HIV cluster detection and response (CDR) provides a framework for identifying rapid HIV transmission and guiding implementation of proven HIV prevention and care strategies. Characterizing the relative benefits of CDR is important for guiding policy makers in resource allocation for HIV prevention. We sought to understand how many HIV infections would need to be averted by CDR activities to achieve various return-on-investment (ROI) thresholds. We conducted an ROI analysis of CDR in 2022, incorporating costs and benefits across US jurisdictions funded for HIV surveillance and prevention. Setting ROI thresholds between 1 and 5, we estimated the number of HIV infections that would need to be averted annually by CDR activities to reach ROI thresholds. A scenario was considered cost saving if the ROI > 1. Based on the number of people in national priority molecular clusters and estimated transmission in these clusters, we determined the percent reduction in transmission within these clusters that would be required to achieve the threshold number of HIV infections averted. The number of HIV infections needing to be averted annually ranged from 19 infections (ROI = 1) to 94 infections (ROI = 5). Among 657 HIV transmissions within national priority molecular clusters, the percent reduction in HIV transmission needed to meet ROI thresholds ranged from 2.9% (ROI = 1) to 14.3% (ROI = 5). In conclusion, CDR activities would need to avert a minimal number of HIV infections nationally to achieve cost savings.

BACKGROUND: Clusters of rapid HIV transmission indicate larger underlying networks that are not effectively reached by HIV prevention, testing, and care services. Starting in 2018, the Centers for Disease Control and Prevention (CDC) funded 59 U.S. health departments (HDs) to detect and respond to HIV clusters; HDs began reporting clusters to CDC in January 2020. METHODS: For clusters reported to CDC, we described cluster characteristics at detection, including detection method; size; HIV transmission category, defined as that of >50% of cluster members; and HD investigation and response activities. RESULTS: During 2020-2022, 45 HDs reported 322 HIV clusters, with most detected by molecular analysis of HIV sequences (75%). Most were detected in the South (46%) and three-quarters were predominant sexual transmission. Median cluster size at detection for molecular clusters was 10 persons (interquartile range 7-18). Among 205 clusters with follow-up data, investigation and response activities were conducted for 95%, including direct outreach to persons in clusters for partner services (64%), medical chart reviews (42%), and focused testing events (13%). Limited data on named partners tested showed that 11% received new HIV diagnoses. CONCLUSIONS: HD HIV cluster detection activities detected many clusters. Response activities were tailored for different clusters and intervened in networks with rapid transmission and high undiagnosed infection, as indicated by high positivity among partners. Cluster detection and response is an important tool to identify and address gaps in HIV prevention, testing, and care that facilitate rapid transmission.

OBJECTIVE: To estimate the influence of bursts of rapid HIV transmission on future transmission and describe populations affected by transmission bursts. DESIGN: Phylogenetic analysis of US National HIV Surveillance System data. METHODS: Time-scaled phylogenetic trees were inferred for six geographic regions using sequences from persons with HIV (PWH) with diagnoses of HIV infection 2014-2019. Transmission bursts were defined as ≥3 adjacent inferred transmission events in the phylogeny during a detection period. We calculated the relative contribution of transmission bursts 2015-2016 to transmission 2017-2019 compared with non-bursts. Then, we detected bursts within any sliding 2-year period 2014-2019 and assessed descriptive associations of characteristics of individuals involved with or descended from transmission bursts using univariate risk ratios. RESULTS: The 5.6% of phylogenetic lineages involved in transmission bursts 2015-2016 contributed to 14.9% of inferred transmission events 2017-2019. The relative contribution of lineages involved in transmission bursts to future transmission was 2.94 times that of lineages not involved in bursts. Younger age at diagnosis, self-identification as transgender or an additional gender identity, or as a cisgender man, male-to-male sexual contact, injection drug use, or male-to-male sexual contact and injection drug use, and diagnosis during acute or early infection were most strongly associated with involvement in or descendance from transmission bursts. CONCLUSIONS: Transmission bursts contribute disproportionately to future HIV transmission, underscoring the value of detecting and responding to rapid transmission to reduce incidence. Bursts of rapid transmission may also contribute to enduring disparities in incidence among some key populations.

BACKGROUND: Clusters of rapid HIV transmission in the United States are increasingly recognized through analysis of HIV molecular sequence data reported to the National HIV Surveillance System. Understanding the full extent of cluster networks is important to assess intervention opportunities. However, full cluster networks include undiagnosed and other infections that cannot be systematically observed in real life. METHODS: We replicated HIV molecular cluster networks during 2015-2017 in the United States using a stochastic dynamic network simulation model of sexual transmission of HIV. Clusters were defined at the 0.5% genetic distance threshold. Ongoing priority clusters had growth of ≥3 diagnoses/year in multiple years; new priority clusters first had ≥3 diagnoses/year in 2017. We assessed the full extent, composition, and transmission rates of new and ongoing priority clusters. RESULTS: Full clusters were 3-9 times larger than detected clusters, with median detected cluster sizes in new and ongoing priority clusters of 4 (range 3-9) and 11 (range 3-33), respectively, corresponding to full cluster sizes with a median of 14 (3-74) and 94 (7-318), respectively. A median of 36.3% (range 11.1%-72.6%) of infections in the full new priority clusters were undiagnosed. HIV transmission rates in these clusters were >4 times the overall rate observed in the entire simulation. CONCLUSIONS: Priority clusters reflect networks with rapid HIV transmission. The substantially larger full extent of these clusters, high proportion of undiagnosed infections, and high transmission rates indicate opportunities for public health intervention and impact.

Tuberculosis (TB) is the leading preventable cause of death among persons living with the human immunodeficiency virus (PLHIV) worldwide. The current US guidelines for the prevention of opportunistic infections in HIV-infected adults and adolescents recommend testing for latent tuberculous infection (LTBI) at the time of HIV diagnosis, regardless of other TB risk factors, and annually thereafter in PLHIV with initial negative LTBI testing results who are at continued high risk of exposure to Mycobacterium tuberculosis.1 Evaluation of PLHIV for TB disease and infection reduces morbidity and mortality, and prevents future TB transmission.2,3 However, we published a study in this Journal that evaluated a nationally representative sample of 2772 PLHIV in care in the United States during 2010–2012, and found that more than 30% of PLHIV diagnosed with HIV infection in the previous 5 years had no documentation of ever being tested for LTBI.4 | | While testing for LTBI among PLHIV in the Netherlands is not routine, the study by van Bentum et al. observed positive LTBI test results among 4.8% of their study population, highlighting the critical importance of screening PLHIV.5

During February 2021-June 2022, the Georgia Department of Public Health (GDPH) detected five clusters of rapid HIV transmission concentrated among Hispanic or Latino (Hispanic) gay, bisexual, and other men who have sex with men (MSM) in metropolitan Atlanta. The clusters were detected through routine analysis of HIV-1 nucleotide sequence data obtained through public health surveillance (1,2). Beginning in spring 2021, GDPH partnered with health districts with jurisdiction in four metropolitan Atlanta counties (Cobb, DeKalb, Fulton, and Gwinnett) and CDC to investigate factors contributing to HIV spread, epidemiologic characteristics, and transmission patterns. Activities included review of surveillance and partner services interview data,(†) medical chart reviews, and qualitative interviews with service providers and Hispanic MSM community members. By June 2022, these clusters included 75 persons, including 56% who identified as Hispanic, 96% who reported male sex at birth, 81% who reported male-to-male sexual contact, and 84% of whom resided in the four metropolitan Atlanta counties. Qualitative interviews identified barriers to accessing HIV prevention and care services, including language barriers, immigration- and deportation-related concerns, and cultural norms regarding sexuality-related stigma. GDPH and the health districts expanded coordination, initiated culturally concordant HIV prevention marketing and educational activities, developed partnerships with organizations serving Hispanic communities to enhance outreach and services, and obtained funding for a bilingual patient navigation program with academic partners to provide staff members to help persons overcome barriers and understand the health care system. HIV molecular cluster detection can identify rapid HIV transmission among sexual networks involving ethnic and sexual minority groups, draw attention to the needs of affected populations, and advance health equity through tailored responses that address those needs.

Gay, bisexual, and other men who have sex with men (MSM) accounted for 68% of new HIV diagnoses in the United States in 2020* (1). Despite advances in treatment and prevention, HIV transmission among MSM continues, in part because of stigma and barriers to accessing prevention and treatment services (2). HIV cluster detection and response, a core strategy of the Ending the HIV Epidemic in the United States initiative,() is an important tool for early identification and response to rapid HIV transmission, including among MSM. To better understand rapid HIV transmission among this population, CDC characterized large HIV molecular clusters detected using analysis of HIV-1 nucleotide sequence data from the National HIV Surveillance System (NHSS).() Among 38 such clusters first detected during 2018-2019 that had grown to include more than 25 persons by December 2021, 29 occurred primarily among MSM. Clusters primarily among MSM occurred in all geographic regions, and 97% involved multiple states. Clusters were heterogeneous in age, gender identity, and race and ethnicity and had rapid growth rates (median=nine persons added per year). The overall transmission rate at cluster detection was 22 transmission events per 100 person-years, more than six times that of previously estimated national transmission rates (3). Most clusters of rapid HIV transmission occur among MSM. Swift response to reach diverse persons and communities with early, tailored, and focused interventions is essential to reducing HIV transmission (4).

OBJECTIVE: This study describes characteristics of large tuberculosis (TB) outbreaks in the United States detected using novel molecular surveillance methods during 2014-2016 and followed for 2 years through 2018. METHODS: We developed 4 genotype-based detection algorithms to identify large TB outbreaks of ≥10 cases related by recent transmission during a 3-year period. We used whole-genome sequencing and epidemiologic data to assess evidence of recent transmission among cases. RESULTS: There were 24 large outbreaks involving 518 cases; patients were primarily U.S.-born (85.1%) racial/ethnic minorities (84.1%). Compared with all other TB patients, patients associated with large outbreaks were more likely to report substance use, homelessness, and having been diagnosed while incarcerated. Most large outbreaks primarily occurred within residences among families and nonfamilial social contacts. A source case with a prolonged infectious period and difficulties in eliciting contacts were commonly reported contributors to transmission. CONCLUSION: Large outbreak surveillance can inform targeted interventions to decrease outbreak-associated TB morbidity.

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 (NHSS) for persons who were aged at least 13years at the time of HIV diagnosis during 2014-2019 and resided in one of 12 United States 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 PR/RT or IN sequence generated from a specimen collected 90days after diagnosis. RESULTS: During 2014-2019, reported levels of baseline PR/RT (with or without IN) testing varied by year from 46.9% to 51.8% without any clear pattern over time. PR/RT with IN testing increased (8.3% to 19.4%), and IN-only testing remained low (1.9% to 1.3%). CONCLUSIONS: While reported levels of baseline PR/RT (with or without IN) 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.

The Respond pillar of the Ending the HIV Epidemic in the U.S. initiative, which consists of activities also known as cluster and outbreak detection and response, offers a framework to guide tailored implementation of proven HIV prevention strategies where transmission is occurring most rapidly. Cluster and outbreak response involves understanding the networks in which rapid transmission is occurring; linking people in the network to essential services; and identifying and addressing gaps in programs and services such as testing, HIV and other medical care, pre-exposure prophylaxis, and syringe services programs. This article reviews the experience gained through 30 HIV cluster and outbreak responses in North America during 2000-2020 to describe approaches for implementing these core response strategies. Numerous jurisdictions that have implemented these response strategies have demonstrated success in improving outcomes related to HIV care and viral suppression, testing, use of prevention services, and reductions in transmission or new diagnoses. Efforts to address important gaps in service delivery revealed by cluster and outbreak detection and response can strengthen prevention efforts broadly through multidisciplinary, multisector collaboration. In this way, the Respond pillar embodies the collaborative, data-guided approach that is critical to the overall success of the Ending the HIV Epidemic in the U.S. initiative.

SARS-CoV-2, the virus that causes COVID-19, is constantly mutating, leading to new variants (1). Variants have the potential to affect transmission, disease severity, diagnostics, therapeutics, and natural and vaccine-induced immunity. In November 2020, CDC established national surveillance for SARS-CoV-2 variants using genomic sequencing. As of May 6, 2021, sequences from 177,044 SARS-CoV-2-positive specimens collected during December 20, 2020-May 6, 2021, from 55 U.S. jurisdictions had been generated by or reported to CDC. These included 3,275 sequences for the 2-week period ending January 2, 2021, compared with 25,000 sequences for the 2-week period ending April 24, 2021 (0.1% and 3.1% of reported positive SARS-CoV-2 tests, respectively). Because sequences might be generated by multiple laboratories and sequence availability varies both geographically and over time, CDC developed statistical weighting and variance estimation methods to generate population-based estimates of the proportions of identified variants among SARS-CoV-2 infections circulating nationwide and in each of the 10 U.S. Department of Health and Human Services (HHS) geographic regions.* During the 2-week period ending April 24, 2021, the B.1.1.7 and P.1 variants represented an estimated 66.0% and 5.0% of U.S. SARS-CoV-2 infections, respectively, demonstrating the rise to predominance of the B.1.1.7 variant of concern(†) (VOC) and emergence of the P.1 VOC in the United States. Using SARS-CoV-2 genomic surveillance methods to analyze surveillance data produces timely population-based estimates of the proportions of variants circulating nationally and regionally. Surveillance findings demonstrate the potential for new variants to emerge and become predominant, and the importance of robust genomic surveillance. Along with efforts to characterize the clinical and public health impact of SARS-CoV-2 variants, surveillance can help guide interventions to control the COVID-19 pandemic in the United States.

The Centers for Disease Control and Prevention (CDC) and state, territorial, and local health departments have expanded efforts to detect and respond to HIV clusters and outbreaks in the United States. In July 2017, CDC created the HIV Outbreak Coordination Unit (OCU) to ensure consistent and collaborative assessment of requests from health departments for consultation or support on possible HIV clusters and outbreaks of elevated concern. The HIV OCU is a multidisciplinary, cross-organization functional unit within CDC's Division of HIV/AIDS Prevention. HIV OCU members have expertise in areas such as outbreak detection and investigation, prevention, laboratory services, surveillance and epidemiology, policy, communication, and operations. HIV OCU discussions facilitate problem solving, coordination, and situational awareness. Between HIV OCU meetings, designated CDC staff members communicate regularly with health departments to provide support and assessment. During July 2017-December 2019, the HIV OCU reviewed 31 possible HIV clusters and outbreaks (ie, events) in 22 states that were detected by CDC, health departments, or local partners; 17 events involved HIV transmission associated with injection drug use, and other events typically involved sexual transmission or overall increases in HIV diagnoses. CDC supported health departments remotely or on site with planning and prioritization; data collection, management, and analysis; communications; laboratory support; multistate coordination; and expansion of HIV prevention services. The HIV OCU has augmented CDC's support of HIV cluster and outbreak assessment and response at health departments and had important internal organizational benefits. Health departments may benefit from developing or strengthening similar units to coordinate detection and response efforts within and across public health agencies and advance the national Ending the HIV Epidemic initiative.

Molecular cluster detection analyzes HIV sequences to identify rapid HIV transmission and inform public health responses. We describe changes in the capability to detect molecular clusters and in geographic variation in transmission dynamics. We examined the reporting completeness of HIV-1 polymerase sequences in quarterly National HIV Surveillance System datasets from December 2015 to December 2019. Priority clusters were identified quarterly. To understand populations recently affected by rapid transmission, we described the transmission risk and race/ethnicity of people in clusters first detected in 2018-2019. During December 2015 to December 2019, national sequence completeness increased from 26% to 45%. Of the 1212 people in the 136 clusters first detected in 2018-2019, 69% were men who have sex with men (MSM) and 11% were people who inject drugs (PWID). State-by-state analysis showed substantial variation in transmission risk and racial/ethnic groups in clusters of rapid transmission. HIV sequence reporting has increased nationwide. Molecular cluster analysis identifies rapid transmission in varied populations and identifies emerging patterns of rapid transmission in specific population groups, such as PWID, who, in 2015-2016, comprised only 1% of people in such molecular clusters. These data can guide efforts to focus, tailor, and scale up prevention and care services for these populations.

We present the Progression and Transmission of HIV (PATH 4.0), a simulation tool for analyses of cluster detection and intervention strategies. Molecular clusters are groups of HIV infections that are genetically similar, indicating rapid HIV transmission where HIV prevention resources are needed to improve health outcomes and prevent new infections. PATH 4.0 was constructed using a newly developed agent-based evolving network modeling (ABENM) technique and evolving contact network algorithm (ECNA) for generating scale-free networks. ABENM and ECNA were developed to facilitate simulation of transmission networks for low-prevalence diseases, such as HIV, which creates computational challenges for current network simulation techniques. Simulating transmission networks is essential for studying network dynamics, including clusters. We validated PATH 4.0 by comparing simulated projections of HIV diagnoses with estimates from the National HIV Surveillance System (NHSS) for 2010-2017. We also applied a cluster generation algorithm to PATH 4.0 to estimate cluster features, including the distribution of persons with diagnosed HIV infection by cluster status and size and the size distribution of clusters. Simulated features matched well with NHSS estimates, which used molecular methods to detect clusters among HIV nucleotide sequences of persons with HIV diagnosed during 2015-2017. Cluster detection and response is a component of the U.S. Ending the HIV Epidemic strategy. While surveillance is critical for detecting clusters, a model in conjunction with surveillance can allow us to refine cluster detection methods, understand factors associated with cluster growth, and assess interventions to inform effective response strategies. As surveillance data are only available for cases that are diagnosed and reported, a model is a critical tool to understand the true size of clusters and assess key questions, such as the relative contributions of clusters to onward transmissions. We believe PATH 4.0 is the first modeling tool available to assess cluster detection and response at the national-level and could help inform the national strategic plan. © 2021 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)

BACKGROUND: Understanding geographic patterns of HIV transmission is critical to designing effective interventions. We characterized geographic proximity by transmission risk and urban-rural characteristics among people with closely related HIV strains suggestive of potential transmission relationships. METHODS: We analyzed U.S. National HIV Surveillance System data for people diagnosed 2010-2016 with a reported HIV-1 partial polymerase nucleotide sequence. We used HIV-TRACE (HIV TRAnsmission Cluster Engine) to identify sequences linked at a genetic distance ≤ 0.5%. For each linked person, we assessed median distances between counties of residence at diagnosis by transmission category and urban-rural classification, weighting observations to account for persons with multiple linked sequences. RESULTS: There were 24,743 persons with viral sequence linkages to at least one other person included in this analysis. Overall, half (50.9%) of persons with linked viral sequences resided in different counties, and the median distance from persons with linked viruses was 11 km/7 miles (IQR 0-145 km/90 mi). Median distances were highest for men who have sex with men (MSM: 14 km/9 mi, IQR 0-179 km/111 mi) and MSM who inject drugs, and median distances increased with increasing rurality (large central metro: 0 km/mi, IQR 0-83 km/52 mi; nonmetro: 103 km/64 mi, IQR 40 km/25 mi-316 km/196 mi). CONCLUSION: Transmission networks in the U.S. involving MSM, MSM who inject drugs, or persons living in small metro and nonmetro counties may be more geographically dispersed, highlighting the importance of coordinated health department efforts for comprehensive follow-up and linkage to care.

OBJECTIVE: To identify correlates of incident HIV infection in rapidly growing HIV molecular clusters. DESIGN: Phylogenetic analysis of HIV public health surveillance data. METHODS: High-priority HIV genetic transmission clusters with evidence of rapid growth in 2012 (i.e., clusters with a pairwise genetic distance </=0.005 substitutions/site and at least 3 cases diagnosed in 2012) were identified using HIV-TRACE. Then, we investigated cluster growth, defined as HIV cases diagnosed in the following 5 years that were genetically linked to these clusters. For clusters that grew during the follow-up period, Bayesian molecular clock phylogenetic inference was performed to identify clusters with evidence of incident HIV infection (as opposed to diagnosis of previously infected cases) during this follow-up period. RESULTS: Of the 116 rapidly growing clusters identified, 73 (63%) had phylogenetic evidence for an incident HIV case during the 5-year follow-up period. Correlates of an incident HIV case arising in clusters included a greater number of diagnosed but virally unsuppressed cases in 2012, a greater number of inferred undiagnosed cases in the cluster in 2012, and a younger time of most recent common ancestor for the cluster. CONCLUSIONS: These findings suggest that incident infections in rapidly growing clusters originate equally from diagnosed but unsuppressed cases and undiagnosed infections. These results highlight the importance of promoting retention in care and viral suppression as well as partner notification and other case-finding activities when investigating and intervening on high-priority molecular transmission clusters.

BACKGROUND: In the United States (U.S.), young (aged 13-24 years) men who have sex with men (MSM) bear a disproportionate burden of HIV. Transmission among MSM has been found to be disassortative by age. METHODS: We analyzed HIV-1 pol sequences reported to the U.S. National HIV Surveillance System from MSM with HIV diagnosed during 2009-2016. Using an HIV genetic transmission network, we identified persons with closely related viruses (i.e., genetic distance </=1.5%) and used multivariable logistic regression to examine changes from 2009-2012 to 2013-2016 in proportions of MSM linked to young MSM who were > 5 years older or of the same race/ethnicity. RESULTS: Among 9,510 young MSM linked to another MSM with a closely related virus, 37% linked to an older MSM and 62% linked to a MSM of the same race/ethnicity. Comparing 2013-2016 with 2009-2012, we found increases in linkage of older MSM to young MSM, with the most substantial increases seen in Hispanic/Latinos aged 13-19 (adjusted prevalence ratio [APR]=1.31, 95% confidence interval [CI]=1.11-1.56) and blacks aged 13-19 (APR=1.23, CI=1.06-1.41) and 20-24 (APR=1.14, CI=1.02-1.28). In contrast, change in linkage patterns among racial/ethnic groups was unremarkable. CONCLUSIONS: We found evidence of increased age mixing among MSM with respect to HIV transmission over time, which coincides temporally with changes in partner-seeking behavior such as increased use of mobile applications. These findings indicate the importance of social factors on HIV sexual and transmission networks and suggest that prevention efforts need to effectively reach MSM of all ages.

Rapid detection of increases in HIV transmission enables targeted outbreak response efforts to reduce the number of new infections. We analyzed US HIV surveillance data and identified spatiotemporal clusters of diagnoses. This systematic method can help target timely investigations and preventive interventions for maximum public health benefit.

After many years of decline, the incidence of human immunodeficiency virus (HIV) infection in the United States has stabilized [1]. The Department of Health and Human Services recently proposed an ambitious plan to end the HIV epidemic in the United States, with goals of reducing new HIV infections by 75% in 5 years and by at least 90% in 10 years [2, 3]. Making progress toward these goals will require effective strategies to identify where effective testing, treatment, and prevention tools are most needed and will have the greatest impact. Recent work in HIV molecular epidemiology has demonstrated that analysis of HIV sequence data can be a powerful tool to better understand where transmission is occurring and help guide prevention efforts [4–6].

From 2000 to 2014, the number of annual diagnoses of human immunodeficiency virus (HIV) infection in Massachusetts declined 47% (1). In August 2016, however, the Massachusetts Department of Public Health (MDPH) received reports of five new HIV cases among persons who inject drugs from a single community health center in the City of Lawrence (2). On average, less than one case per month among persons who inject drugs had been reported in Lawrence during 2014–2015 from all providers. Surveillance identified additional cases of HIV infection among such persons linked to Lawrence and Lowell, in northeastern Massachusetts, during 2016–2017. In 2018, MDPH and CDC conducted an investigation to characterize the outbreak and recommend control measures.

OBJECTIVES: HIV nucleotide sequence data can identify clusters of persons with genetically similar strains suggesting transmission. We simulated the effect of lowered data completeness, defined by the percent of persons with diagnosed HIV with a reported sequence, on transmission patterns and detection of growing HIV transmission clusters. METHODS: We analyzed HIV surveillance data for persons with HIV diagnosed during 2008-2014 who resided in Michigan or Washington. We calculated genetic distances, constructed the inferred transmission network for each jurisdiction, and compared transmission network characteristics and detection of growing transmission clusters in the full dataset with artificially reduced datasets. RESULTS: Simulating lower levels of completeness resulted in decreased percentages of persons linked to a cluster from high completeness (full dataset) to low completeness (5%) (Michigan: 54% to 18%; Washington, 46% to 16%). Patterns of transmission between certain populations remained robust as data completeness level was reduced. As data completeness was artificially decreased, sensitivity of cluster detection substantially diminished in both states. In Michigan, sensitivity decreased from 100% with the full dataset, to 62% at 50% completeness and 21% at 25% completeness. In Washington, sensitivity decreased from 100% with the full dataset, to 71% at 50% completeness and 29% at 25% completeness. CONCLUSIONS: Lower sequence data completeness limits the ability to detect clusters that may benefit from investigation; however, inferences can be made about transmission patterns even with low data completeness, given sufficient numbers. Data completeness should be prioritized, as lack of or delays in detection of transmission clusters could result in additional infections.

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.

For many infectious diseases, for example foodborne infections, tuberculosis, and Ebola virus, identifying and controlling outbreaks is a routine component of the public health response. Although this approach has not been a traditional focus of prevention efforts for HIV, outbreaks of HIV occur (as demonstrated by a 2015 outbreak in Scott County, Indiana, with almost 200 cases of HIV infection diagnosed in less than a year).1 Identifying HIV transmission clusters and outbreaks has traditionally been challenging for several reasons, including delays between infection and diagnosis, mobility of populations leading to geographically dispersed transmission clusters, and limitations in identifying sex and drug partners who may be infected.

We previously reported use of genotype surveillance data to predict outbreaks among incident tuberculosis clusters. We propose a method to detect possible outbreaks among endemic tuberculosis clusters. We detected 15 possible outbreaks, of which 10 had epidemiologic data or whole-genome sequencing results. Eight outbreaks were corroborated.

SETTING: Current guidelines recommend latent tuberculous infection (LTBI) testing at the time of human immunodeficiency virus (HIV) diagnosis and annually thereafter for persons at high risk of LTBI. OBJECTIVES: To estimate LTBI testing prevalence and describe the characteristics of HIV-infected persons who would benefit from annual LTBI testing. DESIGN: We estimated the proportions of LTBI testing among a nationally representative sample of HIV-infected adults in care between 2010 and 2012, and compared the patient characteristics of those with a positive LTBI test result to those with a negative result using chi2 tests. RESULTS: Among 2772 patients, 68.8% had been tested for LTBI at least once since HIV diagnosis, and 39.4% had been tested during the previous 12 months. Among patients tested at least once, 6.9% tested positive, 80.7% tested negative, and 12.4% had an indeterminate or undocumented result. Patients with a positive test were significantly more likely to be foreign-born, have lower educational attainment, and a household income at or below the federal poverty level. CONCLUSIONS: More than 30% of HIV-infected patients had never been tested for LTBI. Providers should test all patients for LTBI at the time of HIV diagnosis. The patient characteristics associated with a positive LTBI test result may guide provider decisions about annual testing.

OBJECTIVES: To describe cases and estimate the annual incidence of tuberculosis in correctional facilities. METHODS: We analyzed 2002 to 2013 National Tuberculosis Surveillance System case reports to characterize individuals who were employed or incarcerated in correctional facilities at time they were diagnosed with tuberculosis. Incidence was estimated with Bureau of Justice Statistics denominators. RESULTS: Among 299 correctional employees with tuberculosis, 171 (57%) were US-born and 82 (27%) were female. Among 5579 persons incarcerated at the time of their tuberculosis diagnosis, 2520 (45%) were US-born and 495 (9%) were female. Median estimated annual tuberculosis incidence rates were 29 cases per 100 000 local jail inmates, 8 per 100 000 state prisoners, and 25 per 100 000 federal prisoners. The foreign-born proportion of incarcerated men 18 to 64 years old increased steadily from 33% in 2002 to 56% in 2013. Between 2009 and 2013, tuberculosis screenings were reported as leading to 10% of diagnoses among correctional employees, 47% among female inmates, and 42% among male inmates. CONCLUSIONS: Systematic screening and treatment of tuberculosis infection and disease among correctional employees and incarcerated individuals remain essential to tuberculosis prevention and control. (Am J Public Health. Published online ahead of print September 15, 2016: e1-e7. doi:10.2105/AJPH.2016.303423).

Tuberculosis is an infectious disease that may result from recent transmission or from an infection acquired many years in the past; there is no diagnostic test to distinguish the two causes. Cases resulting from recent transmission are particularly concerning from a public health standpoint. To describe recent tuberculosis transmission in the United States, we used a field-validated plausible source-case method to estimate cases likely resulting from recent transmission during January 2011-September 2014. We classified cases as resulting from either limited or extensive recent transmission based on transmission cluster size. We used logistic regression to analyze patient characteristics associated with recent transmission. Of 26,586 genotyped cases, 14% were attributable to recent transmission, 39% of which were attributable to extensive recent transmission. The burden of cases attributed to recent transmission was geographically heterogeneous and poorly predicted by tuberculosis incidence. Extensive recent transmission was positively associated with American Indian/Alaska Native (adjusted prevalence ratio [aPR] = 3.6 (95% confidence interval [CI] 2.9-4.4), Native Hawaiian/Pacific Islander (aPR = 3.2, 95% CI 2.3-4.5), and black (aPR = 3.0, 95% CI 2.6-3.5) race, and homelessness (aPR = 2.3, 95% CI 2.0-2.5). Extensive recent transmission was negatively associated with foreign birth (aPR = 0.2, 95% CI 0.2-0.2). Tuberculosis control efforts should prioritize reducing transmission among higher-risk populations.

While the number of reported tuberculosis (TB) cases in the United States has declined over the past two decades, TB morbidity among foreign-born persons has remained persistently elevated. A recent unexpected decline in reported TB cases among foreign-born persons beginning in 2007 provided an opportunity to examine contributing factors and inform future TB control strategies. We investigated the relative influence of three factors on the decline: 1) changes in the size of the foreign-born population through immigration and emigration, 2) changes in distribution of country of origin among foreign-born persons, and 3) changes in the TB case rates among foreign-born subpopulations. Using data from the U.S. National Tuberculosis Surveillance System and the American Community Survey, we examined TB case counts, TB case rates, and population estimates, stratified by years since U.S. entry and country of origin. Regression modeling was used to assess statistically significant changes in trend. Among foreign-born recent entrants (<3 years since U.S. entry), we found a 39.5% decline (-1,013 cases) beginning in 2007 (P<0.05 compared to 2000-2007) and ending in 2011 (P<0.05 compared to 2011-2014). Among recent entrants from Mexico, 80.7% of the decline was attributable to a decrease in population, while the declines among recent entrants from the Philippines, India, Vietnam, and China were almost exclusively (95.5%-100%) the result of decreases in TB case rates. Among foreign-born non-recent entrants (≥3 years since U.S. entry), we found an 8.9% decline (-443 cases) that resulted entirely (100%) from a decrease in the TB case rate. Both recent and non-recent entrants contributed to the decline in TB cases; factors contributing to the decline among recent entrants varied by country of origin. Strategies that impact both recent and non-recent entrants (e.g., investment in overseas TB control) as well as those that focus on non-recent entrants (e.g., expanded targeted testing of high-risk subgroups among non-recent entrants) will be necessary to achieve further declines in TB morbidity among foreign-born persons.

Tuberculosis genotyping data are frequently used to estimate the proportion of tuberculosis cases in a population that are attributable to recent transmission (RT). Multiple factors influence genotype-based estimates of RT and limit the comparison of estimates over time and across geographic units. Additionally, methods used for these estimates have not been validated against field-based epidemiologic assessments of RT. Here we describe a novel genotype-based approach to estimation of RT based on the identification of plausible-source cases, which facilitates systematic comparisons over time and across geographic areas. We compared this and other genotype-based RT estimation approaches with the gold standard of field-based assessment of RT based on epidemiologic investigation in Arkansas, Maryland, and Massachusetts during 1996-2000. We calculated the sensitivity and specificity of each approach for epidemiologic evidence of RT and calculated the accuracy of each approach across a range of hypothetical RT prevalence rates plausible for the United States. The sensitivity, specificity, and accuracy of genotype-based RT estimates varied by approach. At an RT prevalence of 10%, accuracy ranged from 88.5% for state-based clustering to 94.4% with our novel approach. Our novel, field-validated approach allows for systematic assessments over time and across public health jurisdictions of varying geographic size, with an established level of accuracy.

Genotyping of Mycobacterium tuberculosis isolates contributes to tuberculosis (TB) control through detection of possible outbreaks. However, 20% of U.S. cases do not have an isolate for testing, and 10% of cases with isolates do not have a genotype reported. TB outbreaks in populations with incomplete genotyping data might be missed by genotyping-based outbreak detection. Therefore, we assessed the representativeness of TB genotyping data by comparing characteristics of cases reported during January 1, 2009-December 31, 2010, that had a genotype result with those cases that did not. Of 22,476 cases, 14,922 (66%) had a genotype result. Cases without genotype results were more likely to be patients <19 years of age, with unknown HIV status, of female sex, U.S.-born, and with no recent history of homelessness or substance abuse. Although cases with a genotype result are largely representative of all reported U.S. TB cases, outbreak detection methods that rely solely on genotyping data may underestimate TB transmission among certain groups.