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. 2013;8(1):e53193.
doi: 10.1371/journal.pone.0053193. Epub 2013 Jan 7.

Estimation of HIV-testing rates to maximize early diagnosis-derived benefits at the individual and population level

Dario A Dilernia  1 , Daniela C Monaco, Carina Cesar, Alejandro J Krolewiecki, Samuel R Friedman, Pedro Cahn, Horacio Salomon
Affiliations

Estimation of HIV-testing rates to maximize early diagnosis-derived benefits at the individual and population level

Dario A Dilernia et al. PLoS One. 2013.

Abstract

Background: In HIV infection, initiation of treatment is associated with improved clinical outcom and reduced rate of sexual transmission. However, difficulty in detecting infection in early stages impairs those benefits. We determined the minimum testing rate that maximizes benefits derived from early diagnosis.

Methods: We developed a mathematical model of HIV infection, diagnosis and treatment that allows studying both diagnosed and undiagnosed populations, as well as determining the impact of modifying time to diagnosis and testing rates. The model's external consistency was assessed by estimating time to AIDS and death in absence of treatment as well as by estimating age-dependent mortality rates during treatment, and comparing them with data previously reported from CASCADE and DHCS cohorts.

Results: In our model, life expectancy of patients diagnosed before 8 years post infection is the same as HIV-negative population. After this time point, age at death is significantly dependent on diagnosis delay but initiation of treatment increases life expectancy to similar levels as HIV-negative population. Early mortality during HAART is dependent on treatment CD4 threshold until 6 years post infection and becomes dependent on diagnosis delay after 6 years post infection. By modifying testing rates, we estimate that an annual testing rate of 20% leads to diagnosis of 90% of infected individuals within the first 8.2 years of infection and that current testing rate in middle-high income settings stands close to 10%. In addition, many differences between low-income and middle-high incomes can be predicted by solely modifying the diagnosis delay.

Conclusions: To increase testing rate of undiagnosed HIV population by two-fold in middle-high income settings will minimize early mortality during initiation of treatment and global mortality rate as well as maximize life expectancy. Our results highlight the impact of achieving early diagnosis and the importance of strongly work on improving HIV testing rates.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Schematic view of the model structure.
This flowchart shows the states that constitute the model, in which individuals can be classified at any time of the simulation. For additional details on probabilities and logical rules involved in transition between states see supplemental material.
Figure 2
Figure 2. Comparison of modeĺs outputs related to mortality rates with those previously reported.
Time to AIDS and time to death as well as age-dependent AIDS-related mortality rates are compared with those previously reported for the CASCADE cohort and the DHCS cohort.
Figure 3
Figure 3. Annual mortality rate of total individuals living with HIV for each of the analytical settings.
The model was run with the different fixed times from infection to diagnosis detailed in the figure. At the end of each run, the annual mortality rate was determined. Each run was repeated with the 6 combinations of the three CD4 count threshold to initiate HAART (200, 350 and 500 cells/µl) and two different efficiencies to detect HIV infection through symptomatology (35% and 75%). *Significantly different with a p-value<0.05.
Figure 4
Figure 4. Comparison of average age at death of HIV positive individuals according to the capacity of detecting infection by symptomatology and the access to treatment.
Age at death for the population living with HIV are compared with those individuals who have achieved diagnosis. Predictions show the impact of early diagnoses on extending life of individuals living with HIV, as well as show that improvements in efficiency of detecting HIV infections through symptomatology can significantly extend life expectancy in cases where the diagnosis is achieved later than 8 years post-infection. Predictions show the major impact that access to therapy can have on extending life expectancy even for patients diagnosed in advanced stages of infection.
Figure 5
Figure 5. Mortality rate in the first year of HAART for each of the analytical settings.
For the present analysis, the percentage of newly treated patients that die during the first year of initiation of the first HAART regimen was estimated. In this case, data recovered across the whole simulation was analyzed by identifying patients that have initiated HAART and whose date of death occurred within the 12 simulation cycles after initiation of HAART, over the total individuals that have initiated HAART during the model run. *Significantly different with a p-value<0.05.
Figure 6
Figure 6. Proportion of HIV positive individuals unaware of their infection status having CD4 counts below 250 (dark bars) or 350 cells/µl (light bars), for each of the analytical settings.
In this case, model’s outputs were further analyzed to determine the proportion of HIV positive individuals unaware of their infection whose CD4 count have drop below critical levels (250 and 350 cells/µl).
Figure 7
Figure 7. Analysis of the impact of detection rate on diagnosis delay.
The data was obtained from simulations where the diagnosis delay was relaxed. Then testing rate was modified and distribution of year of infection in the undiagnosed population was analyzed. In the figure, each curve corresponds to a different percentile of that distribution. In dark blue is shown the curve for the median.
Figure 8
Figure 8. Comparison of model’s output with observations from different settings.
The model was run under a fixed time to diagnosis of 8 years and 10 years. Predictions obtained using a 8-years delay resemble middle-high income settings while those obtained using 10-years delay resemble low-middle income settings for the proportion of patients with CD4 counts lower than 200 cells/µl (A) and median CD4 count at initiation of HAART (B). Predictions about mortality rate during the first year of HAART are consistent with middle-high income settings but distant from those observed in low-middle income settings (C). Latin American cohorts in panel C are those part of CCASAnet cohort.
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