# Contents

# Executive Summary
The Division of HIV/AIDS Prevention at the Centers for Disease Control and Prevention (CDC) was charged by the National HIV/AIDS Strategy to provide "technical assistance to  localities to collect data to calculate community viral load" . Guidance on Community Viral Load: A Family of Measures, Definitions, and Method for Calculation represents the work of over 50 Workgroup members from more than 25 jurisdictions. This Guidance document introduces the concept of community viral load and provides definitions of, and methods for calculating, community viral load and related measures.
During the past few years, there has been great interest in the scientific literature and at scientific meetings about a new public health HIV measure called "community viral load." It is a population-based measure of HIV-infected individuals' concentration of plasma HIV-1 RNA (viral load). When viral loads are summed across a community and then divided by the number of HIV-infected persons in the community, the average HIV community viral load represents the level of viremia for a geographic area during a defined time. Having a single number that is an indicator of HIV transmission potential and quality of HIV care and treatment for a geographic area is extremely attractive.
For community viral load to be a robust measurement, it must reflect the viral loads of persons diagnosed with HIV, be a reliable measure that is also sensitive to viral load changes in the community, and reflect sexual behaviors or other HIV transmission risks in the community. This measure could be used by local HIV reporting jurisdictions each year to assess progress in treating HIV-infected persons with antiretroviral medications that would lower the community's viremia, which could-potentially-reduce transmission within the community. Community viral load may also have some utility in monitoring the progress of national HIV care and treatment objectives when assessed over time.
During the process of examining local and federal HIV surveillance activities and the data obtained from such activities, a number of issues were identified that may impact the ability of a jurisdiction to successfully and accurately estimate a population's viral load. These include:
- Population selection. A well-defined population should be chosen, possibly even a closed population, so that those who are at risk for transmitting HIV and those at risk for acquiring HIV can be identified and counted, all within a defined geographic area that may not correspond with jurisdictional boundaries.  Varying definitions of "community viral load." There are many analyses that report "community viral load" and the methods and definitions used for each varies somewhat. The history of the community viral load concept and an inventory of community viral load analyses highlight these differences and the varying methods used to calculate community viral load (Appendix A and Table 2). Ideally, measures of community viral load and its related terms should use a common method that would, at a minimum, allow for periodic snapshots to assess changes over time by a jurisdiction, as well as allow for comparisons across localities.  Complete and accurate surveillance/health data. Locations with universal health coverage or a common source of healthcare will fare better than those areas without such infrastructure because the latter will need to create composite data from disparate sources. In addition, jurisdictions have reported significant amounts of missing viral load results for HIV-infected persons, which may bias community viral load estimates. This may be due to a large proportion of HIV-infected persons being out of care in a jurisdiction, or data quality issues for a jurisdiction, or both.
This document proposes a family of viral load measurements, of which one is Community Viral Load (Figure 2); other measures include: Population Viral Load, In-Care Viral Load, and Monitored Viral Load. Technical guidance for estimating Community Viral Load, In-Care Viral Load, and Monitored Viral Load are provided. Analytic methods and tools (spreadsheet and SAS code) for calculating mean viral load, percentage with suppressed viral load (200 copies/mL), percentage with undetectable viral load (50 copies/mL), and for assessing a statistical difference between two mean viral load measures will be made available by CDC to HIV surveillance coordinators. While this Guidance document is intended primarily for HIV surveillance coordinators to be able to calculate Community Viral Load measures for their jurisdictions, it should be recognized that the ability to do so does not rest solely with the HIV surveillance system (see Appendix B for description of surveillance and viral load issues). Using viral load measures to monitor the epidemic is a function of policy, care and treatment, and surveillance as illustrated below:
The effective monitoring of Community Viral Load measures and ultimately documenting progress towards the National HIV/AIDS Strategy goals of reduced HIV incidence and improved health outcomes for persons living with HIV infection in the United States (see Appendix E) requires translation of concepts developed in a few jurisdictions to relevant measures that can be used in all jurisdictions. This document acknowledges the complexity of doing so and offers practical options for measurements under various scenarios where policy, practice, and surveillance intersect.

# Policy

# History of Community Viral Load
In early 2009, based on a mathematical model for South Africa, Granich et al. suggested that universal HIV testing and immediate treatment with antiretroviral medications among persons with heterosexually acquired HIV infection could lead to virtual elimination of HIV disease in 50 years . The underlying mechanism by which this could be possible is to reduce the amount of virus circulating in a person's bloodstream (HIV plasma viral load) by consistent and early use of antiretroviral medications. As the level of viremia and viral shedding decreases, infectiousness also decreases, greatly reducing or eliminating the transmission of HIV . A 2004 study in Taiwan demonstrated that free access to HIV antiretroviral medications for HIV-infected persons decreased HIV transmission .
The earliest use of "community viral load" occurred in 2008 at the Conference of Retroviruses and Opportunistic Infections by Ronald Stall . Based on the work of Millett et al. , the high prevalence of untreated HIV infection was used to explain the disproportionately high rates of HIV infection among African American men who have sex with men (MSM). Due to lack of treatment and a resulting high viremia, the African American MSM community was described to have a much higher "community viral load" than other MSM communities, which accounted for more efficient transmission of HIV and higher infection rates.
One of the first cohort studies to examine HIV concentration levels in the blood of injecting drug users (IDUs) from Vancouver, Canada was published in 2009 . Wood et al. studied a welldescribed population of IDUs that was followed every six months for more than 11 years. HIV viral loads, HIV diagnostic testing, and antiretroviral therapy were monitored for this population and community plasma HIV-1 RNA concentrations over time were ascertained. A statistically significant correlation between the median IDU community viral load and the incidence of new HIV infections was observed. Furthermore, in their analysis, Wood found that when the community viral load among IDUs decreased to <20,000 copies/mL, the association with HIV incidence ceased.
In June 2010, Das et al. published a paper on community viral load in San Francisco . Das and colleagues found a significant association between a declining community viral load and a decline in new HIV diagnoses from 2004 to 2008. Additionally, the authors presented viral load data in novel ways that examined geographic distribution of mean viral load by demographic, risk, and socioeconomic factors. Montaner et al. (2010) published data that helped provide the final link in the mechanism that described viral loads and transmission . Using the community of British Columbia, Canada, they demonstrated that, over time, increased use of antiretroviral therapy was associated with a decrease in the population's viral load and, ultimately, a decrease in new HIV infections.
Collectively, these papers have galvanized the HIV prevention and surveillance communities and spurred analyses exploring the relationship between community viral load and newly diagnosed and reported cases and estimated HIV incidence (see Appendix A, Inventory of VL Analyses).
In addition to ecological analyses suggesting benefits of reduction in community viral load for population-level HIV prevention, use of antiretroviral therapy by the HIV seropositive partner has been shown to be associated with lower HIV incidence in the seronegative partner . Data from the HIV Prevention Trials Network (HPTN) 052/AIDS Clinical Trials Group (ACTG) 5245 have also found that antiretroviral treatment of an HIV-infected partner reduced transmission to the uninfected partner . These data lend support to the Test-and-Treat strategy  that proposes to decrease HIV transmission through the following two pathways:  HIV testing identifies HIV-infected persons who, after learning their status, adopt safer behaviors, which decreases HIV transmission .  HIV-infected individuals who initiate antiretroviral treatment, maintain high levels of adherence, and achieve viral suppression are less infectious, which decreases HIV transmission.
To optimize health outcomes, however, expanded testing efforts must be coupled with initiatives that ensure the newly diagnosed and those already known to have HIV infection are effectively linked to HIV care , receive antiretroviral treatment as indicated, and achieve optimal adherence and suppression of viral replication. (See Appendix D.)

# Establishing a Common Language
Researchers have interpreted community viral load differently (see  2 are typically not available in the local or national HIV surveillance databases. The shortcomings of available data and nascent methodology for its modeling (including data imputations for persons in boxes C-E in Figure 2), has precluded the estimation of Population Viral Load to date. An estimated 21% of HIV-infected Americans are living with undiagnosed HIV infection  (box E, Figure 2) and, thus, their HIV viral loads are unknown but are likely detectable and elevated in the absence of antiretroviral therapy. These persons, who may practice unsafe sex while unaware of their HIV infection, are thought to contribute to the majority of new, sexually acquired HIV infections in the United States .
Viral load data are not available for all persons that have been diagnosed with HIV infection. A significant proportion of such persons are not in care at a given point in time (either never linked to HIV care or not retained in continuous care) and, therefore, may not have HIV viral load monitoring performed (box D, Figure 2). It can be assumed that most of these persons have detectable/not suppressed viral loads as they are unlikely to be receiving continuous, effective antiretroviral therapy; however, the actual distribution of their viral loads is not known (in the absence of special studies). Finally, a fraction of persons that are in care may be missing HIV viral load in HIV surveillance for a variety of reasons, including: incomplete or delayed reporting of viral loads from laboratories to the surveillance system, undetectable viral loads not reportable, viral load results that have not been entered into the surveillance database, patient refusal of HIV disease monitoring, receiving medical care but out of HIV care, and no viral load testing (box C). Community Viral Load describes viral load of all HIV-infected persons diagnosed with HIV infection in a given population. As with "Population Viral Load," viral load measurements for persons that are 'diagnosed but not in care' (box D, Figure 2) are typically missing as is information for those 'in care, no VL' (box C). In order to estimate Community Viral Load as accurately as possible, local public health jurisdictions should implement programmatic activities to expand and routinize HIV testing  so that the proportion of 'undiagnosed' (box E) is as small as possible, and maximize linkage to and retention in care of HIV-diagnosed persons so that the proportion of 'diagnosed but not in care' (box D) is as small as possible. In jurisdictions where (i) the percentage of persons that are in care but have missing viral loads and persons that are diagnosed but not in care (boxes C and D, respectively) when combined is less than 25% of persons, and in particular, if (ii) additional data relating to care and health status are available (such as history of antiretroviral therapy, insurance status, opportunistic infections, and engagement in care), techniques such as multiple imputation may be considered in modeling Community Viral Load for the total diagnosed population (in and out of care). However, the limitations of multiple imputation and the lack of additional data make calculation of Community Viral Load not feasible for most jurisdictions at this time. Methodologies for such analyses are an active area of research. Jurisdictions may consider calculating a variety of measures to describe Monitored Viral Load, including measures of central tendency and dispersion. One approach to examining the quality of HIV care, antiretroviral treatment uptake, and adherence and engagement in care is to determine the percent of persons virologically suppressed, or the percent that are undetectable (lowercase m, measurements). Among persons that are receiving antiretroviral treatment, the proportion that achieves viral suppression is referred to as maximal virologic suppression  and has been endorsed as a quality of HIV care measure by various national groups. Additional measures are discussed in the Technical Guidance section.

# In-
Care
For jurisdictions with overlapping projects such as Test and Treat initiatives, Medical Monitoring Project (MMP), or other clinically oriented projects that capture use of antiretroviral medications, percent suppression is a useful population-based measure of the penetration of the U.S. HIV Treatment Guidelines. HIV care measures calculated from HIV surveillance and clinically collected data will help to evaluate suppression rates among different disproportionately affected populations as highlighted in the National HIV/AIDS Strategy.

# Summary
It is challenging to estimate Population Viral Load, Community Viral Load, and In-Care Viral Load at this time. Jurisdictions able to address missing viral load data among diagnosed resident persons with HIV by using multiple imputation  have been able to calculate Community Viral Load as defined in this document. Those jurisdictions able to impute missing viral load data would also be able to calculate an In-Care Viral Load. For those jurisdictions that are unable to impute missing viral loads for diagnosed cases, they can calculate a Monitored Viral Load Measure.

# Technical Guidance
This section provides guidance on analytic decisions and methodologic considerations for conducting viral load (VL) analyses and calculating VL Measures using HIV surveillance data. Estimation of Community VL, In-Care VL and Monitored VL, as described in Establishing a Common Language, will be the focus of this section. Each subsection topic will include a description, recommendation, or discussion if no recommendation is provided, and an explanation that follows the recommendation.
Selected standardized categorical VL measures are also defined so that comparisons across jurisdictions may be possible. These standardized measures and methodologic recommendations are enclosed within a grey box. An example of VL data to report is included in Figure 4 and a brief discussion of Population VL follows. Because of the variability in types of analyses, a checklist at the end of this section identifies key explanatory elements that should be described in the method section of any Community VL or related analysis.

# Getting started
As described in Appendix B, Surveillance, a number of periodic, routine surveillance activities must occur to ensure HIV surveillance data are accurate and of sufficient quality. Those steps and others, which will be described in this section, provide the framework for Figure 3, Decision tree for conducting viral load analyses.
Note: Geographic analyses may be conducted as a subset of Community VL/In-Care VL or a subset of Monitored VL.

# Inclusion criteria for cases
Depending on the type of analysis an HIV jurisdiction plans to conduct, the case inclusion criteria may differ. For the purposes of standardization, however, the following criteria for case selection as part of a cross-sectional Community VL, In-Care VL, or Monitored VL analysis is recommended:
Explanation: HIV-infected adults and adolescents may transmit HIV through sex and injection drug use. At the time of this writing, the latest full calendar year that could be examined is 2009, allowing for the minimum 12-months lag time for cases to be reported (i.e., using data reported through December 2010), data to be found and entered, etc. Using a calendar year and including

# GEOGRAPHIC analyses

# Proceed with spatial VL/social determinants analysis

# COMMUNITY VIRAL LOAD

# Jurisdictional case*
Are complete and current addresses available? YES NO YES persons alive at the end of that year is consistent with data requests from HRSA and most local HIV planners. For reporting cities within a state, those reporting jurisdictions will also need to conduct an intrastate deduplication process to determine which persons may have moved out of the jurisdiction but remain in the state. All states must participate in the interstate deduplication process. By the case inclusion criteria, HIV-infected persons who died during 2009 or earlier would be excluded. To keep track of those deaths, matching to state vital registry databases will help identify deaths among HIV-infected persons who died in state; matching to at least one national death registry (National Death Index or Social Security Death Index) is necessary to identify deaths that may have occurred out of state. Although each HIV surveillance jurisdiction has incentive to keep track of its jurisdictional cases, residential cases are more important to understanding current HIV transmission in a locality.

# Sample size
The sample size needed to detect a 3-fold geometric mean (GM) difference - between mean viral loads depends on the desired power and standard deviation of the sample. Table 1a presents sample sizes needed with 80% power to detect a difference at the 0.5 level for various GM. The dark row lists the sample size needed to detect a difference of 3-fold in the GM; the sample size is the intersection of the row and column, standard deviation, S. The column of S=1.2 reflects the standard deviation observed in national VL data.   Explanation: Sample size considerations must be taken into account when choosing the cases or study population to examine. For example, Monitored VL among African American men and women may be of great interest to evaluate by a jurisdiction, but if the sample size is inadequate to meet the recommended case inclusion criterion, an alternate method may need to be used, such as combining multiple years of data.
Jurisdictions may want to explore factors related to differences in means of viral loads, such as the difference in the proportion with undetectable or very low VL, which may be expressed as a categorical difference.

# Crude viral load data and differences in lower limits of detection
With the advent of newer assays for quantifying HIV viral load, the lower limit of detection (LLD) of these assays has steadily decreased from <1000 copies/mL in the early 1990s to <400 copies/mL in the early 2000s to <50 copies/mL or fewer in most recent years. Most persons that receive and adhere to antiretroviral therapy will have a viral load below the LLD, and a question arises how to handle their viral loads in the analyses.
Explanation: The distribution of actual VL values below a test's LLD is unknown. Factors that may influence the VL distribution in a jurisdiction include the regimen of and adherence to antiretroviral therapy among the cohort, sensitivity of assay used in measuring these otherwise
We recommend that half of the LLD be used in the analyses of viral loads for persons who have VL below the limit of detection (whether numeric result provided or not) to approximate the likelihood of their actual viral load burden.
Each jurisdiction will need to assess the standard deviation of their local VL data and then determine the appropriate sample size needed to assess VL.
undetectable VL results, and possibly the combination of patient demographics and clinical characteristics of HIV-infected persons at the time. Some researchers have used zero or the value LLD minus one as the VL value for undetectable results in VL analyses. We chose half the value of the LLD as a compromise and for its ease of use. As more data become available, it will be possible to more accurately establish a value for VL results currently classified as undetectable.
Nevertheless, for our purposes of providing a uniform method that can be compared across sites or within a site over time, use of half LLD should be adequate. Consequently, when a viral load assay with LLD <50 copies/mL is used, undetectable viral load results should be replaced with 25 copies/mL for analysis.
When undertaking a calendar-time trend analysis, however, it may be necessary because of different viral load tests in use to truncate the lower limit of detection and possibly the upper limit of detection  . After examining local VL data by year, it should become apparent where those cut points are needed. As an example, if a jurisdiction had complete and refined data from 2005-2009, it may be found that viral load tests with a LLD <400 copies/mL were in wide use in 2005 and 2006, but then were replaced by a more sensitive test with LLD of 75 copies/mL by 2009. For such an example, test results of undetectable, 400 copies/mL or less would be collapsed into a single group and given a VL value of 200 (half LLD) for each year, 2005 through 2009. VL analyses for each year would then be conducted using this common LLD of 400. If a single year, 2009, as a point in time analysis was planned, however, it would be appropriate to use 75 copies/mL as the LLD and use 37.5 as the value for analysis when results of 75 copies/mL or undetectable were reported. In either situation, the LLD or range of VL results used in analysis should be stated.

# Multiple imputation of missing viral load
Although only 15 of 26 jurisdictions responded to an informal survey conducted among Workgroup members, most jurisdictions reported missing VL information for more than 30% of their cases . VL results may be missing for persons not in care, in care but without VL, and for persons who have died or moved away as described in Appendix B, Table B1. This discussion of multiple imputation of missing viral load for calculating Community VL and In-Care VL applies to data that have been refined after completing data management processes described in Appendix B, Surveillance.
Explanation: Two groups of HIV-infected persons have, by surveillance definition, missing VL results-those "in care, no VL" and those "diagnosed but not in care" (boxes C and D, respectively, Figure 2). After data refinement, if the number of persons "in care, no VL" is small, jurisdictions may want to conduct follow-up to better characterize these persons. This information may help inform approaches for data imputation for this group, "in care, no VL."
Jurisdictions that routinely collect supplemental information-such as use of antiretroviral medications, engagement in care, insurance, co-morbidities-may wish to explore data imputation using published methods  that would enable them to calculate a Community VL or  Sites should assess their data and determine if an upper limit of detection is needed. Because of the recommended methods, providing a uniform upper limit of detection is not warranted in most circumstances. If, however, sites have a significant percentage of high VL results, use of an upper limit of detection may be prudent.
We recommend that imputation of missing VL data only be attempted if <25% of cases are missing VL AND jurisdictions are collecting supplemental clinical data.
In-Care VL. However, jurisdictions should carefully weigh the benefits and limitations of multiple imputation before embarking on such analyses.
Methods like multiple imputation that extrapolate from available surveillance VL data to the remainder of the population may not be appropriate for surveillance data. A fundamental requirement of imputation, that data are missing at random and that the distribution of values in the unknown group mirrors the distribution of values in the known, may not be satisfied. The fundamental problem for a "diagnosed case but not in care" identified from surveillance data (i.e., lacking VL data in a surveillance system that routinely receives fairly complete VL data from labs) is that the case is not in care or has moved out of jurisdiction. The viral load distribution of cases not in care cannot be modeled from the distribution of those that are in care. The Workgroup is not aware of any supplemental data available to the majority of surveillance jurisdictions at this time that could address this lack of information.
Even if supplemental information is available to support randomness or similarity (these would likely derive from research samples also reflecting patients in care), there is no clear cut-off for how much information may be imputed (e.g., the recommendation of 75% as the minimum threshold for data completeness). The usefulness of supplemental data for "filling in the blanks" depends on the relevance, completeness, and accuracy of the data; it also depends on the homogeneity of the population that is being modeled. Jurisdictions are advised to consult with statisticians familiar with the methodology when planning analyses that involve imputation.

# Mean viral load
The early VL and prevention studies have used median viral load as the summary analytic measure for community viral load; others have used the mean of the VL results. Because successful clinical use of antiretroviral therapy results in viral suppression, jurisdictions that have successfully engaged HIV-infected persons in care may have over 75% of such persons with undetectable VL; the median would be undetectable, so the mean was used.
Explanation: The rationale for this logarithmic transformation is that it helps to normalize the distribution of viral load values and reduces the influence of outlying measurements for persons having extreme viremia (due to acute infection, advanced HIV disease, concurrent sexually transmitted infections, or random variability).  Since the interpretation of viral load measurements is often more intuitive on a linear scale, we recommend calculation of geometric mean (GM) for viral load. The GM is thus calculated through log transformation by averaging the log transformed values and transforming the average back to the original (linear) scale. The base used for the log transformation has no effect on the final GM estimate. However, using log base 10 has an advantage by its relationship to the value on the original scale; for example, a value of 2 on the log10 scale is 100 on the original scale, 3 corresponding to 1000, 4 corresponding 10000, and so forth.
While our recommendation for calculating the mean viral load is limited to Population VL, Community VL, In-Care VL, and Monitored VL, jurisdictions may wish to calculate a mean VL  This is similar to using the median VL
We recommend that calculation of Community VL and its related viral load Measures (see Figure 2) is performed after transformation of viral load results onto the logarithmic base 10 scale, followed by calculation of the mean.
for a subpopulation. In addition, a geometric mean could be useful in combination with categorical VL measurements (see below). For example, the population can be described in terms of the fraction with undetectable or suppressed VLs and the (geometric) mean of the remaining population.

# Selection of viral load results for analysis
Once the time frame for analysis is decided, the handling of VL values for individuals that may have more than one VL result needs to be considered. Depending on the type of analysis, jurisdictions may want to average the VL results for each individual and then take the mean of those average VLs to compute the Community VL, In-Care VL, or Monitored VL. For other analyses, it may be more appropriate to use the highest, lowest, or most recent VL result. (See Other viral load methods and analyses later in this section.)
As part of the standard case inclusion criteria for a calendar year, the following VL should be selected for analysis:
Explanation: The examination of a single VL result (most recent) is the least restrictive definition of 'being in-care.' Whereas other new measures or definitions of 'in-care' require at least two lab results within a specified timeframe of each other in a 12-months period, no such requirement is necessary for this standardized, point in time examination.
The Community Viral Load Workgroup has developed and tested an Excel spreadsheet that allows for calculation of mean viral load, proportion suppressed, proportion undetectable, and a Z-test to identify a statistically significant difference between two mean viral loads. This spreadsheet accommodates small datasets.
A generic SAS program that incorporates the methodologic guidelines and standards set forth in this document will also be available. The SAS code will be created so that large volumes of data from eHARS can be used for VL calculation by all HIV surveillance jurisdictions.

# Categorical measures of viral load
Viral load results may be grouped into various clinically meaningful categories. Tracking VL categories for a population yearly (e.g., using histograms) may aid in the interpretation of changes in VL results in the population over time, concomitant with changes in proportion of patients receiving highly active antiretroviral therapy or adherence support, or due to other factors.
We recommend use of three categorical VL measures: suppressed/not suppressed, undetectable, and high VL.
Use the most recent VL result per person for analysis-this would be for specimens obtained in 2009 that are the closest to December 31. If there is no VL specimen for a case in 2009, that case would be excluded and would belong to either the "In care, no VL" or "Diagnosed but not in care" boxes of Figure 2 (boxes C and D, respectively).
Although the VL cut points that define each of these categories may not meet all needs, given the nature of ever evolving VL test technology, we have proposed definitions so that all HIV jurisdictions can report using standard definitions of these measures. The three standardized measures that are proposed are as follows:
Explanation: A measurement that may be useful in describing the disease status of local PLWH is percentage of persons that have viral load below LLD or suppressed viral load, defined as VL 200 copies/mL by the latest U.S. HIV Treatment Guidelines . Percentage of persons that have viral load suppression may serve as a rough proxy indicator of the combined access to and adherence to antiretroviral therapy in a given population.
Explanation: This measure can only be assessed in jurisdictions where recent sensitive assays (LLD of 50 copies/mL or lower) are consistently used by laboratories. While this measure is most informative for a clinical setting among persons on antiretroviral therapy, it could also be applied to populations receiving care from a specific facility or across all persons in care using surveillance data.
Explanation: To better characterize the spectrum of viral loads in the population, some jurisdictions may opt to quantify the proportion of VL results that are considered very high (i.e., >100,000 copies/mL), which may indicate high potential transmission risk for a group or the healthcare challenges facing a group in need of immediate antiretroviral therapy .
Another potential categorical measure that jurisdictions may want to examine is the proportion of HIV-infected persons that meet a standardized definition of in-care-e.g., at least two lab results obtained within a calendar year that are at least 60 days apart.
Categorical measures might be more informative than means and may also be more comprehensible to planners, evaluators, policy makers, and others. These measures provide the VL distribution and have the advantage of not implying a normal distribution of values (as means may be perceived by the public). The actual distribution of VL results in surveillance populations is highly skewed. A large proportion of the population has undetectable or very low values (e.g., <200 copies/mL), and the remainder of values are spread across a wide range. Distributions such as these are not well represented by a single measure, especially one that implies a normal distribution. Jurisdictions should examine their data to determine the local viral load distribution and the best measures to describe it, including outlier cutoff values, etc. Other measures are listed in Table 2. Such distributions can also be stratified by CD4+ T-lymphocyte count to reflect stage of illness and the U.S. HIV Treatment Guidelines recommendations on initiation of antiretroviral therapy.
Report percentage 200 copies/mL as suppressed as a categorical VL measure. Conversely, the percentage >200 copies/mL may be reported as not suppressed.
Report percentage 50 copies/mL as undetectable as a categorical VL measure.
Report percentage >100,000 copies/mL as high VL, in need of treatment as a categorical VL measure.

# Address and residency
For analyses that use address (geospatial analyses or linkage to Census geographic information on social determinants data), the current address, if available, should be used. Current address is also useful in establishing whether a jurisdictional case may have moved. While most jurisdictions use patient address from lab reporting, this information is useful only if the address is accurate. Patients with post office boxes or incomplete or inaccurate street addresses cannot be geocoded and placed with certainty on a map.
A timeframe in which current address should be updated could not be agreed upon by the Workgroup. Any analyses including cases by residence or using geographic analyses should specify when addresses were updated and the percentage of cases that could not be geocoded with a high level of confidence.

# Population Viral Load
The Technical Guidance section provides methodologic recommendations for calculating Community VL, In-Care VL, and Monitored VL. Although Population VL is the best Measure for assessing HIV transmission, information is not available for its calculation. However, a component VL measure that is of interest includes an estimation of the number of undiagnosed HIV-infected persons. A back-calculation method estimated that almost 250,000 HIV-infected persons are undiagnosed (21% of the total number of HIV-infected persons) in the United States . Efforts are underway to also estimate the relative proportion of undiagnosed HIV-infected persons by state or metropolitan statistical area. These methods will be available to state health departments in 2011.
VLs will vary greatly among undiagnosed persons, depending on how recently the person was infected. However, they may be assumed to not have suppressed VL.

# Other viral load methods and analyses
Although this Guidance document suggests using one VL result (most recent) of possibly many VL results for a person and recommends a method for calculating VL Measures, viral loads may be analyzed differently. Table 2 includes different VL measures that have been calculated and reported. Each measure has particular strengths and uses. Appendix A lists the references associated with the VL analyses.
The mean viral load provides an estimate of the average level of viral burden and is the most useful for comparing subpopulations and neighborhoods in a jurisdiction. Comparing mean of most recent and total VLs at the geographic level may reflect disparities in access to and use of antiretroviral medications by neighborhood. Comparing means among different subpopulations within a jurisdiction can highlight disparities. The total VL is a useful measure for looking at the combination of HIV prevalence and viremia among prevalent cases.
Depending on the type of analysis that is being conducted, VL results other than the most recent may be appropriate to use. If the purpose of the analysis is to evaluate a new measure of quality of care, the researcher may choose to report the proportion of cases that were virologically suppressed for a calendar year; thus, cases with even a single VL result >200 copies/mL would not be considered suppressed for the year. If the focus of the analysis is on acute antiretroviral need and possible lapses of optimal antiretroviral use, then the proportion of cases that may have a single VL result >100,000 copies/mL may be used. Although we recommend the most recent (to December 31) VL within a calendar year be used to calculate a VL Measure that is a more "current" snapshot, researchers may also want to use the mean of serial VL results obtained throughout the year for a person and then calculate the overall mean VL among all persons in a group for the year. Again, this document is not meant to be a comprehensive list of all possible VL analyses and measures but to point out ones in use and to recommend a common method for calculating VL Measures and reporting of component and a limited number of categorical VL measures.

# Summary for data reporting-see Checklist on page 20
For each type of population-based analysis, Population VL, Community VL, In-Care VL, and Monitored VL, the study population should be described and its size should be reported. The size and relative percentage of each of the component VL measures (boxes A-E), as shown in Figure 2, that defines each VL Measure should also be reported.

# Examples for Data Reporting (bottom up)
Jurisdictions should report: 1) the component VL measures-e.g., percentage of persons "Diagnosed but not in care" in addition to the percentage, "In care and with undetectable - VL," "In care with detectable VL," and "In-care, no VL" when conducting In-Care VL Measurements; and 2) on categorical VL measures to describe the population that is in care and being monitored such as the percentage of persons with suppressed (i.e., 200 copies/mL), the percentage with high viral loads (i.e., >100,000 copies/mL), and the percentage undetectable (i.e., 50 copies/mL).  Describe any methods of adjustment for confounders (e.g., standardization, multivariable modeling)
 Describe any efforts to address potential sources of bias due to missing data, sensitivity analyses (e.g., imputation).

# Results
 Give characteristics of the study population and univariate summaries of outcome measures  Report variability (e.g., standard deviation) and precision (e.g., 95% confidence intervals) around estimates  Present results from adjusted analyses, controlling for potential confounders

# Discussion
 Discuss limitations of the analyses, taking into account potential sources of bias or imprecision; discuss both direction and likely magnitude of the bias (e.g., under or over estimation of Community VL)  Interpret findings in the context of prior analyses-highlight differences and similarities Source: Article refers to a scientific publication; PP is an oral presentation at a scientific meeting; abstract is a short description of study and findings submitted to a meeting; poster is a more detailed paper presentation at a scientific meeting.

# As of May 2011

# Public Health Surveillance
Surveillance is the foundation of public health. It involves the ongoing, systematic collection, analysis, interpretation, and dissemination of data used by public health to reduce morbidity and mortality and to improve health . Each surveillance system has a number of attributes that contributes to its overall effectiveness. These attributes include: simplicity, flexibility, data quality, acceptability, sensitivity, predictive value positive, representativeness, completeness, timeliness, and stability. To ensure ongoing improvement in data quality, efficiency, and usefulness, each surveillance system should be periodically evaluated.
All CDC data originate locally. These data represent a commitment and collaboration between states or jurisdictions and CDC to collect data deemed to be the most vital in fulfilling local and national public health missions. CDC works with local reporting jurisdictions and the Council of State and Territorial Epidemiologists (CSTE) to establish model language and uniformity in state reporting regulations and standardization of data collected for surveillance. CDC works with state and local public health counterparts to decide on national data quality standards, policies, and procedures that can be used to meet surveillance data quality standards. also against at least one national death registry  to ensure an accurate count of the number of people living with HIV disease in their jurisdiction.

# HIV Surveillance
While accurate case counts are important to understand disease burden and where HIV-infected persons seek medical care is important for resource allocation, it is equally important to understand where and how new cases arise for targeted prevention efforts. Jurisdictional boundaries and residency do not predict where healthcare is sought or risk-seeking behaviors occur. For example, persons may cross into an adjacent state to receive health services or engage in high-risk behaviors in a neighboring jurisdiction. Some states have developed lab reporting regulations based on the patient's residence and patient's place of care to accurately describe care patterns. As part of understanding HIV transmission, surveillance areas with residents that cross state lines to engage in high-risk HIV behaviors should take this into consideration when conducting any assessment of HIV transmission potential for their residents.

# Assessing those in care
In general, HIV surveillance relies on reported lab test (i.e., CD4+ T-lymphocyte count or viral load) results as a marker of persons receiving health care and thus being "in care." While it may be possible to identify HIV-infected persons who are receiving care but lacking lab test results, such as persons identified from linking to Ryan White Care AIDS Drug Assistance Program databases, such cases can be followed-up to address the reasons for missing lab results. To determine whether someone was linked to and retained in ongoing HIV healthcare, serial lab results over an extended period would need to be observed in surveillance data.
A limited number of HIV jurisdictions are able to conduct ongoing medical chart review of HIVinfected persons that reside in their jurisdiction. Those jurisdictions have collected supplemental data that are not routinely collected as part of HIV surveillance, such as engagement in care, insurance, and use and type of antiretroviral medications. Other jurisdictions have been funded to participate in special surveillance/prevention activities and, as such, may also be conducting ongoing medical chart reviews and collecting similar type of supplemental data.
States annually estimate the proportion of HIV-infected persons living in their state that are in and out of care. Some HIV reporting jurisdictions may report for their jurisdictional cases, not residential cases, because they are more likely to have complete information for their jurisdictional cases .

# Viral load test results
To ensure that all (detectable and undetectable) viral load results are received by HIV surveillance, jurisdictions must expend great time and resources. As a first step, state public health reporting laws or regulations provide the authority for State Health Departments to receive such information from laboratories. It may be necessary to include language that specifies that all (detectable and undetectable) HIV viral load results are reportable. This type of policy lays the foundation for receiving viral load results but cannot account for the personal interaction needed in establishing relationships with laboratories for reporting and then ongoing work to ensure continued reporting. The magnitude of this can be appreciated by the fact that some jurisdictions have over 80 laboratories that report viral load results .
The volume of viral load reporting is large and is likely to increase. U.S. HIV Treatment Guidelines recommend periodic assessments among persons that are on antiretroviral therapy or among persons whose HIV disease is being monitored, and the number of persons living with HIV is increasing. It is not uncommon for a laboratory to cease VL reporting for a short period as new laboratory staff are hired or veteran laboratory staff take vacations. To identify such lapses in reporting, HIV surveillance jurisdictions monitor laboratories for their volume of reporting over time.
The complete absence of any lab test result (CD4+ T-lymphocyte count or viral load) may suggest the person is not in care, moved away, is receiving care out of jurisdiction, or died, or there is a significant reporting issue (see Table B1). There are some instances, however, where an HIV-infected person resides and receives care within an HIV reporting jurisdiction but no medical care information is shared with HIV surveillance. There are a limited number of federal and private facilities that do not share or allow HIV surveillance staff to have access to medical records of persons being cared for by the facility. Efforts to address these barriers are underway with some facilities.
As technology has advanced, laboratories are moving toward reporting lab results electronically, but many laboratories still do not have this capability. Some HIV reporting jurisdictions have a backlog of paper lab results, and some jurisdictions with early electronic lab reporting have maintained electronic HIV lab reports in a separate database apart from eHARS. Viral load results reported on paper that have not been entered into eHARS are not available for analysis. Separately stored electronic lab files that have not been entered into eHARS may also not be accessible for analytic purposes.
The extent of missing lab results is best described from a recent survey of selected HIV reporting jurisdictions. Of the 15 jurisdictions that responded, 26-89% of persons living with HIV and reported to surveillance were missing a viral load result . The large range of missing viral load results may be attributable to many factors as described in Table B1.
The issue of jurisdictional versus residential case status has some implications for missing lab results. Persons diagnosed in one jurisdiction, by convention, remain in the database as a case in that jurisdiction even if the person moves across the country and receives care in their new home state. The recommended procedure for each HIV reporting area is to enter all lab results into their HIV database, regardless of jurisdictional or residential case status. After all jurisdictions upload their data to CDC, it may be possible to link lab results to jurisdictional cases that have moved; at this time, there is no mechanism that allows for sharing this information with local jurisdictions by CDC or between local jurisdictions directly. Early in the epidemic, when HIV diagnosis was often shortly followed by death, describing jurisdictional cases was reasonable. Today, with effective antiretroviral treatment available and the impetus to look more closely at transmission and prevention of new HIV infections, residential cases may also be an important population to surveil.

# HRSA
The Health Resources and Services Administration , HRSA, is one of several agencies, including CDC and NIH, under the leadership of U.S. Department of Health and Human Services. HRSA is responsible for improving access to health care services for people who are uninsured, isolated, or medically vulnerable.
Since the early 1990s, the Ryan White HIV/AIDS Program has provided an array of HIV-related and health care services to help vulnerable HIV-infected persons manage their disease. Based on the numbers of persons infected with HIV, service needs among these persons, local resources available, and an assessment of unmet need and service gaps, HRSA provides states with funding to bridge these gaps. Medical care and antiretroviral medications (through the AIDS Drug Assistance Program) are just two services that HRSA funds through and in partnership with states.
Each year, jurisdictions provide an epidemiologic description of HIV-infected persons to HRSA's HIV/AIDS Bureau. Most of the data used to describe the epidemic in a jurisdiction originates from HIV surveillance, so it is important to try to harmonize terms and processes that meet both HRSA and CDC needs.

# Definitions and terms
- Persons living with HIV (with and without AIDS) in a jurisdiction.
 HIV surveillance would refer to these individuals as resident cases, which is distinct from jurisdictional cases. HIV surveillance case "ownership" is limited to jurisdictional cases that are residents at diagnosis.
- Persons with unmet need are diagnosed with HIV infection but not in care for a defined 12month period .
 HIV surveillance uses lab test results (CD4+ T-lymphocyte count or viral load) as a marker for being in care. Those persons without a lab result for 12 months or greater would be considered not in care (or out of care). HRSA includes lab results and receipt of antiretroviral medications within a 12-month period as markers of being in care.
- Persons "in care" are receiving primary healthcare for their HIV disease. This care should be consistent with the U.S. HIV Treatment Guidelines .
 HIV surveillance uses lab test results as a marker for being in-care. While ongoing evidence of laboratory testing is strongly suggestive of ongoing encounters with the healthcare system, the quality and appropriateness of these medical encounters cannot be fully assessed with routine HIV surveillance data. For most surveillance jurisdictions, with geographically dispersed cases and varying health facility-specific rules and policies, costs are prohibitive to collect detailed medical care information. Collecting this type of medical encounter information is possible for circumscribed populations of HIV-infected persons and is part of the CDC's Medical Monitoring Project (MMP), which includes a representative sample of HIV-infected persons who are engaged in HIV care.
- HIV-infected persons are retained in care as assessed by at least two clinical visits in a year that are at least 60 days apart .
 HIV surveillance uses lab results as a marker for being in-care. Based on the temporal distribution of the collection date for each lab test, it is possible to assess whether two medical encounters occurred within a calendar year, spaced at least 60 days apart. It would be difficult, however, for surveillance data to determine with certainty if the lab tests were part of the same continuous care or might represent fragmented care as, for example, one test may be ordered by an HIV healthcare provider and another test ordered by an emergency department physician.
- Persons in care that begin antiretroviral treatment achieve virologic suppression after 6 month   HIV surveillance can provide a rough proxy assessment of reporting the proportion of persons in care that have viral load results 200 copies/mL for a calendar year. Because antiretroviral medication use and initiation is not collected by HIV case surveillance, this type of clinical measure is best addressed by MMP.