Guidance on Community Viral Load : a Family of Measures, Definitions, and Method for Calculation
Lancet Infect Dis. Writer manuscript; available in PMC 2015 Jul 23.
Published in final edited form every bit:
PMCID: PMC4512165
NIHMSID: NIHMS584522
Community viral load as a mensurate for assessment of HIV treatment as prevention
William C. Miller
1Partitioning of Infectious Diseases, Department of Medicine, School of Medicine, University of North Carolina at Chapel Colina, Chapel Hill, NC, USA
twoSection of Epidemiology, Gillings' School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Kimberly A. Powers
1Segmentation of Infectious Diseases, Department of Medicine, School of Medicine, University of Due north Carolina at Chapel Loma, Chapel Hill, NC, U.s.a.
2Department of Epidemiology, Gillings' School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
M. Kumi Smith
2Department of Epidemiology, Gillings' School of Global Public Health, Academy of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Myron Southward. Cohen
1Division of Infectious Diseases, Department of Medicine, Schoolhouse of Medicine, University of North Carolina at Chapel Loma, Chapel Hill, NC, USA
twoDepartment of Epidemiology, Gillings' Schoolhouse of Global Public Wellness, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
3Department of Microbiology and Immunology, School of Medicine, University of North Carolina at Chapel Colina, Chapel Hill, NC, The states
Summary
Customs viral load (CVL), an aggregation of individual viral loads of HIV-infected persons within a divers community, has been proposed as a useful metric for monitoring HIV treatment uptake and quantifying its touch on on transmission. The starting time publications regarding CVL appeared in 2009 and the metric was subsequently incorporated into the United states of america National HIV/AIDS Strategy. Although CVL is intuitively appealing, it has several theoretical limitations and biases that require further evaluation. These limitations and biases can be categorized in iv areas related to: 1) selection and measurement issues in computing CVL amid HIV-infected persons, 2) the importance of HIV prevalence in determining the potential for ongoing HIV manual, iii) interpretation of CVL and its bear on on ongoing HIV transmission in a customs, and iv) the ecological fallacy (ecological bias). These potential issues deserve careful assessment as CVL is being considered as a public health metric to assess the impact of HIV care on prevention.
Community viral load (CVL) is a recently developed metric that has been evaluated in attempts to quantify a population's exposure to antiretroviral therapy (Art) and assess the outcome of treating HIV-infected persons on HIV transmission.ane–4 CVL is "an aggregate biological measure of viral load for a item geographic location or community,"1 typically calculated as the arithmetics mean, geometric mean (the average of the base of operations 10 logarithm), median, or sum of the highest or well-nigh recent VL of all reported HIV-infected persons with available viral load measurements in a particular population in a given time menstruation.one,3,4 The primal hypothesis underlying the use of CVL is that as ART coverage increases, greater numbers of HIV-infected persons will exist virologically suppressed, leading to reduced CVL, and consequently, reduced incidence of HIV infection in the full general population.1 CVL has ii potential uses: 1) a enquiry measure of the potential for ongoing HIV transmission inside a community, and ii) a surveillance metric for monitoring ART uptake and effectiveness. Underlying both of these uses is the assumption that CVL is a primal determinant of HIV incidence within a customs.
The uptake of this measure has been rapid. Since its introduction in British Columbiatwo,3 and San Franciscoane, CVL has been used in Washington, DCiv, Ugandafive, and elsewhere.six Lower CVL was modestly associated with reductions in measures of new HIV diagnoses over time in some studies,1–3 just not in others.four Although ecological in nature and limited by the use of new HIV diagnoses equally a proxy for HIV incidence,7 these associations have been interpreted as being driven past the individual-level issue of ART on HIV transmission.1,3 Motivated by these studies, CVL has been incorporated into requests for proposals from the U.South. National Institutes of Healthviii and guidance from the U.S. Centers for Disease Control and Prevention (CDC).9,x More than chiefly, the U.S. National HIV/AIDS Strategy has called for reducing CVL equally an "innovative solution" that "may aid reduce the number of new HIV infections in specific communities that may, in turn, reduce disparities in HIV infection."11
The concept of CVL, including its incorporation into CDC recommendations and US National HIV/AIDS strategy, has had several positive effects. Foremost, surveillance programs in the U.S., Europe, and elsewhere have get sensitized to the importance of the infectiousness of the HIV-infected private. Monitoring and surveillance programs have been mobilized to identify persons in and out of intendance and expand efforts to capture HIV testing, treatment, and viral load data. This increased attention to monitoring will undoubtedly increase the likelihood of truly applying public health principles to the HIV epidemic.12
All the same, the increased attention to HIV surveillance necessitates a careful examination of the utility of proposed metrics. Although CVL and HIV incidence are plausibly related causally, the data available to estimate CVL and its link to incidence are limited. Some of these limitations have been stated,one,7,13 but the rapid encouragement to implement CVL as a priority measure out suggests that some of these limitations may not have been fully appreciated. Hither, nosotros address several limitations and potential biases of CVL in four general categories related to: 1) pick and measurement issues in calculating CVL among HIV-infected individuals, two) the importance of HIV prevalence in determining ongoing HIV transmission, 3) interpretation of CVL and its affect on ongoing transmission in a customs, and four) the ecological fallacy (ecological bias). Each of these issues relates to the apply of CVL for estimating the potential for ongoing transmission within a community; the outset effect too relates to the apply of CVL as a metric for monitoring Fine art uptake. Our goal is to encourage cautious utilize of aggregate viral load metrics, and to advocate evolution of alternative population metrics and study designs.
Bug with Selection and Measurement
Non-representative samples of the HIV-infected population
Amass viral load measures depend on a person's diagnosis, engagement in care, measurement of his/her viral load, and reporting of that viral load to a public health dominance. In many settings, a meaning proportion of HIV-infected persons are not linked to or engaged in intendance. The HIV care pour, reflecting HIV diagnosis, linkage to care, retentiveness in care, and advisable treatment, reflects the incompleteness of HIV care.14–16 In 2010 in the United States, an estimated 1,178,350 persons had HIV infection, merely only 61.6% were linked to care and only 40.viii% retained in HIV care.16 Consequently, whatever estimate of the CVL based on persons linked to and/or retained in intendance will exist based on a subset of the HIV-infected population.fourteen–xvi
A sample of persons derived but from those in care will exist a non-random subset of the total HIV-infected population. An in-care-simply population excludes persons with undiagnosed HIV infection and persons with known HIV status who are not engaged in care. The not-in-intendance population cannot receive the benefits of Fine art and therefore will have college viral loads than the in-care population included in the CVL adding (Figure 1), resulting in a biased gauge of CVL.
Schematic representation of viral load distribution in a hypothetical HIV-infected population
Any population of HIV-infected persons will consist of individuals with undetectable viral loads (reddish curve), individuals with detectable viral loads who are receiving handling (blue curve), and individuals with detectable viral loads who are not receiving handling (green curve). Diagram provides qualitative representation of expected viral load distribution; actual distribution volition be specific to a particular population.
Even if testing and medical care coverage of the HIV-infected population was considerably enhanced, CVL would remain underestimated, because persons with astute and early HIV infection are missed with routine HIV testing in nigh settings and therefore will not be identified and linked to care.17–21 Acute/early on HIV infection, reflecting the first few weeks to months of infection, is associated with enhanced transmissibility,22 due to markedly elevated viral loads23,24 and the characteristics of the initial infecting virus.25 The proportion of ongoing manual attributable to acute/early HIV infection will vary by setting, depending on the local characteristics of the epidemic. Estimates of the transmission attributable to acute/early HIV take varied,26 but was as high equally 38% in one African setting with an established epidemic.27 Detection of acute/early infection requires special efforts, such as pooled testing of ELISA negative specimens with HIV RNA PCR or use of p24 antigen ELISAs in combination with traditional antibody ELISAs.17,nineteen Nonetheless, even if these culling testing approaches were widely used, many, if not about, persons with acute/early infection would remain undetected, because detection requires recently infected persons to actively seek testing. Consequently, fifty-fifty with regular HIV testing and linkage to care, CVL measured through the health system will ever underestimate the "true" CVL (i.eastward. population viral load) because the high viral loads of acute cases are excluded.
In its contempo guidance for monitoring CVL, the CDC has best-selling the issue of selection bias.9,x The CDC has proposed a series of aggregate viral load measures reflecting different levels of data availability in the HIV care cascade, including "population viral load", "community viral load", "in-care viral load", and "monitored viral load" (Effigy 2). (Note: We take used the term community viral load to broadly represent these amass viral load measures, except where explicitly stated in the text.) Although the definition of these measures partially addresses the issue of choice bias due to limited availability of information for persons not engaged in care, the magnitude of the bias and the utility of these various measures are non directly addressed. Furthermore, none of these proposed measures accounts for the relative size of the HIV-infected population (i.e., the prevalence), as discussed in the side by side department.
Human relationship between population subgroups and CDC-defined aggregate measures of viral load
In the fix of concentric circles representing an entire hypothetical population (gray) and various population sub-groups (each a different color), subpopulations represented by a given, smaller circle belong to all groups represented by larger, surrounding circles. The size of each circumvolve is proportional to the size of a given sub-group, bold fifteen% HIV prevalence (hypothetical), 79% of cases diagnosed,14 50% of diagnosed cases in care,14 and 75% of cases in care with viral loads bachelor (hypothetical). For each CDC-divers viral load metric listed on the right side of the effigy, the origin of the respective pointer indicates the sub-population (including all smaller sub-populations) contributing to the metric. Notably, none of the proposed metrics accounts for the size of the uninfected population (i.e., 1 – prevalence).
To demonstrate the potential bear on of choice bias related to the HIV care cascade and acute/early HIV infection, we conducted a simple exercise using plausible estimates of the proportion of the HIV-infected population and hateful viral loads nowadays at each stride of the cascade (Figure 3). Based on CDC estimates of the HIV care cascade, the measured CVL (i.eastward. monitored viral load) in a typical US setting may underestimate the true population viral load past nigh 50%. In a best-case scenario, such as in San Francisco, where greater proportions of the HIV-infected population are aware of their infection condition and in intendance (and thus captured by CVL measures), the underestimation may exist smaller, approximately 15%.
Estimated hateful values of true population viral load, community viral load, and monitored viral load as defined past CDC
Population viral load = mean viral load amidst all HIV-infected individuals (theoretical; currently unobserved). Monitored viral load = estimated mean viral load equally measurable in settings with VL data bachelor only for persons in intendance. Customs viral load = estimated hateful viral load for persons in and out of care, excluding undiagnosed, equally measured in a best-case setting (based on San Francisco data). A detailed description of the methods used in generating the figure is independent in the supplemental material. Briefly, figure assumptions and calculations were based on published estimates of the proportions of HIV-infected populations in cardinal subgroups along the HIV intendance cascade (ref [1] for San Francisco, ref [xiv] for the US overall), proportions of infection-unaware persons with acute infection [18 , nineteen], and mean viral loads in each sub-grouping [ane , xviii , 20].
The issue of selection bias could exist addressed with a population-based survey in a clearly defined target population of all persons in a community, including those with and without known HIV infection and measurement of viral loads in infected persons. Such a random sample would overcome the biased sample associated with engagement in care and would provide a more accurate estimation of the distribution of the viral loads in the community. However, a survey-based approach is unlikely to be used routinely for surveillance purposes because of the expense, particularly in depression prevalence settings, where extremely large samples would exist needed. Furthermore, astute HIV cases will be missed with routine HIV tests, unless testing is completed using HIV RNA testing (e.k. pooling).19 Finally, fifty-fifty if selection bias is substantially reduced, several challenges with CVL remain, as described below.
Sampling concerns within persons - Instability of viral loads within persons over time
Viral loads are dynamic within persons over time. While the goal of treatment is long-term suppression, some treated persons experience intermittent28 or long-term virological failure.29 CVL calculation has typically used a single viral load from each patient collected during the grade of a given calendar year.1,iii,four Some persons have multiple viral loads, others one, and some none. Whether single or multiple viral loads are bachelor, the time at which the available or selected viral load was measured could influence the accuracy and appropriateness of the CVL measure, both for predicting ongoing transmission and monitoring Fine art uptake. For example, any viral load measurement recorded while a person is on treatment may underestimate his/her overall contribution to CVL in the course of a given year if he/she subsequently drops out of care. Like bug ascend for those entering care, as viral loads earlier inbound care, which will non routinely contribute to aggregate viral load measurements, will exist higher on average than those recorded afterward inbound care and initiating Art.
The Uninfected Population and HIV Transmission - Prevalence is important
Any population (or "community") for which we wish to sympathise HIV transmission will comprise both HIV-infected and HIV-uninfected persons. HIV incidence will depend non only on the viral loads of infected persons, but also on the relative sizes of the infected and uninfected populations (i.eastward. HIV prevalence), and the rates and patterns of contact betwixt them. To engagement, CVL measures accept not considered the impact of HIV prevalence within the communities under observation.1–4
Consider two communities with the aforementioned population size merely markedly different prevalence of HIV infection. In Customs A, the prevalence of HIV infection is 5%. In Community B, the prevalence of HIV infection is 0.1%. Assume that in each community the mean CVL is 2000 copies/ml and is measured perfectly among all infected people. Too, presume that average contact rates are the same. Despite the equal CVL across communities, the HIV incidence rate will be substantially greater in the community with higher prevalence because the incidence charge per unit is approximately equal to the product of the prevalence of infection, the average contact rate, and per-contact manual probability.30
CVL estimation – Estimating the potential for ongoing transmission in a customs
Within whatever given population, the distribution of viral loads is likely to be multimodal (Figure 1). Among those in care, many persons will have achieved virological suppression, which volition be reflected by a large peak below the limits of detection. The viral loads of the remaining persons in care volition tend to be log-normally distributed.31 For persons out of care, the distribution is expected to be broader with a tail extending far to the correct reflecting persons with especially high viral loads, including those with acute/early on HIV infection. The shape of this composite distribution (Figure i) presents a problem for any single summary measure out of viral loads, including the arithmetics mean, geometric mean, median, or sum.
An aggregate viral load metric is a suboptimal mensurate of the potential for ongoing transmission in a community, even if all viral loads in a community are available. Consider ii, extreme examples of populations with identical HIV prevalence and 10 HIV-infected persons. In each population, the viral loads for all HIV-infected persons are known and the mean CVL is 10,000 copies/ml (Figure iv). In Population A, the 10 HIV-infected persons take viral loads ranging from two,000 to xx,000 with a mean of 10,000 copies/ml. In Population B, ix of the 10 HIV-infected persons accept undetectable viral loads and the tenth person has a viral load of 100,000 copies/ml. Although the CVL based on the mean or sum is identical, the likelihood of ongoing transmission in these communities is probable not the same.
Representation of populations with identical prevalence and community viral loads, but dissimilar potential for ongoing transmission
A) Viral loads distributed betwixt 2000 and twenty,000; B) Viral loads <fifty in ix persons and 100,000 in 10thursday person; C) Population B with person with high viral load in monogamous human relationship with HIV-infected, suppressed person; D) Population B with person with high viral load with multiple uninfected partners.
In addition to his/her viral load, the number of secondary transmissions attributable to any person depends on his/her sexual behavior and the properties of his/her sexual network. In Population B in a higher place (Effigy 4), if the one person with the viral load of 100,000 copies/ml is monogamous with an HIV-infected partner (Figure four, C), the probability of secondary transmissions volition be considerably lower than if s/he has multiple HIV-uninfected partners (Figure 4, D). Similar variations can be considered with Population A.
The sexual networks of these two populations further touch the likelihood of ongoing manual in the customs. If the sexual networks are densely continued between HIV-infected and -uninfected persons, transmission is facilitated.32 But, if the networks are predominated by pairs of stable couples with few inter-couple links, ongoing manual volition be restricted.33 Overall, transmission is critically determined by a combination of several factors including the sexual beliefs, network positions and viral loads of the HIV-infected persons in a population. In other words, in communities with similar mean viral loads, substantial variation in transmission patterns is non just possible, but expected. Consequently, the relationship between CVL and HIV incidence will vary across settings, reducing the utility of CVL as a universal measure out of the likelihood of ongoing transmission within a community.
CVL and the ecological fallacy
In epidemiology, ecological data are useful for assessing group-level furnishings that occur above and beyond the expected contribution of individual-level furnishings. For case, for many infectious diseases, the group-level effects may be greater than expected based on private data, due to interdependence of transmission events.34 Nevertheless, using ecological data lonely to determine individual-level causality is problematic.30 The attribution of causal furnishings at the private level based on group-level observations is referred to equally the ecological fallacy.30
Most studies relating CVL to measures of HIV transmission have noted the limitation of using ecological data, but have still interpreted the observed ecological associations as being driven solely by the private-level effects of Art on HIV transmission.1,3 However, reduced HIV diagnosis rates in areas with lower CVL cannot be attributed with certainty to reduced manual from persons with lower viral loads.
Although ecological associations between CVL and new HIV diagnoses may be partially driven past reduced biological infectiousness of those on treatment, these associations are also determined by additional factors at both the individual and population levels. Improved treatment services may be paired with other HIV prevention efforts that may have a population-level effect. For example, the population benefits of increased Art use amid injection drug users cannot easily be dissociated from the beneficial touch on of needle exchange and other programs targeted to this population.3,35,36 The ecological level of these analyses without data to link to individual transmission events is insufficient to determine whether viral suppression is responsible for reduced incidence, even if selection bias in CVL measurements were adequately addressed and an appropriate summary of the distribution was used.
Alternative metrics for consideration
Many of the shortcomings of CVL are due to the limitations of routinely available public health data. Surveillance data vary in quality, depending on the data sources and the abyss of data capture. Many alternative metrics will be express similarly. Nonetheless, improvements may be possible, even if a perfect measure is unavailable.
One alternative measure is the proportion of persons in the entire population with a viral load greater than a pre-specified threshold, such as whatever detectable viral load ("detectable viral load proportion").37 Other examples of this type of proportion include the proportion of persons with VL >400 or proportion of persons with VL >chiliad. The rationale for this mensurate has three key components: 1) transmission will occur almost exclusively in persons with measurable viral loads, 2) the HIV infection prevalence is incorporated into the measure direct, and 3) persons not in care can be assumed to have a detectable viral load. This third point is disquisitional, just requires that the community have a reasonably valid estimate of the HIV infection prevalence in the customs. Reasonable prevalence estimates volition be available in some settings, merely not others.
From a surveillance perspective, a combination of metrics may be necessary to obtain a complete clarification of the status of the HIV epidemic in a community. Considering handling for an private's ain benefit and treatment for the potential prevention of transmission volition require monitoring the total cascade of HIV testing, diagnosis, date in care, retention in care, and viral suppression. Incorporating aspects of these stages of the intendance cascade into a blended metric may provide a useful measure for enhanced monitoring.
Conclusions
Nosotros have entered a new and exciting phase of HIV prevention. The potential for HIV handling to prevent HIV transmission is substantial and promising. As the impact of broader and earlier ART apply on HIV incidence in a population undergoes further report, the value of CVL as a routine mensurate of a community's ART uptake and potential for ongoing manual will be examined farther. Still, in exercise, few areas currently have sufficient date and retentivity in care to gauge population-level amass viral load measures accurately and meaningfully. Even with acceptable coverage beyond the HIV care cascade, aggregate viral load measures are imperfect metrics of Art uptake due to issues related to acute/early HIV infection and intra-private viral load variations. The use of CVL every bit a mensurate of potential ongoing transmission in a community is complicated past those aforementioned bug, besides as those related to HIV prevalence, sexual behavior, sexual networks, and the ecological fallacy. The relative strengths and weaknesses of CVL and other proposed metrics for monitoring the impact of ART on the HIV epidemic must be addressed in future studies including observational studies, community randomized trials, and mathematical modeling.
Supplementary Material
01
Acknowledgments
We wish to thank the following people for comments on before drafts of this paper: Adaora Adimora, Till Bärnighausen, David Burns, Stephen Cole, Anna Barry Cope, Jeffrey Eaton, Christophe Fraser, Jesus M. Garcia Calleja, Kristen Hampton, Vicki Mobley, Habib O. Ramadhani, James Shelton, Nalyn Siripong, and David Wohl.
Fiscal Support: This piece of work was supported by the National Institutes of Health, NIAID R01AI083059 (WCM, KAP); NIDDK R37DK049381 (MSC); NIAID T32 AI007001 (MKS). The funding sources had no involvement in the development of this work.
Footnotes
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Authors' roles
William C. Miller conceived of the idea for the newspaper, wrote the initial typhoon, and contributed to subsequent editing of the newspaper. Kimberly A. Powers, M. Kumi Smith, and Myron Due south. Cohen contributed intellectually to the development of the paper's content, edited the drafts for intellectual content, and approved the final version.
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Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4512165/
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