HIV prevention in the United States has historically focused on modifying individual behaviors of both HIV-infected and uninfected individuals to reduce transmission ; however, to date, behavioral change has been insufficient to halt the domestic HIV epidemic . Reductions in both heterosexual and vertical transmission have been shown to be associated with receipt of antiretroviral therapy and subsequent reductions in individual viral load [3–10]. Although there is strong evidence regarding the role of viral load on transmission at the individual level, limited population-level information exists that clearly demonstrates the effects of viral load reduction and HIV transmission at a community level.
Recent studies have supported the value of monitoring viral load at a community level as a means of measuring HIV incidence. In British Columbia, an ecological study found that increased ARV therapy coverage resulted in decreased viral loads and an overall decrease in the number of new HIV diagnoses each year . More specifically, in Vancouver, researchers showed that a longitudinal measure of community plasma HIV-1 RNA concentration among a network of IDUs revealed a direct relationship between viral RNA concentration and HIV incidence . The same study demonstrated that the association between viral plasma RNA concentrations was predictive of HIV-1 incidence independent of high-risk sexual behavior and shared syringe use. Das et al.  showed a direct association between population-level mean viral load or ‘community viral load’ (CVL) and incidence in San Francisco using HIV/AIDS surveillance data.
In concert with these findings, recent emphasis has been placed on the development of community-level interventions to reduce HIV transmission in high-prevalence communities. The National HIV/AIDS Strategy proposes to use the CVL to monitor reductions in disparities and mortality among HIV-infected populations . With the Test and Treat approach, communities routinely screen all persons for HIV infection, link and engage them in care, and provide ARV therapy to those who are eligible [15–17]. The ultimate goal of this approach is achievement of individual viral suppression, which would result in reduced HIV transmission at the community and population level. The District of Columbia is one of the first cities nationally to assess the feasibility of implementing a modified Test and Treat approach through the National Institutes of Health (NIH)-funded Testing and Linkage to Care Plus study (TLC-Plus) .
According to the Joint United Nations Program on HIV/AIDS definition, the District has a generalized HIV epidemic with a 3.2% prevalence rate at the end of 2008 [19,20]. The complexity of the epidemic is reflected by focal HIV epidemics among MSM and among the District's black populations. Rates by race/ethnicity, age, risk category, and geographic region substantiate the presence of a severe epidemic in the city with 4.7% in blacks, 7.6% in persons 40–49 years of age, 14.1% in MSM, 5.2% in heterosexuals, and rates of 2% or greater in seven of the city's eight geopolitical regions or ‘wards’ [19,21–23]. Given the magnitude of the epidemic in the District of Columbia, measurement of the CVL has the potential to provide additional information about drivers of HIV transmission and quality of HIV care. The objectives of this analysis were to assess the availability of viral load data and to use those data to measure the District's CVL overall, longitudinally, by subpopulation, and by geographic distribution.
HIV/AIDS case surveillance data from the District of Columbia Department of Health (DCDOH) HIV/AIDS, Hepatitis, STD, and TB Administration (HAHSTA)-enhanced HIV/AIDS Reporting System (eHARS) were used to identify newly diagnosed HIV/AIDS cases and prevalent HIV/AIDS cases between 1 January 2004 and 31 December 2008. The District transitioned from code-based HIV reporting to confidential name-based reporting in November 2006. This analysis includes only name-based HIV /AIDS cases 13 years of age or older at the time of diagnosis. As required by District of Columbia Municipal Code, laboratories must report CD4 cell count and viral load results from patients diagnosed with HIV/AIDS to the DCDOH HAHSTA in addition to case information . Using these data, the most recently available viral load for all HIV-infected individuals residing in the District of Columbia at the end of each year was obtained.
The ‘community’ in this analysis was defined as District residents diagnosed with HIV/AIDS, reported to the DCDOH, and in care for their HIV infection, as evidenced by receipt of viral load data by the surveillance program. Mean CVL was defined as the mean of the most recent viral load test for HIV-infected individuals between 2004 and 2008. Total CVL was calculated by adding the values of the most recent viral load for all HIV-infected individuals during this period. To assess trends in CVL, the mean viral load was calculated for each year and as of 31 December 2008. Only HIV-infected individuals with at least one viral load test within the period were included in the analysis of mean and total CVL, excluding individuals with no viral load data. For those cases with viral load data in 2008, the mean CVL was stratified by demographics and mode of transmission.
Owing to variability in the level of detection according to the assay used, an undetectable viral load was described as one deemed below the limits of detection, according to the source laboratory. In this instance, the viral load recorded for analysis was one viral load copy per millimeter less than the established value for the assay. If log-transformed values were reported, the values were back-transformed before inclusion in the analysis. To account for these variations in reporting, a viral load of 400 copies/ml or less was established as the cutoff for an undetectable viral load in the multivariate analysis.
The District of Columbia is divided into eight geopolitical regions called ‘wards’. For the purposes of this analysis, a geographic community is described as the ward of residence of an individual at the time of the most recent viral load. Address information at the time of the most recent viral load was geocoded using Geographic Information Systems (GIS) for all living HIV/AIDS cases at the ward and census tract levels and maps were generated that visually displayed CVL.
Cases of HIV/AIDS in the District of Columbia include only cases that reside in the District at the time of diagnosis. Owing to migration in and out of the city, current addresses for all cases were identified when possible. Current address was available for 92% of all prevalent cases in District of Columbia. Addresses for individuals with an address outside of the District, missing addresses, and incarcerated and homeless were all categorized as ‘other’. In addition, two indicators of socioeconomic status – poverty rates and percentage of high school diplomas – were mapped by ward using data available from the 2000 census .
Multivariate logistic regression was used to identify potential correlates of CVL data availability and factors associated with having an undetectable viral load. Logistic regression models were adjusted for demographic characteristics, mode of transmission, and insurance status. Linear regression models were used to assess trends in mean CVL over time for the District of Columbia as a whole, taking into account the availability of viral load data and mode of transmission. Viral load data was log-transformed to approximate a normal distribution and generalized estimating equations were used to account for the correlation of viral load data in multiple years. Negative binomial models were used to assess for temporal differences in newly diagnosed cases and to assess the relationship between newly diagnosed cases and mean CVL. Robust standard errors were used to account for over dispersion of the data. Cases that were diagnosed with AIDS within 12 months of their HIV diagnosis (‘late testers’), geographic community, and time were controlled for in the multivariate linear models. Data analysis was conducted using Statistical Analysis Software (SAS) version 9.1 (SAS Institute, Inc., Cary, North Carolina, USA), and the threshold for statistical significance was set at P value less than 0.05.
The analysis was reviewed and deemed exempt by both the George Washington University and the District of Columbia Department of Health Institutional Review Boards.
Characteristics of cases by availability of viral load data
Between 2004 and 2008, a total of 15 467 individuals diagnosed with HIV/AIDS and who were alive during this period were assessed. Table 1 shows the baseline characteristics of these cases stratified by viral load availability within the analysis period. Forty-eight percent (n = 7 426) of individuals contributed at least one viral load test to the analysis. The mean age, time since diagnosis, and median CD4 cell count at diagnosis differed significantly between those with viral load data and those without (P < 0.0001). Among cases with ‘other’ residence, 13.2% of those with viral load data and 13.4% of those without viral load data had a current address outside of the District. After adjusting, availability of viral load data was significantly associated with sex, race/ethnicity, diagnostic status, ward, and insurance. Males, blacks, Hispanics, AIDS cases, and those with ‘other’ ward and insurance information were significantly less likely to have viral load data. In contrast, persons of other races, cases infected through MSM/IDU, and those with public insurance were more likely to have viral load data.
Trends in community viral load and new HIV/AIDS diagnoses
The completeness of viral load data increased throughout the analysis period from 4.8% of prevalent cases in 2004 to 33.4% of prevalent cases in 2008 (Fig. 1). When examining trends in the mean viral load over the 5-year period, the mean CVL significantly decreased over time (P < 0.0001) and the number and proportion of cases with undetectable viral loads increased substantially from 15.4 to 57.7%. The trend in mean CVL remained significant after adjusting for HIV mode of transmission and the number of cases contributing viral load data (P < 0.0001). The number of newly diagnosed cases of HIV/AIDS in District of Columbia was virtually the same in 2004 (n = 1 062) as it was in 2008 (n = 1 069), although there were some fluctuations during the interim years. However, when assessing the overall trend, there was a significant increase (P = 0.021) in newly diagnosed cases after controlling for mean CVL, late testers, and ward. The statistically significant decreases observed in mean CVL in the District among those with viral load data were not significantly associated with the number of newly diagnosed HIV/AIDS cases after controlling for time, late testers, and ward (P = 0.11)
Community viral load by subgroup and factors associated with an undetectable viral load
Table 2 shows the mean and total CVL for those individuals with available viral load data in 2008 and the proportion with undetectable viral loads stratified by select characteristics. Cases categorized as ‘other’ mode of transmission were excluded from this portion of the analysis due to the small number of persons (n = 3). The overall mean CVL in District of Columbia for the remaining 4 684 HIV-infected individuals that had at least one viral load test conducted in 2008 was 33 847 copies/ml (95% CI 27 653–40 042). The total CVL was 158 541 289 copies/ml. Higher mean viral loads were observed among women, blacks, 20–29 year olds, those infected through heterosexual transmission, residents of ward 8, and those without insurance coverage. The highest total viral loads were among males, blacks, those infected through heterosexual transmission, residents of ward 8, and those with public insurance.
In 2008, the overall proportion of cases with an undetectable viral load was 57.4%. Whites, those persons currently age 60 and older, MSMs, residents of ward 3, and those with private insurance had the highest proportion of undetectable viral load within their respective subgroups. When we examined factors associated with having an undetectable viral load, blacks were significantly less likely than whites to have an undetectable viral load (adjusted odds ratio (aOR) 0.44, 95% confidence interval (CI) 0.35–0.55) and those infected through IDU were significantly less likely to have an undetectable viral load compared with MSMs (aOR 0.81, 95% CI 0.66, 0.99). As age increased, the likelihood of having an undetectable viral load also increased from a 3.5-fold higher likelihood among 30–39 year olds to an 11.1 times higher likelihood among persons currently age 60 and older, compared with those 13–19 years old. Significant associations were not observed in relation to sex or ward of residence. However, after adjusting for age, ward of residence, and insurance status, further stratification by race and mode of transmission revealed that blacks, Hispanics, and those of other races, among all transmission categories, were significantly less likely to have undetectable viral loads as compared with whites. Specifically, black MSM (aOR 0.44, 95% CI 0.33–0.59), black IDU (aOR 0.21, 95% CI 0.05–0.79), Hispanic/other race IDU (aOR 0.08, 95% CI 0.02–0.38), black heterosexuals (aOR 0.34, 95% CI 0.15–0.79), and Hispanic/other race IDU (aOR 0.60, 95% CI 0.23–1.55) were significantly less likely to have undetectable viral loads when compared with their white counterparts.
Geographic distribution of mean and total community viral load and socioeconomic status
Mean and total CVL in 2008 by census tract are shown in Fig. 2. The distribution of both mean and total CVL appear to be homogenously distributed, except in the northwest regions of the city. The remaining areas of the city appeared to have higher CVLs consistent with the higher prevalence observed in these particular areas. When socioeconomic status, as measured by poverty level and percentage without a high school diploma, was assessed along with mean and total viral load, census tracts in the southeast regions of the city, corresponding to wards 6, 7, and 8, had the highest poverty rates, lowest proportions of high school diplomas, and correspondingly higher viral loads.
Longitudinal analysis of HIV/AIDS surveillance data in the District reveals that almost half of HIV-infected individuals had viral load data available between 2004 and 2008. Compared with data from San Francisco and New York City, where 75 and 64% of persons had available viral load data [13,26], respectively, the completeness rate is lower. However, completeness of viral load data increased substantially over the 5-year period indicative of improved laboratory reporting. The varied availability of viral load data by demographics may be reflective of access to care, care seeking patterns, or migration out of the District of Columbia. Blacks and Hispanics were less likely to have available viral load data, which may be indicative of disparities in access to care. AIDS cases and persons without ward-level data were also less likely to have available viral load data, which may be related to incarceration, lack of engagement in care, or migration outside of the District among late-stage infected persons. As increased emphasis is placed on using viral load data to measure population-based outcomes, capturing complete laboratory data through routine electronic laboratory reporting will be possible and allow for further understanding of these variations.
The city's mean and total CVL are consistent with the District's severe and complex epidemic. The mean CVL of over 33 000 copies/ml in 2008 is suggestive of a highly infectious population and the total CVL of over 158 × 106 copies/ml reflects not only increasingly available data but also a large viral burden. The mean CVL observed among different population groups is consistent with the HIV-related disparities identified through surveillance data in the District . White men and women, who often have better clinical outcomes, as demonstrated in both the District and in other jurisdictions, had the lowest mean and total CVL. In addition, older individuals were more likely to have an undetectable viral load, perhaps due to more routine access to care, longer times on treatment, or better adherence. The highest mean and total viral load and lowest proportions of undetectable viral loads were found among black men and women. This is consistent with epidemiologic findings regarding clinical indicators of HIV disease both in the District and nationally, as blacks are significantly more likely to have lower CD4 cell counts at diagnosis and to have poorer clinical outcomes [19,22,29,30]. Geographically, the highest mean and total CVLs, and the worst socioeconomic indicators, were clustered around predominantly African–American, impoverished neighborhoods, which also have some of the highest HIV/AIDS prevalence rates in the city [19,21,23,25].
Consistent with CVL trends in other cities [13,26], the District's mean CVL significantly decreased over the 5-year period. However, in contrast to findings in cities such as San Francisco and Vancouver, no association was found between trends in the mean CVL and newly diagnosed HIV/AIDS cases. This may be explained by factors related to surveillance activities in the District. First, more than half of living HIV/AIDS cases in the District of Columbia were missing viral load test results during the study period; therefore, the mean CVL observed may over or underestimate the true mean. Second, the District of Columbia began the transition from code-based to confidential name-based HIV reporting in 2006; thus, the increase in newly diagnosed cases may reflect changes in reporting requirements and case investigation strategies. Furthermore, the District expanded routine HIV testing in 2006 with the goal of identifying previously undiagnosed infections; therefore, an increase in the number of newly reported cases would be expected. Finally, previous studies have assessed the association between incident cases and CVL; our analysis focused on newly reported cases, which do not necessarily reflect new infections. This lack of association does not suggest that treatment as prevention is not a viable prevention strategy in District of Columbia. As the District's surveillance system matures, and viral load, incidence, and antiretroviral treatment data become readily available, additional longitudinal analyses of CVL and its association with incident infections will assist in measuring the potential impact of the test and treat approach on reducing HIV transmission.
Regardless of the potential for bias, measuring the mean CVL and the proportion of cases with undetectable viral load routinely is beneficial for HIV surveillance systems and public health practitioners, as it allows them to monitor their response to the epidemic. During the study period, the District expanded routine HIV testing, emphasized immediate linkages to care, and facilitated engagement in routine care. We have previously reported that these activities resulted in identifying an increased number of cases, earlier HIV diagnosis, as reflected in higher median CD4 cell counts at diagnosis, more rapid entry into care, and a decreased proportion of late testers [19,27]. Declines in the mean CVL, as well as the increasing proportion of cases with undetectable viral loads, confirm that these activities have positively impacted HIV-related clinical outcomes.
There are several limitations to this analysis. First, using surveillance data allowed for the inclusion of diagnosed and reported HIV/AIDS cases only and did not include persons unaware of their infections or acutely infected. Estimates of the proportion of individuals unaware of their infection in the District range from 47.4% among heterosexuals to 41.2% among MSM, both of which are significantly higher than the national estimates of 25–27% [21–23,31,32]. Second, individuals that are diagnosed with HIV but have not yet been reported to the surveillance system, are not in care, or are in care but whose viral load data have not been reported are unaccounted for in this analysis. In addition, data are not currently available to allow for linkage of individual-level viral load data with antiretroviral treatment data in order to identify treatment successes and failures and further quantify rates of viral suppression. Fourth, with 52% of individuals missing viral load data in the HIV/AIDS surveillance database, this could have introduced bias in the estimates. In order to mitigate the effect of missing viral load data, multiple imputation can be considered in subsequent analyses . Finally, it is important to acknowledge the susceptibility of this analysis to the ecological fallacy. Causal inferences could not be made that directly link HIV transmission to individuals or even communities with high viral load.
In conclusion, results from this analysis further support the use of CVL as a surveillance biomarker. CVL has the potential to serve as a tool for understanding transmission patterns, providing markers of access to care and treatment, and assessing trends in high HIV prevalence areas. The mean and total viral load provide a provocative picture of the overall viral burden in the District of Columbia with the mean CVL reflecting the transmission potential of the average HIV-infected individual and the total CVL indicating the transmission potential within the city as a whole. Geospatial and subgroup analyses of CVL may also be useful for guiding the development of targeted public health interventions and treatment services to disproportionately affected areas and populations with the goals of reducing disparities in HIV prevention, care access, and treatment utilization among those populations with the highest viral load burden.
Measurement of CVL using an ecological approach provides a methodology that is feasible and cost-effective and can be used by public health programs when individual-level transmission and treatment data may not be readily available. As surveillance data is increasingly used to measure outcomes, such as those in the National HIV/AIDS Strategy and TLC Plus study, CVL measurement can assist in monitoring and evaluating the impact of HIV prevention and treatment programs such as expanded HIV testing, antiretroviral coverage, and behavioral interventions at a population level. Improvements in the completeness of capturing viral load data are underway and are necessary in order to take full advantage of these data; however, in the interim, CVL measurement may provide an additional tool to assess the HIV epidemic and population-level outcomes in the District and other communities combating severe epidemics.
A.D.C., MD, MPH-Principal Investigator on surveillance technical support for the Public Health – Academic Partnership, primary responsibility for study; designed and supervised all analyses for manuscript, and holds the primary writing responsibility for the manuscript.
M.B. was responsible for data cleaning and management, conducted analyses for manuscript, and was integral to writing of manuscript.
S.W. conducted all analyses for manuscript and assisted materially with writing manuscript.
A.G. was responsible for day-to-day functions of surveillance and was integral to conceptual approach to manuscript.
T.W. was integral to conceptual approach of manuscript.
S.H. participated in writing and review of manuscript.
A.E.G. was integral in writing and review of the manuscript.
This analysis was conducted as a part of the Public Health/Academic Partnership between the District of Columbia Department of Health, HIV/AIDS, Hepatitis, STD, TB Administration and The George Washington University School of Public Health and Health Services, Department of Epidemiology and Biostatistics, Contract Number POHC-2006-C-0030.
All authors from the George Washington University, as well as the District of Columbia Department of Health, reviewed and approved the final draft of the paper. Additionally, under this contract, the District of Columbia Department of Health had the right to review and approve the final version of the manuscript.
For their assistance and expertise throughout the study, the authors acknowledge Dr Gregory Pappas, Senior Deputy Director of the District of Columbia Department of Health HAHSTA, and Drs Manya Magnus and Irene Kuo of the George Washington University Department of Epidemiology and Biostatistics. They would also like to thank Ms Rowena Samala and Ms Laura Burke for their assistance in cleaning and preparing the laboratory data and completion of the GIS mapping, respectively.
Conflicts of interest
There are no conflicts of interest.
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