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# CD4 cell count and viral load monitoring in patients undergoing antiretroviral therapy in Uganda: cost effectiveness study

BMJ 2011; 343 (Published 09 November 2011) Cite this as: BMJ 2011;343:d6884
1. James G Kahn, professor1,
2. Elliot Marseille, principal2,
3. David Moore, research scientist3,
4. Rebecca Bunnell, acting director, division of community health45,
5. Willy Were, medical epidemiologist4,
6. Richard Degerman, data management adviser4,
7. Jordan W Tappero, director, health systems reconstruction office45,
8. Paul Ekwaru, statistician4,
9. Frank Kaharuza, chief, epidemiology branch4,
10. Jonathan Mermin, director, division of HIV/AIDS prevention45
1. 1Philip R Lee Institute for Health Policy Studies and Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
2. 2Health Strategies International, Oakland, CA
3. 3Department of Medicine, Faculty of Medicine, University of British Columbia and British Columbia Centre for Excellence in HIV/AIDS, Vancouver, Canada
4. 4CDC-Uganda, National Center for HIV, Viral Hepatitis, STD and TB Prevention (NCHHSTP), Centers for Disease Control and Prevention, Entebbe, Uganda
5. 5Centers for Disease Control and Prevention, Atlanta, GA, USA
1. Correspondence to: J G Kahn, University of California, 3333 California Street, Suite 265, San Francisco CA, US 94118 jgkahn{at}ucsf.edu
• Accepted 21 September 2011

## Abstract

Objective To examine the cost and cost effectiveness of quarterly CD4 cell count and viral load monitoring among patients taking antiretroviral therapy (ART).

Design Cost effectiveness study.

Setting A randomised trial in a home based ART programme in Tororo, Uganda.

Participants People with HIV who were members of the AIDS Support Organisation and had CD4 cell counts <250 ×106 cells/L or World Health Organization stage 3 or 4 disease.

### Cost effectiveness model

Our model was designed to assess the cost and health value of each incremental use of resources for ART monitoring. This incremental cost effectiveness ratio (ICER) is the difference in cost between two monitoring options, divided by the difference in DALYs averted. We compared each monitoring option with the next less expensive alternative. The clinical option was the least expensive, followed by clinical/CD4, and clinical/CD4/viral load. We did not calculate cost effectiveness of clinical/CD4/viral load monitoring compared with clinical monitoring as this would numerically blend the two laboratory monitoring strategies and thus obscure their independent value.

The increase in costs between monitoring options reflects differences in costs per person year for the monitoring tests themselves (that is, CD4 and viral load); differences in costs of antiretroviral regimens (because of unequal rates of progression to more costly second line treatment); and outpatient and inpatient care. These were estimated based on use during the three years’ median follow-up during the trial. Mortality influences total costs by affecting the number of person years of care in each arm. We projected costs of future HIV care associated with the lives saved during the trial to a total of 15 years from start of treatment, using the observed arm specific costs and rates of clinical outcomes during the trial.

The increase in health benefits (that is, DALYs averted) between monitoring options reflects differences in mortality, severe morbidity, and the DALYs incurred with these clinical events. Specifically:

DT=NDA×DD+Σ (NMA×DM)

where DT=total DALYs averted; NDA=number of deaths averted; DD=DALYs averted per death averted; NMA=number of severe morbid events averted; and DM=DALYs averted per morbid event averted.

The DALYs from morbidity are summed across 14 diagnoses. The DALYs associated with mortality are calculated for the three year trial and then over the subsequent 12 years.

### Data inputs

We relied mainly on data from the trial to determine the value of health and intervention cost inputs,11 16 17 supplemented by published sources and expert opinion within the trial. Table 1 summarises the data inputs, which are discussed below for the base case. Alternative input values and assumptions are described for the sensitivity analyses.

Table 1

### Sensitivity analyses

Table 4 reports the effect on ICERs of variation in key inputs and modelling assumptions. We examined uncertainty in the mortality rate as suggested by the 95% confidence intervals for observed adjusted hazard ratios in the intention to treat analysis. For clinical/CD4/viral load, with the most favourable hazard ratio and mortality rate, the ICER drops below $1600. With the worst mortality, the clinical/CD4/viral load strategy is dominated by clinical/CD4 (more expensive and less effective). For clinical/CD4, lower mortality worsens the ICER because the ICER approaches the cost effectiveness of keeping individuals alive on ART (that is, cost and a year’s DALYs v death and no cost). When mortality is higher, the clinical/CD4 arm becomes less expensive and effective than clinical, so the ICER is calculated in the reverse direction. Table 4 Sensitivity analyses for key inputs and modelling assumptions for cost effectiveness analysis, antiretroviral monitoring study, Tororo and Busia Districts, Uganda, 2003-7 View this table: The trial’s per protocol (as treated) analysis examined only the period after monitoring began (at 90 days), removing the early period of high mortality unrelated to ART monitoring. This analysis found adjusted mortality rates of 2.2, 2.0, and 3.5 per 100 patient years for the clinical/CD4/viral load, clinical/CD4, and clinical arms, respectively. The clinical/CD4 arm had an ICER of$88 per DALY averted compared with clinical alone, and the viral load option was dominated compared with the clinical/CD4 arm.

Varying the rate of change in regimen between 50% and 150% of base case values (for all arms simultaneously) had little effect on the ICER for viral load ($4882-5466). It has a proportionately larger effect on the ICER for CD4 versus clinical ($401-“dominant”). This is because of the small and similar switch rates in clinical/CD4/viral load and clinical/CD4 arms and the larger base case value of regimen switch rates in the clinical arm.

### Limitations

There are important limitations to our study. We treated mortality comparisons, which dominate our model’s health and cost effects, as meaningful regardless of statistical significance. Thus, the base case results reflect observed differences in mortality that were small and not quite significant for clinical/CD4 versus clinical monitoring (combined mortality and AIDS defined events were significantly different) and far from significant for clinical/CD4/viral load versus clinical/CD4 monitoring . As a result, they could be considered overly optimistic. Despite this lenient approach (counting as real a difference for which there is little evidence), viral load had a highly unfavourable ICER. Our sensitivity analyses highlight how the uncertainty about these comparisons contributes to a substantial likelihood that viral load monitoring is dominated. Furthermore, in the per protocol analysis, which is arguably appropriate for an intervention that starts three months after the initiation of treatment, the clinical/CD4/viral load arm had a mortality point estimate slightly higher than the clinical/CD4 arm.

We lacked data on mortality rates beyond the trial. To calculate long range ICERs, we assumed ongoing arm specific mortality rates as observed in the trial. The assumption of higher or lower rates after the trial, however, did not change our findings qualitatively—that is, clinical/CD4 monitoring retained its superior cost effectiveness. Specifically, lower long term mortality, as observed late in the trial, led to a more attractive ICER for clinical/CD4 versus clinical monitoring, and higher mortality increases the ICER just 50%. Within trial ICERs generate similar results. Thus, our findings seem robust to uncertainties in mortality.

The study had a home based component and was thus structurally atypical. The relative cost effectiveness among groups, however, is likely to be similar to facility based programmes as the overall cost effectiveness of ART was similar to facility based analyses in the Côte d’Ivoire and South Africa.25 26 In addition, all three strategies used quarterly monitoring. In some countries, governments promote biannual monitoring. Results for less frequent monitoring might differ from ours.

We did not examine the benefits of ART in preventing HIV transmission. In settings where viral load monitoring improves rates of full viral suppression, there would probably be benefits. These might not be as great as initially assumed as the effect of reduced viral load on HIV transmission is not dichotomous; even a plasma viral load of 5000-10 000 copies/mL would reduce the risk of transmission considerably compared with 500 000 copies/mL. Thus, the effect of identifying relatively slight increases in viral load a few weeks or months earlier could be less important than efforts to increase access to ART and retention in care.

We did not assess all plausible configurations of laboratory monitoring. For example, a strategy including clinical and virological but not CD4 monitoring could potentially reduce costs while detecting virological failure early. Viral load might be a better indicator than CD4 cell counts of treatment success and thus clinically advantageous. Yet our study did not indicate improved efficacy from adding viral load to CD4 monitoring. Furthermore, logistical challenges to viral load monitoring are substantial in Africa. CD4 cell counts are more widely available in peripheral health centres because of lower costs, ease of operation and maintenance, and use for determining eligibility for ART. Reliance on viral load could potentially work with efficient referral to a central laboratory or new technology, but currently in Uganda, routine viral load monitoring is unlikely to be feasible outside a research setting or major cities. As less expensive and logistically simpler viral load testing becomes available, a study comparing routine CD4 cell count monitoring with routine viral load monitoring alone would be valuable.

### Implications

Standards in clinical practice well attuned to resource rich settings might not maximise the opportunities available in resource constrained settings. Monitoring CD4 cell counts seems desirable clinically and economically in these settings. In contrast, viral load monitoring might be a relatively poor investment compared with offering ART to another person or monitoring CD4 cell counts for those currently taking ART. This approach supports current WHO guidelines.27

According to WHO, the attractiveness of the ICER can be determined by comparison with the country’s annual gross domestic product per capita. An intervention with an ICER below the annual gross domestic product per capita is considered “very cost effective.” An ICER below three times the annual gross domestic product per capita is considered “cost effective.”28 The annual gross domestic product per capita in Uganda in 2008 was an estimated 1200 international dollars ($485 using currency exchange rates).13 Thus by the gross domestic product per capita standard, CD4 count monitoring is “very cost effective” and viral load monitoring is not cost effective. In closing, we want to highlight another path to increased access to high quality ART in resource poor settings. We strongly support efforts to reduce the price of second line antiretroviral regimens and laboratory tests. If such efforts could replicate the impressive past decreases in the price of first line regimens and the also important reductions for CD4 counts, the types of analyses reported here would be less necessary. #### What is already known on this topic • ART in sub-Saharan Africa costs$500 to $1000 per disability adjusted life year (DALY) averted • Previous estimates of the cost effectiveness of laboratory monitoring relied on computer simulation models and varied widely #### What this study adds • Adding routine monitoring of CD4 count costs$174 per DALY averted, less than for ART. CD4 monitoring is cost effective by improving clinical outcomes and reducing changes to more expensive antiretroviral drugs

• Adding routine monitoring of viral load costs \$5181 per DALY averted, far more than for ART or CD4 monitoring and in a subset of analyses is both less effective and more costly than clinical or CD4 monitoring. Viral load monitoring has unattractive cost effectiveness because of low clinical benefit and high test costs

## Notes

Cite this as: BMJ 2011;343:d6884

## Footnotes

• Contributors: JGK and EM designed the analysis, constructed the cost effectiveness model, and wrote the manuscript. DM, RD, and JM provided data and interpretation for the clinical trial results. RD and PE provided healthcare use and cost data from the trial. DM, RB, WW, JWT, PE, FK, and JM reviewed and edited the manuscript to assure accurate interpretation and representation of the trial context, clinical practices, and economic findings. JGK is guarantor.

• Funding: This study was funded by US Centers for Disease Control and Prevention, Kenya, and US National Institute of Drug Abuse (R01 DA15612). The findings and conclusions in this manuscript are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention.

• Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

• Ethical approval: This study was approved by the science and ethics committee of the Uganda Virus Research Institute, the Uganda National Council of Science and Technology; CDC Institutional Review Board; UCSF CHR 04024782.

• Data sharing: Detailed costing data available on request.

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