Cost-effectiveness of pre-exposure prophylaxis for HIV prevention among female sex workers in Ethiopia: a Markov model analysis

Cost-effectiveness of pre-exposure prophylaxis for HIV prevention among female sex workers in Ethiopia: a Markov model analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Cost-effectiveness of pre-exposure prophylaxis for HIV prevention among female sex workers in Ethiopia: a Markov model analysis Idiris Genemo, Desalegn Feleke, Abdi Gari, Yohannes Ejigu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8681752/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Background Pre-exposure prophylaxis (PrEP) is a highly effective HIV prevention strategy for high-risk populations, such as female sex workers. However, its uptake in Ethiopia remains limited. Due to the scarcity of local evidence, a cost-effectiveness analysis is needed to inform the potential expansion of PrEP services nationwide. This study aimed to assess the cost-effectiveness of PrEP in preventing HIV infection among female sex workers in Ethiopia. Methods A static Markov model was adapted to analyze HIV transmission and disease progression in a hypothetical cohort of 1,000 female sex workers receiving PrEP. Primary data were collected to estimate the cost of providing PrEP to female sex workers, while most model parameters were obtained from the published literature. Cost-effectiveness was assessed by calculating the incremental cost-effectiveness ratio of the PrEP intervention compared to no PrEP use. The effectiveness of PrEP was measured in QALYs gained over the lifetime of the cohort, and the ICER was estimated to determine the cost-effectiveness of the intervention. One-way sensitivity and probabilistic sensitivity analysis were conducted to assess uncertainty in the model results. Result The estimated unit cost of PrEP per client per year was USD 137.7. The PrEP intervention resulted in an incremental cost of USD 1,202,379 and an incremental gain of 1,231.50 QALYs compared with no PrEP. At the current level of PrEP coverage, the incremental cost-effectiveness ratio (ICER) was 976.35 USD per QALY gained. Under full PrEP coverage, the ICER decreased to USD 107.25 per QALY gained. At this level, PrEP would be considered a cost-effective intervention based on the threshold used for the cost-effectiveness analysis. The cost-effectiveness of PrEP was highly sensitive to PrEP efficacy, the level of uptake, and the cost of antiretroviral therapy (ART). Conclusion Pre-exposure prophylaxis for HIV prevention in female sex workers is not cost-effective at the current low level of PrEP coverage in Ethiopia. However, if coverage is expanded to full uptake and a lifetime time horizon is considered, PrEP could be a cost-effective intervention in Ethiopia. Pre-exposure prophylaxis cost-effectiveness Ethiopia Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Millions of new cases of HIV/AIDS are reported annually, making it a major global public health concern. Despite significant drops in the numbers of new HIV infections globally, the epidemic among females aged 15 to 24 in certain countries is still out of control. Adolescent girls and young women account for 67% of new infections among young people in sub-Saharan Africa. It is a global goal to increase HIV prevention for high-risk women in sub-Saharan Africa, with a special emphasis on the needs of young women and girls; however, sub-Saharan initiatives have little financial space for increasing their HIV prevention budget [ 1 – 3 ]. According to most estimates of new infections in Ethiopia, HIV continues to spread across the population. Over 60% of these new infections are occurring in Amhara, Oromia, SNNP, and Tigray regions [ 4 ]. Following the WHO guidance, the Ethiopian Ministry of Health (FMoH) and its partners have initiated a trial of oral PrEP as an additional HIV prevention strategy since 2019, based on results from a pilot study conducted in nine public health facilities and six drop-in-centers. The pilot assessed the service's acceptability among key national groups and its feasibility for nationwide rollout, indicating that scaling up is feasible. The national PrEP program targets HIV-negative female sex workers and partners of serodiscordant couples, administering a fixed-dose combination of Tenofovir and Lamivudine (TDF/3TC) for one month during the initial visit and for three months at subsequent visits [ 5 ]. National policy and decision makers need to determine where PrEP might fit best within already-established HIV prevention programs and budgets, and the possible ramifications of implementing such policy changes as PrEP becomes a viable option for inclusion in the HIV prevention toolbox [ 6 ]. To scale up and expand the implemented PrEP service nationwide, understanding its cost and cost-effectiveness is crucial for advocating for increased funding and expanding PrEP access to key populations [ 7 ]. However, the cost and cost-effectiveness of pre-exposure prophylaxis for HIV prevention in the key population in the Ethiopian context are unknown, and its implementation was a pilot study without support from implementation research. This lack of data makes it difficult for policymakers and stakeholders to make informed decisions regarding PrEP, including its expansion and resource allocation. Therefore, this study will fill this gap by determining the cost-effectiveness of pre-exposure prophylaxis for HIV/AIDS prevention for female sex workers in Ethiopia. Methods Study design A static Markov model was adapted to analyze HIV transmission and disease progression in a hypothetical cohort of female sex workers (N=1000) receiving PrEP treatment. To assess the program's cost-effectiveness, the outcome model's inputs were obtained from a literature review, while the cost of PrEP was mainly estimated from the Key Population (KP) center in Wolaita zone, southern Ethiopia. Model Structure To explain HIV infection and disease progression, we adapted a previously published state-transition Markov model with stable transition probabilities [8]. The hypothetical cohorts would be categorized into one of four distinct health states upon entering this model. These states were classified according to the infection and disease progression categories: susceptible and uninfected (S), HIV-positive without AIDS (HIV), HIV-positive with AIDS (AIDS), and Dead (D). Two separate Markov models were developed for two groups to differentiate the impact of the PrEP intervention from the non-intervention group (where women are not utilizing the PrEP intervention). Accordingly, the four health conditions were footnoted by Sp for the PrEP utilizing group and Sn for the non-PrEP group ( Fig. 1 ). Interventions According to the Ethiopian national guidelines, female sex workers began oral daily PrEP following a negative HIV antibody test and have a willingness to follow daily PrEP. As part of the PrEP protocol, female sex workers will take oral TDF/3TC every day for three months after being tested for STIs, pregnancy, and hepatitis B surface antigen, and will visit doctors every three months. During the visit, clients will be checked for sexually transmitted infections (STIs) and pregnancy. Also, condoms and counselling on risk reduction will be given out to the clients. In addition, a renal function test is conducted every six months for this group. However, if they become HIV-positive, they will be connected to an ART clinic and will start ART follow-up [5]. In this study, two strategies were compared: the status quo (strategy 1) of female sex workers and pre-exposure prophylaxis (strategy 2) added for female sex workers. Strategy 1 served as a baseline and represents the natural progression of HIV infection among female sex workers because they did not receive PrEP. The likelihood of acquiring HIV in this group was higher than in the intervention groups due to the absence of PrEP protection (Fig. 1). Strategy 2, considered the main preventive measure against HIV infection, this group of female sex workers benefited from the additional protection that PrEP intervention offered. This intervention can reduce the estimated HIV relative risk by 90% (95% CI: 67%–94%) [9] compared to those not receiving the intervention if they are fully adherent to PrEP (>85%). This study assumed that a hypothetical cohort of female sex workers (N=1000) receiving PrEP will remain on PrEP for the lifetime horizon and they are fully adherent to the PrEP. Model input parameters We used secondary data that was fed into models, which were sourced from various literature sources. These data are primarily based on studies conducted in Ethiopia; when Ethiopian data are not available, studies from similar contexts are used (Table 2). One exception is the cost of implementing PrEP, which was primarily estimated based on the Key Population (KP) center in Wolaita Zone, southern Ethiopia. The model's baseline or background mortality for each health status was derived from the WHO's Ethiopian life table (Table 1). Patients with AIDS and HIV-related states (who are susceptible to death) were included in the added disease-related mortality. The sum of disease-related mortality and baseline mortality for HIV/AIDS states represented the overall mortality. Table 1 : Age-specific background mortality for cohorts (from the WHO life table for Ethiopia) No. Age category Mortality risk 1 15-19 0.0039876 2 20-24 0.0050747 3 25-29 0.0065768 4 30-34 0.0089441 5 35-39 0.0127647 6 40-45 0.0180433 7 45-49 0.0248633 The model parameters used in a Markov model were summarized as follows. Table 2: Transition probabilities for the Markov model Transition probabilities Baseline value Sensitivity range References Sn to HIVn 0.053 0.037–0.076 [10] Sp to HIVp 0.0053 0.014-0.028 Calculated[10] Sn to Dn and Sp to Dp 0.0058 0.004-0.0076 [11] HIVn to AIDSn & HIVp to AIDSp 0.063 0.0421-0.0853 [12] HIVn to Dn and HIVp to Dp 0.012 0.08-0.014 [13] AIDSn to Dn and AIDSp to Dp 0.019 0.014-0.029 [14] PrEP efficacy 0.9 0.67- 0.94 [9] PrEP uptake 16% [15] Cost parameters (USD) PrEP cost $137.7 $103-172 Primary data ART treatment cost (HIVn, HIVp, AIDSn, and AIDSp) $235 $176-294 [16] Screening for STIs and health checkups cost for HIV+ persons $49.9 $37-62 [16] AIDS hospitalization cost $87.7 $66-110 [17] Effects parameters (annual QALYs) Sp (susceptible in PrEP) 0.97 0.9-1 Assumed Sn (susceptible without PrEP) 0.95 0.93-0.97 Assumed HIVn and HIVp 0.94 0.87–1 [18] AIDSn and AIDSp 0.45 0.35-0.55 [18] Discount rate Cost 0.03 0-0.05 [19] Effect 0.03 0-0.05 [19] * PrEP, pre-exposure prophylaxis. Transition probabilities are per year, and unit costs are also per year. *The transition probability (Sp to HIVp) is the product of the transition probability (Sn to HIVn) and 1- (PrEP efficacy) * Screening cost for infected individuals of HIV and AIDS for the two groups includes laboratory tests, including organ function tests and other sexually transmitted infections (STIs), except the test for HIV and health checkups (it was taken from a study conducted in Arbaminch hospital, southern Ethiopia). * The cost of AIDS hospitalization (per year) was the mean cost of hospitalization for AIDSn and AIDSp (it was taken from a cost study conducted in Adigrat Hospital). * Sensitivity ranges were used for the one-way sensitivity analysis. Cost of the PrEP program PrEP costs were estimated from the healthcare sector perspective at the KP center in Wolaita Zone, southern Ethiopia. Costs were classified into recurrent and capital components. Recurrent costs comprised drugs, cost of supplies including laboratory reagents, and personnel and administrative costs, which were calculated by multiplying the quantity of inputs or staff involved by their respective unit costs or salaries. For capital costs, such as building, equipment, and furniture, the space of the building or the number of inputs used was measured, along with their unit cost and useful life, as well as their discount rate and annuitization factor [20]. The base case year for the cost estimation was the Ethiopian Fiscal Year 2023/24, and 3% discount rate [19] Both costs and effects were used. Cost was initially calculated in Ethiopian birr and then converted to US dollars. For a different timing of cost adjustments to inflation, the consumer price index of 7% [20] and the official exchange rate, as per the National Bank of Ethiopia (1 USD = 57.3 ETB) [20], was used. Costs associated with HIV after entering the HIV and AIDS states (i.e., cost of ART, screening, and hospitalization costs in the AIDS state) were extracted from published literature in Ethiopia and adjusted to 2024 US$. Cost-effectiveness analysis Each health condition in the Markov model was assigned a distinct cost and a quality-adjusted life year (QALY) value. The total number of QALYs in each year was used to evaluate the effectiveness of PrEP. Then, the annual costs and QALYs for each of the cohort's 1,000 hypothetical individuals were added to determine the overall costs and QALYs. Finally, the ICERs were computed to evaluate the cost-effectiveness of PrEP for each strategy. To determine the cost-effectiveness of PrEP, we used Pichon-Riviere’s [21] cost-effectiveness thresholds for 174 countries, including Ethiopia, based on life expectancy at birth and health expenditures per capita. According to this study, Ethiopia’s cost-effective threshold for intervention is between $39 and $194 per QALY [22]. Sensitivity analysis To observe the impact on the PrEP’s cost-effectiveness, uncertainty related to inputs such as QALY estimations for the ICER calculation and time horizon was altered. A sensitivity analysis was used in this study to determine how different values of an independent variable (or input parameters) affect cost-effectiveness analysis under a particular set of assumptions. This analysis was measured and evaluated using both one-way (tornado analysis) and probabilistic sensitivity analysis (PSA). Input probability for tornado and PSA was varied based on input parameter tables reported and an assumed beta distribution for the probabilities, while costs were varied by ±25% of the mean value and assumed a gamma distribution. A tornado diagram was created by condensing the findings of the one-way sensitivity analysis into the parameters that had the most effect on cost-effectiveness. In addition, a Monte-Carlo simulation with 1000 iterations was conducted for probabilistic sensitivity analysis in order to determine the likelihood that the PrEP would become financially viable. Data analysis was performed using Microsoft Excel, with Visual Basic for Applications (VBA) macros used for sensitivity analysis. Results Cost of providing PrEP for female sex workers in Wolaita Sodo town A total of 41 female sex workers were initiated on oral daily PrEP after a negative HIV antibody test for HIV prevention, who fulfilled eligibility criteria in the Wolaita Sodo health center in the year 2023/24 EFY. This year, the estimated total cost of the PrEP program for female sex workers was around 5,646 USD, of which 45.4% went towards supplies, and 28.7% went towards the drug; the detail is summarized in Table 8. The annual unit cost of PrEP per FSW is estimated to be about 137.7 USD The estimated total cost of PrEP, and without PrEP The total cost with and without PrEP intervention was computed from the sum of costs for every status in each cycle (i.e., sum of 54 cycles (or lifetime horizon). The effectiveness of the interventions is determined using QALYs using the same methods. We separated the costs into the state of the hypothetical cohort for both interventions. The estimated cost was also divided into costs by status for both interventions, as shown in Table 3 below. For the susceptible health status in the group without PrEP, we assumed that the cost of health care would be $0. However, those cohorts who are in both groups and have HIV and AIDS status will incur costs for screening, health checkups, and ART services. In addition, those with AIDS have hospitalization costs. Table 3: Total cost by status for both with and without PrEP intervention Susceptible cost HIV AIDS Total cost Without PrEP $0 (0%) $1,422,838 (49 %) $1,496,689 (51%) $2,919,527 With PrEP $2,809,901 (85.5%) $243,071 (7.4%) $234,703 (7.1%) $3,287,675 In the group without PrEP, the estimated total cost for HIV and AIDS status was approximately 48.7% and 51.3%, respectively. However, the cost of the PrEP intervention group for the susceptible status accounted for about 85.5% of the total cost, while the cost for HIV was about 7.4%, and the cost for the AIDS status accounted for around 7.1%. The effectiveness of PrEP in terms of QALYs When the effectiveness of the PrEP strategy is measured in QALYs gained, as compared to the non-PrEP intervention, the total QALY gained with PrEP is higher than that of the other strategy. In the PrEP strategy group, the QALY gained by susceptible women is about 94% of the total (Table 4). Table 4: Estimated effects for both with and without PrEP intervention in different states Susceptible HIV AIDS Total QALYs Without PrEP 10,696 4,994 1,808 17,498 With PrEP 19,793 853 283 20,929 However, the QALYs gained for susceptible groups in the no-PrEP intervention were 61% of the total QALYs gained. The total QALYs gained by the no-PrEP strategy, by both HIV and AIDS status, will be higher than the PrEP strategy, because the number of patients moving to the other status will increase in these groups. HIV infections averted through the PrEP intervention The impact of HIV infections averted is estimated through the cumulative number of HIV infections averted in the cohort of women's lifetime horizon. This was done by comparing the number of HIV infections in the two strategies. The percentage of impact was defined as the number of HIV infections averted by implementing the PrEP strategy, divided by the total number of HIV infections that would have occurred without PrEP. The overall impact of PrEP was estimated at various coverage levels. With Ethiopia's current PrEP coverage (16% uptake) among FSWs, approximately 1,715 HIV infections (19.8%) were prevented through PrEP. If coverage were increased to full access (100% uptake), about 6,960 HIV infections (80%) among female sex workers would be prevented. Cost-effectiveness of pre-exposure prophylaxis for HIV prevention The PrEP intervention has an incremental cost of $1,202,379 and an incremental QALY gain of 1,231.50 QALYs compared to no-PrEP. Accordingly, the PrEP intervention has an ICER of $976.35 per QALY gained, which is not cost-effective based on Pichon-Riviere et al.'s [22] thresholds Table 5. Table 5: Cost-effectiveness analysis of PrEP for lifetime horizon Strategy Total Cost Total QALYs Incremental Cost ($) Incremental QALYs ICER($/QALYs) PrEP 4,121,906 18,729.21 - - - No PrEP 2,919,527 17,497.70 1,202,379 1,231.50 976.35 However, because PrEP coverage is so low, we performed various coverage scenario studies to determine at what level of coverage the PrEP program will become cost-effective. Cost-effectiveness analysis of the PrEP strategy at different coverage levels 25% of PrEP coverage: if PrEP is initiated in one-fourth of the cohorts, the PrEP intervention provides better value when the percentage of female sex workers who initiate PrEP increases. If 25% of the cohorts initiate PrEP, the total Costs and overall effects increase because more patients remain susceptible due to the prevention benefit. Table 6 below demonstrates that initiating PrEP in 25% of FSW has an incremental cost-effectiveness ratio of $448 per QALY gained compared to the situation with no PrEP strategy. Table 6: Cost-effectiveness analysis of lifetime PrEP program with 25% of PrEP coverage Different coverage of the PrEP strategy Strategy Total Cost Total QALYs ICER ($/QALYs) 25% No PrEP 2,919,527 17,498 448.05 PrEP 3,829,707 19,529 50% No PrEP 2,919,527 17,498 196.5 PrEP 3,492,292 20,412 100% No PrEP 2,919,527 17,498 107.25 PrEP 3,287,675 20,930 50% PrEP coverage: If PrEP is initiated on half of the cohorts, the coverage of PrEP among the female sex workers will be increased to 50%, which will result in more QALYs to be gained, along with an increase in cost. With this coverage, the cost-effectiveness would be cost $196.5 per QALY gained. 100% of PrEP coverage: In this case, when the whole female sex workers initiated PrEP intervention and when the lifetime horizon is considered, it would cost $107.25 per QALY gained. The PrEP strategy would be cost-effective based on Pichon-Riviere’s threshold [21]. However, if we consider only the next 20 years, and all female sex workers initiated on the PrEP strategy, its cost would be $694/QALYs, which would not be cost-effective. Thus, initiating PrEP in the younger age group made the PrEP strategy more cost-effective, and coverage of this program determines its cost-effectiveness. Also, implementing this program for a short period of time might not be that important because, in short-term implementation, study results showed that it will not be a cost-effective intervention. The cost-effectiveness results of all age groups with a lifetime horizon when PrEP was initiated on whole cohorts are summarized below in Table 7. Table 7: Cost-effectiveness analysis for female sex workers on PrEP compared to no intervention for different age groups in a lifetime horizon if PrEP is initiated in whole cohorts (i.e.100% PrEP) Age group Total cost (USD) Total QALYs Incremental Cost (US$) Incremental QALYs ICER value ($/QALYs) 15-19 3,287,675 20,930.42 368,148 3,432.72 107.25 20-24 3,080,538 19,732.69 414,106 2,999.25 138.07 25-29 2,844,594 18,351.30 463,663 2,537.50 182.72 30-34 2,588,679 16,828.80 512,113 2,081.87 245.99 35-39 2,322,358 15,217.97 555,073 1,657.25 334.94 40-44 2,053,412 13,565.48 589,387 1,277.30 461.43 45-49 1,784,357 11,885.75 611,292 947.83 644.94 Sensitivity analysis One-way sensitivity analysis (Tornado diagram) To perform a one-way sensitivity analysis, we used a tornado diagram at 100% coverage of PrEP, where PrEP is a cost-effective program. This diagram illustrates the effects of one parameter change on the ICER. By altering each input parameter, we examined the model outputs to identify the most significant parameters. The significant parameters identified were the cost of PrEP, the transition probability from susceptible to HIV, the cost of ART, and PrEP efficacy. The ICER is considered as an expected value (EV) in this diagram, and displayed on the vertical axis, which is 107.25 $/QALYs as shown in the Fig. 2 below. Each bar represents the range of expected values generated by varying the corresponding variable. For instance, when the unit cost of PrEP decreased from $172 to $103, PrEP would be more cost-effective and changed ICER from 311 $/QALYs to -99 $/QALYs, which makes the strategy cost-saving. When PrEP efficacy was less than 67%, PrEP would be less effective, and when it exceeded 94%, it became more cost-effective. Probabilistic sensitivity analysis (PSA) Probabilistic sensitivity analysis was conducted using a Monte Carlo approach, based on 1,000 randomly generated simulations of input parameter values. The result of a probabilistic sensitivity analysis (PSA) conducted using Monte Carlo simulation with 1000 iterations was presented in the cost-effectiveness acceptability curve (CEAC), indicating the probability of cost-effectiveness at different levels of willingness-to-pay thresholds (Fig. 3). The results showed that the probability of being cost-effective when the PrEP strategy was compared to the no intervention was only about 45% at a willingness to pay threshold of 194$ per QALY. Also, the probability of being cost-effective at the mean value of Pichon-Riviere et al. (i.e.124 $/QALYs) was only about 26%. However, when compared to 0.5 times the GDP per capita of Ethiopia (564.5$/QALYs), the probability of being cost-effective would be increased to 97%. Moreover, this strategy was 100 % cost-effective when compared to one time the GDP per capita of Ethiopia (i.e., 1129 USD). The Fig. 4 below shows the ICER scatter plot and how the changes in the effects (QALYs) are sensitive to changes in the cost. It represents the uncertainty surrounding the cost-effectiveness of an intervention (PrEP) compared to a no-PrEP intervention. Each dot represents one simulation run, and the results showed that the effects were not sensitive to the cost, and the level of uncertainty around the cost-effectiveness of an intervention (PrEP) is low. Discussion This study evaluated the cost-effectiveness of PrEP intervention in FSW in Ethiopia using a hypothetical cohort of 1000 in a Markov Model. This study estimated that providing PrEP services costs an average of $137.70 per FSW per year. According to Pichon-Riviere’s [21] Threshold, PrEP intervention is not cost-effective given Ethiopia’s current coverage level. However, this intervention might be a cost-effective program if its coverage is increased to roughly 100%. The finding of the cost of providing PrEP in this study is comparable with the study that modelled the impact and cost-effectiveness of oral pre-exposure prophylaxis in 13 low-resource countries, including Ethiopia. This study reveals that Ethiopia's unit cost of delivering PrEP was $106, which is somewhat less than our finding. This difference might result from the study’s design, which combined the Incidence Patterns Model and the Goals model for its cost estimation [23]. Additionally, the 2017 cost estimates were derived from the Global Price Reporting Mechanism and based on Kenya’s gross national income per capita. Accordingly, in this study, the annual cost of PrEP per person in countries such as Haiti, Malawi, Zimbabwe, Mozambique, and Uganda was between $117 and $133 [23]. This finding is relatively higher, which could be explained by the previously mentioned factors, as well as regional variations and disparities in socioeconomic status. This study demonstrated that the use of PrEP for HIV prevention in FSW could have an important impact on the HIV epidemics in Ethiopia, and distributing PrEP for all cohorts seems to be cost-effective in Ethiopia. Currently, Ethiopia does not yet have a standard or officially accepted cost per QALY willingness-to-pay threshold for the cost-effectiveness analysis. To determine whether an intervention is cost-effective in a given jurisdiction, Pichon-Riviere et al [21] Calculated cost-effectiveness thresholds for 174 nations, including Ethiopia, based on growth in life expectancy and health expenditures. To compare the ICER with this criterion, we also used the WTP threshold of 0.5 and one times Ethiopia's GDP per capita, which is approximately 1,129 US dollars. Despite the good value provided by PrEP use in high-risk groups, particularly in view of other competing priorities such as providing ART to infected individuals, and new prevention technologies is very important [24], scaling up PrEP for this group (female sex workers) with current coverage in our context might not be effective, but with increased coverage (when 100% of FSW receive PrEP), it will be very cost-effective. The results suggested that providing oral PrEP to younger female sex workers would increase uptake of PrEP for this group and improve its cost-effectiveness. Also, any reduction in PrEP cost would improve its cost-effectiveness in Ethiopia, which is similar to the literature in other countries [8,24,25]. Other similar studies conducted in South Africa on female sex workers and adolescent girls showed that multi-purpose HIV and pregnancy prevention technologies were cost-effective for female sex workers and women aged 16–24, but not for women aged 25–49 [26]. According to their study, as the age of the cohort increases, the likelihood of pre-exposure prophylaxis being cost-effective decreases, which aligns with the findings of this study. Initiating PrEP in a larger proportion of FSW increased total costs and increased total effects; however, it decreased average cost because when a larger proportion of FSW start on PrEP intervention, more clients remain susceptible without acquiring HIV, and if the proportion of FSW who received PrEP decreases, most of the cohorts infected and enter into HIV and AIDS states. However, Juusola J [27] found that initiating PrEP in a larger proportion of MSM averts more infections, but at increasing cost per QALY gained ($216,480/QALY gained when 100% of MSM receive PrEP), and using PrEP only in high-risk MSM can improve its cost-effectiveness. Also, they found that decreasing the PrEP cost can improve its cost-effectiveness [24], which is consistent with our study. Overall, this study's findings suggested that PrEP could be a cost-effective way to reduce new HIV infections in our settings. However, the cost-effectiveness of PrEP is highly dependent on PrEP coverage, cost (including annual PrEP and ART costs), the epidemiological context, and PrEP efficacy. A price reduction could decrease the cost-effectiveness ratio and improve the cost-effectiveness of the strategy. Given the ICERs’ sensitivity to several variables, PrEP might be more cost-effective for the younger FSM population with high coverage and other subgroups. Also, our study suggests that implementing this program for a long period of time with increased coverage for the high-risk group would increase its cost-effectiveness. When policymakers want to expand the program in our context, they should consider that the coverage of PrEP in female sex workers determines its cost-effectiveness. This study has its own strengths and limitations. First strength was that our study was the first study to assess the cost-effectiveness of PrEP for HIV prevention in Ethiopia. Second, this study performed both one-way and probabilistic sensitivity analysis to check the robustness of the findings (or uncertainty), including input parameter changes that may happen in the future. Finally, this study also conducted scenario analysis by increasing PrEP coverage. However, this study has its limitations, as we used a static Markov model instead of a dynamic transmission model, where dynamic models are widely used to describe epidemiologic dynamics in populations where the infection probability changes over time, depending on the number of infected people, and the infection probability was assumed to be static in the Markov model used in this study [8]. Our study did not assume that PrEP caused a change in risk behavior and also assumed the adherence to PrEP was 100%, but this was not the most realistic way to model, and the threshold we used was not officially recognized by the government of Ethiopia, where there is no clearly defined threshold for comparison that may affect our conclusion. Conclusion This study reveals that, based on Pichon-Riviere et al.'s cost-effectiveness threshold, the current level of PrEP coverage for HIV prevention among FSW in Ethiopia is not a cost-effective intervention. However, if the coverage is increased to reach nearly all FSW, and a lifetime horizon is considered, then it could be a cost-effective program. In addition, targeting younger FSWs, identifying strategies to reduce PrEP costs, and increasing coverage among high-risk groups would provide substantial health benefits by minimizing HIV infections, thereby making PrEP a highly cost-effective intervention. Furthermore, variables such as the cost of PrEP, the cost of ART, and PrEP efficacy are key drivers of ICER. Therefore, the implementation and scale-up of this program require a careful evaluation of its cost-effectiveness. Abbreviations AIDSn FSW with AIDS in the PrEP group AIDSn FSW with AIDS in the without PrEP group ART Antiretroviral therapy CDC Center for Disease Control and Prevention CEAs Cost-effectiveness analyses Dn Death of FSW in the PrEP group Dp Death of FSW in the without PrEP group DIC Drop-in-center ETB Ethiopian birr FSW Female sex workers HIVn Human immunodeficiency virus HIVp Human immunodeficiency virus KP Key populations MSM Male who has sex with males PEPFAR President’s Emergency Plan for AIDS Relief PrEP Pre-exposure prophylaxis Sn Susceptible FSW in the PrEP group Sp Susceptible FSW in the without PrEP group TDF/3TC Tenofovir disoproxil fumarate/emtricitabine Declarations Ethics approval and consent to participate The study neither involves direct human subjects research nor patient-level data. Therefore, following certain clinical research guidelines, like the Declaration of Helsinki, was not applicable . However, the study was conducted in compliance with general ethical principles for health research, including confidentiality, voluntary participation, and informed consent of healthcare providers. Funding DFF was financially supported by Jimma University, the Institute of Health, School of Postgraduate Studies to conduct a primary cost data collection in the study area. Data availability No datasets were generated or analyzed during the current study. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Author contribution All authors made a significant contribution to the work reported, DFF AGN ISG study conception and design, conducted the literature review, data requests, analysis, and prepared the Manuscript, YET, critically reviewed the article, oversaw the interpretation, and improved its report writing. All authors have read and approved the final version of the manuscript. Acknowledgements Wolaita Sodo health center and its staff provided key primary data regarding the delivery of PrEP service. Additionally, we would like to thank Jimma University, the Institute of Health, School of Post Graduate Studies, for providing financial support to carry out this research. References UNAIDS. Miles to go closing gaps, breaking barriers, righting injustices. Global AIDS update. 2018. U.S. President’s Emergency Plan for AIDS Relief. Dreaming of an AIDS-free future girls from the dreams-supported sauti project in Tanzania. 2018. UNAIDS. Turning point for Africa — An historic opportunity to end AIDS as a public health threat by 2030 and launch a new era of sustainability. 2030. FHAPCO. HIV/AIDS National Strategic Plan for Ethiopia 2021-2025. 2021;4:26-32;176. Ministy of Health. National Comprehensive HIV Prevention, Care and Treatment Refresher Training for Health care Providers. Ethiopia; 2022. Allen R et al. The role of costing in the introduction and scale-up of HIV pre-exposure prophylaxis: evidence from integrating PrEP into routine maternal and child health and family planning clinics in western Kenya. 2019; https://doi.org/10.1002/jia2.25296/full Yamamoto N et al. Evaluating the cost-effectiveness of a pre-exposure prophylaxis program for HIV prevention for men who have sex with men in Japan. Scientific Reports. Nature Research; 2022;12. https://doi.org/10.1038/s41598-022-07116-4 C. Pretorius et al. Modelling impact and cost-effectiveness of oral pre-exposure prophylaxis in 13 low-resource countries. 2020; https://doi.org/10.1002/jia2.25451/full Jones et al. HIV incidence among women engaging in sex work in sub-Saharan Africa: a systematic review and meta-analysis. medRxiv : the preprint server for health sciences. 2023; https://doi.org/10.1101/2023.10.17.23297108 Yared Belete Belay,Eskinder Eshetu Ali, Karen Y. Chung, Gebremedhin Beedemariam Gebretekle BS. Cost-Utility Analysis of Dolutegravir- Versus Efavirenz-Based Regimens as a First-Line Treatment in Adult HIV/AIDS Patients in Ethiopia. PharmacoEconomics - Open. Springer International Publishing; 2021;5:655–64. https://doi.org/10.1007/s41669-021-00275-6 Yimam Getaneh, Fentabil Getnet, Feng Ning, Abdur Rashid, Lingjie Liao FY and YS. HIV-1 Disease Progression and Drug Resistance Mutations among Children on First-Line Antiretroviral Therapy in Ethiopia. Biomedicines. 2023;11:1–14. https://doi.org/10.3390/biomedicines11082293 WHO. People living with HIV People acquiring HIV People dying from HIV-related causes. Who. 2023;1–8. EPHI. HIV Related Estimates and Projections in Ethiopia for the year 2021-2022. Ethiopia; 2022. Shibesh BF, Admas AB, Lake AW, Getu SB, Worede DT. Uptake of retroviral pre-exposure prophylaxis and its associated factors among female sex workers, Northwest Ethiopia. AIDS Research and Therapy. 2023;20:1–5. https://doi.org/10.1186/s12981-023-00573-5 Asfaw Demissie Bikilla, Degu Jerene, Bjarne Robberstad and BL. Cost estimates of HIV care and treatment with and without anti-retroviral therapy at Arba Minch hospital in southern Ethiopia. Cost Effectiveness and Resource Allocation. 2009;7:1–7. https://doi.org/10.1186/1478-7547-7-6 Zemenfeskidus Hadgu. The costs of HIV / AIDS care and treatment in Adigrat General Hospital , eastern zone of Tigray National Regional State , North Ethiopia The costs of HIV / AIDS care and treatment in Adigrat General Hospital , eastern zone of Tigray National Regional Stat. 2011; Mengistu et al. Health related quality of life and its association with social support among people living with HIV/AIDS receiving antiretroviral therapy in Ethiopia: a systematic review and meta-analysis. Health and Quality of Life Outcomes. BioMed Central; 2022;20:1–8. https://doi.org/10.1186/s12955-022-01985-z M.F. Drummond et.al. Methods for the Economic Evaluation of Health Care Programmes. 2015;4th Edition. UNAIDS. Costing Guidelines for HIV Prevention Strategies. Health policy and planning. 2001; 12:326–31. Pichon-Riviere A, Drummond M, Palacios A, Garcia-Marti S, Augustovski F. Determining the efficiency path to universal health coverage: cost-effectiveness thresholds for 174 countries based on growth in life expectancy and health expenditures. The Lancet Global Health. 2023;11:e833–42. https://doi.org/10.1016/S2214-109X(23)00162-6 Jessie L. Juusola, M.S.1, Margaret L. Brandeau, Ph.D.1, Douglas K. Owens, M.D., M.S.2,3, and Eran Bendavid, M.D. MS 3. The Cost-Effectiveness of Preexposure Prophylaxis for HIV Prevention in Men Who Have Sex with Men in the United States. Ann Intern Med. 2012; https://doi.org/10.1059/0003-4819-156-8-201204170-00001 Heun Choi1 et al. Cost-effectiveness analysis of pre-exposure prophylaxis for the prevention of HIV in men who have sex with men in South Korea: a mathematical modelling study. Scientific Reports. Nature Research; 2020;10. https://doi.org/10.1038/s41598-020-71565-y Quaife M, Terris-Prestholt F, Eakle R, Escobar MAC, Kilbourne-Brook M, Mvundura M, et al. The cost-effectiveness of multi-purpose HIV and pregnancy prevention technologies in South Africa. Journal of the International AIDS Society. 2018;21. https://doi.org/10.1002/jia2.25064 Additional Declarations No competing interests reported. Supplementary Files Annex.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 07 Apr, 2026 Reviews received at journal 26 Mar, 2026 Reviewers agreed at journal 19 Mar, 2026 Reviews received at journal 17 Mar, 2026 Reviewers agreed at journal 17 Mar, 2026 Reviewers agreed at journal 17 Mar, 2026 Reviewers agreed at journal 17 Mar, 2026 Reviewers invited by journal 17 Mar, 2026 Editor assigned by journal 27 Jan, 2026 Submission checks completed at journal 27 Jan, 2026 First submitted to journal 23 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8681752","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":609137514,"identity":"ad9e7ed3-5e0e-43f4-9101-cb201035c0d7","order_by":0,"name":"Idiris Genemo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYHACAzBibG8AsS1I0dJzAMSWIFYLCEgkgEnC6vmlmzc+/FFwT5555vOrG34USDDwt3cn4NUiOedYsTGPQbFh4+ycsps9QIdJnDm7Ab+rbuSYSTMYJDACtaTd4AFqMZDIxa/FHqhF8odBgn3jzDNpN/8Qo8VAIsdMgscgIbFxBvux20TZInEjDeSXhOTGnhy22zIGEjwE/cI/IxkYYn8SbDe2H392880fGzn+9l78WuDAsIEHHEE8xCkHAXkG9gfEqx4Fo2AUjIIRBQCRakYaiZXZdwAAAABJRU5ErkJggg==","orcid":"","institution":"Jimma University","correspondingAuthor":true,"prefix":"","firstName":"Idiris","middleName":"","lastName":"Genemo","suffix":""},{"id":609137515,"identity":"8ae4dbbf-6ecc-4cd0-bb9d-d3fddc6f50b3","order_by":1,"name":"Desalegn Feleke","email":"","orcid":"","institution":"Wolaita Sodo University","correspondingAuthor":false,"prefix":"","firstName":"Desalegn","middleName":"","lastName":"Feleke","suffix":""},{"id":609137516,"identity":"3d482c8c-6ee8-46a9-b27a-ea7b91bfb705","order_by":2,"name":"Abdi Gari","email":"","orcid":"","institution":"Haramaya University","correspondingAuthor":false,"prefix":"","firstName":"Abdi","middleName":"","lastName":"Gari","suffix":""},{"id":609137517,"identity":"58f8ae88-546d-41d3-aa7b-e2ea53f1d6d2","order_by":3,"name":"Yohannes Ejigu","email":"","orcid":"","institution":"Jimma University","correspondingAuthor":false,"prefix":"","firstName":"Yohannes","middleName":"","lastName":"Ejigu","suffix":""}],"badges":[],"createdAt":"2026-01-23 18:08:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8681752/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8681752/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105050776,"identity":"05292b11-84ee-4eb7-a961-82d09f8ae6be","added_by":"auto","created_at":"2026-03-20 10:11:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":137345,"visible":true,"origin":"","legend":"\u003cp\u003eA schematic representation of the Markov Model for two separate groups without PrEP (A) and with PrEP groups (B), for the meaning of each abbreviation see the Abbreviations section\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8681752/v1/c7b125b97c60d1314c77caf4.png"},{"id":105050784,"identity":"5f1d7257-55d9-4ff9-8abb-74028e2e43e4","added_by":"auto","created_at":"2026-03-20 10:11:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":113777,"visible":true,"origin":"","legend":"\u003cp\u003eTornado diagram for one-way sensitivity analysis of cost-effectiveness of PrEP\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8681752/v1/28ac2cb2140dbd56320aba53.png"},{"id":105050780,"identity":"307013fb-f227-4a7a-9d8d-f4c75702f634","added_by":"auto","created_at":"2026-03-20 10:11:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":53885,"visible":true,"origin":"","legend":"\u003cp\u003eA cost-effectiveness acceptability curve for the PrEP versus No PrEP\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8681752/v1/c768beed7c08ec12f9b9e38f.png"},{"id":105050778,"identity":"48bbaf67-8888-4e13-af9a-1d12ed36ade1","added_by":"auto","created_at":"2026-03-20 10:11:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":143190,"visible":true,"origin":"","legend":"\u003cp\u003eICER scatter plots of 1000 iterations for the PrEP versus No PrEP\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8681752/v1/700663c33660a8c969fe806a.png"},{"id":105904071,"identity":"eaa68c7f-a5a5-44c2-8c05-0c0c776a83cb","added_by":"auto","created_at":"2026-04-01 10:03:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1353215,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8681752/v1/da615a3a-4cb8-4881-8dfc-78e65442480a.pdf"},{"id":105050781,"identity":"f03fdc03-1e75-44a4-a7f1-110e873d765e","added_by":"auto","created_at":"2026-03-20 10:11:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16749,"visible":true,"origin":"","legend":"","description":"","filename":"Annex.docx","url":"https://assets-eu.researchsquare.com/files/rs-8681752/v1/dd5d0d9b7a6efc79c2bb57b8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cost-effectiveness of pre-exposure prophylaxis for HIV prevention among female sex workers in Ethiopia: a Markov model analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMillions of new cases of HIV/AIDS are reported annually, making it a major global public health concern. Despite significant drops in the numbers of new HIV infections globally, the epidemic among females aged 15 to 24 in certain countries is still out of control. Adolescent girls and young women account for 67% of new infections among young people in sub-Saharan Africa. It is a global goal to increase HIV prevention for high-risk women in sub-Saharan Africa, with a special emphasis on the needs of young women and girls; however, sub-Saharan initiatives have little financial space for increasing their HIV prevention budget [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to most estimates of new infections in Ethiopia, HIV continues to spread across the population. Over 60% of these new infections are occurring in Amhara, Oromia, SNNP, and Tigray regions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Following the WHO guidance, the Ethiopian Ministry of Health (FMoH) and its partners have initiated a trial of oral PrEP as an additional HIV prevention strategy since 2019, based on results from a pilot study conducted in nine public health facilities and six drop-in-centers. The pilot assessed the service's acceptability among key national groups and its feasibility for nationwide rollout, indicating that scaling up is feasible. The national PrEP program targets HIV-negative female sex workers and partners of serodiscordant couples, administering a fixed-dose combination of Tenofovir and Lamivudine (TDF/3TC) for one month during the initial visit and for three months at subsequent visits [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNational policy and decision makers need to determine where PrEP might fit best within already-established HIV prevention programs and budgets, and the possible ramifications of implementing such policy changes as PrEP becomes a viable option for inclusion in the HIV prevention toolbox [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo scale up and expand the implemented PrEP service nationwide, understanding its cost and cost-effectiveness is crucial for advocating for increased funding and expanding PrEP access to key populations [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, the cost and cost-effectiveness of pre-exposure prophylaxis for HIV prevention in the key population in the Ethiopian context are unknown, and its implementation was a pilot study without support from implementation research. This lack of data makes it difficult for policymakers and stakeholders to make informed decisions regarding PrEP, including its expansion and resource allocation. Therefore, this study will fill this gap by determining the cost-effectiveness of pre-exposure prophylaxis for HIV/AIDS prevention for female sex workers in Ethiopia.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA static Markov model was adapted to analyze HIV transmission and disease progression in a hypothetical cohort of female sex workers (N=1000) receiving PrEP treatment. To assess the program\u0026apos;s cost-effectiveness, the outcome model\u0026apos;s inputs were obtained from a literature review, while the cost of PrEP was mainly estimated from the Key Population (KP) center in Wolaita zone, southern Ethiopia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel Structure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explain HIV infection and disease progression, we adapted a previously published state-transition Markov model with stable transition probabilities [8]. The\u0026nbsp;hypothetical\u0026nbsp;cohorts would be categorized into one of four distinct health states upon entering this model. These states were classified according to the infection and disease progression categories: susceptible and uninfected (S), HIV-positive without AIDS (HIV), HIV-positive with AIDS (AIDS), and Dead (D).\u003c/p\u003e\n\u003cp\u003eTwo separate Markov models were developed for two groups to differentiate the impact of the PrEP intervention from the non-intervention group (where women are not utilizing the PrEP intervention). Accordingly, the four health conditions were footnoted by Sp for the PrEP utilizing group and Sn for the non-PrEP group (\u003cem\u003eFig. 1\u003c/em\u003e).\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eInterventions\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eAccording to the Ethiopian national guidelines, female sex workers began oral daily PrEP following a negative HIV antibody test and have a willingness to follow daily PrEP. As part of the PrEP protocol, female sex workers will take oral TDF/3TC every day for three months after being tested for STIs, pregnancy, and hepatitis B surface antigen, and will visit doctors every three months. During the visit, clients will be checked for sexually transmitted infections (STIs) and pregnancy. Also, condoms and counselling on risk reduction will be given out to the clients. In addition, a renal function test is conducted every six months for this group. However, if they become HIV-positive, they will be connected to an ART clinic and will start ART follow-up [5].\u003c/p\u003e\n\u003cp\u003eIn this study, two strategies were compared: the status quo (strategy 1) of female sex workers and pre-exposure prophylaxis (strategy 2) added for female sex workers. Strategy 1 served as a baseline and represents the natural progression of HIV infection among female sex workers because they did not receive PrEP. The likelihood of acquiring HIV in this group was higher than in the intervention groups due to the absence of PrEP protection (Fig. 1).\u003c/p\u003e\n\u003cp\u003eStrategy 2,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003econsidered the main preventive measure against HIV infection, this group of female sex workers benefited from the additional protection that PrEP intervention offered. This intervention can reduce the estimated HIV relative risk by 90% (95% CI: 67%\u0026ndash;94%) [9] compared to those not receiving the intervention if they are fully adherent to PrEP (\u0026gt;85%). This study assumed that a hypothetical cohort of female sex workers (N=1000) receiving PrEP will remain on PrEP for the lifetime horizon and they are fully adherent to the PrEP.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel input parameters \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used secondary data that was fed into models, which were sourced from various literature sources. These data are primarily based on studies conducted in Ethiopia; when Ethiopian data are not available, studies from similar contexts are used (Table 2). One exception is the cost of implementing PrEP, which was primarily estimated based on the Key Population (KP) center in Wolaita Zone, southern Ethiopia.\u003c/p\u003e\n\u003cp\u003eThe model\u0026apos;s baseline or background mortality for each health status was derived from the WHO\u0026apos;s Ethiopian life table (Table 1). Patients with AIDS and HIV-related states (who are susceptible to death) were included in the added disease-related mortality. The sum of disease-related mortality and baseline mortality for HIV/AIDS states represented the overall mortality.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e: Age-specific background mortality for cohorts (from the WHO life table for Ethiopia)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cem\u003eNo.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e\u003cem\u003eAge category\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cem\u003eMortality risk\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e15-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.0039876\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e20-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.0050747\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e25-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.0065768\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e30-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.0089441\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e35-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.0127647\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cem\u003e6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e40-45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.0180433\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cem\u003e7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e45-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.0248633\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe model parameters used in a Markov model were summarized as follows.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2:\u003c/strong\u003e Transition probabilities for the Markov model\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"671\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4967%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTransition probabilities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6511%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.745%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity range\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.438%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReferences\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4967%;\"\u003e\n \u003cp\u003eSn to HIVn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6511%;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.745%;\"\u003e\n \u003cp\u003e0.037\u0026ndash;0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.438%;\"\u003e\n \u003cp class=\"MsoNormal\" align=\"left\"\u003e\u003cspan lang=\"EN-US\"\u003e[10]\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4967%;\"\u003e\n \u003cp\u003eSp to HIVp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6511%;\"\u003e\n \u003cp\u003e0.0053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.745%;\"\u003e\n \u003cp\u003e0.014-0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.438%;\"\u003e\n \u003cp\u003eCalculated[10]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4967%;\"\u003e\n \u003cp\u003eSn to Dn and Sp to Dp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6511%;\"\u003e\n \u003cp\u003e0.0058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.745%;\"\u003e\n \u003cp\u003e0.004-0.0076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.438%;\"\u003e\n \u003cp class=\"MsoNormal\" align=\"left\"\u003e\u003cspan lang=\"EN-US\"\u003e[11]\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4967%;\"\u003e\n \u003cp\u003eHIVn to AIDSn \u0026amp; HIVp to AIDSp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6511%;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.745%;\"\u003e\n \u003cp\u003e0.0421-0.0853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.438%;\"\u003e\n \u003cp class=\"MsoNormal\" align=\"left\"\u003e\u003cspan lang=\"EN-US\"\u003e[12]\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4967%;\"\u003e\n \u003cp\u003eHIVn to Dn and HIVp to Dp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6511%;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.745%;\"\u003e\n \u003cp\u003e0.08-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.438%;\"\u003e\n \u003cp class=\"MsoNormal\" align=\"left\"\u003e\u003cspan lang=\"EN-US\"\u003e[13]\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4967%;\"\u003e\n \u003cp\u003eAIDSn to Dn and AIDSp to Dp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6511%;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.745%;\"\u003e\n \u003cp\u003e0.014-0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.438%;\"\u003e\n \u003cp class=\"MsoNormal\" align=\"left\"\u003e\u003cspan lang=\"EN-US\"\u003e[14]\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4967%;\"\u003e\n \u003cp\u003ePrEP efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6511%;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.745%;\"\u003e\n \u003cp\u003e0.67- 0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.438%;\"\u003e\n \u003cp class=\"MsoNormal\" align=\"left\"\u003e\u003cspan lang=\"EN-US\"\u003e[9]\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4967%;\"\u003e\n \u003cp\u003ePrEP uptake\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6511%;\"\u003e\n \u003cp\u003e16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.745%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.438%;\"\u003e\n \u003cp class=\"MsoNormal\" align=\"left\"\u003e\u003cspan lang=\"EN-US\"\u003e[15]\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4967%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCost parameters (USD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6511%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.745%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6387%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4967%;\"\u003e\n \u003cp\u003ePrEP cost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6511%;\"\u003e\n \u003cp\u003e$137.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.745%;\"\u003e\n \u003cp\u003e$103-172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6387%;\"\u003e\n \u003cp\u003ePrimary data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4967%;\"\u003e\n \u003cp\u003eART treatment cost (HIVn, HIVp, AIDSn, and AIDSp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6511%;\"\u003e\n \u003cp\u003e$235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.745%;\"\u003e\n \u003cp\u003e$176-294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6387%;\"\u003e\n \u003cp class=\"MsoNormal\" align=\"left\"\u003e\u003cspan lang=\"EN-US\"\u003e[16]\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4967%;\"\u003e\n \u003cp\u003eScreening for STIs and health checkups cost for HIV+ persons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6511%;\"\u003e\n \u003cp\u003e$49.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.745%;\"\u003e\n \u003cp\u003e$37-62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6387%;\"\u003e\n \u003cp class=\"MsoNormal\" align=\"left\"\u003e\u003cspan lang=\"EN-US\"\u003e[16]\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4967%;\"\u003e\n \u003cp\u003eAIDS hospitalization cost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6511%;\"\u003e\n \u003cp\u003e$87.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.745%;\"\u003e\n \u003cp\u003e$66-110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6387%;\"\u003e\n \u003cp class=\"MsoNormal\" align=\"left\"\u003e\u003cspan lang=\"EN-US\"\u003e[17]\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4967%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEffects parameters (annual QALYs)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 3.3388%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4967%;\"\u003e\n \u003cp\u003eSp (susceptible in PrEP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6511%;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.745%;\"\u003e\n \u003cp\u003e0.9-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6387%;\"\u003e\n \u003cp\u003eAssumed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4967%;\"\u003e\n \u003cp\u003eSn (susceptible without PrEP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6511%;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.745%;\"\u003e\n \u003cp\u003e0.93-0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6387%;\"\u003e\n \u003cp\u003eAssumed\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4967%;\"\u003e\n \u003cp\u003eHIVn and HIVp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6511%;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.745%;\"\u003e\n \u003cp\u003e0.87\u0026ndash;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6387%;\"\u003e\n \u003cp class=\"MsoNormal\" align=\"left\"\u003e\u003cspan lang=\"EN-US\"\u003e[18]\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4967%;\"\u003e\n \u003cp\u003eAIDSn and AIDSp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6511%;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.745%;\"\u003e\n \u003cp\u003e0.35-0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6387%;\"\u003e\n \u003cp class=\"MsoNormal\" align=\"left\"\u003e\u003cspan lang=\"EN-US\"\u003e[18]\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4967%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiscount rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6511%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.745%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6387%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4967%;\"\u003e\n \u003cp\u003eCost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6511%;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.7993%;\"\u003e\n \u003cp\u003e0-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8255%;\"\u003e\n \u003cp class=\"MsoNormal\" align=\"left\"\u003e\u003cspan lang=\"EN-US\"\u003e[19]\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4967%;\"\u003e\n \u003cp\u003eEffect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6511%;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.7993%;\"\u003e\n \u003cp\u003e0-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8255%;\"\u003e\n \u003cp class=\"MsoNormal\" align=\"left\"\u003e\u003cspan lang=\"EN-US\"\u003e[19]\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* PrEP, pre-exposure prophylaxis. Transition probabilities are per year, and unit costs are also per year.\u003c/p\u003e\n\u003cp\u003e*The transition probability (Sp to HIVp) is the product of the transition probability (Sn to HIVn) and 1- (PrEP efficacy)\u003c/p\u003e\n\u003cp\u003e* Screening cost for infected individuals of HIV and AIDS for the two groups includes laboratory tests, including organ function tests and other sexually transmitted infections (STIs), except the test for HIV and health checkups (it was taken from a study conducted in Arbaminch hospital, southern Ethiopia).\u003c/p\u003e\n\u003cp\u003e* The cost of AIDS hospitalization (per year) was the mean cost of hospitalization for AIDSn and AIDSp (it was taken from a cost study conducted in Adigrat Hospital).\u003c/p\u003e\n\u003cp\u003e* Sensitivity ranges were used for the one-way sensitivity analysis.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCost of the PrEP program\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrEP costs were estimated from the healthcare sector perspective at the KP center in Wolaita Zone, southern Ethiopia. Costs were classified into recurrent and capital components. Recurrent costs comprised drugs, cost of supplies including laboratory reagents, and personnel and administrative costs, which were calculated by multiplying the quantity of inputs or staff involved by their respective unit costs or salaries.\u003c/p\u003e\n\u003cp\u003eFor capital costs, such as building, equipment, and furniture, the space of the building or the number of inputs used was measured, along with their unit cost and useful life, as well as their discount rate and annuitization factor\u0026nbsp;[20].\u003c/p\u003e\n\u003cp\u003eThe base case year for the cost estimation was the Ethiopian Fiscal Year\u0026nbsp;2023/24,\u0026nbsp;and\u0026nbsp;3% discount rate [19] Both costs and effects were used.\u0026nbsp;Cost\u0026nbsp;was initially calculated in Ethiopian birr and then converted to US dollars. For a different timing of cost adjustments to inflation,\u0026nbsp;the consumer price index of 7% [20] and the official exchange rate, as per the National Bank of Ethiopia (1 USD = 57.3 ETB) [20], was used.\u003c/p\u003e\n\u003cp\u003eCosts associated with HIV after entering the HIV and AIDS states (i.e., cost of ART, screening, and hospitalization costs in the AIDS state) were extracted from published literature in Ethiopia and adjusted to 2024 US$.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCost-effectiveness analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach health condition in the Markov model was assigned a distinct cost and a quality-adjusted life year (QALY) value. The total number of QALYs in each year was used to evaluate the effectiveness of PrEP. Then, the annual costs and QALYs for each of the cohort\u0026apos;s 1,000 hypothetical individuals were added to determine the overall costs and QALYs. Finally, the\u0026nbsp;ICERs were computed to evaluate the cost-effectiveness of PrEP for each strategy.\u003c/p\u003e\n\u003cp\u003eTo determine the cost-effectiveness of PrEP, we used Pichon-Riviere\u0026rsquo;s [21]\u0026nbsp;cost-effectiveness thresholds for 174 countries, including Ethiopia, based on life expectancy at birth and health expenditures per capita.\u0026nbsp;According to this study, Ethiopia\u0026rsquo;s cost-effective threshold for intervention is between\u0026nbsp;$39 and $194\u0026nbsp;per QALY [22].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo observe the impact on the PrEP\u0026rsquo;s cost-effectiveness, uncertainty related to inputs such as QALY estimations for the ICER calculation and time horizon was altered. A sensitivity analysis was used in this study to determine how different values of an independent variable (or input parameters) affect cost-effectiveness analysis under a particular set of assumptions.\u003c/p\u003e\n\u003cp\u003eThis analysis was measured and evaluated using both one-way (tornado analysis) and probabilistic sensitivity analysis (PSA). Input probability for tornado and PSA was varied based on input parameter tables reported and an assumed beta distribution for the probabilities, while costs were varied by \u0026plusmn;25% of the mean value and assumed a gamma distribution.\u003c/p\u003e\n\u003cp\u003eA tornado diagram was created by condensing the findings of the one-way sensitivity analysis into the parameters that had the most effect on cost-effectiveness. In addition, a Monte-Carlo simulation with 1000 iterations was conducted for probabilistic sensitivity analysis in order to determine the likelihood that the PrEP would become financially viable. Data analysis was performed using Microsoft Excel, with Visual Basic for Applications (VBA) macros used for sensitivity analysis.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003e\u003cstrong\u003eCost of providing PrEP for female sex workers in Wolaita Sodo town\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eA total of 41 female sex workers were initiated on oral daily PrEP after a negative HIV antibody test for HIV prevention, who fulfilled eligibility criteria in the Wolaita Sodo health center in the year 2023/24 EFY. This year, the estimated total cost of the PrEP program for female sex workers was around 5,646 USD, of which 45.4% went towards supplies, and 28.7% went towards the drug; the detail is summarized in Table 8. The annual unit cost of PrEP per FSW is estimated to be about 137.7 USD\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eThe estimated total cost of PrEP, and without PrEP\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe total cost with and without PrEP intervention was computed from the sum of costs for every status in each cycle (i.e., sum of 54 cycles (or lifetime horizon). The effectiveness of the interventions is determined using QALYs using the same methods. We separated the costs into the state of the hypothetical cohort for both interventions. The estimated cost was also divided into costs by status for both interventions, as shown in Table 3 below. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the susceptible health status in the group without PrEP, we assumed that the cost of health care would be $0. However, those cohorts who are in both groups and have HIV and AIDS status will incur costs for screening, health checkups, and ART services. In addition, those with AIDS have hospitalization costs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u003c/strong\u003e Total cost by status for both with and without PrEP intervention\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSusceptible cost\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eHIV\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAIDS\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eTotal cost\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eWithout PrEP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cem\u003e$0\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e(0%)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cem\u003e$1,422,838\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e(49 %)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cem\u003e$1,496,689\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e(51%)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cem\u003e$2,919,527\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eWith PrEP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cem\u003e$2,809,901\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e(85.5%)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cem\u003e$243,071\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e(7.4%)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cem\u003e$234,703\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e(7.1%)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cem\u003e$3,287,675\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIn the group without PrEP, the estimated total cost for HIV and AIDS status was approximately 48.7% and 51.3%, respectively. However, the cost of the PrEP intervention group for the susceptible status accounted for about 85.5% of the total cost, while the cost for HIV was about 7.4%, and the cost for the AIDS status accounted for around 7.1%.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eThe effectiveness of PrEP in terms of QALYs\u0026nbsp;\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eWhen the effectiveness of the PrEP strategy is measured in QALYs gained, as compared to the non-PrEP intervention, the total QALY gained with PrEP is higher than that of the other strategy. In the PrEP strategy group, the QALY gained by susceptible women is about 94% of the total (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4:\u003c/strong\u003e Estimated effects for both with and without PrEP intervention in different states\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSusceptible\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIDS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal QALYs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWithout PrEP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cem\u003e10,696\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cem\u003e4,994\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cem\u003e1,808\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cem\u003e17,498\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWith PrEP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cem\u003e19,793\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cem\u003e853\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cem\u003e283\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cem\u003e20,929\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eHowever, the QALYs gained for susceptible groups in the no-PrEP intervention were 61% of the total QALYs gained. The total QALYs gained by the no-PrEP strategy, by both HIV and AIDS status, will be higher than the PrEP strategy, because the number of patients moving to the other status will increase in these groups.\u003c/p\u003e\n\u003ch2\u003eHIV infections averted through the PrEP intervention\u003c/h2\u003e\n\u003cp\u003eThe impact of HIV infections averted is estimated through the cumulative number of HIV infections averted in the cohort of women\u0026apos;s lifetime horizon. This was done by comparing the number of HIV infections in the two strategies. The percentage of impact was defined as the number of HIV infections averted by implementing the PrEP strategy, divided by the total number of HIV infections that would have occurred without PrEP.\u003c/p\u003e\n\u003cp\u003eThe overall impact of PrEP was estimated at various coverage levels. With Ethiopia\u0026apos;s current PrEP coverage (16% uptake) among FSWs, approximately 1,715 HIV infections (19.8%) were prevented through PrEP. If coverage were increased to full access (100% uptake), about 6,960 HIV infections (80%) among female sex workers would be prevented.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCost-effectiveness of pre-exposure prophylaxis for HIV prevention\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PrEP intervention has an incremental cost of $1,202,379 and an incremental QALY gain of 1,231.50 QALYs compared to no-PrEP. Accordingly, the PrEP intervention has an ICER of $976.35 per QALY gained, which is not cost-effective based on Pichon-Riviere et al.\u0026apos;s \u0026nbsp;[22] thresholds Table 5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5:\u003c/strong\u003e Cost-effectiveness analysis of PrEP for lifetime horizon\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"677\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStrategy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Cost\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal QALYs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncremental Cost ($)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncremental QALYs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eICER($/QALYs)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrEP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cem\u003e4,121,906\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cem\u003e18,729.21\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo PrEP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cem\u003e2,919,527\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cem\u003e17,497.70\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cem\u003e1,202,379\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cem\u003e1,231.50\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u003cem\u003e976.35\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eHowever, because PrEP coverage is so low, we performed various coverage scenario studies to determine at what level of coverage the PrEP program will become cost-effective.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCost-effectiveness analysis of the PrEP strategy at different coverage levels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e25% of PrEP coverage:\u0026nbsp;\u003c/strong\u003eif PrEP is initiated in one-fourth of the cohorts, the PrEP intervention provides better value when the percentage of female sex workers who initiate PrEP increases. If 25% of the cohorts initiate PrEP, the total Costs and overall effects increase because more patients remain susceptible due to the prevention benefit. Table 6 below demonstrates that initiating PrEP in 25% of FSW has an incremental cost-effectiveness ratio of $448 per QALY gained compared to the situation with no PrEP strategy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6:\u003c/strong\u003e Cost-effectiveness analysis of lifetime PrEP program with 25% of PrEP coverage\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"630\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDifferent coverage of the PrEP strategy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStrategy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Cost\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal QALYs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eICER\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e($/QALYs)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e25%\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003eNo PrEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cem\u003e2,919,527\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cem\u003e17,498\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cem\u003e448.05\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003ePrEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cem\u003e3,829,707\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cem\u003e19,529\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e50%\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003eNo PrEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e2,919,527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e17,498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 145px;\"\u003e\n \u003cp\u003e196.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003ePrEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e3,492,292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e20,412\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e100%\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003eNo PrEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e2,919,527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e17,498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 145px;\"\u003e\n \u003cp\u003e107.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003ePrEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e3,287,675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e20,930\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e50% PrEP coverage:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cspan id=\"_Toc179895397\"\u003eIf PrEP is initiated on half of the cohorts, the coverage of PrEP among the female sex workers will be increased to 50%, which will result in more QALYs to be gained, along with an increase in cost. With this coverage, the cost-effectiveness would be cost $196.5 per QALY gained.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e100% of PrEP coverage:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eIn this case, when the whole female sex workers initiated PrEP intervention and when the lifetime horizon is considered, it would cost $107.25 per QALY gained. The PrEP strategy would be cost-effective based on Pichon-Riviere\u0026rsquo;s threshold [21].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, if we consider only the next 20 years, and all female sex workers initiated on the PrEP strategy, its cost would be $694/QALYs, which would not be cost-effective.\u003c/p\u003e\n\u003cp\u003eThus, initiating PrEP in the younger age group made the PrEP strategy more cost-effective, and coverage of this program determines its cost-effectiveness. Also, implementing this program for a short period of time might not be that important because, in short-term implementation, study results showed that it will not be a cost-effective intervention. The cost-effectiveness results of all age groups with a lifetime horizon when PrEP was initiated on whole cohorts are summarized below in Table 7.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7:\u003c/strong\u003e Cost-effectiveness analysis for female sex workers on PrEP compared to no intervention for different age groups in a lifetime horizon if PrEP is initiated in whole cohorts (i.e.100% PrEP)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"661\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cem\u003eAge group\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cem\u003eTotal cost (USD)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cem\u003eTotal QALYs\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u003cem\u003eIncremental Cost (US$)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cem\u003eIncremental \u0026nbsp; \u0026nbsp; \u0026nbsp;QALYs\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cem\u003eICER value ($/QALYs)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cem\u003e15-19\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e3,287,675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e20,930.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e368,148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e3,432.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e107.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cem\u003e20-24\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e3,080,538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e19,732.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e414,106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e2,999.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e138.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cem\u003e25-29\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2,844,594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e18,351.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e463,663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e2,537.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e182.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cem\u003e30-34\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2,588,679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e16,828.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e512,113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e2,081.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e245.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cem\u003e35-39\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2,322,358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e15,217.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e555,073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e1,657.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e334.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cem\u003e40-44\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2,053,412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e13,565.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e589,387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e1,277.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e461.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cem\u003e45-49\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1,784,357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e11,885.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e611,292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e947.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e644.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eOne-way sensitivity analysis (Tornado diagram)\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eTo perform a one-way sensitivity analysis, we used a tornado diagram at 100% coverage of PrEP, where PrEP is a cost-effective program. This diagram illustrates the effects of one parameter change on the ICER. By altering each input parameter, we examined the model outputs to identify the most significant parameters. The significant parameters identified were the cost of PrEP, the transition probability from susceptible to HIV, the cost of ART, and PrEP efficacy. The ICER is considered as an expected value (EV) in this diagram, and displayed on the vertical axis, which is 107.25 $/QALYs as shown in the Fig. 2 below.\u003c/p\u003e\n\u003cp\u003eEach bar represents the range of expected values generated by varying the corresponding variable. For instance, when the unit cost of PrEP decreased from $172 to $103, PrEP would be more cost-effective and changed ICER from 311 $/QALYs to -99 $/QALYs, which makes the strategy cost-saving. When PrEP efficacy was less than 67%, PrEP would be less effective, and when it exceeded 94%, it became more cost-effective.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProbabilistic sensitivity analysis (PSA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProbabilistic sensitivity analysis was conducted using a Monte Carlo approach, based on 1,000 randomly generated simulations of input parameter values. The result of a probabilistic sensitivity analysis (PSA) conducted using Monte Carlo simulation with 1000 iterations was presented in the cost-effectiveness acceptability curve (CEAC), indicating the probability of cost-effectiveness at different levels of willingness-to-pay thresholds (Fig. 3).\u003c/p\u003e\n\u003cp\u003eThe results showed that the probability of being cost-effective when the PrEP strategy was compared to the no intervention was only about 45% at a willingness to pay threshold of 194$ per QALY. Also, the probability of being cost-effective at the mean value of\u0026nbsp;Pichon-Riviere et al. (i.e.124\u0026nbsp;$/QALYs) was only about 26%. However, when compared to 0.5 times the GDP per capita of Ethiopia (564.5$/QALYs), the probability of being cost-effective would be increased to 97%. Moreover, this strategy was 100 % cost-effective when compared to one time the GDP per capita of Ethiopia (i.e., 1129 USD).\u003c/p\u003e\n\u003cp\u003eThe Fig. 4 below shows the ICER scatter plot and how the changes in the effects (QALYs) are sensitive to changes in the cost. It represents the uncertainty surrounding the cost-effectiveness of an intervention (PrEP) compared to a no-PrEP intervention. Each dot represents one simulation run, and the results showed that the effects were not sensitive to the cost, and the level of uncertainty around the cost-effectiveness of an intervention (PrEP) is low.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study evaluated the cost-effectiveness of PrEP intervention in FSW in Ethiopia using a hypothetical cohort of 1000 in a Markov Model. This study estimated that providing PrEP services costs an average of $137.70\u0026nbsp;per FSW per year. According to\u0026nbsp;Pichon-Riviere\u0026rsquo;s [21] Threshold, PrEP intervention is not cost-effective given Ethiopia\u0026rsquo;s current coverage level. \u0026nbsp; However, this intervention might be a cost-effective program if its coverage is increased to roughly 100%.\u003c/p\u003e\n\u003cp\u003eThe finding of the cost of providing PrEP in this study is comparable with the study that modelled the impact and cost-effectiveness of oral pre-exposure prophylaxis in 13 low-resource countries, including Ethiopia. This study reveals that Ethiopia\u0026apos;s unit cost of delivering PrEP was $106, which is somewhat less than our finding. This difference might result from the study\u0026rsquo;s design, which combined\u0026nbsp;the Incidence Patterns Model and the Goals model\u0026nbsp;for its cost estimation [23].\u0026nbsp;Additionally, the 2017 cost estimates were derived from the Global Price Reporting Mechanism and based on Kenya\u0026rsquo;s gross national income per capita. Accordingly, in this\u0026nbsp;study, the annual cost of PrEP per person in countries such as\u0026nbsp;Haiti, Malawi, Zimbabwe, Mozambique, and Uganda was between $117 and $133\u0026nbsp;[23]. This finding is relatively higher,\u0026nbsp;which could be explained by the previously mentioned factors, as well as regional variations and disparities in socioeconomic status.\u003c/p\u003e\n\u003cp\u003eThis study demonstrated that the use of PrEP for HIV prevention in FSW could have an important impact on the HIV epidemics in Ethiopia, and distributing PrEP for all cohorts seems to be cost-effective in Ethiopia. Currently, Ethiopia does not yet have a standard or officially accepted cost per QALY willingness-to-pay threshold for the cost-effectiveness analysis. To determine whether an intervention is cost-effective in a given jurisdiction, Pichon-Riviere et al [21] Calculated cost-effectiveness thresholds for 174 nations, including Ethiopia, based on growth in life expectancy and health expenditures. To compare the ICER with this criterion, we also used the WTP threshold of 0.5 and one times Ethiopia\u0026apos;s GDP per capita, which is approximately 1,129 US dollars.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite the good value provided by PrEP use in high-risk groups, particularly in view of other competing priorities such as providing ART to infected individuals, and new prevention technologies is very important [24], scaling up PrEP for this group (female sex workers) with current coverage in our context might not be effective, but with increased coverage (when 100% of FSW receive PrEP), it will be very cost-effective. The results suggested that providing oral PrEP to younger female sex workers would increase uptake of PrEP for this group and improve its cost-effectiveness. Also, any reduction in PrEP cost would improve its cost-effectiveness in Ethiopia, which is similar to the literature in other countries [8,24,25].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOther similar studies conducted in South Africa on female sex workers and adolescent girls showed that multi-purpose HIV and pregnancy prevention technologies were cost-effective for female sex workers and women aged 16\u0026ndash;24, but not for women aged 25\u0026ndash;49 [26]. According to their study, as the age of the cohort increases, the likelihood of pre-exposure prophylaxis being cost-effective decreases, which aligns with the findings of this study.\u003c/p\u003e\n\u003cp\u003eInitiating PrEP in a larger proportion of FSW increased total costs and increased total effects; however, it decreased average cost because when a larger proportion of FSW start on PrEP intervention, more clients remain susceptible without acquiring HIV, and if the proportion of FSW who received PrEP decreases, most of the cohorts infected and enter into HIV and AIDS states. However, Juusola J [27] found that initiating PrEP in a larger proportion of MSM averts more infections, but at increasing cost per QALY gained ($216,480/QALY gained when 100% of MSM receive PrEP), and using PrEP only in high-risk MSM can improve its cost-effectiveness. Also, they found that decreasing the PrEP cost can improve its cost-effectiveness [24], which is consistent with our study.\u003c/p\u003e\n\u003cp\u003eOverall, this study\u0026apos;s findings suggested that PrEP could be a cost-effective way to reduce new HIV infections in our settings. However, the cost-effectiveness of PrEP is highly dependent on PrEP coverage, cost (including annual PrEP and ART costs), the epidemiological context, and PrEP efficacy. A price reduction could decrease the cost-effectiveness ratio and improve the cost-effectiveness of the strategy. Given the ICERs\u0026rsquo; sensitivity to several variables, PrEP might be more cost-effective for the younger FSM population with high coverage and other subgroups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlso, our study suggests that implementing this program for a long period of time with increased coverage for the high-risk group would increase its cost-effectiveness. When policymakers want to expand the program in our context, they should consider that the coverage of PrEP in female sex workers determines its cost-effectiveness.\u003c/p\u003e\n\u003cp\u003eThis study has its own strengths and limitations. First strength was that our study was the first study to assess the cost-effectiveness of PrEP for HIV prevention in Ethiopia. Second, this study performed both one-way and probabilistic sensitivity analysis to check the robustness of the findings (or uncertainty), including input parameter changes that may happen in the future.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, this study also conducted scenario analysis by increasing PrEP coverage. However, this study has its limitations, as we used a static Markov model instead of a dynamic transmission model, where dynamic models are widely used to describe epidemiologic dynamics in populations where the infection probability changes over time, depending on the number of infected people, and the infection probability was assumed to be static in the Markov model used in this study [8]. Our study did not assume that PrEP caused a change in risk behavior and also assumed the adherence to PrEP was 100%, but this was not the most realistic way to model, and the threshold we used was not officially recognized by the government of Ethiopia, where there is no clearly defined threshold for comparison that may affect our conclusion.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study reveals that, based on Pichon-Riviere et al.'s cost-effectiveness threshold, the current level of PrEP coverage for HIV prevention among FSW in Ethiopia is not a cost-effective intervention. However, if the coverage is increased to reach nearly all FSW, and a lifetime horizon is considered, then it could be a cost-effective program. In addition, targeting younger FSWs, identifying strategies to reduce PrEP costs, and increasing coverage among high-risk groups would provide substantial health benefits by minimizing HIV infections, thereby making PrEP a highly cost-effective intervention. Furthermore, variables such as the cost of PrEP, the cost of ART, and PrEP efficacy are key drivers of ICER. Therefore, the implementation and scale-up of this program require a careful evaluation of its cost-effectiveness.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAIDSn FSW with AIDS in the PrEP group\u003c/p\u003e\n\u003cp\u003eAIDSn FSW with AIDS in the without PrEP group\u003c/p\u003e\n\u003cp\u003eART Antiretroviral therapy\u003c/p\u003e\n\u003cp\u003eCDC Center for Disease Control and Prevention\u003c/p\u003e\n\u003cp\u003eCEAs Cost-effectiveness analyses\u003c/p\u003e\n\u003cp\u003eDn Death of FSW in the PrEP group\u003c/p\u003e\n\u003cp\u003eDp Death of FSW in the without PrEP group\u003c/p\u003e\n\u003cp\u003eDIC Drop-in-center\u003c/p\u003e\n\u003cp\u003eETB Ethiopian birr\u003c/p\u003e\n\u003cp\u003eFSW Female sex workers\u003c/p\u003e\n\u003cp\u003eHIVn Human immunodeficiency virus\u003c/p\u003e\n\u003cp\u003eHIVp Human immunodeficiency virus\u003c/p\u003e\n\u003cp\u003eKP Key populations\u003c/p\u003e\n\u003cp\u003eMSM Male who has sex with males\u003c/p\u003e\n\u003cp\u003ePEPFAR President\u0026rsquo;s Emergency Plan for AIDS Relief\u003c/p\u003e\n\u003cp\u003ePrEP Pre-exposure prophylaxis\u003c/p\u003e\n\u003cp\u003eSn Susceptible FSW in the PrEP group\u003c/p\u003e\n\u003cp\u003eSp Susceptible FSW in the without PrEP group\u003c/p\u003e\n\u003cp\u003eTDF/3TC Tenofovir disoproxil fumarate/emtricitabine \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study neither involves direct human subjects research nor patient-level data. Therefore, following certain clinical research guidelines, like the Declaration of Helsinki, was \u003cstrong\u003enot applicable\u003c/strong\u003e. However, the study was conducted in compliance with general ethical principles for health research, including confidentiality, voluntary participation, and informed consent of healthcare providers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDFF was financially supported by\u0026nbsp;Jimma University, the Institute of Health, School of Postgraduate Studies to conduct a primary cost data collection in the study area.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo datasets were generated or analyzed during the current study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors made a significant contribution to the work reported, DFF AGN ISG study conception and design, conducted the literature review, data requests, analysis, and prepared the Manuscript, YET, critically reviewed the article, oversaw the interpretation, and improved its report writing. All authors have read and approved the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWolaita Sodo health center and its staff provided key primary data regarding the delivery of PrEP service. Additionally, we would like to thank Jimma University, the Institute of Health, School of Post Graduate Studies, for providing financial support to carry out this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eUNAIDS. Miles to go closing gaps, breaking barriers, righting injustices. Global AIDS update. 2018. \u003c/li\u003e\n\u003cli\u003eU.S. President\u0026rsquo;s Emergency Plan for AIDS Relief. Dreaming of an AIDS-free future girls from the dreams-supported sauti project in Tanzania. 2018. \u003c/li\u003e\n\u003cli\u003eUNAIDS. Turning point for Africa \u0026mdash; An historic opportunity to end AIDS as a public health threat by 2030 and launch a new era of sustainability. 2030. \u003c/li\u003e\n\u003cli\u003eFHAPCO. HIV/AIDS National Strategic Plan for Ethiopia 2021-2025. 2021;4:26-32;176. \u003c/li\u003e\n\u003cli\u003eMinisty of Health. National Comprehensive HIV Prevention, Care and Treatment Refresher Training for Health care Providers. Ethiopia; 2022. \u003c/li\u003e\n\u003cli\u003eAllen R et al. The role of costing in the introduction and scale-up of HIV pre-exposure prophylaxis: evidence from integrating PrEP into routine maternal and child health and family planning clinics in western Kenya. 2019; https://doi.org/10.1002/jia2.25296/full\u003c/li\u003e\n\u003cli\u003eYamamoto N et al. Evaluating the cost-effectiveness of a pre-exposure prophylaxis program for HIV prevention for men who have sex with men in Japan. Scientific Reports. Nature Research; 2022;12. https://doi.org/10.1038/s41598-022-07116-4\u003c/li\u003e\n\u003cli\u003eC. Pretorius et al. Modelling impact and cost-effectiveness of oral pre-exposure prophylaxis in 13 low-resource countries. 2020; https://doi.org/10.1002/jia2.25451/full\u003c/li\u003e\n\u003cli\u003eJones et al. HIV incidence among women engaging in sex work in sub-Saharan Africa: a systematic review and meta-analysis. medRxiv : the preprint server for health sciences. 2023; https://doi.org/10.1101/2023.10.17.23297108\u003c/li\u003e\n\u003cli\u003eYared Belete Belay,Eskinder Eshetu Ali, Karen Y. Chung, Gebremedhin Beedemariam Gebretekle BS. Cost-Utility Analysis of Dolutegravir- Versus Efavirenz-Based Regimens as a First-Line Treatment in Adult HIV/AIDS Patients in Ethiopia. PharmacoEconomics - Open. Springer International Publishing; 2021;5:655\u0026ndash;64. https://doi.org/10.1007/s41669-021-00275-6\u003c/li\u003e\n\u003cli\u003eYimam Getaneh, Fentabil Getnet, Feng Ning, Abdur Rashid, Lingjie Liao FY and YS. HIV-1 Disease Progression and Drug Resistance Mutations among Children on First-Line Antiretroviral Therapy in Ethiopia. Biomedicines. 2023;11:1\u0026ndash;14. https://doi.org/10.3390/biomedicines11082293\u003c/li\u003e\n\u003cli\u003eWHO. People living with HIV People acquiring HIV People dying from HIV-related causes. Who. 2023;1\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eEPHI. HIV Related Estimates and Projections in Ethiopia for the year 2021-2022. Ethiopia; 2022. \u003c/li\u003e\n\u003cli\u003eShibesh BF, Admas AB, Lake AW, Getu SB, Worede DT. Uptake of retroviral pre-exposure prophylaxis and its associated factors among female sex workers, Northwest Ethiopia. AIDS Research and Therapy. 2023;20:1\u0026ndash;5. https://doi.org/10.1186/s12981-023-00573-5\u003c/li\u003e\n\u003cli\u003eAsfaw Demissie Bikilla, Degu Jerene, Bjarne Robberstad and BL. Cost estimates of HIV care and treatment with and without anti-retroviral therapy at Arba Minch hospital in southern Ethiopia. Cost Effectiveness and Resource Allocation. 2009;7:1\u0026ndash;7. https://doi.org/10.1186/1478-7547-7-6\u003c/li\u003e\n\u003cli\u003eZemenfeskidus Hadgu. The costs of HIV / AIDS care and treatment in Adigrat General Hospital , eastern zone of Tigray National Regional State , North Ethiopia The costs of HIV / AIDS care and treatment in Adigrat General Hospital , eastern zone of Tigray National Regional Stat. 2011; \u003c/li\u003e\n\u003cli\u003eMengistu et al. Health related quality of life and its association with social support among people living with HIV/AIDS receiving antiretroviral therapy in Ethiopia: a systematic review and meta-analysis. Health and Quality of Life Outcomes. BioMed Central; 2022;20:1\u0026ndash;8. https://doi.org/10.1186/s12955-022-01985-z\u003c/li\u003e\n\u003cli\u003eM.F. Drummond et.al. Methods for the Economic Evaluation of Health Care Programmes. 2015;4th Edition. \u003c/li\u003e\n\u003cli\u003eUNAIDS. Costing Guidelines for HIV Prevention Strategies. Health policy and planning. 2001; 12:326\u0026ndash;31. \u003c/li\u003e\n\u003cli\u003ePichon-Riviere A, Drummond M, Palacios A, Garcia-Marti S, Augustovski F. Determining the efficiency path to universal health coverage: cost-effectiveness thresholds for 174 countries based on growth in life expectancy and health expenditures. The Lancet Global Health. 2023;11:e833\u0026ndash;42. https://doi.org/10.1016/S2214-109X(23)00162-6\u003c/li\u003e\n\u003cli\u003eJessie L. Juusola, M.S.1, Margaret L. Brandeau, Ph.D.1, Douglas K. Owens, M.D., M.S.2,3, and Eran Bendavid, M.D. MS 3. The Cost-Effectiveness of Preexposure Prophylaxis for HIV Prevention in Men Who Have Sex with Men in the United States. Ann Intern Med. 2012; https://doi.org/10.1059/0003-4819-156-8-201204170-00001\u003c/li\u003e\n\u003cli\u003eHeun Choi1 et al. Cost-effectiveness analysis of pre-exposure prophylaxis for the prevention of HIV in men who have sex with men in South Korea: a mathematical modelling study. Scientific Reports. Nature Research; 2020;10. https://doi.org/10.1038/s41598-020-71565-y\u003c/li\u003e\n\u003cli\u003eQuaife M, Terris-Prestholt F, Eakle R, Escobar MAC, Kilbourne-Brook M, Mvundura M, et al. The cost-effectiveness of multi-purpose HIV and pregnancy prevention technologies in South Africa. Journal of the International AIDS Society. 2018;21. https://doi.org/10.1002/jia2.25064\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cost-effectiveness-and-resource-allocation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cera","sideBox":"Learn more about [Cost Effectiveness and Resource Allocation](http://resource-allocation.biomedcentral.com)","snPcode":"12962","submissionUrl":"https://submission.nature.com/new-submission/12962/3","title":"Cost Effectiveness and Resource Allocation","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pre-exposure prophylaxis, cost-effectiveness, Ethiopia","lastPublishedDoi":"10.21203/rs.3.rs-8681752/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8681752/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePre-exposure prophylaxis (PrEP) is a highly effective HIV prevention strategy for high-risk populations, such as female sex workers. However, its uptake in Ethiopia remains limited. Due to the scarcity of local evidence, a cost-effectiveness analysis is needed to inform the potential expansion of PrEP services nationwide. This study aimed to assess the cost-effectiveness of PrEP in preventing HIV infection among female sex workers in Ethiopia.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA static Markov model was adapted to analyze HIV transmission and disease progression in a hypothetical cohort of 1,000 female sex workers receiving PrEP. Primary data were collected to estimate the cost of providing PrEP to female sex workers, while most model parameters were obtained from the published literature. Cost-effectiveness was assessed by calculating the incremental cost-effectiveness ratio of the PrEP intervention compared to no PrEP use. The effectiveness of PrEP was measured in QALYs gained over the lifetime of the cohort, and the ICER was estimated to determine the cost-effectiveness of the intervention. One-way sensitivity and probabilistic sensitivity analysis were conducted to assess uncertainty in the model results.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eThe estimated unit cost of PrEP per client per year was USD 137.7. The PrEP intervention resulted in an incremental cost of USD 1,202,379 and an incremental gain of 1,231.50 QALYs compared with no PrEP. At the current level of PrEP coverage, the incremental cost-effectiveness ratio (ICER) was 976.35 USD per QALY gained. Under full PrEP coverage, the ICER decreased to USD 107.25 per QALY gained. At this level, PrEP would be considered a cost-effective intervention based on the threshold used for the cost-effectiveness analysis. The cost-effectiveness of PrEP was highly sensitive to PrEP efficacy, the level of uptake, and the cost of antiretroviral therapy (ART).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ePre-exposure prophylaxis for HIV prevention in female sex workers is not cost-effective at the current low level of PrEP coverage in Ethiopia. However, if coverage is expanded to full uptake and a lifetime time horizon is considered, PrEP could be a cost-effective intervention in Ethiopia.\u003c/p\u003e","manuscriptTitle":"Cost-effectiveness of pre-exposure prophylaxis for HIV prevention among female sex workers in Ethiopia: a Markov model analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-20 10:11:33","doi":"10.21203/rs.3.rs-8681752/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-07T20:43:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-26T11:25:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"134646535450384462402113699891030198994","date":"2026-03-19T18:49:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-17T19:39:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325948578032545001556146405325415053576","date":"2026-03-17T18:07:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"264239742390382247636102709480365692234","date":"2026-03-17T16:08:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168460471554448867970166426015847675612","date":"2026-03-17T14:16:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-17T13:50:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-27T07:55:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-27T07:55:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cost Effectiveness and Resource Allocation","date":"2026-01-23T17:59:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cost-effectiveness-and-resource-allocation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cera","sideBox":"Learn more about [Cost Effectiveness and Resource Allocation](http://resource-allocation.biomedcentral.com)","snPcode":"12962","submissionUrl":"https://submission.nature.com/new-submission/12962/3","title":"Cost Effectiveness and Resource Allocation","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8c2c3269-55e6-4447-976b-724d6d001540","owner":[],"postedDate":"March 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T20:54:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-20 10:11:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8681752","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8681752","identity":"rs-8681752","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}