Increased Incidence of Antimicrobial-Resistant Nontyphoidal Salmonella Infections, United States, 2004–2016

Felicita Medalla; Weidong Gu; Cindy R. Friedman; Michael Judd; Jason Folster; Patricia M. Griffin; Robert M. Hoekstra

Disclosures

Emerging Infectious Diseases. 2021;27(6):1662-1672. 

In This Article

Methods

Laboratory-based Enteric Disease Surveillance

Public health laboratories in 50 states and many local health departments receive human Salmonella isolates from clinical laboratories and report serotype information to the Centers for Disease Control and Prevention (CDC) through Laboratory-Based Enteric Disease Surveillance (LEDS).[17] We excluded serotypes Typhi, Paratyphi A, Paratyphi B (tartrate-negative), and Paratyphi C, which account for <1% of Salmonella infections in the United States, and whose only known reservoir are humans.[2,16,17,23] In this article, we use the term Salmonella to refer to nontyphoidal Salmonella.

National Antimicrobial Resistance Monitoring System

The National Antimicrobial Resistance Monitoring System (NARMS) is a collaboration among CDC, the US Food and Drug Administration, the US Department of Agriculture, and state and local health departments to monitor resistance among enteric bacteria isolated from humans, retail meat, and food animals.[16,24] Public health laboratories in 50 state and 4 local health departments submit every 20th Salmonella isolate received from clinical laboratories to CDC for antimicrobial drug susceptibility testing.[16,19,24]

During 2004–2016, CDC tested Salmonella isolates for susceptibility to agents representing 8–9 antimicrobial classes: aminoglycosides, β-lactam/β-lactamase inhibitors, cephems, macrolides (tested since 2011), penicillins, quinolones, folate pathway inhibitors, phenicols, and tetracyclines.[16] MICs were determined by broth microdilution with Sensititer (ThermoFisher, https://www.thermofisher.com) and interpreted using criteria from the Clinical and Laboratory Standards Institute (CLSI) when available.[7,16] Using CLSI criteria, we defined ceftriaxone resistance as MIC ≥4 μg/mL, ampicillin resistance as MIC ≥32 μg/mL, and nonsusceptibility to ciprofloxacin as MIC ≥0.12 μg/mL. The ciprofloxacin definition includes both resistant and intermediate CLSI categories because Salmonella infections with intermediate susceptibility to ciprofloxacin have been associated with poor treatment outcomes.[6,7,16]

Resistance Categories

We defined clinically important resistance as resistance to ceftriaxone, nonsusceptibility to ciprofloxacin, or resistance to ampicillin on the basis of the following criteria: third-generation cephalosporins (e.g., ceftriaxone) and fluoroquinolones (e.g., ciprofloxacin) are used for empiric treatment of severe infections; fluoroquinolones are not recommended for children; and ampicillin is useful for susceptible infections.[2] We defined and ranked from highest to lowest 3 mutually exclusive categories of clinically important resistance (Appendix Figure 1 https://wwwnc.cdc.gov/EID/article/27/6/20-4486-App1.pdf):[20] ceftriaxone/ampicillin resistance (because all ceftriaxone-resistant isolates are ampicillin-resistant); ciprofloxacin nonsusceptibility (nonsusceptible to ciprofloxacin but susceptible to ceftriaxone); and ampicillin-only resistance (ampicillin-resistant but susceptible to ceftriaxone and ciprofloxacin). We included ciprofloxacin-nonsusceptible isolates that were ceftriaxone-resistant in the ceftriaxone/ampicillin category because they are of greatest public health and treatment concern. Many isolates in each category had resistance to other agents. We defined multidrug resistance as resistance to ≥3 classes of antimicrobial agents.[16,19]

Bayesian Hierarchical Model to Estimate Changes

We used 2004–2016 data from LEDS, NARMS, and the US Census Bureau as input in the models.[16,17,25] For LEDS, we used the number of culture-confirmed infections by state and year (state-year). We combined serotyped isolates other than Enteritidis, Typhimurium, Newport, I 4,[5],12:i:-, and Heidelberg into an "other" category. We assigned unserotyped and partially serotyped isolates from each state into the 6 serotype categories (Enteritidis, Typhimurium, Newport, I 4,[5],12:i:-, Heidelberg, and other) on the basis of the average proportion of serotyped isolates in each category from 2004–2016. For NARMS, we used resistance proportions among fully serotyped isolates per state-year. We used US Census population data for each state-year to express incidence per 100,000 persons per year.[25]

A similar Bayesian hierarchical model approach was used from a previous study to estimate the incidence of resistant infections.[20,26] However, we found a Poisson model for LEDS data better captured the uncertainty of Salmonella incidence observed at the state-year level instead of the normal distribution used in our previous study.[20] The model incorporated the random effects of state, year, and state-year interaction to borrow strength from contiguous states and previous years.[20,26–28] Alaska and Hawaii were excluded because they are not adjacent to any state; the District of Columbia was also excluded, which began submitting isolates to NARMS in 2008.[16,19,20] We used an approach similar to our previous study to make adjustments for data from Florida, which reported low numbers of isolates compared with its 6 closest states.[17,18,20]

We applied the models to generate estimates (referred to as posterior estimates) for Salmonella infection incidence rates, resistance proportions, and resistant infection incidence rates (referred to as resistance incidence) by state-year for each of the 6 serotype categories by using Markov chain Monte Carlo simulations.[20,26–28] For each serotype category, we estimated resistance incidence for overall clinically important resistance, the 3 mutually exclusive categories of clinically important resistance, and multidrug resistance. For all Salmonella, we calculated overall estimates by summing estimates across the 6 serotype categories. We calculated state-year resistance incidence estimates per 100,000 persons per year as estimated incidence for state-year × estimated resistance proportion for state-year. For estimation of resistance incidence by geographic region, we used the 4 US Census region categories (Midwest, Northeast, South, and West) and aggregated posterior estimates of resistance incidence by year for all states in each region.[25] For each resistance category, we calculated mean estimates and 95% credible intervals (CrIs) from posterior estimates and mean crude rates by year for the 48 states and those stratified by serotype and region categories (Figure 1; Appendix Figures 2–5) for an overall side-by-side comparison.[20,25,26]

Figure 1.

Estimated annual incidence of culture-confirmed nontyphoidal Salmonella infections with any clinically important resistance, by serotype and region, United States, 2004–2016. Estimated changes in resistance incidence (mean and 95% credible intervals of the posterior differences per 100,000 persons per year) were derived using Bayesian hierarchical models. Crude resistance incidence rates were derived by multiplying infection incidence and resistance proportion for state-year. Any clinically important resistance was defined as resistant to ceftriaxone, resistant to ampicillin, or ciprofloxacin nonsusceptible. The "other" category comprised serotypes other than Enteritidis, Typhimurium, Newport, I 4,[5],12:i:-, and Heidelberg. US Census regions were used to define 4 geographic regions. NTS, all nontyphoidal Salmonella serotypes.

To assess changes in resistance incidence, we compared the mean resistance incidence from 2015–2016 with that from two 5-year reference periods during 2004–2016: 2004–2008 and 2010–2014. These reference periods are consistent with those used in NARMS annual reports to assess changes in resistance percentages.[16] All 50 states have participated in NARMS since 2003; the 2004–2008 period is the early years of nationwide participation and the 2010–2014 period is the recent past. For each resistance and serotype category, we calculated the difference between the posterior estimates of resistance incidence for 2015–2016 and those for each year in the 5-year reference periods for each region to obtain the mean difference and the 95% CrIs. We did not assume homogeneous rates across multiple years using this approach. For all Salmonella, we calculated the change in resistance incidence, which represents the net change (increase or decrease), for each resistance category, by summing the estimated changes derived for the 6 serotype categories. We describe statistically significant changes (i.e., in which the 95% CrIs do not include 0).

Extrapolating to the US Population

We multiplied the mean estimates of culture-confirmed infections by 29, which is the estimated number of total infections for every culture-confirmed infection in the general population, to estimate the total number of resistant infections for each period and changes in total resistant per 100,000 persons per year during 2015–2016 compared with the reference periods for each resistance category.[1] We used the average 2015–2016 population estimates for the 50 states (322 million) to extrapolate to the US population.[25]

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