Estimating the costs and perceived benefits of oral pre-exposure prophylaxis (PrEP) delivery in ten counties of Kenya: a costing and a contingent valuation study

BackgroundKenya included oral PrEP in the national guidelines as part of combination HIV prevention, and subsequently began providing PrEP to individuals who are at elevated risk of HIV infection in 2017. However, as scale-up continued, there was a recognized gap in knowledge on the cost of delivering oral PrEP. This gap limited the ability of the Government of Kenya to budget for its PrEP scale-up and to evaluate PrEP relative to other HIV prevention strategies. The following study calculated the actual costs of oral PrEP scale-up as it was being delivered in ten counties in Kenya. This costing also allowed for a comparison of various models of service delivery in different geographic regions from the perspective of service providers in Kenya. In addition, the analysis was also conducted to understand factors that indicate why some individuals place a greater value on PrEP than others, using a contingent valuation technique.MethodsData collection was completed between November 2017 and September 2018. Costing data was collected from 44 Kenyan health facilities, consisting of 23 public facilities, 5 private facilities and 16 drop-in centers (DICEs) through a cross-sectional survey in ten counties. Financial and programmatic data were collected from financial and asset records and through interviewer administered questionnaires. The costs associated with PrEP provision were calculated using an ingredients-based costing approach which involved identification and costing of all the economic inputs (both direct and indirect) used in PrEP service delivery. In addition, a contingent valuation study was conducted at the same 44 facilities to understand factors that reveal why some individuals place a greater value on PrEP than others. Interviews were conducted with 2,258 individuals (1,940 current PrEP clients and 318 non-PrEP clients). A contingent valuation method using a “payment card approach” was used to determine the maximum willingness to pay (WTP) of respondents regarding obtaining access to oral PrEP services.ResultsThe weighted cost of providing PrEP was $253 per person year, ranging from$217 at health centers to $283 at dispensaries. Drop-in centers (DICEs), which served about two-thirds of the client volume at surveyed facilities, had a unit cost of$276. The unit cost was highest for facilities targeting MSM ($355), while it was lowest for those targeting FSW ($248). The unit cost for facilities targeting AGYW was $323 per person year. The largest percentage of costs were attributable to personnel (58.5$2 per month (mean was $4.07 per month). This covers only one-third of the monthly cost of the medication (approximately$6 per month) and less than 10% of the full cost of delivering PrEP ($21 per month). A sizable proportion of current clients (27%) were unwilling to pay anything for PrEP. Certain populations put a higher value on PrEP services, including: FSW and MSM, Muslims, individuals with higher education, persons between the ages of 20 and 35, and households with a higher income and expenditures.DiscussionThis is the most recent and comprehensive study on the cost of PrEP delivery in Kenya. These results will be used in determining resource requirements and for resource mobilization to facilitate sustainable PrEP scale-up in Kenya and beyond. This contingent valuation study does have important implications for Kenya's PrEP program. First, it indicates that some populations are more motivated to adopt oral PrEP, as indicated by their higher WTP for the service. MSM and FSW, for example, placed a higher value on PrEP than AGYW. Higher educated individuals, in turn, put a much higher value on PrEP than those with less education (which may also reflect the higher “ability to pay” among those with more education). This suggests that any attempt to increase demand or improve PrEP continuation should consider these differences in client populations. Cost recovery from existing PrEP clients would have potentially negative consequences for uptake and continuation

Modélisation (Bio) Mathématique des interactions HSV-2/VIH à partir de données expérimentales

LE KREMLIN-B.- PARIS 11-BU Méd (940432101) / SudocSudocFranceF

Statistical power and estimation of incidence rate ratios obtained from BED incidence testing for evaluating HIV interventions among young people

CITATION: Auvert, B., Mahiane, G. S., Lissouba, P. & Moreau, T. 2011. Statistical power and estimation of incidence rate ratios obtained from BED incidence testing for evaluating HIV interventions among young people. PLoS ONE, 6(8): e21149, doi:10.1371/journal.pone.0021149.The original publication is available at http://journals.plos.org/plosoneBackground: The objectives of this study were to determine the capacity of BED incidence testing to a) estimate the effect of a HIV prevention intervention and b) provide adequate statistical power, when used among young people from sub-Saharan African settings with high HIV incidence rates. Methods: Firstly, after having elaborated plausible scenarios based on empirical data and the characteristics of the BED HIV-1 Capture EIA (BED) assay, we conducted statistical calculations to determine the BED theoretical power and HIV incidence rate ratio (IRR) associated with an intervention when using BED incidence testing. Secondly, we simulated a cross-sectional study conducted in a population among whom an HIV intervention was rolled out. Simulated data were analyzed using a log-linear Poisson model to recalculate the IRR and its confidence interval, and estimate the BED practical power. Calculations were conducted with and without corrections for misclassifications. Results: Calculations showed that BED incidence testing can yield a BED theoretical power of 75% or more of the power that can be obtained in a classical cohort study conducted over a duration equal to the BED window period. Statistical analyses using simulated populations showed that the effect of a prevention intervention can be estimated with precision using classical statistical analysis of BED incidence testing data, even with an imprecise knowledge of the characteristics of the BED assay. The BED practical power was lower but of the same magnitude as the BED theoretical power. Conclusions: BED incidence testing can be applied to reasonably small samples to achieve good statistical power when used among young people to estimate IRR. © 2011 Auvert et al.http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0021149ArticlePublisher's versio

Dataset for: Segmented Polynomials for Incidence Rate Estimation from Prevalence data

The study considers the problem of estimating incidence of a non remissible infection (or disease) with possibly differential mortality using data from a(several) cross-sectional prevalence survey(s). Fitting segmented polynomial models is proposed to estimate the incidence as a function of age, using the maximum likelihood method. The approach allows automatic search for optimal position of knots and model selection is performed using the Akaike Information Criterion. The method is applied to simulated data and to estimate HIV incidence among men in Zimbabwe using data from both the NIMH Project Accept (HPTN 043) and Zimbabwe Demographic Health Surveys (2005-2006)

Fits to the BED OD data for a single seroconverting mother from the ZVITAMBO Trial.

<p>Predicted values obtained from: A. LMM. Linear regression of the square root of OD values against log time (<i>t</i>) since the last HIV negative test (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049661#pone.0049661.e031" target="_blank">Equation (7</a>)). B. NLMM (U). Fitting the non-linear function given by <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049661#pone.0049661.e034" target="_blank">Equation (8</a>) to the untransformed BED OD data. C. NLMM (L). Fitting the non-linear function given by <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049661#pone.0049661.e036" target="_blank">Equation (9</a>) to the log-transformed BED OD data. D. Using the fit described in C, but now plotting log_e(OD) on the ordinate.</p

HIV incidence, estimated using BED, with the mean recency duration estimated using four different methods.

<p>HIV incidence (with 95% confidence intervals) among women during their first year postpartum in the ZVITAMBO Trial, calculated using estimates of the mean recency duration from non-linear mixed modeling (NLMM), linear mixed modeling (LMM), survival analysis (SA) and graphical analysis (Graph).</p

Graphical approach for estimating the mean recency duration.

<p>The graph shows a scatter plot of all BED OD values obtained from seroconverting women from the ZVITAMBO study, where the time between the last negative and first positive HIV tests did not exceed 120 days and where the woman provided at least four HIV positive samples. Horizontal line marks a pre-set OD cut-off of 0.8; vertical lines mark a pre-set cut-off of <i>T</i> = 365-days and a line whose position can be varied until the number of points in rectangles A and B are the same. Points in the other four rectangles are not used in this estimating procedure.</p

Fits to the BED OD data for ZVITAMBO mothers providing at least six BED samples following seroconversion.

<p>Fitting the non-linear function given by <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049661#pone.0049661.e036" target="_blank">Equation (9</a>) to the log-transformed BED OD data for 12 different women in the ZVITAMBO Trial who provided either six or seven separate BED results following seroconversion, and where the time between last negative and first positive HIV tests was at most 120 days. Plots of log_e(OD) against estimated time since seroconversion.</p

Mean recency duration for seroconverting postpartum women in the ZVITAMBO Trial, estimated using five different approaches.

<p>The optical density cut-off was fixed at 0.8 for all methods, minimum of two samples per case were required and the maximum allowable time between the last negative and first positive HIV tests was 120 days.</p

The numbers of independent BED tests provided by the 353 women who seroconverted during follow-up in the ZVITAMBO Trial, when either no exclusion criteria were applied, or where case were excluded if either there was only <i>s</i> = 1 sample per case, or the maximum time (<i>t</i>^max) between the last negative and first positive HIV tests was greater than 120 days.

<p>The numbers of independent BED tests provided by the 353 women who seroconverted during follow-up in the ZVITAMBO Trial, when either no exclusion criteria were applied, or where case were excluded if either there was only <i>s</i> = 1 sample per case, or the maximum time (<i>t</i>^max) between the last negative and first positive HIV tests was greater than 120 days.</p