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JAIDS Journal of Acquired Immune Deficiency Syndromes:
doi: 10.1097/QAI.0b013e31828d2fc1
Letters to the Editor

Transmitted Antiretroviral Drug Resistance in New York City, 2006–2010: The First Five Years of Routine Genotype Surveillance

Torian, Lucia V. PhD; Forgione, Lisa A. MA

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The New York City Department of Health and Mental Hygiene, HIV Epidemiology and Field Services Program, New York, NY.

Correspondence to: Lucia V. Torian, PhD, Deputy Director, HIV Epidemiology and Field Services Program, 42-09 28th Street, CN#44, Long Island City, NY 11101 (e-mail: ltorian@health.nyc.gov).

Supported by a Cooperative Agreement with the Centers for Disease Control and Prevention, PS08-80, 202, #UC62/CCU223595.

The authors have no conflicts of interest to disclose.

To the Editors:

Readers of 2 articles recently published in JAIDS1,2 may be interested in the transmitted drug resistance (TDR) observed during the first 5 full calendar years of routine population-based genotype surveillance in New York City. During 2006–2010, the proportion of persons with no history of antiretroviral (ARV) drug use who were first genotyped within 3 months of initial diagnosis date rose from 27% to 46% (P < 0.001) among all newly diagnosed patients and 39%–58% among patients initiating care. The prevalence of TDR remained essentially stable, rising from 10.5% in 2006 to 11.7% in 2009 and falling back to 10.9% in 2010 (P for year-to-year trend 0.063). The Stanford surveillance drug resistance mutation (SDRM) list and interpretation algorithm, which were specifically designed for epidemiologic analyses such as ours,3 were used to define TDR, and the Stanford Sierra web service was used to analyze the nucleotide sequences (94% of which are subtype B) reported by the testing laboratories to the New York State Department of Health.4,5

Genotyping varied significantly by demographic characteristics, risk factor, viral load (VL), and stage of illness at diagnosis (Table 1). Whites, men who have sex with men (MSM), and persons living in nonpoverty zip codes were significantly more likely to receive a genotype than nonwhites, other transmission risk groups, and those living in poverty zip codes. Persons aged 20–49 years were more likely to be genotyped than those aged <20 or ≥50 years. Patients with CD4 <350 cells were significantly more likely to be genotyped than persons with CD4 ≥350, as were patients whose HIV diagnosis was concurrent with a diagnosis of AIDS and patients with VL >100,000 RNA copies per milliliter.

TABLE 1-a Baseline G...
TABLE 1-a Baseline G...
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There were no significant differences in TDR by demographic characteristics or risk factors, although newly diagnosed persons aged 13–19 had marginally significantly [adjusted odds ratio 1.41, 95% confidence interval (CI) 0.92 to 2.17, P = 0.092] increased TDR. Persons with new diagnoses classified as recent infections by the serologic testing algorithm for recent HIV seroconversion (STARHS), which uses the proportion of anti-HIV IgG to total IgG to differentiate recent (mean 162 days) from chronic infection,6,7 were significantly more likely to have TDR (13.8%) than persons with new diagnoses classified as chronic HIV infection (9.7%, adjusted odds ratio 1.39, 95% CI 1.13 to 1.70), as was found in North Carolina and the NYC MSM cohort. Overall, single class TDR was 7% for any nonnucleoside reverse transcriptase inhibitor (NNRTI), 3.7% for any nucleoside reverse transcriptase inhibitor (NRTI), and 2.8% for any protease inhibitor (PI). Two-class TDR was observed in 2.5% and 3-class TDR in 0.4% of patients. Two-class TDR was 1.5%, 0.6%, and 0.4% for the NRTI–NNRTI, NRTI–PI, and NNRTI–PI combinations, respectively. No significant changes over time were observed for the 3 classes separately or the multiclass combinations.

TABLE 1-b Baseline G...
TABLE 1-b Baseline G...
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The prevalence of single class TDR did not differ significantly from that for single class TDR reported for all of New York State during the earliest 3 years (2006–2008) of the 5-year time period reported here; overall, the TDR was also similar.8 However, multiclass TDR was higher in NYC than in NYS for the NRTI–NNRTI (1.5% vs 0.8%) and NRTI–PI combinations (0.6% vs 0.4%). Also, there were some differences between the city and the state in the most commonly observed mutations associated with resistance to NNRTI (eg, K103N, Y181C, G190A in the city vs K103N, E138A, and K103R in the state) and NRTI (eg, M184V, K70R, and M41L in the city and G333E, V118I, and M41L in the state). The most frequently observed PI mutations in the city were L90M, M46I, and V82A, whereas L10I, A71T, and L10V were most frequently observed in the state.

The TDR among recently infected persons in NYC did not increase over time as it did in North Carolina, nor did it vary to the degree observed in the MSM cohort. However, as in those analyses, the surveillance data showed that NNRTI was the class to which the recently infected were most frequently resistant, K103N was the most common NNRTI mutation among both recent and established infections, and >60% of those recently infected were MSM. Multiclass resistance was 3.1% among recent and 2.5% among chronic infections.

Our analysis is not directly comparable with the others because each used a different SDRM list to define TDR,3,9,10 different patient inclusion criteria, and different definitions of acute/early HIV infection. Because of the extensive overlap in the mutation lists, the impact of these differences on the overall TDR estimate is likely to be minimal.11 The patient inclusion criteria also contain a considerable overlap. Finally, our STARHS classification overlaps with the combinations of patient history and serologic and virologic criteria used in the other 2 analyses. Thus, there is sufficient similarity in SDRMs, the patients analyzed, and the definitions of acute/recent infection to make the comparison appropriate.

In summary, the overall TDR was essentially stable at 11% in NYC from 2006 to 2010. Although genotyping increased over time, it remained associated with CD4 count, that is, newly diagnosed persons with CD4 < 350, VL > 100,000 on initiation of care and those diagnosed with concurrent HIV/AIDS were more likely to be genotyped than persons with higher CD4 counts, most likely because CD4 <350 and VL >100,000 would have prompted initiation of ART based on the federal guidelines that were in effect during the last 3 years of the analysis.12 As clinicians subscribing to current guidelines increasingly initiate ART regardless of CD4 count13 and as uptake of the recommendation to genotype at initiation of care improves, we expect the genotype surveillance system to represent an increasingly greater proportion of patients in care, thus broadening its power to capture TDR and other population-based outcomes of interest.

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REFERENCES

1. Yanik EL, Napravnik S, Hurt CB, et al.. Prevalence of transmitted antiretroviral drug resistance differs between acutely and chronically HIV-infected patients. J Acquir Immune Defic Syndr. 2012;61:258–262.

2. Castor D, Low A, Evering T, et al.. Transmitted drug resistance and phylogenetic relationships among acute and early HIV-1 infected individuals in New York City. J Acquir Immune Defic Syndr. 2012;61:1–7.

3. Shafer RW, Rhee S-Y, Pillay D, et al.. HIV-1 protease and reverse transcriptase mutations for drug resistance surveillance. AIDS. 2007;21:215–223.

4. Liu TF, Shafer RW. Web resources for HIV type 1 genotypic resistance interpretation. Clin Infect Dis. 2006;42:1608–1618.

5. HIV drug resistance database. Available at http://www.hivdb.stanford.edu. Accessed February 1, 2013.

6. Parekh B, Kennedy MS, Dobbs T, et al.. Quantitative detection of increasing HIV type 1 antibodies after seroconversion: a simple assay for detecting recent HIV infection and estimating incidence. AIDS Res Hum Retroviruses. 2002;18:295–307.

7. Parekh BS, Danson DL, Hargrove J, et al.. Determination of mean recency period for estimation of HIV type 1 incidence with the BED-capture EIA in persons infected with diverse subtypes. AIDS Res Hum Retroviruses. 2011;27:265–273.

8. Readhead AR, Gordon DE, Wang Z, et al.. Transmitted antiretroviral drug resistance in New York State, 2006–2008: results from a new surveillance system. PLoS One. 2012;8:e40533.

9. Bennett DE, Camacho RJ, Kuritzkes DR, et al.. Drug resistance mutations for surveillance of transmitted HIV-1 drug resistance. 2009 update. PLoS One. 2009;4:e4724.

10. Johnson VA, Brun-Vezinet F, Clotet B, et al.. Update of the drug resistance mutations in HIV-1. Top HIV Med. 2007;15:119–125.

11. Green H, Tilston P, Fearnhill E, et al.. The impact of different definitions on the estimated rate of transmitted HIV drug resistance in the United Kingdom. J Acquir Immune Defic Syndr. 2008;49:196–204.

12. Panel on Antiretroviral Guidelines for Adults and Adolescents, US Department of Health and Human Services. Guidelines for the use of antiretroviral agents in HIV-1 infected adults and adolescents; 2007. Available at http://aidsinfo.nih.gov/ContentFiles/AdultandAdolescentGL000721.pdf. Accessed February 1, 2013.

13. Panel on Antiretroviral Guidelines for Adults and Adolescents, US Department of Health and Human Services. Guidelines for the use of antiretroviral agents in HIV-1 infected adults and adolescents; 2012. Available at http://aidsinfo.nih.gov/ContentFiles/AdultandAdolescentGL000721.pdf. Accessed February 1, 2013.

© 2013 Lippincott Williams & Wilkins, Inc.

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