Receipt and timing of HIV drug resistance testing in six U.S. jurisdictions

Dasgupta, Sharoda; Hall, H. Irene; Hernández, Ángela; Ocfemia, M. Cheryl Bañez; Saduvala, Neeraja; Oster, Alexandra M.

doi:10.1080/09540121.2017.1316356

Cited by 9 publications

(7 citation statements)

References 18 publications

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“…The current analysis also found demographic and geographic disparities in the proportion of persons with a reported PR/RT (with or without integrase) sequence similar to findings of a previous analysis of NHSS data [13]. It is important to note, however, that observed differences in levels of PR/RT (with or without integrase) sequence reporting were small and are unlikely to significantly bias findings from molecular cluster analyses.…”

Section: Discussionsupporting

confidence: 82%

Baseline HIV drug-resistance testing: 12 US jurisdictions, 2014–2019

Hugueley

McClung

Saduvala³

et al. 2022

Aids

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Objective:To understand recent patterns in reported baseline HIV drug-resistance testing over time in the United States.Design:Data from the National HIV Surveillance System for persons who were aged at least 13 years at the time of HIV diagnosis during 2014–2019 and resided in one of 12 US jurisdictions with high levels of reporting in 2014 and 2015.Methods:Among persons included in the analysis, we calculated the total proportion of HIV diagnoses occurring during 2014–2019 with a reported baseline sequence by year of diagnosis and sequence type. A baseline sequence was defined as any protease/ reverse transcriptase (PR/RT) or integrase sequence generated from a specimen collected 90 days or less after diagnosis.Results:During 2014–2019, reported levels of baseline PR/RT (with or without integrase) testing varied by year from 46.9% to 51.8% without any clear pattern over time. PR/RT with integrase testing increased (8.3–19.4%) and integrase-only testing remained low (1.9–1.3%).Conclusion:While reported levels of baseline PR/RT (with or without integrase) testing have remained sufficiently high for the purposes of molecular cluster detection, higher levels would strengthen jurisdictions’ and the Centers for Disease Control and Prevention's ability to monitor trends in HIV drug-resistance and detect and respond to HIV molecular clusters. Efforts to increase levels of reported baseline testing likely need to address both gaps in testing as well as reporting.

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Section: Discussionsupporting

confidence: 82%

Baseline HIV drug-resistance testing: 12 US jurisdictions, 2014–2019

Hugueley

McClung

Saduvala³

et al. 2022

Aids

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“…Cluster identification depends on completeness and timeliness of sequence data reporting, which can be affected by provider practices of ordering testing and laboratory processing and reporting. 33 Consequently, the 13 clusters identified may not be representative of all recent and rapid transmission in the United States. For example, a 2015 HIV outbreak among persons who inject drugs in Indiana was not detected by this analysis because Indiana did not participate in molecular HIV surveillance.…”

Section: Discussionmentioning

confidence: 99%

Identifying Clusters of Recent and Rapid HIV Transmission Through Analysis of Molecular Surveillance Data

Oster

Panneer

Ocfemia

et al. 2018

JAIDS Journal of Acquired Immune Deficiency Syndromes

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136

138

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Background: Detecting recent and rapid spread of HIV can help prioritize prevention and early treatment for those at highest risk of transmission. HIV genetic sequence data can identify transmission clusters, but previous approaches have not distinguished clusters of recent, rapid transmission. We assessed an analytic approach to identify such clusters in the United States. Methods: We analyzed 156,553 partial HIV-1 polymerase sequences reported to the National HIV Surveillance System and inferred transmission clusters using two genetic distance thresholds (0.5% and 1.5%) and two time periods for diagnoses (all years and 2013–2015, i.e., recent diagnoses). For rapidly growing clusters (with ≥5 diagnoses during 2015), molecular clock phylogenetic analysis estimated the time to most recent common ancestor for all divergence events within the cluster. Cluster transmission rates were estimated using these phylogenies. Results: A distance threshold of 1.5% identified 103 rapidly growing clusters using all diagnoses and 73 using recent diagnoses; at 0.5%, 15 clusters were identified using all diagnoses and 13 using recent diagnoses. Molecular clock analysis estimated that the 13 clusters identified at 0.5% using recent diagnoses had been diversifying for a median of 4.7 years, compared with 6.5–13.2 years using other approaches. The 13 clusters at 0.5% had a transmission rate of 33/100 person-years, compared with previous national estimates of 4/100 person-years. Conclusions: Our approach identified clusters with transmission rates 8 times those of previous national estimates. This method can identify groups involved in rapid transmission and help programs effectively direct and prioritize limited public health resources.

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“…HIV genotype sequence reporting was not mandatory during the study period, potentially impacting completeness of molecular cluster membership [17]. Additionally, resistance testing was not ordered for all new HIV diagnoses, also impacting completeness [18].…”

Section: Study Design and Data Collectionmentioning

confidence: 99%

Characterization of Molecular Cluster Detection and Evaluation of Cluster Investigation Criteria Using Machine Learning Methods and Statewide Surveillance Data in Washington State

Erly

Herbeck

Kerani

et al. 2020

Viruses

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Molecular cluster detection can be used to interrupt HIV transmission but is dependent on identifying clusters where transmission is likely. We characterized molecular cluster detection in Washington State, evaluated the current cluster investigation criteria, and developed a criterion using machine learning. The population living with HIV (PLWH) in Washington State, those with an analyzable genotype sequences, and those in clusters were described across demographic characteristics from 2015 to2018. The relationship between 3- and 12-month cluster growth and demographic, clinical, and temporal predictors were described, and a random forest model was fit using data from 2016 to 2017. The ability of this model to identify clusters with future transmission was compared to Centers for Disease Control and Prevention (CDC) and the Washington state criteria in 2018. The population with a genotype was similar to all PLWH, but people in a cluster were disproportionately white, male, and men who have sex with men. The clusters selected for investigation by the random forest model grew on average 2.3 cases (95% CI 1.1–1.4) in 3 months, which was not significantly larger than the CDC criteria (2.0 cases, 95% CI 0.5–3.4). Disparities in the cases analyzed suggest that molecular cluster detection may not benefit all populations. Jurisdictions should use auxiliary data sources for prediction or continue using established investigation criteria.

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Receipt and timing of HIV drug resistance testing in six U.S. jurisdictions

Cited by 9 publications

References 18 publications

Baseline HIV drug-resistance testing: 12 US jurisdictions, 2014–2019

Baseline HIV drug-resistance testing: 12 US jurisdictions, 2014–2019

Identifying Clusters of Recent and Rapid HIV Transmission Through Analysis of Molecular Surveillance Data

Characterization of Molecular Cluster Detection and Evaluation of Cluster Investigation Criteria Using Machine Learning Methods and Statewide Surveillance Data in Washington State

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