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Volume 20, Number 4—April 2014


Ciprofloxacin Resistance and Gonorrhea Incidence Rates in 17 Cities, United States, 1991–2006

Harrell W. ChessonComments to Author , Robert D. Kirkcaldy, Thomas L. Gift, Kwame Owusu-Edusei, and Hillard S. Weinstock
Author affiliations: Centers for Disease Control and Prevention, Atlanta, Georgia, USA

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Table 3

Selected results of regression analyses of the temporal association of ciprofloxacin resistance and gonorrhea incidence rates in 17 cities, United States, 1991–2006*

Independent variable Gonorrhea incidence rate (log), year t Ciprofloxacin resistance rate, year t
Gonorrhea incidence rate (log), year t – 1 0.571 (0.057)† 0.015 (0.012)
Gonorrhea incidence rate (log), year t – 2 0.043 (0.080) 0.000 (0.012)
Gonorrhea incidence rate (log), year t – 3 −0.057 (0.077) 0.009 (0.010)
Ciprofloxacin resistance, year t – 1 −0.096 (0.488) 0.854 (0.154)†
Ciprofloxacin resistance, year t – 2 1.41 (0.538)† 0.395 (0.192)‡
Ciprofloxacin resistance, year t – 3 0.793 (0.492) −0.127 (0.177)
Sum of gonorrhea incidence rate (log) coefficients 0.557 (0.070) 0.024 (0.014)
Joint significance of gonorrhea incidence rate (log) coefficients: F test F = 46.6† F = 1.09
Sum of ciprofloxacin resistance coefficients 2.11 (0.506) 1.12 (0.146)
Joint significance of ciprofloxacin resistance coefficients: F test F = 8.88† F = 66.7†
Adjusted R2 0.971 0.859

*Values are coefficients (SEs) unless otherwise indicated. Both of the above regressions also included a constant term and binary (dummy) variables for city and year (not reported in table) and were estimated by using ordinary least squares. These findings, that past levels of ciprofloxacin resistance helped to predict current gonorrhea incidence rates but that past gonorrhea incidence rates did not help to predict current ciprofloxacin resistance levels, were generally consistent when linear regression corrected for first-order autocorrelated errors was used rather than ordinary least squares and/or when including additional covariates (% Black, % 15–29 y of age, robbery rate, unemployment rate, and per capita income).

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