Increasingly resistant bacteria in sickle cell disease patients indicate need to evaluate extendedspectrum cephalosporin therapy.

Few long-term multicenter investigations have evaluated the relationships between aggregate antimicrobial drug use in hospitals and bacterial resistance. We measured fluoroquinolone use from 1999 through 2003 in a network of US hospitals. The percentages of fluoroquinolone-resistant

Antimicrobial drug resistance in bacterial pathogens is of national and international concern (

Hospitals included in this study were participants in the Surveillance and Control of Pathogens of Epidemiologic Importance (SCOPE)–MediMedia Information Technology (MMIT) Antimicrobial Surveillance Network. MMIT (

Total grams for each fluoroquinolone used during each year were electronically extracted from individual patient billing records and aggregated to reflect hospitalwide usage. The total number of patient days (PD) for the corresponding time period at each hospital was determined from the sum of individual patient lengths of stay. These data were used to express normalized antimicrobial drug use in defined daily doses per 1,000 patient days (DDD/1,000PD) as recommended by the World Health Organization (WHO) (

Hospital antibiograms were requested from each participating hospital for each study year. To be included in the analysis, antibiograms must have reported data on organisms from all clinical sites (i.e., systemic and urinary isolates) and all units (including intensive care units). Ciprofloxacin or levofloxacin susceptibility was used to determine the percentage of fluoroquinolone-resistant

Mixed-effects repeated-measures analysis of variance was used to analyze changes in fluoroquinolone use and resistance over the study period, and the Tukey HSD (honestly significantly difference) test was used to compare differences between individual years. Relationships between total and individual fluoroquinolone usage and resistance in target pathogens for each year were determined by univariate linear regression. To analyze the relationship between fluoroquinolone use and percent resistance over the course of the study period, the method of generalized estimating equations (GEE) was used to construct a population-averaged longitudinal model (

Characteristic | Mean ± SD | Median (range) |
---|---|---|

No. admissions | 19,122 ± 12,208 | 14,720 (5,206–40,676) |

No. patient-days | 96,488 ± 64,719 | 76,408 (19,244–219,634) |

Case mix index | 1.51 ± 0.245 | 1.52 (1.13–2.01) |

Length of hospital stay, d | 5 ± 0.67 | 5 (3.6–6.6) |

No. staffed beds | 358 ± 203 | 310 (105–778) |

No. intensive care unit beds | 22 ± 16 | 18 (3–80) |

No. surgical procedures/1,000 admissions | 346 ± 184 | 281 (163–779) |

*SD, standard deviation.

Fluoroquinolone use and resistance over study period. FQ, fluoroquinolone; Levo, levofloxacin; Cipro, ciprofloxacin; Moxi, moxifloxacin; Gati, gatifloxacin; DDD/1,000PD, defined daily doses/1,000 patient-days; FQ-R PSA, fluoroquinolone-resistant

The diversity of use of individual fluoroquinolones in hospitals changed during the study period.

The results of the univariate regressions between fluoroquinolone use and resistance are summarized in

FQ use | FQ-R | MRSA | |||||
---|---|---|---|---|---|---|---|

Total FQ | Levofloxacin | Ciprofloxacin | Total FQ | Levofloxacin | Ciprofloxacin | ||

1999 | R^{2} | 0.233 | 0.255 | ||||

p | 0.116 | 0.0937 | |||||

n | 12 | 12 | |||||

2000 | R^{2} | 0.519 | 0.112 | ||||

p | 0.348 | 0.175 | |||||

n | 19 | 18 | |||||

2001 | R^{2} | 0.481 | 0.335 | 0.067 | 0.159 | ||

p | 0.001 | 0.009 | 0.2821 | 0.1134 | |||

n | 19 | 19 | 19 | 17 | |||

2002 | R^{2} | 0.178 | 0.112 | 0.09 | 0.145 | ||

p | 0.056 | 0.137 | 0.184 | 0.097 | |||

n | 21 | 21 | 21 | 20 | |||

2003 | R^{2} | 0.104 | 0.012 | 0.01 | 0.157 | 0.02 | 0.001 |

p | 0.16 | 0.641 | 0.66 | 0.092 | 0.538 | 0.9924 | |

n | 20 | 20 | 20 | 19 | 19 | 19 |

*Linear regression of fluoroquinolone (FQ) use versus percent resistance for hospitals. R^{2}, coefficient of determination; n, number of hospitals.

Drug | FQ-R | MRSA | ||
---|---|---|---|---|

Coefficient | p value | Coefficient | p value | |

Total FQ | ||||

Previous year's resistance | 0.875 | <0.001 | 0.804 | <0.001 |

Total FQ use | 0.002 | 0.883 | 0.025 | 0.155 |

Time | -0.312 | 0.554 | 1.04 | 0.040 |

Constant | 6.75 | 0.001 | 4.61 | 0.058 |

Levofloxacin | ||||

Previous year's resistance | 0.868 | <0.001 | 0.818 | <0.001 |

Levofloxacin use | 0.005 | 0.548 | 0.012 | 0.033 |

Time | -0.317 | 0.579 | 1.04 | 0.041 |

Constant | 6.78 | 0.001 | 6.38 | 0.001 |

Ciprofloxacin | ||||

Previous year's resistance | 0.866 | <0.001 | 0.845 | <0.001 |

Ciprofloxacin use | -0.018 | 0.226 | -0.004 | 0.848 |

Time | -0.393 | 0.475 | 0.991 | 0.079 |

Constant | 8.185 | <0.001 | 6.55 | 0.033 |

*Association of fluoroquinolone and pathogen resistance over time controlling for prior year resistance. GEE, generalized estimating equations; FQ-R

A) Changes in fluoroquinolone use (x axis) and resistance in

The results of this longitudinal, multicenter study of fluoroquinolone use and bacterial resistance suggest a complex relationship between fluoroquinolone use in hospitals and percentage of methicillin-resistant

Using a model incorporating the previous year's percent resistance as well as fluoroquinolone use and time over the entire study period, we did not find an additional effect of total fluoroquinolone use on percent resistance. Many factors beyond the volume of use of an antimicrobial agent affect the emergence and spread of antimicrobial resistance to that drug in the hospital setting. Cross-transmission between patients, acquisition of organisms from the hospital environment, and the use of different antimicrobial agents with linked resistance to the agent under study are all factors that also affect the number of resistant isolates in a given hospital (

Because percent antimicrobial resistance in a given period is highly correlated to previous percent resistance (i.e., the observations are not independent), we incorporated the previous year's percent resistance into our longitudinal model. Failing to account for this autocorrelation may result in spurious associations (

The results of this study raise a number of questions for further investigation. Why did the association between fluoroquinolone use and resistance become weaker in the later study years? Does this finding represent random fluctuation or an underlying trend? Mean fluoroquinolone use, after increasing through the first 3 study years, reached a plateau in the last 2 years (

Also of interest is the effect of individual fluoroquinolones on resistance. In our univariate analyses, levofloxacin use had a much stronger association with resistance in both pathogens than did use of ciprofloxacin. Levofloxacin also showed the only significant relationship (with percent MRSA) among the longitudinal models. However, the univariate models did not control for prior resistance levels. Also, the population-averaged modeling approach results in a pooling of effects across all hospitals, resulting in some degree of effect attenuation. Indeed, the estimates from a GEE analysis can sometimes be smaller than those estimates produced from a corresponding mixed-effect model (

This study has a number of limitations. The study hospitals do not represent a random sample of US hospitals; further studies are required to determine whether the results are broadly applicable. We were unable to control for differences between hospitals in their methods of antibiogram construction, including methods and reporting of duplicate isolates, which can affect reported resistance (

Our results suggest that the ecologic relationship between the hospital use of fluoroquinolones and antimicrobial resistance in

We thank Jim Letcavage and Annie Mahoney for providing antimicrobial drug–use data and the hospital pharmacists, infection control practitioners, microbiologists, and physicians who participated in the Surveillance and Control of Pathogens of Epidemiologic Importance–MMIT Antimicrobial Surveillance Network.

Bayer provides fellowship support to CM. REP received a grant from Merck and Bayer.

Dr. MacDougall is an infectious diseases fellow at the Virginia Commonwealth University School of Pharmacy. His primary research interests are the relationship between antimicrobial drug use and resistance and the development and impact of antimicrobial stewardship programs.