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Key Points: Question: Can development of an individualized treatment rule identify patients presenting to emergency departments/urgent care with suicidal ideation or suicide attempts who are likely to benefit from psychiatric hospitalization? Findings: A decision analytic model found that hospitalization was associated with reduced suicide attempt risk among patients who attempted suicide in the past day but not among others with suicidality. Accounting for heterogeneity, suicide attempt risk was found to increase with hospitalization in 24% of patients and decrease in 28%. Meaning: Results of this study suggest that implementing an individualized treatment rule could identify many additional patients who may benefit from or be harmed by hospitalization. Importance: Psychiatric hospitalization is the standard of care for patients presenting to an emergency department (ED) or urgent care (UC) with high suicide risk. However, the effect of hospitalization in reducing subsequent suicidal behaviors is poorly understood and likely heterogeneous. Objectives: To estimate the association of psychiatric hospitalization with subsequent suicidal behaviors using observational data and develop a preliminary predictive analytics individualized treatment rule accounting for heterogeneity in this association across patients. Design, Setting, and Participants: A machine learning analysis of retrospective data was conducted. All veterans presenting with suicidal ideation (SI) or suicide attempt (SA) from January 1, 2010, to December 31, 2015, were included. Data were analyzed from September 1, 2022, to March 10, 2023. Subgroups were defined by primary psychiatric diagnosis (nonaffective psychosis, bipolar disorder, major depressive disorder, and other) and suicidality (SI only, SA in past 2-7 days, and SA in past day). Models were trained in 70.0% of the training samples and tested in the remaining 30.0%. Exposures: Psychiatric hospitalization vs nonhospitalization. Main Outcomes and Measures: Fatal and nonfatal SAs within 12 months of ED/UC visits were identified in administrative records and the National Death Index. Baseline covariates were drawn from electronic health records and geospatial databases. Results: Of 196 610 visits (90.3% men; median [IQR] age, 53 [41-59] years), 71.5% resulted in hospitalization. The 12-month SA risk was 11.9% with hospitalization and 12.0% with nonhospitalization (difference, −0.1%; 95% CI, −0.4% to 0.2%). In patients with SI only or SA in the past 2 to 7 days, most hospitalization was not associated with subsequent SAs. For patients with SA in the past day, hospitalization was associated with risk reductions ranging from −6.9% to −9.6% across diagnoses. Accounting for heterogeneity, hospitalization was associated with reduced risk of subsequent SAs in 28.1% of the patients and increased risk in 24.0%. An individualized treatment rule based on these associations may reduce SAs by 16.0% and hospitalizations by 13.0% compared with current rates. Conclusions and Relevance: The findings of this study suggest that psychiatric hospitalization is associated with reduced average SA risk in the immediate aftermath of an SA but not after other recent SAs or SI only. Substantial heterogeneity exists in these associations across patients. An individualized treatment rule accounting for this heterogeneity could both reduce SAs and avert hospitalizations. This predictive analytics model develops a preliminary individualized treatment rule to estimate the association between psychiatric hospitalization and subsequent suicidal behaviors in patients at risk for suicide. [ABSTRACT FROM AUTHOR]

Background: Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA). Methods: A 2018–2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample. Results: In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors. Conclusions: Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection. [ABSTRACT FROM AUTHOR]

Background: Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan. Methods: This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018–2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample. Results: 32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables. Conclusions: Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments. [ABSTRACT FROM AUTHOR]

Although research shows that more depressed patients respond to combined antidepressants (ADM) and psychotherapy than either alone, many patients do not respond even to combined treatment. A reliable prediction model for this could help treatment decision-making. We attempted to create such a model using machine learning methods among patients in the US Veterans Health Administration (VHA). A 2018–2020 national sample of VHA patients beginning combined depression treatment completed self-report assessments at baseline and 3 months (n = 658). A learning model was developed using baseline self-report, administrative, and geospatial data to predict 3-month treatment response defined by reductions in the Quick Inventory of Depression Symptomatology Self-Report and/or in the Sheehan Disability Scale. The model was developed in a 70 % training sample and tested in the remaining 30 % test sample. 30.0 % of patients responded to treatment. The prediction model had a test sample AUC-ROC of 0.657. A strong gradient was found in probability of treatment response from 52.7 % in the highest predicted quintile to 14.4 % in the lowest predicted quintile. The most important predictors were episode characteristics (symptoms, comorbidities, history), personality/psychological resilience, recent stressors, and treatment characteristics. Restrictions in sample definition, a low recruitment rate, and reliance on patient self-report rather than clinician assessments to determine treatment response limited the generalizability of results. A machine learning model could help depressed patients and providers predict likely response to combined ADM-psychotherapy. Parallel information about potential harms and costs of alternative treatments would be needed, though, to inform optimal treatment selection. • 30 % of depressed Veterans Health Administration patients responded to combined antidepressant-psychotherapy treatment. • A machine learning model was developed to predict differential response. • The model was significantly predictive. • Parallel modeling across alternative treatments could help optimize treatment. [ABSTRACT FROM AUTHOR]

Importance: Several statistical models for predicting suicide risk have been developed, but how accurate such models must be to warrant implementation in clinical practice is not known.Objective: To identify threshold values of sensitivity, specificity, and positive predictive value that a suicide risk prediction method must attain to cost-effectively target a suicide risk reduction intervention to high-risk individuals.Design, Setting, and Participants: This economic evaluation incorporated published data on suicide epidemiology, the health care and societal costs of suicide, and the costs and efficacy of suicide risk reduction interventions into a novel decision analytic model. The model projected suicide-related health economic outcomes over a lifetime horizon among a population of US adults with a primary care physician. Data analysis was performed from September 19, 2019, to July 5, 2020.Interventions: Two possible interventions were delivered to individuals at high predicted risk: active contact and follow-up (ACF; relative risk of suicide attempt, 0.83; annual health care cost, $96) and cognitive behavioral therapy (CBT; relative risk of suicide attempt, 0.47; annual health care cost, $1088).Main Outcomes and Measures: Fatal and nonfatal suicide attempts, quality-adjusted life-years (QALYs), health care sector costs and societal costs (in 2016 US dollars), and incremental cost-effectiveness ratios (ICERs) (with ICERs ≤$150 000 per QALY designated cost-effective).Results: With a specificity of 95% and a sensitivity of 25%, primary care-based suicide risk prediction could reduce suicide death rates by 0.5 per 100 000 person-years (if used to target ACF) or 1.6 per 100 000 person-years (if used to target CBT) from a baseline of 15.3 per 100 000 person-years. To be cost-effective from a health care sector perspective at a specificity of 95%, a risk prediction method would need to have a sensitivity of 17.0% or greater (95% CI, 7.4%-37.3%) if used to target ACF and 35.7% or greater (95% CI, 23.1%-60.3%) if used to target CBT. To achieve cost-effectiveness, ACF required positive predictive values of 0.8% for predicting suicide attempt and 0.07% for predicting suicide death; CBT required values of 1.7% for suicide attempt and 0.2% for suicide death.Conclusions and Relevance: These findings suggest that with sufficient accuracy, statistical suicide risk prediction models can provide good health economic value in the US. Several existing suicide risk prediction models exceed the accuracy thresholds identified in this analysis and thus may warrant pilot implementation in US health care systems. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to estimate the cost-effectiveness of esketamine, a novel intranasally dosed antidepressant, for patients in the United States with treatment-resistant depression.Methods: A decision-analytic model parameterized with efficacy data from phase 3 randomized trials of esketamine was used to simulate the effects of treatment with esketamine versus oral antidepressants over a 5-year horizon, from both societal and health care sector perspectives. Outcomes included remission and response of depression, quality-adjusted life-years (QALYs), costs, and incremental cost-effectiveness ratios (ICERs) for esketamine. Value-based prices were calculated, defined as the per-dose price at which esketamine would become cost-effective given cost-effectiveness thresholds of $50,000/QALY, $100,000/QALY, and $150,000/QALY. Uncertainty in these outcomes was assessed with probabilistic sensitivity analyses. Key model parameters included the efficacy of esketamine versus oral antidepressants (relative risk of 1.39 for remission; 1.32 for response) and the monthly cost of esketamine ($5,572 for month 1; $1,699-$2,244 thereafter).Results: Over 5 years, esketamine was projected to increase time in remission from 25.3% to 31.1% of life-years, resulting in a gain of 0.07 QALYs. Esketamine increased societal costs by $16,617 and health care sector costs by $16,995. Base case ICERs were $237,111/QALY (societal) and $242,496/QALY (health care sector). Probabilistic sensitivity analysis showed a greater than 95% likelihood that esketamine's ICER would be above $150,000/QALY. At a cost-effectiveness threshold of $150,000/QALY, esketamine's value-based price was approximately $140/dose (versus a current price of $240/dose).Conclusions: Esketamine is unlikely to be cost-effective for management of treatment-resistant depression in the United States unless its price falls by more than 40%. [ABSTRACT FROM AUTHOR]

Background: Most guidelines for major depressive disorder recommend initial treatment with either a second-generation antidepressant (SGA) or cognitive behavioral therapy (CBT). Although most trials suggest that these treatments have similar efficacy, their health economic implications are uncertain.Objective: To quantify the cost-effectiveness of CBT versus SGA for initial treatment of depression.Design: Decision analytic model.Data Sources: Relative effectiveness data from a meta-analysis of randomized controlled trials; additional clinical and economic data from other publications.Target Population: Adults with newly diagnosed major depressive disorder in the United States.Time Horizon: 1 to 5 years.Perspectives: Health care sector and societal.Intervention: Initial treatment with either an SGA or group and individual CBT.Outcome Measures: Costs in 2014 U.S. dollars, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios.Results of Base-case Analysis: In model projections, CBT produced higher QALYs (3 days more at 1 year and 20 days more at 5 years) with higher costs at 1 year (health care sector, $900; societal, $1500) but lower costs at 5 years (health care sector, -$1800; societal, -$2500).Results of Sensitivity Analysis: In probabilistic sensitivity analyses, SGA had a 64% to 77% likelihood of having an incremental cost-effectiveness ratio of $100 000 or less per QALY at 1 year; CBT had a 73% to 77% likelihood at 5 years. Uncertainty in the relative risk for relapse of depression contributed the most to overall uncertainty in the optimal treatment.Limitation: Long-term trials comparing CBT and SGA are lacking.Conclusion: Neither SGAs nor CBT provides consistently superior cost-effectiveness relative to the other. Given many patients' preference for psychotherapy over pharmacotherapy, increasing patient access to CBT may be warranted.Primary Funding Source: Department of Veterans Affairs, National Institute of Mental Health. [ABSTRACT FROM AUTHOR]

Importance: Electroconvulsive therapy (ECT) is a highly effective treatment for depression but is infrequently used owing to stigma, uncertainty about indications, adverse effects, and perceived high cost.Objective: To assess the cost-effectiveness of ECT compared with pharmacotherapy/psychotherapy for treatment-resistant major depressive disorder in the United States.Design, Setting, and Participants: A decision analytic model integrating data on clinical efficacy, costs, and quality-of-life effects of ECT compared with pharmacotherapy/psychotherapy was used to simulate depression treatment during a 4-year horizon from a US health care sector perspective. Model input data were drawn from multiple meta-analyses, randomized trials, and observational studies of patients with depression. Where possible, data sources were restricted to US-based studies of nonpsychotic major depression. Data were analyzed between June 2017 and January 2018.Interventions: Six alternative strategies for incorporating ECT into depression treatment (after failure of 0-5 lines of pharmacotherapy/psychotherapy) compared with no ECT.Main Outcomes and Measures: Remission, response, and nonresponse of depression; quality-adjusted life-years; costs in 2013 US dollars; and incremental cost-effectiveness ratios. Strategies with incremental cost-effectiveness ratios of $100 000 per quality-adjusted life-year or less were designated cost-effective.Results: Based on the Sequenced Treatment Alternatives to Relieve Depression trial, we simulated a population with a mean (SD) age of 40.7 (13.2) years, and 62.2% women. Over 4 years, ECT was projected to reduce time with uncontrolled depression from 50% of life-years to 33% to 37% of life-years, with greater improvements when ECT is offered earlier. Mean health care costs were increased by $7300 to $12 000, with greater incremental costs when ECT was offered earlier. In the base case, third-line ECT was cost-effective, with an ICER of $54 000 per quality-adjusted life-year. Third-line ECT remained cost-effective in a range of univariate, scenario, and probabilistic sensitivity analyses. Incorporating all input data uncertainty, we estimate a 74% to 78% likelihood that at least 1 of the ECT strategies is cost-effective and a 56% to 58% likelihood that third-line ECT is the optimal strategy.Conclusions and Relevance: For US patients with treatment-resistant depression, ECT may be an effective and cost-effective treatment option. Although many factors influence the decision to proceed with ECT, these data suggest that, from a health-economic standpoint, ECT should be considered after failure of 2 or more lines of pharmacotherapy/psychotherapy. [ABSTRACT FROM AUTHOR]

Current Indian guidelines recommend twice-annual CD4 testing to monitor first-line antiretroviral therapy (ART), with a plasma HIV RNA test to confirm failure if CD4 declines, which would prompt a switch to second-line ART. We used a mathematical model to assess the clinical benefits and cost-effectiveness of alternative laboratory monitoring strategies in India. We simulated a cohort of HIV-infected patients initiating first-line ART and compared 11 strategies with combinations of CD4 and HIV RNA testing at varying frequencies. We included adaptive strategies that reduce the frequency of tests after 1 year from 6 to 12 months for virologically suppressed patients. We projected life expectancy, time on failed first-line ART, cumulative 10-year HIV transmissions, lifetime cost (2014 US dollars), and incremental cost-effectiveness ratios (ICERs). We defined strategies as cost-effective if their ICER was <1 × the Indian per capita gross domestic product (GDP, $1,600). We found that the current Indian guidelines resulted in a per person life expectancy (from mean age 37) of 150.2 months and a per person cost of $2,680. Adding annual HIV RNA testing increased survival by ∼8 months; adaptive strategies were less expensive than similar nonadaptive strategies with similar life expectancy. The most effective strategy with an ICER <1 × GDP was the adaptive HIV RNA strategy (ICER $840/year). Cumulative 10-year transmissions decreased from 27.2/1,000 person-years with standard-of-care to 20.9/1,000 person-years with adaptive HIV RNA testing. In India, routine HIV RNA monitoring of patients on first-line ART would increase life expectancy, decrease transmissions, be cost-effective, and should be implemented. [ABSTRACT FROM AUTHOR]

Background: Preexposure prophylaxis (PrEP) is effective at preventing HIV infection among men who have sex with men (MSM), but there is uncertainty about how to identify high-risk MSM who should receive PrEP. Methods: We used a mathematical model to assess the cost-effectiveness of using the HIV Incidence Risk Index for MSM (HIRI-MSM) questionnaire to target PrEP to high-risk MSM. We simulated strategies of no PrEP, PrEP available to all MSM, and eligibility thresholds set to HIRI-MSM scores between 5 and 45, in increments of 5 (where a higher score predicts greater HIV risk). Based on the iPrEx, IPERGAY, and PROUD trials, we evaluated PrEP efficacies from 44% to 86% and annual costs from $5900 to 8700. We designate strategies with incremental cost-effectiveness ratio (ICER) ≤$100,000/quality-adjusted life-year (QALY) as "cost-effective." Results: Over 20 years, making PrEP available to all MSM is projected to prevent 33.5% of new HIV infections, with an ICER of $1,474,000/QALY. Increasing the HIRI-MSM score threshold reduces the prevented infections, but improves cost-effectiveness. A threshold score of 25 is projected to be optimal (most QALYs gained while still being cost-effective) over a wide range of realistic PrEP efficacies and costs. At low cost and high efficacy (IPERGAY), thresholds of 15 or 20 are optimal across a range of other input assumptions; at high cost and low efficacy (iPrEx), 25 or 30 are generally optimal. Conclusions: The HIRI-MSM provides a clinically actionable means of guiding PrEP use. Using a score of 25 to determine PrEP eligibility could facilitate cost-effective use of PrEP among high-risk MSM who will benefit from it most. [ABSTRACT FROM AUTHOR]

In sub-Saharan Africa, HIV-infected adults who fail second-line antiretroviral therapy (ART) often do not have access to third-line ART. We examined the clinical impact and cost-effectiveness of making third-line ART available in Côte d'Ivoire.We used a simulation model to compare 4 strategies after second-line ART failure: continue second-line ART (C-ART2), continue second-line ART with an adherence reinforcement intervention (AR-ART2), immediate switch to third-line ART (IS-ART3), and continue second-line ART with adherence reinforcement, switching patients with persistent failure to third-line ART (AR-ART3). Third-line ART consisted of a boosted-darunavir plus raltegravir-based regimen. Primary outcomes were 10-year survival and lifetime incremental cost-effectiveness ratios (ICERs), in $/year of life saved (YLS). ICERs below $3585 (3 times the country per capita gross domestic product) were considered cost-effective.Ten-year survival was 6.0% with C-ART2, 17.0% with AR-ART2, 35.4% with IS-ART3, and 37.2% with AR-ART3. AR-ART2 was cost-effective ($1100/YLS). AR-ART3 had an ICER of $3600/YLS and became cost-effective if the cost of third-line ART decreased by <1%. IS-ART3 was less effective and more costly than AR-ART3. Results were robust to wide variations in the efficacy of third-line ART and of the adherence reinforcement, as well as in the cost of second-line ART.Access to third-line ART combined with an intense adherence reinforcement phase, used as a tool to distinguish between patients who can still benefit from their current second-line regimen and those who truly need third-line ART would provide substantial survival benefits. With minor decreases in drug costs, this strategy would be cost-effective. [ABSTRACT FROM AUTHOR]

Objective: To compare the clinical outcomes and cost-effectiveness of routine HIV screening in Portugal to the current practice of targeted and on-demand screening. Design: We used Portuguese national clinical and economic data to conduct a model-based assessment. Methods: We compared current HIV detection practices to strategies of increasingly frequent routine HIV screening in Portuguese adults aged 18-69. We considered several subpopulations and geographic regions with varying levels of undetected HIV prevalence and incidence. Baseline inputs for the national case included undiagnosed HIV prevalence 0.16%, annual incidence 0.03%, mean population age 43 years, mean CD4 count at care initiation 292 cells/μL, 63% HIV test acceptance, 78% linkage to care, and HIV rapid test cost €6 under the proposed routine screening program. Outcomes included quality-adjusted survival, secondary HIV transmission, cost, and incremental cost-effectiveness. Results: One-time national HIV screening increased HIV-infected survival from 164.09 quality-adjusted life months (QALMs) to 166.83 QALMs compared to current practice and had an incremental cost-effectiveness ratio (ICER) of €28,000 per quality-adjusted life year (QALY). Screening more frequently in higher-risk groups was cost-effective: for example screening annually in men who have sex with men or screening every three years in regions with higher incidence and prevalence produced ICERs of €21,000/QALY and €34,000/QALY, respectively. Conclusions: One-time HIV screening in the Portuguese national population will increase survival and is cost-effective by international standards. More frequent screening in higher-risk regions and subpopulations is also justified. Given Portugal’s challenging economic priorities, we recommend prioritizing screening in higher-risk populations and geographic settings.

Objective: Autoimmune encephalitis (AE) is a highly treatable neurologic condition that can cause psychosis. Screening for AE is not currently recommended in routine workup for first-episode psychosis (FEP), owing partly to the high cost of testing for AE-associated neuronal autoantibodies., Methods: This study used a decision-analytic model to estimate the cost-effectiveness of routine serum screening for AE compared with clinically targeted screening in patients with FEP. Model parameters drawn from prior published literature included the prevalence of neuronal autoantibodies in FEP (4.5%), serum autoantibody panel cost (US $291), remission probability with antipsychotics (0.58), and remission probability with immunotherapy for patients diagnosed with AE (0.85). Outcomes included quality-adjusted life-years (QALYs), costs, and incremental cost-effectiveness ratios (ICERs), assessed over a 5-year horizon from the US health care sector and societal perspectives. ICER thresholds of $50,000/QALY to $150,000/QALY were used to define cost-effectiveness. The analysis was conducted between June 2018 and January 2020., Results: Routine screening led to mean QALY gains of 0.008 among all patients and 0.174 among the subgroup of patients with neuronal autoantibodies. Mean costs increased by $780 from a societal perspective and $1,150 from a health care sector perspective, resulting in ICERs of $99,330/QALY and $147,460/QALY, respectively. Incorporating joint input data uncertainty, the likelihood routine screening has an ICER ≤ $150,000/QALY was 55% from a societal perspective and 37% from a health care sector perspective. The model parameter with the greatest contribution to overall uncertainty was the effectiveness of immunotherapy relative to antipsychotics., Conclusions: Routine screening for AE in patients with FEP may be cost-effective in the United States. As further immunotherapy effectiveness data become available, a more definitive recommendation to perform routine screening could be warranted., (© Copyright 2020 Physicians Postgraduate Press, Inc.)

Background: Preexposure prophylaxis (PrEP) is effective at preventing HIV infection among men who have sex with men (MSM), but there is uncertainty about how to identify high-risk MSM who should receive PrEP., Methods: We used a mathematical model to assess the cost-effectiveness of using the HIV Incidence Risk Index for MSM (HIRI-MSM) questionnaire to target PrEP to high-risk MSM. We simulated strategies of no PrEP, PrEP available to all MSM, and eligibility thresholds set to HIRI-MSM scores between 5 and 45, in increments of 5 (where a higher score predicts greater HIV risk). Based on the iPrEx, IPERGAY, and PROUD trials, we evaluated PrEP efficacies from 44% to 86% and annual costs from $5900 to 8700. We designate strategies with incremental cost-effectiveness ratio (ICER) ≤$100,000/quality-adjusted life-year (QALY) as "cost-effective.", Results: Over 20 years, making PrEP available to all MSM is projected to prevent 33.5% of new HIV infections, with an ICER of $1,474,000/QALY. Increasing the HIRI-MSM score threshold reduces the prevented infections, but improves cost-effectiveness. A threshold score of 25 is projected to be optimal (most QALYs gained while still being cost-effective) over a wide range of realistic PrEP efficacies and costs. At low cost and high efficacy (IPERGAY), thresholds of 15 or 20 are optimal across a range of other input assumptions; at high cost and low efficacy (iPrEx), 25 or 30 are generally optimal., Conclusions: The HIRI-MSM provides a clinically actionable means of guiding PrEP use. Using a score of 25 to determine PrEP eligibility could facilitate cost-effective use of PrEP among high-risk MSM who will benefit from it most.