Next Article in Journal
Antibiotic Treatment vs. Non-Antibiotic Treatment in Bovine Clinical Mastitis During Lactation with Mild and Moderate Severity
Previous Article in Journal
Evaluation of Infections Caused by Carbapenem-Resistant Pseudomonas aeruginosa, Acinetobacter baumannii, and Klebsiella pneumoniae in an Intensive Care Unit: A Retrospective Study
Previous Article in Special Issue
Antibiotic Resistance Awareness in Kosovo: Insights from the WHO Antibiotic Resistance: Multi-Country Public Awareness Survey
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Antibiotics
Volume 14
Issue 7
10.3390/antibiotics14070701
antibiotics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Distribution and Determinants of Antibiotic Self-Medication: A Cross-Sectional Study in Chinese Residents

by
Guo Huang
1,2,
Pu Ge
3,
Mengyun Sui
4,
He Zhu
1,2,
Sheng Han
1,2,* and
Luwen Shi
1,2,*
1
Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
2
International Research Center for Medicinal Administration, Peking University, Beijing 100191, China
3
School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
4
Division of Chronic Non-Communicable Diseases and Injury Prevention, Shanghai Municipal Center for Disease Control and Prevention, Shanghai 201106, China
*
Authors to whom correspondence should be addressed.
Antibiotics 2025, 14(7), 701; https://doi.org/10.3390/antibiotics14070701
Submission received: 2 June 2025 / Revised: 30 June 2025 / Accepted: 10 July 2025 / Published: 12 July 2025
(This article belongs to the Special Issue Antibiotic Use in the Communities—2nd Edition)
Browse Figure
Versions Notes

Abstract

Antimicrobial resistance (AMR) represents a critical global health threat, with inappropriate antibiotic self-medication (ASM) being a key contributor. China—as the world’s largest antibiotic consumer—faces significant challenges despite regulatory efforts, compounded by limited contemporary data during the COVID-19 pandemic. A nationwide cross-sectional study was conducted using the 2021 China Family Health Index Survey (n = 11,031 participants across 120 cities). Trained investigators administered face-to-face questionnaires assessing ASM practices, decision-making factors, and sociodemographic characteristics. Multivariate logistic regression identified determinants of ASM. Overall, ASM prevalence was 33.7% (n = 3717), with no urban-rural difference (p > 0.05). Physician advice (78.2%), drug safety (67.1%), and efficacy (64.2%) were primary selection criteria; rural residents prioritized drug price and salesperson recommendations more than their urban counterparts (p < 0.01). Key predictors included higher ASM odds among females (OR = 1.30, 95%CI:1.18–1.43), middle-aged adults (46–59 years; OR = 1.20, 95%CI:1.02–1.42), those with health insurance (resident: OR = 1.33; commercial: OR = 1.62), and individuals with drinking histories (OR = 1.20, 95%CI:1.10–1.31). Lower odds were associated with primary education (OR = 0.69, 95%CI:0.58–0.81), unemployment (OR = 0.88, 95%CI:0.79–0.98), and absence of chronic diseases (OR = 0.56, 95%CI:0.47–0.67). One-third of Chinese residents engaged in ASM during the pandemic, driven by intersecting demographic and behavioral factors. Despite converging urban-rural prevalence rates, distinct decision-making drivers necessitate context-specific interventions, including strengthened pharmacy regulation in rural areas, tailored education programs for high-risk groups, and insurance system reforms to disincentivize self-medication.

1. Introduction

Antimicrobial resistance (AMR) constitutes a critical global health threat, imposing substantial economic and human costs, with projections indicating 10 million annual deaths and a 2.5–3% global GDP reduction by 2050 if unaddressed [1,2]. This crisis is acutely pronounced in low- and middle-income countries like China, where antibiotic overuse remains prevalent—exemplified by >80% antibiotic prescription rates for upper respiratory infections and per capita consumption rates fivefold higher than Western nations [3,4]. Inappropriate antibiotic practices, particularly self-medication (ASM)—defined as antibiotic use without professional diagnosis or prescription—accelerate AMR propagation while risking adverse drug reactions, treatment failures, and increased healthcare expenditures [5,6,7].
Global initiatives, including WHO action plans (2011, 2015) and G20 commitments (2016), underscore AMR’s urgency [8,9,10]. China has responded through surveillance systems (e.g., Center for Antibacterial Surveillance, 2005) and policies like China’s National Action Plan to Contain Antimicrobial Resistance (2016–2020) [11,12]. Despite these efforts, non-prescription antibiotic access persists nationwide, with 63–88% of pharmacies dispensing antibiotics without prescriptions [13,14], fueling widespread ASM. Internationally, ASM prevalence varies significantly—from 50.8% in Asian populations to <20% in France and Australia—reflecting disparities in healthcare access, regulatory enforcement, and cultural norms [15,16,17].
Critical knowledge gaps impede effective antibiotic stewardship interventions in China. First, while existing policies primarily target prescribers, patient-centered strategies remain underdeveloped [18]. Second, disparities in healthcare access between rural and urban areas—such as variations in pharmacy proximity versus hospital wait times—exacerbate ASM in underserved regions [19]. China’s rural healthcare system faces structural constraints that influence medication-seeking behaviors. In 2023, the physician-to-population ratio in rural areas (2.74 per 1000 residents) remained significantly lower than in urban centers (4.13 per 1000) in 2023 [20]. Pharmacist distribution is similarly imbalanced; while China’s national average reached 6.0 licensed pharmacists per 10,000 people as of May 2025, the majority remain concentrated in urban areas [21]. Primary care facilities (township health centers/village clinics) often lack essential diagnostic resources and specialist providers, resulting in fragmented care delivery [22]. Consequently, rural residents frequently bypass local facilities for urban hospitals, with studies indicating the majority of patients seek care in cities for serious conditions [23]. These access barriers, compounded by financial constraints and travel burdens, create fertile ground for antibiotic self-medication as a pragmatic alternative. Third, although the COVID-19 pandemic has altered antibiotic usage patterns, population-level data during this period are scarce [24]. Finally, behavioral determinants—including substance use and socioeconomic factors—are not comprehensively assessed despite their documented influence on health decision-making [25].
This study addresses these gaps through a national survey of Chinese residents in 2021, aiming to quantify ASM distribution post-COVID-19 emergence, identify key considerations driving antibiotic procurement, and analyze sociodemographic, behavioral, and regional determinants of ASM, thereby informing targeted antimicrobial stewardship strategies aligned with Healthy China 2030 goals.

2. Results

2.1. Study Participants

The study enrolled 11,031 eligible participants, comprising 8008 (72.60%) urban and 3023 (27.40%) rural residents. Over half were female, with nearly half aged ≤30 years. A normal BMI range was observed in 68.40% of participants, while more than half were married, highly educated, and unemployed. Most (70.57%) reported a monthly household income per capita exceeding CNY 3000 (USD 420), and 17.63% had ≥1 chronic disease. Non-smokers and non-drinkers constituted 19.82% and 40.37% of the cohort, respectively.
Compared to urban residents, those in rural areas exhibited significantly higher proportions of individuals aged ≥60 years, without spouses, unemployed, with monthly household income per capita < CNY 3000, covered by out-of-pocket payment medical insurance, and without alcohol consumption history, but lower proportions of higher education attainment, absence of chronic diseases, and no smoking history (Table 1).

2.2. Distribution of ASM

Among 11,031 participants, 3717 (33.70%) reported practicing ASM. ASM prevalence varied significantly across sociodemographic strata (Table 2), with higher rates observed among females, middle-aged adults (46–59 years), overweight individuals, married participants, highly educated respondents, white-collar workers, urban residents, and those with chronic diseases, smoking history, or drinking history. Lower ASM rates were associated with out-of-pocket medical insurance and the absence of chronic diseases. No significant differences existed for income.

2.3. Considerations of ASM Practitioners

The 3717 ASM practitioners prioritized three key factors when selecting antibiotics: physicians’ advice (78.18%), drug safety (67.10%), and drug efficacy (64.22%) (Table 3; Figure 1). Rural residents placed significantly greater emphasis on drug price, recommendations from sales personnel, and after-sales service, whereas urban residents valued brand reputation and corporate credibility more highly.

2.4. Determinants of ASM

Multivariate logistic regression identified several independent predictors of ASM (Table 4). Males exhibited 23% lower odds of ASM than females (OR = 0.770; 95% CI: 0.700–0.845; p < 0.0001). Middle-aged adults (46–59 years) had 20.3% higher odds than elderly participants (≥60 years) (OR = 1.203; 95% CI: 1.020–1.418; p = 0.0279). Those with primary/below education showed 31.3% lower odds versus higher-educated counterparts (OR = 0.687; 95% CI: 0.580–0.813; p < 0.0001). Unemployed participants had 12.1% lower odds relative to white-collar workers (OR = 0.879; 95% CI: 0.788–0.981; p = 0.0209). Participants with resident/employee health insurance (OR = 1.327; 95% CI: 1.191–1.478; p < 0.0001) or commercial insurance (OR = 1.624; 95% CI: 1.227–2.149; p = 0.0007) demonstrated higher odds than those relying on out-of-pocket payment. Absence of chronic diseases predicted lower odds (none vs. multiple: OR = 0.561; 95% CI: 0.469–0.671; p < 0.0001; one vs. multiple: OR = 0.715; 95% CI: 0.588–0.869; p = 0.0008). Drinking history increased ASM odds by 20.1% (OR = 1.201; 95% CI: 1.097–1.314; p < 0.0001).

3. Discussion

This nationwide study reveals a 33.7% prevalence of ASM among Chinese residents during the COVID-19 pandemic. Notably, despite significant urban-rural socioeconomic disparities, no difference in ASM prevalence was observed, suggesting this practice remains deeply embedded across diverse communities. This finding highlights the urgent need for context-specific interventions to address AMR, a critical global health threat projected to cause 4.73 million annual deaths in Asia by 2050 that imposes substantial economic burdens [2].
China’s ASM rate aligns with previous national studies reporting 37.1–45.7% prevalence [26,27], but exceeds rates in high-income nations like the UK (5%) and France (18%) [17,28]. While lower than India (58%) [29], it surpasses the WHO’s recommended threshold of 30% [30]. This intermediate position reflects China’s unique healthcare landscape where rapid economic development coexists with persistent self-medication traditions. The convergence of urban and rural ASM rates underscores how cultural norms and medication accessibility may transcend geographic and socioeconomic boundaries [31,32], particularly during healthcare disruptions like the COVID-19 pandemic which affected medication dispensing patterns [33].
Our analysis identified several demographic predictors of ASM. Females exhibited 30% higher odds than males (OR = 1.299), consistent with studies from Sudan and the UK [33,34]. This gender disparity may stem from differential care-seeking behaviors, higher antibiotic prescription rates for women, and greater access to leftover medications [35]. Middle-aged adults (46–59 years) showed elevated risk, potentially due to time constraints and healthcare access barriers [36]. The observed association between higher educational attainment and increased ASM (OR = 1.456 vs. primary education) reveals a critical education paradox: advanced education confers no protective effect on appropriate antibiotic use. This counterintuitive pattern—replicated in Italy [34] and Sudan [37]—suggests knowledge acquisition alone is insufficient for behavior modification. Rather, the findings point to diagnostic overconfidence among educated individuals [38], wherein perceived competence in self-assessment outweighs evidence-based risk awareness. This overconfidence manifests as unwarranted trust in personal judgment regarding antibiotic indication and dosing, inadvertently amplifying AMR risks.
Participants with health insurance demonstrated significantly higher ASM odds (Resident/Employee: OR = 1.327; Commercial: OR = 1.624), aligning with Iranian evidence that insured individuals perceive self-medication as financially lower-risk [39]. Multiple chronic diseases amplified ASM risk (OR = 1.782 vs. disease-free), likely due to symptom familiarity and frequent medication exposure [40], while drinking history increased odds by 20.1% (OR = 1.201), corroborating European associations between substance use and self-medication [41].
While our study identifies critical factors influencing antibiotic selection decisions (e.g., physicians’ advice, safety perceptions), these observations lack explicit theoretical contextualization. Grounding findings in the Beliefs about Medicines Questionnaire (BMQ) framework—which evaluates individuals’ necessity beliefs (perceived need for treatment) and concern beliefs (worries about risks, side effects, or dependence)—provides a structured lens to interpret behavioral drivers [42]. For instance, public reliance on physician advice reflects heightened necessity beliefs, where antibiotics are perceived as essential for symptom resolution. Conversely, safety considerations align directly with BMQ’s concern beliefs, encompassing fears of adverse effects or long-term harm from inappropriate use. Notable urban-rural divergences emerged in antibiotic selection criteria. Rural residents prioritized drug price, salesperson recommendations, and after-sales service, whereas urban consumers emphasized brand reputation and corporate credibility. These differences likely reflect rural healthcare access barriers, medication literacy gaps, and targeted marketing strategies by community pharmacies [43,44]. The substantial rural reliance on salesperson advice is particularly concerning given frequent deficiencies in formal medical training among pharmacy staff.
To curb irrational antibiotic use, collaborative engagement among the public, physicians, and policymakers is imperative. Furthermore, current study findings necessitate implementing more precise, multifaceted interventions.
Fundamental to curbing ASM is the rigorous enforcement of prescription-only dispensing policies, particularly targeting rural pharmacies where non-prescription access persists despite nationwide prohibitions [45]. This necessitates implementing real-time e-prescription verification systems with blockchain auditing to prevent circumvention, coupled with substantial penalties for violations. Concurrently, mandatory pharmacist certification programs must integrate AMR stewardship training [46,47], specifically addressing commercial pressures that drive inappropriate sales in competitive markets [48]. To disrupt the supply reservoir for self-medication, precise tablet dispensing aligned with treatment duration [49,50] should be mandated nationwide, supplemented by community medication take-back networks utilizing existing pharmacy distribution channels.
Public education campaigns must transcend generic awareness to address population-specific misconceptions. Urban interventions should leverage brand psychology by collaborating with reputable manufacturers to embed stewardship messaging in packaging (e.g., “This trusted brand protects your family through evidence-based use”), while deploying WeChat-based “AMR Risk Calculators” that gamify antibiotic literacy [51]. For rural populations, community radio dramas illustrating AMR consequences and IVR prescription reminders can counter salesperson influence [52]. Critically, campaigns must explicitly target educated demographics whose knowledge-practice disconnect reflects diagnostic overconfidence—reframing WHO’s World Antibiotic Awareness Week materials to challenge misconceptions of antibiotics as “panaceas” [53] through cognitive dissonance strategies.
Insurance system reforms should introduce tiered co-payment structures that impose disincentives for non-guideline-compliant antibiotic prescriptions while ensuring full coverage for WHO Essential Medicines List antibiotics [54]. Urban schemes could incorporate prescription behavior monitoring into physician reimbursement algorithms, whereas rural initiatives require subsidized narrow-spectrum antibiotics to alleviate price-driven compromises. China should further capitalize on its telemedicine infrastructure through integrated digital health platforms: WeChat health portals could deliver BMQ-informed patient education modules for urban users, while AI-powered diagnostic support tools on telemedicine platforms enhance prescription accuracy in remote clinics, reducing unnecessary antibiotic demand [55].
Finally, as trusted health authorities, healthcare providers are pivotal in correcting public misconceptions about antibiotic use. Physicians and pharmacists must actively manage patient expectations through evidence-based communication strategies, particularly when counseling high-risk groups identified in this study (e.g., insured females 46–59) [56]. Crucially, providers should lead by example: reducing non-evidence-based antibiotic prescriptions—a persistent issue in primary care—directly challenges the false perception of antibiotics as universally effective remedies. Sustainable change requires systematic implementation and reinforcement of standardized clinical guidelines for rational antibiotic use across all practice settings [57,58].
Several methodological limitations warrant acknowledgment. First, the cross-sectional design precludes causal inference regarding ASM determinants. Second, self-reported data introduces potential recall and social desirability biases. Third, specific antibiotic names and spectral classifications (e.g., broad- vs. narrow-spectrum) were not captured in the survey instrument, restricting our ability to assess differential AMR risks associated with antibiotic types. Fourth, regional specificity limits generalizability beyond China. Finally, the exclusion of healthcare provider perspectives omits critical stakeholders in antibiotic dispensing. Future research should employ longitudinal designs to track ASM evolution post-pandemic, incorporate provider insights, and integrate the WHO AWaRe classification for quantifying spectrum-specific antibiotic exposure, with particular focus on identified high-risk groups such as rural residents prioritizing affordability.

4. Materials and Methods

4.1. Study Design and Population

Data were derived from the 2021 China Family Health Index Survey (CFHI-2021) [59], conducted from 10 July to 15 September 2021, across 120 cities in 22 provinces and 5 autonomous regions. Trained investigators in each city administered questionnaires via the online Questionnaire Star platform (https://www.wjx.cn/ (assess on 10 July 2021)), using a one-on-one, face-to-face approach. After investigators entered unique questionnaire IDs, respondents independently completed the survey by accessing the provided link. For participants with cognitive capacity but physical limitations, investigators recorded responses based on verbal input. The inclusion criteria were as follows: (1) Age ≥16 years; (2) Response to the ASM question; (3) Provision of written informed consent.

4.2. The Questionnaire and Data Collection

The structured questionnaire comprised two sections: 1. Sociodemographic and clinical characteristics: Gender, age, BMI, marital status, education, occupation, monthly household income per capita, medical insurance, chronic disease count, smoking/drinking history, and residence (urban/rural). Urban/rural residency was classified based on China’s household registration (hukou) system. Participants reporting non-agricultural or urban resident hukou status were categorized as urban residents. Those reporting agricultural hukou status were categorized as rural residents. This approach aligns with established socioeconomic stratification in China [60]. Notably, 2. antibiotic self-medication behavior: Question 1 (single-response): “Have you ever purchased and used antibiotics without a prescription?” (Yes/No). Question 2 (multiple-response, for ASM practitioners only): “What factors influenced your antibiotic purchase decisions?” Participants selected from 18 options: (1) Drug efficacy; (2) Drug safety; (3) Dosage form (e.g., capsules, patches); (4) Drug price; (5) Insurance reimbursement eligibility; (6) Ease of administration; (7) Taste of medication; (8) Packaging aesthetics; (9) Physicians’ advice; (10) Pharmacists’ advice; (11) Family members’ suggestions; (12) Friends’ suggestions; (13) Recommendations from sales personnel; (14) Personal experience; (15) Brand reputation; (16) Corporate credibility; (17) Advertising influence; and (18) After-sales service. Respondents selected 1–18 options.

4.3. Statistical Analysis

Descriptive statistics summarized categorical variables as frequencies (%) and normally distributed continuous variables as mean ± standard deviation. Chi-square tests identified potential determinants of ASM and urban-rural differences. Binary logistic regression assessed associations between ASM (dependent variable) and sociodemographic factors (independent variables), reporting adjusted odds ratios (adjusted ORs) with 95% confidence intervals (CIs). Statistical significance was set at p < 0.05. Analyses used SAS 9.4 (SAS Institute, Cary, NC, USA).

5. Conclusions

This study reveals persistent antibiotic ASM practices across both urban and rural China, mediated through factors including gender, education level, insurance status, and health-related behaviors. The convergence of ASM prevalence despite divergent regional socioeconomic profiles necessitates multifaceted interventions. Future research should employ longitudinal designs to track post-pandemic ASM trends and incorporate healthcare provider perspectives. Immediate action is imperative to integrate pharmacist education, public awareness campaigns, and insurance reforms within China’s evolving primary healthcare framework to mitigate the escalating AMR crisis.

Author Contributions

Conceptualization, G.H., P.G., H.Z., S.H., and L.S.; Data curation, G.H. and H.Z.; Formal analysis, G.H., P.G., and M.S.; Funding acquisition, L.S.; Investigation, M.S.; Methodology, G.H., P.G., and S.H.; Project administration, S.H. and L.S.; Resources, P.G., S.H., and L.S.; Software, G.H., M.S., H.Z., and S.H.; Supervision, H.Z., S.H., and L.S.; Validation, P.G., M.S., H.Z., S.H., and L.S.; Visualization, G.H. and P.G.; Writing—original draft, G.H. and M.S.; Writing—review and editing, S.H. and L.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (grant number 82273899).

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Committee of Ji’nan University, Guangzhou, China (JNUKY-2021-018).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study can be made available upon request from the corresponding author.

Acknowledgments

We would like to thank all the participants enrolled in this study and local health professionals for contributing to the study.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Antimicrobial Resistance Collaborators. Global burden of bacterial antimicrobial resistance in 2019: A systematic analysis. Lancet 2022, 399, 629–655. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  2. O’Neill, J. Antimicrobial Resistance: Tackling a Crisis for the Health and Wealth of Nations. 2014. Available online: https://amr-review.org/ (accessed on 28 April 2025).
  3. Li, J.; Song, X.; Yang, T.; Chen, Y.; Gong, Y.; Yin, X.; Lu, Z. A systematic review of antibiotic prescription associated with upper respiratory tract infections in China. Medicine 2016, 95, e3587. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  4. Zhang, Q.Q.; Ying, G.G.; Pan, C.G.; Liu, Y.S.; Zhao, J.L. Comprehensive evaluation of antibiotics emission and fate in the river basins of China: Source analysis, multimedia modeling, and linkage to bacterial resistance. Environ. Sci. Technol. 2015, 49, 6772–6782. [Google Scholar] [CrossRef] [PubMed]
  5. Ateshim, Y.; Bereket, B.; Major, F.; Emun, Y.; Woldai, B.; Pasha, I.; Habte, E.; Russom, M. Prevalence of self-medication with antibiotics and associated factors in the community of Asmara, Eritrea: A descriptive cross sectional survey. BMC Public Health 2019, 19, 726. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  6. Sachdev, C.; Anjankar, A.; Agrawal, J. Self-medication with antibiotics: An element increasing resistance. Cureus 2022, 14, e30844. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  7. Zeb, S.; Mushtaq, M.; Ahmad, M.; Saleem, W.; Rabaan, A.A.; Naqvi, B.S.Z.; Garout, M.; Aljeldah, M.; Al-Shammari, B.R.; Al-Faraj, N.J.; et al. Self-medication as an important risk factor for antibiotic resistance: A multi-institutional survey among students. Antibiotics 2022, 11, 842. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  8. World Health Organization. Global Action Plan on Antimicrobial Resistance, Geneva, Switzerland. 2015. Available online: https://apps.who.int/iris/bitstream/handle/10665/193736/9789241509763_eng.pdf?sequence=1&isAllowed=y (accessed on 25 May 2025).
  9. World Health Organization. Antimicrobial Resistance: No Action Today, No Cure Tomorrow, Geneva, Switzerland. 2011. Available online: https://www.who.int/dg/speeches/detail/antimicrobial-resistance-no-action-today-no-cure-tomorrow (accessed on 28 April 2025).
  10. G20 Summits. Communiqué of the G20 Leaders, Hangzhou, China. 2016. Available online: http://www.g20chn.org/hywj/dncgwj/201609/t20160906_3392.html (accessed on 23 May 2025).
  11. Li, H.; Yan, S.; Li, D.; Gong, Y.; Lu, Z.; Yin, X. Trends and patterns of outpatient and inpatient antibiotic use in China’s hospitals: Data from the Center for Antibacterial Surveillance, 2012–2016. J. Antimicrob. Chemother. 2019, 74, 1731–1740. [Google Scholar] [CrossRef] [PubMed]
  12. National Health and Family Planning Commission of the People’s Republic of China. China’s National Action Plan to Contain Antimicrobial Resistance. 2016. Available online: http://www.nhc.gov.cn/yzygj/s3593/201608/f1ed26a0c8774e1c8fc89dd481ec84d7.shtml (accessed on 25 May 2025).
  13. Fang, Y. China should curb non-prescription use of antibiotics in the community. BMJ 2014, 348, g4233. [Google Scholar] [CrossRef] [PubMed]
  14. Shi, L.; Chang, J.; Liu, X.; Zhai, P.; Hu, S.; Li, P.; Hayat, K.; Kabba, J.A.; Feng, Z.; Yang, C.; et al. Dispensing antibiotics without a prescription for acute cough associated with common cold at community pharmacies in shenyang, northeastern china: A cross-sectional study. Antibiotics 2020, 9, 163. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  15. Widowati, I.G.A.R.; Budayanti, N.N.S.; Januraga, P.P.; Duarsa, D.P. Self-medication and self-treatment with short-term antibiotics in Asian countries: A literature review. Pharm. Educ. 2021, 21, 152–162. [Google Scholar] [CrossRef]
  16. Huh, K.; Chung, D.R.; Kim, S.H.; Cho, S.Y.; Ha, Y.E.; Kang, C.I.; Peck, K.R.; Song, J.H. Factors affecting the public awareness and behavior on antibiotic use. Eur. J. Clin. Microbiol. Infect. Dis. 2018, 37, 1547–1552. [Google Scholar] [CrossRef] [PubMed]
  17. Demoré, B.; Mangin, L.; Tebano, G.; Pulcini, C.; Thilly, N. Public knowledge and behaviours concerning antibiotic use and resistance in France: A cross-sectional survey. Infection 2017, 45, 513–520. [Google Scholar] [CrossRef] [PubMed]
  18. Xiao, Y.; Zhang, J.; Zheng, B.; Zhao, L.; Li, S.; Li, L. Changes in Chinese policies to promote the rational use of antibiotics. PLoS Med. 2013, 10, e1001556. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  19. Do, N.T.T.; Vu, H.T.L.; Nguyen, C.T.K.; Punpuing, S.; Khan, W.A.; Gyapong, M.; Asante, K.P.; Munguambe, K.; Gómez-Olivé, F.X.; John-Langba, J.; et al. Community-based antibiotic access and use in six low-income and middle-income countries: A mixed-method approach. Lancet Glob. Health 2021, 9, e610–e619. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  20. National Bureau of statistics of China. China Statistical Yearbook 2024. Available online: https://www.stats.gov.cn/sj/ndsj/2024/indexeh.htm (accessed on 30 June 2025).
  21. National Medical Products Administration (NMPA) Center for Qualification of Licensed Pharmacists. National Licensed Pharmacist Statistics, May 2025. Available online: http://www.cqlp.org/info/link.aspx?id=7719&page=1 (accessed on 30 June 2025).
  22. Shi, J.; Jin, H.; Shi, L.; Chen, C.; Ge, X.; Lu, Y.; Zhang, H.; Wang, Z.; Yu, D. The quality of primary care in community health centers: Comparison among urban, suburban and rural users in Shanghai, China. BMC Fam. Pract. 2020, 21, 178. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  23. Li, X.; Lu, J.; Hu, S.; Cheng, K.K.; De, M.J.; Meng, Q.; Mossialos, E.; Xu, D.R.; Yip, W.; Zhang, H.; et al. The primary health-care system in China. Lancet 2017, 390, 2584–2594. [Google Scholar] [CrossRef] [PubMed]
  24. Monnet, D.L.; Harbarth, S. Will coronavirus disease (COVID-19) have an impact on antimicrobial resistance? Euro Surveill. 2020, 25, 2001886. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  25. Yang, Y.; Fan, S.; Chen, W.; Wu, Y. Broader open data needed in psychiatry: Practice from the psychology and behavior investigation of chinese residents. Alpha Psychiatry 2024, 25, 564–565. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  26. Ye, D.; Chang, J.; Yang, C.; Yan, K.; Ji, W.; Aziz, M.M.; Gillani, A.H.; Fang, Y. How does the general public view antibiotic use in China? Result from a cross-sectional survey. Int. J. Clin. Pharm. 2017, 39, 927–934. [Google Scholar] [CrossRef] [PubMed]
  27. Deng, P.; Yu, J.; Zhou, N.; Hu, M. Access to medicines for acute illness and antibiotic use in residents: A medicines household survey in Sichuan Province, western China. PLoS ONE 2018, 13, e0201349. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  28. McNulty, C.A.; Boyle, P.; Nichols, T.; Clappison, P.; Davey, P. Don’t wear me out--the public’s knowledge of and attitudes to antibiotic use. J. Antimicrob. Chemother. 2007, 59, 727–738. [Google Scholar] [CrossRef] [PubMed]
  29. Virmani, S.; Nandigam, M.; Kapoor, B.; Makhija, P.; Nair, S. Antibiotic use among health science students in an Indian University: A cross sectional study. Clin. Epidemiol. Glob. Health 2017, 5, 176–179. [Google Scholar] [CrossRef]
  30. World Health Organization. Using Indicators to Measure Country Pharmaceutical Situations: Fact Book on WHO Level I and Level II Monitoring Indicators. 2006. Available online: https://iris.who.int/bitstream/handle/10665/354554/WHO-TCM-2006.2-eng.pdf (accessed on 23 May 2025).
  31. Grigoryan, L.; Burgerhof, J.G.; Degener, J.E.; Deschepper, R.; Lundborg, C.S.; Monnet, D.L.; Scicluna, E.A.; Birkin, J.; Haaijer-Ruskamp, F.M.; SAR consortium. Attitudes, beliefs and knowledge concerning antibiotic use and self-medication: A comparative European study. Pharmacoepidemiol. Drug Saf. 2007, 16, 1234–1243. [Google Scholar] [CrossRef] [PubMed]
  32. Llor, C.; Bjerrum, L. Background for different use of antibiotics in different countries. Clin. Infect. Dis. 2005, 40, 333. [Google Scholar] [CrossRef] [PubMed]
  33. Frazer, J.S.; Frazer, G.R. Analysis of primary care prescription trends in England during the COVID-19 pandemic compared against a predictive model. Fam. Med. Community Health 2021, 9, e001143. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  34. Mitsi, G.; Jelastopulu, E.; Basiaris, H.; Skoutelis, A.; Gogos, C. Patterns of antibiotic use among adults and parents in the community: A questionnaire-based survey in a Greek urban population. Int. J. Antimicrob. Agents 2005, 25, 439–443. [Google Scholar] [CrossRef] [PubMed]
  35. Smith, D.R.M.; Dolk, F.C.K.; Smieszek, T.; Robotham, J.V.; Pouwels, K.B. Understanding the gender gap in antibiotic prescribing: A cross-sectional analysis of English primary care. BMJ Open 2018, 8, e020203. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  36. McNulty, C.A.; Boyle, P.; Nichols, T.; Clappison, P.; Davey, P. The public’s attitudes to and compliance with antibiotics. J. Antimicrob. Chemother. 2007, 60, 63–68. [Google Scholar] [CrossRef] [PubMed]
  37. Awad, A.; Eltayeb, I.; Matowe, L.; Thalib, L. Self-medication with antibiotics and antimalarials in the community of Khartoum State, Sudan. J. Pharm. Pharm. Sci. 2005, 8, 326–331. [Google Scholar] [PubMed]
  38. Bogale, A.A.; Amhare, A.F.; Chang, J.; Bogale, H.A.; Betaw, S.T.; Gebrehiwot, N.T.; Fang, Y. Knowledge, attitude, and practice of self-medication with antibiotics among community residents in Addis Ababa, Ethiopia. Expert. Rev. Anti Infect. Ther. 2019, 17, 459–466. [Google Scholar] [CrossRef] [PubMed]
  39. Vahedi, S.; Jalali, F.S.; Bayati, M.; Delavari, S. Predictors of self-medication in Iran: A notional survey study. Iran. J. Pharm. Res. 2021, 20, 348–358. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  40. Grigoryan, L.; Haaijer-Ruskamp, F.M.; Burgerhof, J.G.; Mechtler, R.; Deschepper, R.; Tambic-Andrasevic, A.; Andrajati, R.; Monnet, D.L.; Cunney, R.; Di-Matteo, A.; et al. Self-medication with antimicrobial drugs in Europe. Emerg. Infect. Dis. 2006, 12, 452–459. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  41. Lukovic, J.A.; Miletic, V.; Pekmezovic, T.; Trajkovic, G.; Ratkovic, N.; Aleksic, D.; Grgurevic, A. Self-medication practices and risk factors for self-medication among medical students in Belgrade, Serbia. PLoS ONE 2014, 9, e114644. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  42. Nie, B.; Chapman, S.C.E.; Chen, Z.; Wang, X.; Wei, L. Utilization of the beliefs about medicine questionnaire and prediction of medication adherence in China: A systematic review and meta-analysis. J. Psychosom. Res. 2019, 122, 54–68. [Google Scholar] [CrossRef] [PubMed]
  43. Liang, Y.; Zhou, M.; Yu, W.; Wang, H. Changes in social support among patients with hematological malignancy undergoing hematopoietic stem cell transplantation in Souzhou, China. Indian J. Cancer 2020, 57, 253–261. [Google Scholar] [CrossRef] [PubMed]
  44. Awuah, R.B.; Asante, P.Y.; Sakyi, L.; Biney, A.A.E.; Kushitor, M.K.; Agyei, F.; de-Graft Aikins, A. Factors associated with treatment-seeking for malaria in urban poor communities in Accra, Ghana. Malar. J. 2018, 17, 168. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  45. Maibach, E.W.; Leiserowitz, A.; Roser-Renouf, C.; Mertz, C.K. Identifying like-minded audiences for global warming public engagement campaigns: An audience segmentation analysis and tool development. PLoS ONE 2011, 6, e17571. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  46. Chandran, D.S.; Manickavasagam, P.P. Sale of antibiotics without prescription in stand-alone pharmacies in Tamil Nadu. J. Fam. Med. Prim. Care 2022, 11, 5516–5520. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  47. Bianco, A.; Licata, F.; Trovato, A.; Napolitano, F.; Pavia, M. Antibiotic-dispensing practice in community pharmacies: Results of a cross-sectional study in Italy. Antimicrob. Agents Chemother. 2021, 65, e02729-20. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  48. Li, Y.; Xu, J.; Wang, F.; Wang, B.; Liu, L.; Hou, W.; Fan, H.; Tong, Y.; Zhang, J.; Lu, Z. Overprescribing in China, driven by financial incentives, results in very high use of antibiotics, injections, and corticosteroids. Health Aff. 2012, 31, 1075–1082. [Google Scholar] [CrossRef] [PubMed]
  49. Licata, F.; Angelillo, S.; Oliverio, A.; Bianco, A. The Internet: Friend or foe of antibiotic resistance? Results of a cross-sectional study among Italian university students. Antibiotics 2021, 10, 1091. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  50. Bianco, A.; Licata, F.; Zucco, R.; Papadopoli, R.; Pavia, M. Knowledge and practices regarding antibiotics use: Findings from a cross-sectional survey among Italian adults. Evol. Med. Public Health 2020, 2020, 129–138. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  51. Wang, D.; Ge, P.; Xue, D.M.; Hu, C.; Huang, G.; Qi, J.; Hong, W.Y.; Pan, L.; Han, X.; Zhang, J.; et al. Prevalence and associated factors influencing the use of antibiotics for self-medication among Chinese residents: A cross-sectional study in 2021. Arch. Public Health 2025, 83, 107. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  52. World Health Organization. Antibiotic Resistance: Multi-Country Public Awareness Survey, Geneva, Switzerland 2015. Available online: http://apps.who.int/iris/bitstream/10665/194460/1/9789241509817_eng.pdf (accessed on 23 April 2025).
  53. Xiao, Y.; Li, L. Legislation of clinical antibiotic use in China. Lancet Infect. Dis. 2013, 13, 189–191. [Google Scholar] [CrossRef] [PubMed]
  54. Bartoloni, A.; Cutts, F.; Leoni, S.; Austin, C.C.; Mantella, A.; Guglielmetti, P.; Roselli, M.; Salazar, E.; Paradisi, F. Patterns of antimicrobial use and antimicrobial resistance among healthy children in Bolivia. Trop. Med. Int. Health 1998, 3, 116–123. [Google Scholar] [CrossRef] [PubMed]
  55. Chang, J.; Xu, S.; Zhu, S.; Li, Z.; Yu, J.; Zhang, Y.; Zu, J.; Fang, Y.; Ross-Degnan, D. Assessment of non-prescription antibiotic dispensing at community pharmacies in China with simulated clients: A mixed cross-sectional and longitudinal study. Lancet Infect. Dis. 2019, 19, 1345–1354. [Google Scholar] [CrossRef] [PubMed]
  56. Stivers, T. Managing patient pressure to prescribe antibiotics in the clinic. Paediatr. Drugs 2021, 23, 437–443. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  57. Bianco, A.; Licata, F.; Nobile, C.G.; Napolitano, F.; Pavia, M. Pattern and appropriateness of antibiotic prescriptions for upper respiratory tract infections in primary care paediatric patients. Int. J. Antimicrob. Agents 2022, 59, 106469. [Google Scholar] [CrossRef] [PubMed]
  58. Bianco, A.; Papadopoli, R.; Mascaro, V.; Pileggi, C.; Pavia, M. Antibiotic prescriptions to adults with acute respiratory tract infections by Italian general practitioners. Infect. Drug Resist. 2018, 11, 2199–2205. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  59. China Family News. CFHI-2021 China Family Health Index Survey General Report. Available online: https://www.cfnews.org.cn/newsinfo/2685237.html (accessed on 21 May 2025).
  60. Song, Q.; Smith, J.P. Hukou system, mechanisms, and health stratification across the life course in rural and urban China. Health Place 2019, 58, 102150. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
Antibiotics 14 00701 g001
Figure 1. Considerations distribution of ASM Practitioners.
Figure 1. Considerations distribution of ASM Practitioners.
Antibiotics 14 00701 g001
Table 1. General characteristics of Chinese residents.
Table 1. General characteristics of Chinese residents.
Variablesn (%)χ2p-Value
Urban RuralTotal
Total8008 (72.60)3023 (27.40)11,031 (100.00)
Gender 0.08310.7731
Male3647 (45.54)1386 (45.86)5033 (45.63)
Female4361 (54.56)1637 (54.15)5998 (54.37)
Age (years) 179.8654<0.0001
0–303377 (42.17)1288 (42.61)4665 (42.29)
31–452325 (29.03)676 (22.36)3001 (27.21)
46–591652 (20.63)566 (18.72)2218 (20.11)
60–654 (8.17)493 (16.31)1147 (10.40)
BMI (kg/m2) 3.79980.1496
<18.51103 (13.77)459 (15.18)1562 (14.16)
18.5–24.95510 (68.81)2035 (67.32)7545 (68.40)
25–1395 (17.42)529 (17.50)1924 (17.44)
Spouse 34.7433<0.0001
Yes2243 (60.34)3983 (54.56)6226 (56.44)
No1474 (39.66)3331 (45.54)4805 (43.56)
Education level 832.9165<0.0001
Primary or below473 (5.91)654 (21.63)1127 (10.22)
Secondary2272 (28.37)1145 (37.88)3417 (30.98)
Higher5263 (65.72)1224 (40.49)6487 (58.81)
Occupation 527.0434<0.0001
Unemployed4496 (56.14)2379 (78.70)6875 (62.32)
Blue-collar992 (12.39)295 (9.76)1287 (11.67)
White-collar2520 (31.47)349 (11.54)2869 (26.01)
Monthly household income per capita 1020.805<0.0001
0–30001714 (21.40)1532 (50.68)3246 (29.43)
3001–60003229 (40.32)1025 (33.91)4254 (38.56)
6001–3065 (38.27)466 (15.42)3531 (32.01)
Medical insurance 60.1299<0.0001
Resident/employee6083 (75.96)2206 (72.97)8289 (75.14)
Commercial203 (2.53)34 (1.12)237 (2.15)
Government-funded168 (2.10)38 (1.26)206 (1.87)
Out-of-pocket payment1554 (19.41)745 (24.64)2299 (20.84)
Number of chronic diseases 11.71850.0029
none6644 (82.97)2442 (80.78)9086 (82.37)
Single932 (11.64)369 (12.21)1301 (11.79)
Multiple432 (5.39)212 (7.01)644 (5.84)
Smoking history 15.2551<0.0001
Yes1514 (18.91)672 (22.23)2186 (19.82)
No6494 (81.09)2351 (77.77)8845 (80.18)
Drinking history 42.7765<0.0001
Yes3383 (42.25)1070 (35.40)4453 (40.37)
No4652 (57.75)1953 (64.60)6578 (59.63)
Table 2. Distributions of ASM among Chinese residents.
Table 2. Distributions of ASM among Chinese residents.
VariablesASM [n (%)]χ2p-Value
YesNoTotal
Total3717 (33.70)7314 (66.30)11,031 (100.00)
Gender 6.6819 0.0097
Male1632 (43.91)3401 (46.50)5033 (45.63)
Female2085 (56.09)3913 (53.50)5998 (54.37)
Age (years) 55.2949 <0.0001
0–301423 (38.28)3242 (44.33)4665 (42.29)
31–451020 (27.44)1981 (27.09)3001 (27.21)
46–59876 (23.57)1342 (18.35)2218 (20.11)
60–398 (10.71)749 (10.24)1147 (10.40)
BMI (kg/m2) 14.5356 0.0007
<18.5471 (12.67)1091 (14.92)1562 (14.16)
18.5–24.92548 (68.55)4997 (68.32)7545 (68.40)
25–698 (18.78)1226 (16.72)1924 (17.44)
Spouse 34.7433 <0.0001
Yes2243 (60.34)3983 (54.46)6226 (56.44)
No1474 (39.66)3331 (45.54)4805 (43.56)
Education level
Primary or below324 (8.72)803 (10.98)1127 (10.22)14.7739 0.0006
Secondary1148 (30.89)2269 (31.02)3417 (30.98)
Higher2245 (60.40)4242 (58.00)6487 (58.81)
Occupation 48.6309 <0.0001
Unemployed2156 (58.00)4719 (64.52)6875 (62.32)
Blue-collar455 (12.24)832 (11.38)1287 (11.67)
White-collar1106 (29.76)1763 (24.10)2869 (26.01)
Monthly household income per capita 4.7330 0.0938
0–30001045 (28.11)2201 (30.09)3246 (29.43)
3001–60001454 (39.12)2800 (38.28)4254 (38.56)
6001–1218 (32.77)2313 (31.62)3531 (32.01)
Medical insurance 60.5866 <0.0001
Resident/employee2931 (78.85)5358 (72.98)8289 (75.14)
Commercial95 (2.56)142 (1.94)237 (2.15)
Government-funded70 (1.88)136 (1.86)206 (1.87)
Out-of-pocket payment621 (16.71)1678 (22.94)2299 (20.84)
Number of chronic diseases 65.5118 <0.0001
none2921 (78.58)6165 (84.29)9086 (82.37)
Single501 (13.48)800 (10.94)1301 (11.79)
Multiple295 (7.94)349 (4.77)644 (5.84)
Smoking history 6.7482 0.0094
Yes788 (21.20)1398 (18.99)2186 (19.82)
No2929 (78.80)5916 (80.89)8845 (80.18)
Drinking history 19.8502 <0.0001
Yes1609 (43.29)2844 (38.88)4453 (40.37)
No2108 (56.71)4470 (61.12)6578 (59.63)
Residence 11.0567 0.0009
Urban2772 (74.58)5236 (71.59)8008 (72.60)
Rural945 (25.42)2078 (28.41)3023 (27.40)
Table 3. Considerations of ASM Practitioners by residence.
Table 3. Considerations of ASM Practitioners by residence.
Variablesn (%)χ2p-Value
Urban RuralTotal
Total2772 (74.58)945 (25.42)3717 (100.00)
Clinical factors
1 Drug efficacy 1787 (64.47)600 (63.49)2387 (64.22)0.2910 0.5896
2 Drug safety1867 (67.35)627 (66.35)2494 (67.10)0.3211 0.5710
3 Dosage form (e.g., capsules, patches)609 (21.97)199 (21.06)808 (21.74)0.3442 0.5574
Economic and accessibility
4 Drug price1036 (37.37)455 (48.15)1491 (40.11)34.0566 <0.0001
5 Insurance reimbursement eligibility847 (30.56)292 (30.90)1139 (30.64)0.0392 0.8430
Convenience and experience
6 Ease of administration581 (20.96)176 (18.62)757 (20.37)2.3697 0.1237
7 Taste of medication264 (9.52)78 (8.25)342 (9.20)1.3602 0.2435
8 Packaging aesthetics112 (4.04)34 (3.60)146 (3.93)0.3657 0.5453
Social and personal advice
9 Physicians’ advice2181 (78.68)725 (76.72)2906 (78.18)1.5873 0.2077
10 Pharmacists’ advice1632 (58.87)551 (58.31)2183 (58.73)0.0937 0.7596
11 Family members’ suggestions1181 (42.60)421 (44.55)1602 (43.10)1.0879 0.2969
12 Friends’ suggestions753 (27.16)238 (25.19)991 (26.66)1.4120 0.2347
13 Recommendations from sales personnel732 (26.41)308 (32.59)1040 (27.98)13.3816 0.0003
14 Personal experience1502 (54.18)511 (54.07)2013 (54.16)0.0035 0.9530
Brand and corporate
15 Brand reputation835 (30.12)205 (21.69)1040 (27.98)24.8509 <0.0001
16 Corporate credibility620 (22.37)165 (17.46)785 (21.12)10.1830 0.0014
17 Advertising influence243 (8.77)83 (8.78)326 (8.77)0.0002 0.9874
18 After-sales service269 (9.70)120 (12.70)389 (10.47)6.7430 0.0094
Table 4. Multivariate logistic regression on ASM among Chinese residents.
Table 4. Multivariate logistic regression on ASM among Chinese residents.
VariablesβSEWald χ2p-ValueOR (95%CI)
Intercept−0.2714 0.1474 3.3924 0.0655
Gender (Ref: Female)
Male−0.2619 0.0480 29.7837 <0.00010.770 (0.700, 0.845)
Age (Ref: 60–)
0–30−0.0321 0.0953 0.1133 0.7364 0.968 (0.803, 1.167)
31–45−0.0108 0.0857 0.0159 0.8997 0.989 (0.836, 1.170)
46–590.1848 0.0841 4.8313 0.0279 1.203 (1.020, 1.418)
BMI (Ref: 25–)
<18.5−0.1350 0.0771 3.0688 0.0798 0.874 (0.751, 1.016)
18.5–24.9−0.0362 0.0553 0.4279 0.5130 0.964 (0.865, 1.075)
Spouse (Ref: No)
Yes0.0408 0.0610 0.4482 0.5032 1.042 (0.924, 1.174)
Education level (Ref: Higher)
Primary or below−0.3759 0.0863 18.9833 <0.00010.687 (0.580, 0.813)
Secondary−0.0769 0.0509 2.2792 0.1311 0.926 (0.838, 1.023)
Occupation (Ref: White-collar)
Unemployed−0.1291 0.0559 5.3329 0.0209 0.879 (0.788, 0.981)
Blue-collar−0.0905 0.0729 1.5406 0.2145 0.913 (0.792, 1.054)
Monthly household income per capita (Ref: 6001–)
0–30000.0330 0.0570 0.3365 0.5618 1.034 (0.924, 1.156)
3001–60000.0203 0.0492 0.1707 0.6795 1.021 (0.927, 1.124)
Medical insurance (Ref: Out-of-pocket payment)
Resident/employee0.2826 0.0552 26.2324 <0.00011.327 (1.191, 1.478)
Commercial0.4848 0.1430 11.4930 0.0007 1.624 (1.227, 2.149)
Government-funded0.2163 0.1572 1.8926 0.1689 1.241 (0.912, 1.690)
Number of chronic diseases (Ref: Multiple)
None−0.5776 0.0913 39.9822 <0.00010.561 (0.469, 0.671)
Single−0.3353 0.0997 11.3167 0.0008 0.715 (0.588, 0.869)
Smoking history (Ref: No)
Yes0.0849 0.0608 1.9542 0.1621 1.089 (0.966, 1.226)
Drinking history (Ref: No)
Yes0.1830 0.0461 15.7526 <0.00011.201 (1.097, 1.314)
Residence (Ref: Rural)
Urban0.0454 0.0501 0.8211 0.3648 1.046 (0.949, 1.154)
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Huang, G.; Ge, P.; Sui, M.; Zhu, H.; Han, S.; Shi, L. Distribution and Determinants of Antibiotic Self-Medication: A Cross-Sectional Study in Chinese Residents. Antibiotics 2025, 14, 701. https://doi.org/10.3390/antibiotics14070701

AMA Style

Huang G, Ge P, Sui M, Zhu H, Han S, Shi L. Distribution and Determinants of Antibiotic Self-Medication: A Cross-Sectional Study in Chinese Residents. Antibiotics. 2025; 14(7):701. https://doi.org/10.3390/antibiotics14070701

Chicago/Turabian Style

Huang, Guo, Pu Ge, Mengyun Sui, He Zhu, Sheng Han, and Luwen Shi. 2025. "Distribution and Determinants of Antibiotic Self-Medication: A Cross-Sectional Study in Chinese Residents" Antibiotics 14, no. 7: 701. https://doi.org/10.3390/antibiotics14070701

APA Style

Huang, G., Ge, P., Sui, M., Zhu, H., Han, S., & Shi, L. (2025). Distribution and Determinants of Antibiotic Self-Medication: A Cross-Sectional Study in Chinese Residents. Antibiotics, 14(7), 701. https://doi.org/10.3390/antibiotics14070701

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop