The distribution of antibiotic use and its association with antibiotic resistance

  1. Scott W Olesen
  2. Michael L Barnett
  3. Derek R MacFadden
  4. John S Brownstein
  5. Sonia Hernández-Diaz
  6. Marc Lipsitch
  7. Yonatan H Grad  Is a corresponding author
  1. Harvard TH Chan School of Public Health, United States
  2. University of Toronto, Canada
  3. Boston Children's Hospital, United States

Abstract

Antibiotic use is a primary driver of antibiotic resistance. However, antibiotic use can be distributed in different ways in a population, and the association between the distribution of use and antibiotic resistance has not been explored. Here we tested the hypothesis that repeated use of antibiotics has a stronger association with population-wide antibiotic resistance than broadly-distributed, low-intensity use. First, we characterized the distribution of outpatient antibiotic use across US states, finding that antibiotic use is uneven and that repeated use of antibiotics makes up a minority of antibiotic use. Second, we compared antibiotic use with resistance for 72 pathogen-antibiotic combinations across states. Finally, having partitioned total use into extensive and intensive margins, we found that intense use had a weaker association with resistance than extensive use. If the use-resistance relationship is causal, these results suggest that reducing total use and selection intensity will require reducing broadly-distributed, low-intensity use.

Data availability

State-level, aggregate antibiotic use and resistance data used in the main analyses are in Figure 3 - Source data 1 and 2. We do not own and cannot publish disaggregated MarketScan or Medicare data. MarketScan data are available by commercial license from Truven Health (marketscan.truvenhealth.com). Medicare data are available from ResDAC (www.resdac.org). ResDAC requires an application ensuring that requesting researchers comply with Common Rule, HIPAA, and CMS security and privacy requirements. Disaggregated ResistanceOpen data are restricted due to hospitals' privacy concerns. ResistanceOpen data are available by request from HealthMap (www.resistanceopen.org).

Article and author information

Author details

  1. Scott W Olesen

    Department of Immunology and Infectious Diseases, Harvard TH Chan School of Public Health, Boston, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5400-4945
  2. Michael L Barnett

    Department of Health Policy and Management, Harvard TH Chan School of Public Health, Boston, United States
    Competing interests
    No competing interests declared.
  3. Derek R MacFadden

    Division of Infectious Diseases, Department of Medicine, University of Toronto, Toronto, Canada
    Competing interests
    No competing interests declared.
  4. John S Brownstein

    Boston Children's Hospital, Boston, United States
    Competing interests
    No competing interests declared.
  5. Sonia Hernández-Diaz

    Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, United States
    Competing interests
    No competing interests declared.
  6. Marc Lipsitch

    Department of Immunology and Infectious Diseases, Harvard TH Chan School of Public Health, Boston, United States
    Competing interests
    Marc Lipsitch, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1504-9213
  7. Yonatan H Grad

    Department of Immunology and Infectious Diseases, Harvard TH Chan School of Public Health, Boston, United States
    For correspondence
    ygrad@hsph.harvard.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5646-1314

Funding

National Institute of General Medical Sciences (U54GM088558)

  • Scott W Olesen
  • Marc Lipsitch

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2018, Olesen et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Scott W Olesen
  2. Michael L Barnett
  3. Derek R MacFadden
  4. John S Brownstein
  5. Sonia Hernández-Diaz
  6. Marc Lipsitch
  7. Yonatan H Grad
(2018)
The distribution of antibiotic use and its association with antibiotic resistance
eLife 7:e39435.
https://doi.org/10.7554/eLife.39435

Share this article

https://doi.org/10.7554/eLife.39435

Further reading

    1. Epidemiology and Global Health
    2. Microbiology and Infectious Disease
    Scott W Olesen, Marc Lipsitch, Yonatan H Grad

    We are writing to reply to the comment by Pouwels et al., 2019 about our recent study (Olesen et al., 2018) on antibiotic use and antibiotic resistance.

    1. Epidemiology and Global Health
    Yuan Zhang, Dan Tang ... Xing Zhao
    Research Article

    Background:

    Biological aging exhibits heterogeneity across multi-organ systems. However, it remains unclear how is lifestyle associated with overall and organ-specific aging and which factors contribute most in Southwest China.

    Methods:

    This study involved 8396 participants who completed two surveys from the China Multi-Ethnic Cohort (CMEC) study. The healthy lifestyle index (HLI) was developed using five lifestyle factors: smoking, alcohol, diet, exercise, and sleep. The comprehensive and organ-specific biological ages (BAs) were calculated using the Klemera–Doubal method based on longitudinal clinical laboratory measurements, and validation were conducted to select BA reflecting related diseases. Fixed effects model was used to examine the associations between HLI or its components and the acceleration of validated BAs. We further evaluated the relative contribution of lifestyle components to comprehension and organ systems BAs using quantile G-computation.

    Results:

    About two-thirds of participants changed HLI scores between surveys. After validation, three organ-specific BAs (the cardiopulmonary, metabolic, and liver BAs) were identified as reflective of specific diseases and included in further analyses with the comprehensive BA. The health alterations in HLI showed a protective association with the acceleration of all BAs, with a mean shift of –0.19 (95% CI −0.34, –0.03) in the comprehensive BA acceleration. Diet and smoking were the major contributors to overall negative associations of five lifestyle factors, with the comprehensive BA and metabolic BA accounting for 24% and 55% respectively.

    Conclusions:

    Healthy lifestyle changes were inversely related to comprehensive and organ-specific biological aging in Southwest China, with diet and smoking contributing most to comprehensive and metabolic BA separately. Our findings highlight the potential of lifestyle interventions to decelerate aging and identify intervention targets to limit organ-specific aging in less-developed regions.

    Funding:

    This work was primarily supported by the National Natural Science Foundation of China (Grant No. 82273740) and Sichuan Science and Technology Program (Natural Science Foundation of Sichuan Province, Grant No. 2024NSFSC0552). The CMEC study was funded by the National Key Research and Development Program of China (Grant No. 2017YFC0907305, 2017YFC0907300). The sponsors had no role in the design, analysis, interpretation, or writing of this article.