Introduction
Persons with acute HIV infection (AHI) contribute disproportionately to the transmission of HIV [1,2] [3,4] [5] [6]
Despite the public health importance of AHI, this syndrome is rarely recognized. Many persons with AHI develop symptoms consistent with acute retroviral syndrome [7] [3,8–13] [8,9,12]
To date, however, North Carolina remains the only state in the United States to use a comprehensive testing strategy including HIV RNA testing through a standardized pooling algorithm [12,14] [15] [16]
Methods
Study design
This cross-sectional study was designed to develop and evaluate targeted testing criteria to detect AHI using program data collected by HIV testing program of North Carolina. We included data from all publicly funded counseling and testing centers in North Carolina.
Study population
The study population comprised all persons undergoing HIV testing at publicly funded testing sites during the study period, 22 October 2002 to 30 April 2005. Testing was confidential and linked to patient information using a system of unique identifiers according to state public health statutes. Persons provided informed consent for HIV testing according to the state protocol. All persons identified as having HIV infection were contacted by disease intervention specialists, and partner tracing was routinely performed. The procedures of this study were approved by the University of North Carolina Institutional Review Board.
The study population was restricted to persons older than 10 years of age. For analyses, the following persons were excluded: county of residence or zip code outside of North Carolina, established HIV infection, or missing demographic information.
Data collection: patient information
Persons presenting for HIV testing in North Carolina routinely provide personal information during pretest counseling. This information includes age, sex, race, insurance, reason for visit, and HIV risk category. The type of testing site, residential county and residential zip code were also recorded. The location of the testing site was not considered during model development.
In addition to the routinely collected data from the testing centers, we used reported HIV case surveillance data to estimate HIV disease burden in each county of residence [17]
Data collection: HIV testing
Sera submitted for HIV testing were processed centrally at the North Carolina State Laboratory of Public Health and maintained at −70°C. A standard Vironostika HIV-1 enzyme immunoassay (EIA) (bioMerieux, Durham, North Carolina, USA) and western blot (BioRad, Hercules, California, USA) were used for antibody screening. Specimens with negative or indeterminate antibody test results were tested with PCR for the presence of RNA using a specimen-pooling algorithm to increase specificity and reduce costs [12,13]
In addition to these standard testing procedures, we sought to identify persons with recent HIV infection among those with positive sera. During early HIV infection, antibodies are typically of low titer and avidity. We used a detuned antibody testing procedure (a ‘less-sensitive’ HIV antibody EIA that remains negative for an average of 170 days after a standard ‘sensitive’ antibody EIA becomes positive) to identify recent infections [18] [18]
HIV outcome definitions
We defined HIV negative as a person with negative HIV enzyme-linked immunosorbent assay (ELISA) and negative HIV RNA nucleic acid amplification test at initial or subsequent testing. We defined acute HIV infection as a person with a negative or indeterminate HIV antibody result and a positive HIV RNA test. We defined recent HIV infection as a person with positive HIV antibody result using routine HIV ELISA and western blot, but with a negative less-sensitive detuned ELISA, suggesting recent seroconversion. We defined established HIV infection as a person with positive ELISA, western blot and detuned ELISA.
Statistical analysis
We performed all analyses using Stata SE, version 8.2 (StataCorp, College Station, Texas, USA). After initial examination of frequencies and distributions, the analyses proceeded in two stages: development and validation.
The study population was divided into two groups based on the period of data collection and infection status. To develop risk assessment strategies, we used data collected during the first 2 years of the program and restricted the population to persons with recent HIV infection or no HIV infection. Validation of the sensitivity of the risk assessment strategies was initially performed using acute infections identified during this 2-year period. Further validation of sensitivity for detection of AHI and validation of specificity and the proportion of the population tested were performed with data collected during the first 6 months of the third year of data collection.
We developed the risk assessment procedure using the population comprising persons with recent HIV infection or no infection. We used recent HIV infection as the outcome because the number of persons with AHI was small, thereby limiting model stability. We initially examined the bivariable relationship between each candidate predictor variable and the outcome. For continuous variables, both continuous and categorical representations were evaluated. In each case, performance was comparable, and only categorical representations are presented here. We excluded candidate predictor variables with small cell sizes in the bivariable analyses (cell size < 10 observations) from further analyses, including referrals for symptoms; client referral; referrals from drug treatment, family planning, prenatal, tuberculosis, court, immigration, or occupational settings; and the following reported risks – child of a woman with HIV, hemophilia, and healthcare exposure. Two variables, sex with a man and sex with a woman, were excluded from multivariable models because of collinearity with the sex/sexual preference variable.
We used multiple logistic regression to develop a model-based individual risk assessment procedure. We identified candidate predictor variables in bivariable analyses using simple logistic regression. Prevalence odds ratios (OR) with 95% confidence intervals were estimated. Those variables associated with recent HIV infection with P -value less than 0.20 in bivariable analyses were included in the full logistic model [19] P -value was less than 0.05 for all remaining variables. In addition to the stopping criteria based on P -value, we examined Akaike's Information Criterion and the Bayesian Information Criterion with similar results. Goodness of fit was examined with the Hosmer–Lemeshow test.
The principal goal in the development of the risk assessment strategies was to reduce the number of persons referred for pooled HIV RNA testing. Consequently, the principal results of interest are the sensitivity and the proportion of the population that would be tested with a given strategy. For multivariable models, we determined cut-offs from the predicted probabilities derived from each unique combination of variables. We then identified those cut-offs that would limit the population tested to 70 or 50% of the total population. We also examined a cut-off that would yield sensitivity of at least 90%.
In addition to the model-based individual risk assessment, we developed simple criteria using information that would be readily available to a health agency. These criteria were based on clinic type and geographical area to provide a simple strategy for implementation at a central testing laboratory.
Results
During the first 2 years of the project (development phase), 227 162 persons underwent testing in public North Carolina testing centers. Of these, 3045 (1.3%) reported home addresses out of North Carolina. Among the remaining 224 117 persons, 224 087 (99.99%) had valid county or zip code information.
Using our definitions of HIV status, we identified 875 (0.39%) persons with newly-diagnosed but established HIV infection, who were excluded from further analyses. Recent HIV infection was identified in 237 (0.11%) and AHI in 44 persons (0.02%). The remaining population (n = 222 931, 99.48%) was considered HIV negative by serology and pooled HIV RNA screening.
To develop risk assessment strategies, we restricted the population to persons with recent or no HIV infection and complete demographic and behavioral information (n = 215 528). Persons with AHI were used in the validation phase. This complete case dataset excluded five (2.1%) persons with recent HIV infection and 7679 (3.4%) persons without HIV infection who were missing demographic or behavioral information.
Testing was performed in a sexually transmitted disease (STD) setting most frequently (41.5%; Table 1 ). Nearly half of the population was African–American (45.4%). Two-thirds of the population comprised female patients (66.3%), with 30.2% heterosexual men and 3.5% men who have sex with men (MSM).
Table 1: Characteristics, prevalence and associations with recent HIV infection among persons undergoing HIV testing in North Carolina (development data; n = 215 484).
Model development
In bivariable (unadjusted) models, MSM were much more likely than women to have recent HIV infection [OR = 24.5, 95% confidence interval (CI) = 18.0–33.3]. Heterosexual men were about twice as likely as women to have recent HIV infection (Table 1 ). The most important reported risk factors were sexual intercourse with an HIV-infected person (OR = 19.1, 95% CI = 14.0–26.0) and sexual intercourse with MSM (OR = 15.6, 95% CI = 11.8–20.6). However, a small proportion of the population reported these risks (1.5% and 2.9%, respectively). Persons living in counties with more than 200 reported cases of HIV infection during the 2 years prior to testing were also more likely to have recent HIV infection (OR = 4.7, 95% CI = 2.6–8.4), compared with persons living in counties with zero to 10 cases. Testing site was also strongly associated with infection status. Several testing sites, including family planning, tuberculosis, antenatal, drug treatment, and community health centers, had particularly low prevalence of recent infection (0.014%).
In multivariable models, demographics including sexual preference, testing site, and geographical location were important predictors of recent HIV infection (Table 2 ). Sexual intercourse with an HIV-infected person was the only reported risk behavior associated with infection status. In the full model, the overall accuracy was reasonably high (ROC area = 0.867). Reduction of the model had minimal effect on overall accuracy (ROC area = 0.861).
Table 2: Full and reduced multivariable predictive models for recent HIV infection among persons undergoing HIV testing in North Carolina (development data; n = 215 484).
Model performance
Both the full and reduced model showed reasonable performance. In each case, a cut-off chosen to test 50% of the population would have identified approximately 92% of persons with recent HIV infection (Table 3 ). Testing 70% of the population would have identified approximately 98% of infections. Identification of 90% of persons with recent infection would require testing 40–43% of the population using the reduced or full model.
Table 3: Performance of model-based and simple criteria for detection of recent HIV infection.
Model validation
We validated model performance in two groups. First, we examined the sensitivity of the models for detecting persons with AHI during years 1 and 2. Note that the models were developed using persons with recent infection, excluding those with acute infection. The sensitivity of the full and reduced models for AHI was more than 90% in all cases, comparable to the sensitivity in model development (Table 4 ).
Table 4: Validation of performance of model-based and simple criteria.
We also examined the performance of the models in the first half of year 3, evaluating sensitivity in a limited population of persons with AHI, specificity, and proportion of the population tested. In both the full and reduced model, testing approximately 70% of the population would have detected 94% of the persons with AHI. Using the cut-off for 50% of the population led to identification of 87.5% of the cases. Using the cut-off for sensitivity of more than 90% also identified 87.5%, but resulted in only approximately 40% of the population undergoing testing.
Simple criteria
To provide alternative simple criteria, we examined the potential of targeting testing based on the type of testing site and geographical location. These group level variables provide a more feasible strategy for implementation.
Limiting testing to single types of testing sites (e.g. STD clinics) was inadequate. In the development period (years 1 and 2), restricting testing to STD clinics only identified approximately 40% of recent infections, while testing about 40% of the population (Table 3 ), indicating little enrichment for recent infections in the STD clinics as compared with the full population. Testing in the counseling and testing sites (CTS) yielded 34% of the infections, but represented only 10% of the population.
Several types of testing sites yielded very few infections. If testing for recent infection was limited to CTS, STD, prison/jail, field visits, and ‘other’, while excluding family planning, tuberculosis, antenatal, drug treatment and community health centers, 95% of recent infections would have been detected, but only 64% of the population would have been tested.
By combining the site type information and the geographical region of the state, we could improve performance substantially. If testing was restricted to counties with more than one case over the 2 years and simultaneously limited to the higher risk testing sites (CTS, STD, prison/jail, field visits, and other), 95% of the recent infections would have been identified while testing 53% of the population. This performance is comparable to the model-based criteria.
Simple criteria: validation period
Generally, performance of the simple criteria in the validation samples was comparable to that in the development period with a few exceptions (Table 4 ). Restricting to the higher risk clinics only would have detected 97.7% of AHI in years 1 and 2, but only 87.5% of infections in year 3. These clinics represented 64% of the population. Adding the restriction of counties with at least one recent case reduced the population tested to 54%, but only 81% of AHI in year 3 would have been identified.
Antenatal clinics
Pregnant women represent a special group because of the potential to prevent HIV infection in their infants. We examined the effect of adding the antenatal clinics to the simple criteria described above. Adding the antenatal clinics to the higher risk testing sites increased sensitivity slightly but led to substantially more of the population being tested (Table 3 ). These changes were consistent in the validation groups (Table 4 ).
Discussion
In an effort to assist programs to identify persons with AHI efficiently, we examined both model-based and simple criteria for predicting persons with a higher likelihood of AHI. Our primary goal was to reduce the number of persons requiring special testing to identify AHI while simultaneously identifying a large proportion of persons with AHI. Using simple criteria (testing site and county of residence), we were able to identify 80–95% of persons with AHI while testing approximately 54% of the population. More complex, model-based criteria performed similarly.
In the development phase, we used recent HIV infection as our primary outcome, rather than AHI. We made this decision for three reasons. First, the number of persons with AHI was small (∼5% of the total number of persons with HIV infection in our sample), thereby reducing the stability of model estimates. Second, excluding AHI in years 1 and 2 gave us a separate study sample to validate the sensitivity of the risk assessment criteria. Finally, and perhaps most importantly, we recognized that most jurisdictions are not currently using the pooled testing algorithm for AHI, but might have access, or could gain access, to data regarding recent HIV infections using CDC-endorsed and WHO-endorsed tests for recent infection. These data can be obtained through standard surveillance procedures with the addition of ‘detuned’ ELISA testing. Therefore, by demonstrating that this process yielded a reasonable risk assessment strategy, we believe other areas could replicate our efforts locally.
Model-based strategies for risk assessment included a limited number of predictors for HIV infection. Reduction of the model had relatively little effect on model accuracy. Consequently, five variables (i.e. sexual preference, testing site, county, race, and sexual exposure to a person with HIV infection) were sufficient to predict recent (and acute) HIV infection with reasonable accuracy. In particular, men who have sex with men had a substantially greater likelihood of infection than heterosexual men or women.
Despite the reasonable accuracy of the model-based strategies, individual-level risk assessment would be challenging programmatically. The individual strategy would require each specimen to be sorted, adding to data management and technician costs. In contrast, the strategies using simple criteria could be sorted as a group (e.g. by using separate forms distributed to targeted clinics), reducing the work required. Given that performance of the simple strategies was reasonably comparable to the model-based strategies, we feel that from a programmatic perspective, this approach is preferable.
AHI testing is not without cost, but is likely to be highly cost-effective. In our previous work, we found that pooled RNA testing added $3.63 to the cost of testing each specimen in North Carolina (almost doubling the per-specimen testing cost in this moderate-prevalence state) [12] [20,21] [20]
This tension between the programmatic cost and societal cost-effectiveness is highly germane in the current era, in which HIV testing in the United States is being expanded to include low-risk groups (e.g. clients routine primary healthcare [15] [21]
Identifying persons with AHI is a laudable public health goal. Persons with AHI have high viral loads in both blood and genital secretions, facilitating transmission [3,4] [22]
Despite the importance of AHI, efforts to identify persons with acute infection have not been given high priority by national or global public health authorities, primarily because of perceived and real costs. Using a rational approach for targeting acute infection testing to those at greater risk, we have demonstrated that one could reduce the number of specimens requiring acute HIV testing by about 50% while still detecting 80–90% of infections. On the basis of these data, we propose that careful assessment of the testing sites that are responsible for the majority of recent infections in a public health jurisdiction could provide a useful mechanism to implement a testing strategy for AHI. If the identification of persons with AHI can be targeted to clinical settings where it is most likely to be detected on a wide scale, the potential to reduce the burden of HIV disease in this country is substantial.
Acknowledgements
The study was supported by the National Institutes of Health (NIMH-R01 068686).
We are indebted to the staff of the North Carolina State Laboratory of Public Health Serology/Virology Laboratory for all antibody testing and nucleic acid amplification testing for the North Carolina Department of Health and Human Services Screening and Tracing Active Transmission program, the University of North Carolina Center for AIDS Research Retrovirology Core Laboratory, and to the Disease Intervention Specialists of the HIV/STD Prevention and Care Branch of the North Carolina Department of Health and Human Services. We are also indebted to Evelyn Foust and Dr Steve Cline for their leadership within the North Carolina Department of Health and Human Services.
W.C.M. contributed to the study design and implementation, conducted the data analyses, and drafted the manuscript. P.A.L. contributed to the study design, oversaw the development and implementation of the STAT program, and edited the manuscript. S.Mc. assisted with the implementation of the study, data management and edited the manuscript. T.Q.N. assisted with study implementation, data management, data analyses, and edited the manuscript. D.W. contributed to study design, implementation, data collection and reviewed the manuscript. C.D.P. contributed to study design and implementation and drafted and edited the manuscript.
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