⁻⁶ (−9.94×10⁻⁵, 8.39×10⁻⁵)

0.868

−5.48×10⁻⁵ (−1.39×10⁻⁴, 2.89×10⁻⁵)

0.199

Aminoglycosides

−4.88×10⁻⁷ (−1.82×10⁻⁶, 8.44×10⁻⁷)

0.473

7.89×10⁻⁸ (−1.37×10⁻⁶, 1.52×10⁻⁶)

0.915

Amphenicols

−1.34×10⁻⁵ (−2.66×10⁻⁵, −1.62×10⁻⁷)

0.047

1.48×10⁻⁵ (8.88×10⁻⁷, 2.87×10⁻⁵)

0.037

β−lactamase inhibitors

−1.23×10⁻⁷ (−4.00×10⁻⁷, 1.54×10⁻⁷)

0.383

2.70×10⁻⁸ (−2.41×10⁻⁷, 2.95×10⁻⁷)

0.844

Carbapenems

6.26×10⁻⁷ (−5.15×10⁻⁷, 1.77×10⁻⁶)

0.282

−1.18×10⁻⁶ (−2.63×10⁻⁶, 2.62×10⁻⁷)

0.109

Cephalosporins

−1.03×10⁻⁹ (−9.58×10⁻⁸, 9.37×10⁻⁸)

0.983

−3.42×10⁻⁸ (−1.48×10⁻⁷, 7.91×10⁻⁸)

0.554

Vancomycin & norvancomycin

1.87×10⁻⁵ (1.06×10⁻⁵, 2.69×10⁻⁵)

< 0.001

1.36×10⁻⁵ (5.30×10⁻⁶, 2.20×10⁻⁵)

0.001

Teicoplanin

−9.71×10⁻⁶ (−1.89×10⁻⁵, −5.50×10⁻⁷)

0.038

7.62×10⁻⁷ (−9.30×10⁻⁶, 1.08×10⁻⁵)

0.882

Lincomycin−Clindamycin

1.20×10⁻⁶ (6.82×10⁻⁷, 1.71×10⁻⁶)

< 0.001

−7.13×10⁻⁸ (−7.11×10⁻⁷, 5.68×10⁻⁷)

0.827

Macrolides

−5.38×10⁻⁸ (−1.66×10⁻⁷, 5.79×10⁻⁸)

0.345

2.96×10⁻⁸ (−1.08×10⁻⁷, 1.67×10⁻⁷)

0.673

Oxazolidinones

8.08×10⁻⁶ (2.58×10⁻⁶, 1.36×10⁻⁵)

0.004

3.85×10⁻⁶ (−3.43×10⁻⁶, 1.11×10⁻⁵)

0.300

Penicillin

7.72×10⁻⁸ (−2.84×10⁻⁸, 1.83×10⁻⁷)

0.152

1.03×10⁻⁹ (−1.12×10⁻⁷, 1.14×10⁻⁷)

0.986

Polymyxin

−2.46×10⁻⁶ (−2.10×10⁻⁵, 1.61×10⁻⁵)

0.795

3.63×10⁻⁶ (−1.93×10⁻⁵, 2.66×10⁻⁵)

0.757

Quinolones

−3.53×10⁻⁷ (−5.62×10⁻⁷, −1.44×10⁻⁷)

0.001

−1.74×10⁻⁷ (−4.11×10⁻⁷, 6.22×10⁻⁸)

0.149

Sulfonamides

7.85×10⁻⁷ (−2.10×10⁻⁷, 1.78×10⁻⁶)

0.122

4.91×10⁻⁷ (−6.32×10⁻⁷, 1.62×10⁻⁶)

0.391

Tetracyclines

−1.76×10⁻⁷ (−3.27×10⁻⁷, −2.54×10⁻⁸)

0.022

3.01×10⁻⁸ (−1.41×10⁻⁷, 2.01×10⁻⁷)

0.731

　　Note. VR E. faecium: Enterococcus faecium; VR E. faecalis: Enterococcus faecalis. 95% CI: 95% confidence interval.

Trends in Antibiotic Consumption and Antimicrobial Resistance in China: An Ecological Analysis from 2016 to 2022

作者简介:
Fei Zhao, Associate Researcher, PhD, Research Direction: Pharmacoepidemiology, Tel: 86-10-85133648, E-mail: zhaofei21cn@126.com

Zinan Zhao, Chief Pharmacist, Master's Degree, Research Direction: Clinical Pharmacy, Tel: 86-10-85133648, E-mail: bellazhao2015@163.com

通讯作者: Pengfei Jin, Prof, Tel: 86-10-85133620, E-mail: j790101@163.com

收稿日期: 2025-06-30

录用日期: 2025-10-17

注释:

1) Authors’ Contributions: 2) Competing Interests: 3) Ethic:

Trends in Antibiotic Consumption and Antimicrobial Resistance in China: An Ecological Analysis from 2016 to 2022

1. Department of Pharmacy, Beijing Hospital; National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Science; Beijing Key Laboratory of Assessment of Clinical Drugs Risk and Individual Application (Beijing Hospital), Beijing 100730, China

Author Bio:
Fei Zhao, Associate Researcher, PhD, Research Direction: Pharmacoepidemiology, Tel: 86-10-85133648, E-mail: zhaofei21cn@126.com

Zinan Zhao, Chief Pharmacist, Master's Degree, Research Direction: Clinical Pharmacy, Tel: 86-10-85133648, E-mail: bellazhao2015@163.com

Corresponding author: Pengfei Jin, Prof, Tel: 86-10-85133620, E-mail: j790101@163.com

All authors declare no competing interests.

Not applicable.

^&These authors contributed equally to this work.

Received Date: 2025-06-30

Accepted Date: 2025-10-17

Keywords:

全文HTML

INTRODUCTION

Antimicrobial resistance (AMR) poses a major threat to human health globally^[1]. AMR killed at least 1.27 million people worldwide in 2019 and may result in 10 million deaths annually by 2050 if the current trend in AMR is not suppressed^[2]. Among the resistant pathogens, carbapenem-resistant organisms (CRO) and vancomycin-resistant enterococci (VRE) are of particular concern, especially in healthcare settings, where they can cause severe infections in people with weakened immune systems^[3-5].

Global control of these resistant bacteria is challenging. The epidemiology of AMR in China shows both similarities and differences with global patterns. According to nationwide bacterial resistance surveillance data in China, the resistance rates of carbapenem-resistant Klebsiella pneumoniae (CRKP) to meropenem (MEM) and imipenem (IPM) were 2.9% and 3.0% in 2005, respectively, but increased to 26.3% and 25.0%, respectively, by 2020, paralleling global trends^[6]. In contrast, vancomycin resistance in Enterococcus faecium (E. faecium) and Enterococcus faecalis (E. faecalis) has remained at relatively low levels in China^[7,8]. This differs from the United States and Europe. In a recent report on the threat of antibiotic resistance in the United States 2019, approximately 30% of all healthcare-associated enterococcal infections were resistant to vancomycin. Notably, more than 70% of E. faecium isolates were resistant to vancomycin in solid organ transplant units^[9]. VRE resistance rates show a consistently increasing trend across Europe^[4,10,11].

Antimicrobial drugs reduce the global burden of common infectious diseases. Globally, an increase in the annual consumption of various types of antibiotics is apparent^[12]. However, the widespread and indiscriminate use of antimicrobial drugs is a major driving force of AMR^[13]. Several studies have shown that antibiotic drug use is closely associated with antibiotic resistance^[14]. Most of these studies provided snapshots of the correlation between antibiotic consumption and AMR. However, the correlation between antibiotic consumption and AMR has not been consistent. Gong et al. reported a strong correlation between the consumption of carbapenems and carbapenem-resistant Klebsiella pneumoniae^[15]. Conversely, Pérez-Lazo reported a strong negative correlation between imipenem and imipenem- and meropenem-resistant K. pneumoniae^[16]. Additionally, in the context of the global increase in antibiotic consumption, the resistance rates of VRE in China have remained lower than those reported in other countries, whereas the resistance rates of CRO have shown an upward trend, similar to that observed in other countries^[17]. Therefore, additional studies are required to provide comprehensive insights into the antimicrobial resistance of CRO and VRE with respect to antibiotic consumption in China.

In this study, we examined the association between resistance to six WHO-designated priority pathogens and the consumption of 15 related classes of antibiotics to provide a clearer understanding of how improved medical practices and informed public health policies can help reduce the spread of resistant bacteria.

METHODS

Study Design

We conducted an ecological study of data pertaining to January 2016 and December 2022.

Data Sources

We collected the drug-resistant proportions of carbapenem-resistant organisms (CRO) and vancomycin-resistant enterococci (VRE) from 31 provinces from the China Antibacterial Resistance Surveillance System (CARSS), and at the national level on the Chinese mainland between 2016–2022. Resistant bacteria included carbapenem-resistant Escherichia coli (CREC), Acinetobacter baumannii (CRAB), Klebsiella pneumoniae (CRKP), Pseudomonas aeruginosa (CRPA), vancomycin-resistant Enterococcus faecium (VR^{E. faecium}) and Enterococcus faecalis (VR E. faecalis)).

The CARSS is a nationwide, voluntary bacterial resistance surveillance network covering secondary and tertiary hospitals that submits standardized laboratory results according to unified operating procedures and quality control requirements^[18]. By 2022, 1997, hospitals had submitted drug resistance data to the CARSS, with 29% being secondary hospitals and 71% being tertiary hospitals^[19]. Participation in the CARSS is dynamic, but all submitted data follow the same format and laboratory quality standards, which ensure comparability across sites^[20]. Hierarchical information on bacterial drug resistance was obtained to understand the epidemic trend and new threats of bacterial drug resistance nationwide^[21].

Antibiotic consumption data were drawn from a subset of hospitals reported to the National Hospital Information Network of the Chinese Pharmaceutical Association. We selected 829 hospitals for antibiotic use analyses, choosing sites such that the distribution of hospital levels (secondary vs. tertiary) and provincial coverage broadly matched that of CARSS contributors. Hospitals with continuous reporting from 2016 to 2022 were prioritized to enhance temporal comparability. Consumption data comprised annual hospital-level totals for 161 antibiotics, which were extracted and classified into 15 pharmacological classes, including 1) Aminoglycosides, 2) Amphenicols, 3) Carbapenem, 4) Cephalosporins, 5) Lincomycin and Clindamycin, 6) Macrolides, 7) Oxazolidinones, 8) Penicillin, 9) Polymyxin, 10) Quinolone, 11) Sulfonamides, 12) Teicoplanin, 13) Tetracyclines, 14) Vancomycin and norvancomycin, and 15) β-Amidase inhibitor (including β-Amidase inhibitor, Cephalosporins/β-Amidase inhibitor, and Penicillin/β-Amidase inhibitor)^[22] (Supplementary Table S1). The selection of antibiotics was guided by two criteria: their clinical relevance for treating infections caused by the six WHO-designated priority pathogens, and prior evidence of association with antimicrobial resistance in published literature (Supplementary Table S2). Antibiotic consumption was standardized to defined daily doses (DDDs) and reported annually as DDDs per patient-day at both provincial and national levels. We collected data on all antibacterial agents used systemically. Data regarding antifungals or antivirals were not considered.

To ensure comparability between AMR and antibiotic consumption datasets, we harmonized both sources at the provincial–year level. Only hospitals that submitted data within a given year were included without the imputation of missing values. In addition, the population of different provinces was collected as a covariate variable from the China Statistics Yearbook from 2016 to 2022^[23].

Statistical Analysis

The median and interquartile range (25th and 75th percentiles) were reported for DDDs as continuous variables with a non-normal distribution, and the resistance proportions were summarized for each bacterial species. Temporal trends in nationwide resistance were analyzed using beta regression. The resistance proportion of the six bacteria was considered the dependent variable, whereas antibiotic consumption and provincial population by year were considered independent variables. To account for possible changes in hospital participation and clustering of data within provinces over seven consecutive years, we aggregated data at the provincial-year level and applied hierarchical models with province-level random effects. The association between resistance proportions as dependent and independent variables was examined using hierarchical models with a beta-link function. Before multivariate model fitting, we excluded variables with VIF > 10 to avoid multicollinearity. Continuous independent variables (e.g., year, population, and antibiotic consumption) were mean-centered prior to modeling to improve numerical stability and interpretability of regression coefficients. This approach reduces the bias from uneven hospital counts across provinces and partially mitigates effects of inconsistent hospital-level participation. Statistical significance was set than 0.05. All statistical analyses were performed using the R software (R Core Team 2024).

Role of Funding Sources

The funder played no role in the study design, data collection, data analysis, data interpretation, or manuscript writing. The corresponding author had full access to all data in the study and had the final responsibility for the decision to submit the manuscript for publication.

DISCUSSION

This nationwide ecological study provided an overview of trends in carbapenem-resistant organisms (CRO) and vancomycin-resistant enterococci (VRE), along with antibiotic consumption in mainland China from 2016 to 2022. Although the observed declines in CRAB, CRPA, and VRE were encouraging, the continued increase in CRKP underscores the persistent threat of carbapenem resistance. Furthermore, significant changes in the DDDs of various antibiotic classes were observed from 2016 to 2022, providing evidence for the global trend of antibiotic overuse.

The association between drug resistance and antibiotic use was also investigated. Several studies have indicated a strong correlation between antibiotic drug use and antibiotic resistance^[24]. Lopez-Lozano et al. found empirical evidence of nonlinear relationships between population antibiotic consumption and resistance rates in five European settings^[25]. Abejew et al. systematically reviewed the correlation between antibiotic resistance and global antibiotic consumption globally^[14]. These results inspired the present study and provided a theoretical basis. Maugeri et al. found that antibiotic consumption is one of the main predictors of antimicrobial resistance in European countries^[26]. Similarly, in our study, the consumption of carbapenem antibiotics was a predictor of CRAB, CRPA, and CRKP resistance, whereas the use of Vancomycin and Norvancomycin predicted VR E. faecium and VR E. faecalis resistance, consistent with most global research results^[27,28].

Many studies have shown a comprehensive association between antibiotic consumption and resistance to CRPA and CRKP due to their complex drug resistance mechanisms and cross-resistance^[29,30]. Similarly, in our study, exploring the correlation between a single antibiotic and the resistance proportions of CRO and VRE was insufficient. We did not find an association between the consumption of carbapenem antibiotics and resistance to CRAB, CRPA, CRKP, and CREC, nor between the consumption of vancomycin and vancomycin and resistance to VR E. faecalis when considering only the association with a single drug class. However, this association was evident after adjusting for consumption of all other antibiotics. Therefore, it is necessary to consider the effects of other antibiotics on the development of drug resistance. Furthermore, the use of polymyxin was negatively associated with the proportions of CRKP and CREC, and penicillin usage showed a negative correlation with the resistance of CRPA and CRKP. Similar results have been previously reported^[14]. These findings underscore the complex relationship between antibiotic use and emergence of drug-resistant bacterial strains, highlighting the critical need for judicious antibiotic management.

Previous studies and our study indicate that the resistance rate to VRE in China differs from that in Europe and the United States, remaining low^[31]. Although the use of various antibiotics has significantly increased in China, there is a clear association between vancomycin and VRE consumption. Therefore, compared with the high drug resistance of VRE in Europe and the United States, it is necessary to strengthen surveillance by conducting more sampling in China, especially in high-risk hospital units, such as hematology, ICU, and renal units^[32-34]. However, the observed changes in statistical significance between the unadjusted and adjusted models likely reflect confounding from concurrent antibiotic consumption and the complex interplay among antibiotic classes. Although our ecological design could not confirm causal interactions, these findings are consistent with the possibility of synergistic or indirect effects, particularly in the case of vancomycin use and VRE resistance.

Our findings have important implications for antimicrobial stewardship and public health policies. Continued efforts are needed to sustain the downward trends in CRAB, CRPA, and VRE, likely supported by strengthened infection prevention and rational prescriptions. Simultaneously, targeted interventions, including stricter carbapenem stewardship, improved diagnostic capacity, and expanded access to effective alternative therapies, are urgently required to curb the alarming increase in the incidence of CRKP. Finally, pronounced interprovincial variability highlights the need for region-specific strategies tailored to local epidemiology and resource capacities. Collectively, these measures could contribute to a more comprehensive national response to antimicrobial resistance in China.

Compared with previous studies, our analysis presents novelties that also represent some of its strengths. First, we evaluated the association between antibiotic consumption and AMR at the provincial level in mainland China. These results provide antibiotics as indicators of better AMR control. Second, our study included a majority of drug-resistant CRO and VRE bacteria. These results provide a comprehensive overview of bacterial resistance and antibiotic use in mainland China. Finally, the introduction of a hierarchical model enabled us to disentangle the relationship between antibiotic consumption and AMR.

This study has some limitations. First, as an ecological study, we could not infer causality from observed relationships. Based on previous studies and biological plausibility, we selected a set of antibiotics that may be effective in treating bacterial infections that are likely to induce drug resistance. However, these findings should be interpreted with caution, as the ecological design precludes causal inference and cannot disentangle the influence of concurrent infection control measures, regional prescribing practices, or socioeconomic determinants. Second, differences in reporting practices between the two surveillance networks and incomplete hospital participation across years may have introduced bias, although our harmonization procedures and modeling approach helped minimize these effects. Third, all data were available at provincial and yearly levels. Therefore, it was impossible to evaluate the observed relationships within a smaller geographic unit or season. However, this remains a direction for future research. Future research should account for additional confounders and consider use of spatial statistical approaches to explore potential geographic clustering in antimicrobial resistance and antibiotic consumption.

CONCLUSION

The proportions of CRAB, CRPA, VR E. faecium, and VR E. faecalis declined across the study period, whereas the proportion of CRKP increased in mainland China. The DDDs of 10 antibiotic classes exhibited a statistically significant nationwide increase. Our study corroborates previous findings, suggesting that the consumption of carbapenems could be the main indicator of carbapenem-resistant strains of Acinetobacter baumannii and Pseudomonas aeruginosa, whereas the consumption of vancomycin serves as a key indicator of VRE. However, it is crucial to consider the effect of other antibiotics on drug resistance. Further studies at a more granular level are necessary to thoroughly explore the relationship between AMR and antibiotic use.

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doi: 10.3967/bes2025.169

Trends in Antibiotic Consumption and Antimicrobial Resistance in China: An Ecological Analysis from 2016 to 2022

Author Bio:
Fei Zhao, Associate Researcher, PhD, Research Direction: Pharmacoepidemiology, Tel: 86-10-85133648, E-mail: zhaofei21cn@126.com

Zinan Zhao, Chief Pharmacist, Master's Degree, Research Direction: Clinical Pharmacy, Tel: 86-10-85133648, E-mail: bellazhao2015@163.com

Corresponding author: Pengfei Jin, Prof, Tel: 86-10-85133620, E-mail: j790101@163.com

计量

Trends in Antibiotic Consumption and Antimicrobial Resistance in China: An Ecological Analysis from 2016 to 2022

doi: 10.3967/bes2025.169

作者简介:
Fei Zhao, Associate Researcher, PhD, Research Direction: Pharmacoepidemiology, Tel: 86-10-85133648, E-mail: zhaofei21cn@126.com

Zinan Zhao, Chief Pharmacist, Master's Degree, Research Direction: Clinical Pharmacy, Tel: 86-10-85133648, E-mail: bellazhao2015@163.com

通讯作者: Pengfei Jin, Prof, Tel: 86-10-85133620, E-mail: j790101@163.com

English Abstract

Trends in Antibiotic Consumption and Antimicrobial Resistance in China: An Ecological Analysis from 2016 to 2022

1. Department of Pharmacy, Beijing Hospital; National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Science; Beijing Key Laboratory of Assessment of Clinical Drugs Risk and Individual Application (Beijing Hospital), Beijing 100730, China

Author Bio:
Fei Zhao, Associate Researcher, PhD, Research Direction: Pharmacoepidemiology, Tel: 86-10-85133648, E-mail: zhaofei21cn@126.com

Zinan Zhao, Chief Pharmacist, Master's Degree, Research Direction: Clinical Pharmacy, Tel: 86-10-85133648, E-mail: bellazhao2015@163.com

Corresponding author: Pengfei Jin, Prof, Tel: 86-10-85133620, E-mail: j790101@163.com

全文HTML

Study Design

Data Sources

Statistical Analysis

Role of Funding Sources

Drug-resistant Proportions of CRO and VRE Nationwide

DDDs of 15 Antibiotic Classes Nationwide

Association between Drug-resistant Proportion and Antibiotic Consumption

目录

doi: 10.3967/bes2025.169

Trends in Antibiotic Consumption and Antimicrobial Resistance in China: An Ecological Analysis from 2016 to 2022

Author Bio: Fei Zhao, Associate Researcher, PhD, Research Direction: Pharmacoepidemiology, Tel: 86-10-85133648, E-mail: zhaofei21cn@126.com Zinan Zhao, Chief Pharmacist, Master's Degree, Research Direction: Clinical Pharmacy, Tel: 86-10-85133648, E-mail: bellazhao2015@163.com

Corresponding author: Pengfei Jin, Prof, Tel: 86-10-85133620, E-mail: j790101@163.com

计量

出版历程

Trends in Antibiotic Consumption and Antimicrobial Resistance in China: An Ecological Analysis from 2016 to 2022

doi: 10.3967/bes2025.169

作者简介: Fei Zhao, Associate Researcher, PhD, Research Direction: Pharmacoepidemiology, Tel: 86-10-85133648, E-mail: zhaofei21cn@126.com Zinan Zhao, Chief Pharmacist, Master's Degree, Research Direction: Clinical Pharmacy, Tel: 86-10-85133648, E-mail: bellazhao2015@163.com

通讯作者: Pengfei Jin, Prof, Tel: 86-10-85133620, E-mail: j790101@163.com

English Abstract

Trends in Antibiotic Consumption and Antimicrobial Resistance in China: An Ecological Analysis from 2016 to 2022

1. Department of Pharmacy, Beijing Hospital; National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Science; Beijing Key Laboratory of Assessment of Clinical Drugs Risk and Individual Application (Beijing Hospital), Beijing 100730, China

Author Bio: Fei Zhao, Associate Researcher, PhD, Research Direction: Pharmacoepidemiology, Tel: 86-10-85133648, E-mail: zhaofei21cn@126.com Zinan Zhao, Chief Pharmacist, Master's Degree, Research Direction: Clinical Pharmacy, Tel: 86-10-85133648, E-mail: bellazhao2015@163.com

Corresponding author: Pengfei Jin, Prof, Tel: 86-10-85133620, E-mail: j790101@163.com

全文HTML

Study Design

Data Sources

Statistical Analysis

Role of Funding Sources

Drug-resistant Proportions of CRO and VRE Nationwide

DDDs of 15 Antibiotic Classes Nationwide

Association between Drug-resistant Proportion and Antibiotic Consumption

目录

Author Bio:
Fei Zhao, Associate Researcher, PhD, Research Direction: Pharmacoepidemiology, Tel: 86-10-85133648, E-mail: zhaofei21cn@126.com

Zinan Zhao, Chief Pharmacist, Master's Degree, Research Direction: Clinical Pharmacy, Tel: 86-10-85133648, E-mail: bellazhao2015@163.com

作者简介:
Fei Zhao, Associate Researcher, PhD, Research Direction: Pharmacoepidemiology, Tel: 86-10-85133648, E-mail: zhaofei21cn@126.com

Zinan Zhao, Chief Pharmacist, Master's Degree, Research Direction: Clinical Pharmacy, Tel: 86-10-85133648, E-mail: bellazhao2015@163.com

Author Bio:
Fei Zhao, Associate Researcher, PhD, Research Direction: Pharmacoepidemiology, Tel: 86-10-85133648, E-mail: zhaofei21cn@126.com

Zinan Zhao, Chief Pharmacist, Master's Degree, Research Direction: Clinical Pharmacy, Tel: 86-10-85133648, E-mail: bellazhao2015@163.com