Using modeling to inform international guidelines for antiretroviral treatment

Hallett, Timothy B.; Menzies, Nicolas A; Revill, Paul; Keebler, Daniel; Bórquez, Annick; McRobie, Ellen; Eaton, Jeffrey W.

doi:10.1097/qad.0000000000000115

Cited by 25 publications

(11 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To answer these questions, calculations are required to assess the impact of MDA targeted at particular age groupings, especially pre-SAC and SAC, on overall transmission in communities with differing levels of infection exposure. In many areas of infectious disease epidemiology and the design of interventions, the impact of control is today assessed by simulations based on mathematical models using parameter estimates from epidemiological studies (e.g., HIV and Plasmodium falciparum [ 7 , 8 ]). The neglected tropical disease (NTD) field lags behind, in the sense of largely basing target treatment levels on discussion and consensus, without detailed calculations.…”

Section: Introductionmentioning

confidence: 99%

Should the Goal for the Treatment of Soil Transmitted Helminth (STH) Infections Be Changed from Morbidity Control in Children to Community-Wide Transmission Elimination?

et al. 2015

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Should the Goal for the Treatment of Soil Transmitted Helminth (STH) Infections Be Changed from Morbidity Control in Children to Community-Wide Transmission Elimination?

et al. 2015

View full text Add to dashboard Cite

“…Models are increasingly playing key roles in policy decision-making in HIV healthcare. Modelling has been influential in the adoption of current WHO treatment guidelines [ 3 ] and was used in designing the recent PEPFAR combination prevention trials [ 4 ]. Accurately modelling the progression of HIV-infected individuals through these different stages is crucial to provide reliable estimates of incidence, prevalence and number in need of treatment, and hence for robust policy recommendations and assessments.…”

Section: Introductionmentioning

confidence: 99%

CD4+ cell dynamics in untreated HIV-1 infection

Cori

Pickles

Sighem

et al. 2015

Aids

View full text Add to dashboard Cite

Background:CD4+ cell count is a key measure of HIV disease progression, and the basis of successive international guidelines for treatment initiation. CD4+ cell dynamics are used in mathematical and econometric models for evaluating public health need and interventions. Here, we estimate rates of CD4+ decline, stratified by relevant covariates, in a form that is clinically transparent and can be directly used in such models.Methods:We analyse the AIDS Therapy Evaluation in the Netherlands cohort, including individuals with date of seroconversion estimated to be within 1 year and with intensive clinical follow-up prior to treatment initiation. Owing to the fact that CD4+ cell counts are intrinsically noisy, we separate the analysis into long-term trends of smoothed CD4+ cell counts and an observation model relating actual CD4+ measurements to the underlying smoothed counts. We use a monotonic spline smoothing model to describe the decline of smoothed CD4+ cell counts through categories CD4+ above 500, 350–500, 200–350 and 200 cells/μl or less. We estimate the proportion of individuals starting in each category after seroconversion and the average time spent in each category. We examine individual-level cofactors which influence these parameters.Results:Among untreated individuals, the time spent in each compartment was 3.32, 2.70, 5.50 and 5.06 years. Only 76% started in the CD4+ cell count above 500 cells/μl compartment after seroconversion. Set-point viral load (SPVL) was an important factor: individuals with at least 5 log10 copies/ml took 5.37 years to reach CD4+ cell count less than 200 cells/μl compared with 15.76 years for SPVL less than 4 log10 copies/ml.Conclusion:Many individuals already have CD4+ cell count below 500 cells/μl after seroconversion. SPVL strongly influences the rate of CD4+ decline. Treatment guidelines should consider measuring SPVL, whereas mathematical models should incorporate SPVL stratification.

show abstract

“…Cost-effectiveness analysis can support the development of a ‘focused’ strategy for HIV investment,9 10 whereby an allocation of resources across risk groups, geographical areas, time11 and among competing HIV prevention and treatment strategies is chosen that maximises the health attainable, given the available resources. A focused strategy can lead to greater health impact than a strategy in which the same mix of interventions is used uniformly across a highly heterogeneous epidemic 10…”

Section: Introductionmentioning

confidence: 99%

“…This evidence typically comes from a wide range of sources including clinical trials, surveillance studies, long-term observational studies, costing studies and quality of life surveys. Evidence is often synthesised using decision modelling in order to provide estimates of the costs and outcomes associated with different policy choices over a suitable time frame 9…”

Section: Introductionmentioning

confidence: 99%

Appraising the value of evidence generation activities: an HIV modelling study

et al. 2018

Self Cite

View full text Add to dashboard Cite

IntroductionThe generation of robust evidence has been emphasised as a priority for global health. Evidence generation spans a wide range of activities including clinical trials, surveillance programmes and health system performance measurement. As resources for healthcare and research are limited, the desirability of research expenditure should be assessed on the same basis as other healthcare resources, that is, the health gains from research must be expected to exceed the health opportunity costs imposed as funds are diverted to research rather than service provision.MethodsWe developed a transmission and costing model to examine the impact of generating additional evidence to reduce uncertainties on the evolution of a generalised HIV epidemic in Zambia.ResultsWe demonstrate three important points. First, we can quantify the value of additional evidence in terms of the health gain it is expected to generate. Second, we can quantify the health opportunity cost imposed by research expenditure. Third, the value of evidence generation depends on the budgetary policies in place for managing HIV resources under uncertainty. Generating evidence to reduce uncertainty is particularly valuable when decision makers are required to strictly adhere to expenditure plans and when transfers of funds across geographies/programmes are restricted.ConclusionBetter evidence can lead to health improvements in the same way as direct delivery of healthcare. Quantitative appraisals of evidence generation activities are important and should reflect the impact of improved evidence on population health, evidence generation costs and budgetary policies in place.

show abstract

Using modeling to inform international guidelines for antiretroviral treatment

Cited by 25 publications

References 13 publications

Should the Goal for the Treatment of Soil Transmitted Helminth (STH) Infections Be Changed from Morbidity Control in Children to Community-Wide Transmission Elimination?

Should the Goal for the Treatment of Soil Transmitted Helminth (STH) Infections Be Changed from Morbidity Control in Children to Community-Wide Transmission Elimination?

CD4+ cell dynamics in untreated HIV-1 infection

Appraising the value of evidence generation activities: an HIV modelling study

Contact Info

Product

Resources

About