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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, 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.

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.

Duloxetine is a treatment approved by the US Food and Drug Administration for both osteoarthritis (OA) pain and depression, though uptake of duloxetine in knee OA management varies. We examined the cost-effectiveness of adding duloxetine to knee OA care in the absence or presence of depression screening.We used the Osteoarthritis Policy Model, a validated computer microsimulation of knee OA, to examine the value of duloxetine for patients with knee OA who have moderate pain by comparing 3 strategies: 1) usual care, 2) usual care plus duloxetine for patients who screen positive for depression on the Patient Health Questionnaire 9 (PHQ-9), and 3) usual care plus universal duloxetine. Outcome measures included quality-adjusted life years (QALYs), lifetime direct medical costs, and incremental cost-effectiveness ratios (ICERs), discounted at 3% annually. Model inputs, drawn from the published literature and national databases, included annual cost of duloxetine ($721-937); average pain reduction for duloxetine (17.5 points on the Western Ontario and McMaster Universities Osteoarthritis Index pain scale [0-100]), and likelihood of depression remission with duloxetine (27.4%). We considered 2 willingness-to-pay (WTP) thresholds of $50,000/QALY and $100,000/QALY. We varied parameters related to the PHQ-9 and the cost of duloxetine, efficacy, and toxicities to address uncertainty in model inputs.The screening strategy led to an additional 17 QALYs per 1,000 subjects and increased costs by $289/subject (ICER = $17,000/QALY). Universal duloxetine led to an additional 31 QALYs per 1,000 subjects and $1,205 per subject (ICER = $39,300/QALY). Under the majority of sensitivity analyses, universal duloxetine was cost-effective at the $100,000/QALY threshold.The addition of duloxetine to usual care for knee OA patients with moderate pain, regardless of depressive symptoms, is cost-effective at frequently used WTP thresholds.

IMPORTANCE: Several anti–amyloid monoclonal antibodies have been developed for slowing the progression of Alzheimer disease (AD). Among the furthest developed are aducanumab, which received accelerated approval from the US Food and Drug Administration in 2021, and donanemab, which is currently undergoing phase 3 trials. The cost-effectiveness of these treatments has not been established. OBJECTIVES: To estimate the cost-effectiveness of aducanumab and donanemab relative to standard care for early AD in the US. DESIGN, SETTING, AND PARTICIPANTS: A decision analytic model was used to estimate the lifetime health and economic outcomes of adults with early AD, from US healthcare sector and societal perspectives. Simulated patients had a mean (SD) age of 75.2 (5.5) years; 65% had mild cognitive impairment and 35% had mild dementia. Analyses were conducted from April 6, 2021, to January 20, 2022. INTERVENTIONS: Standard care, aducanumab (selected inputs including disease progression hazard ratio [HR] of 0.89 [95% CI, 0.63-1.15], annual price of $28 000, and twice-yearly monitoring with magnetic resonance imaging [MRI] of the brain), or donanemab (selected inputs including disease progression HR of 0.68 [95% CI, 0.44-0.99], annual price of $28 000, and twice-yearly monitoring with MRI of the brain and amyloid positron emission tomography [PET] monitoring). Donanemab was switched to placebo after substantial amyloid reduction on PET imaging, which occurred in 27% of patients at 6 months and 55% of patients at 12 months. MAIN OUTCOMES AND MEASURES: Quality-adjusted life-years (QALYs); costs, in 2020 US dollars; incremental cost-effectiveness ratios (ICERs); and value-based prices, defined as the maximum price at which a treatment would be cost-effective given a cost-effectiveness threshold of ICER of $150 000/QALY. RESULTS: Lifetime QALYs increased by 0.133 with aducanumab and 0.408 with donanemab. Total health care sector and societal costs increased by $130 100 and $127 800, respectively, with aducanumab and by $78 700 and $71 600, respectively, with donanemab, driven largely by drug costs ($119 000 for aducanumab and $44 600 for donanemab). Health care sector and societal ICERs relative to standard care were $981 000/QALY and $964 000/QALY, respectively, for aducanumab and $193 000/QALY and $176 000/QALY, respectively, for donanemab. In sensitivity analysis, aducanumab’s value-based price remained less than $50 000/y, even when assuming a 90% reduction in disease progression. Donanemab’s value-based price surpassed $50 000/y once its efficacy exceeded 50%. CONCLUSIONS AND RELEVANCE: These findings suggest that at current expected prices, neither aducanumab nor donanemab would be cost-effective for early AD in the US. Donanemab’s dosing scheme, in which patients suspend treatment on achieving substantial amyloid reductions, may provide a rubric by which sufficiently effective anti–amyloid antibody treatments could be cost-effective even when priced comparably to other biologics.

Key Points Question What is the best method to predict which patients presenting to the emergency department will make a suicide attempt within 1 and 6 months after the visit? Findings This prognostic study of 1818 patients found that prediction of suicide attempts in the 1 month and 6 months after a patient visited an emergency department was significantly improved using machine learning models applied to data from a brief patient self-report scale, especially when supplemented with data from patients’ electronic health records and/or clinicians’ assessments. Meaning This study suggests that clinicians can improve their ability to identify patients at high risk of suicide by using data from a brief patient self-report scale and electronic health records., Importance Half of the people who die by suicide make a health care visit within 1 month of their death. However, clinicians lack the tools to identify these patients. Objective To predict suicide attempts within 1 and 6 months of presentation at an emergency department (ED) for psychiatric problems. Design, Setting, and Participants This prognostic study assessed the 1-month and 6-month risk of suicide attempts among 1818 patients presenting to an ED between February 4, 2015, and March 13, 2017, with psychiatric problems. Data analysis was performed from May 1, 2020, to November 19, 2021. Main Outcomes and Measures Suicide attempts 1 and 6 months after presentation to the ED were defined by combining data from electronic health records (EHRs) with patient 1-month (n = 1102) and 6-month (n = 1220) follow-up surveys. Ensemble machine learning was used to develop predictive models and a risk score for suicide. Results A total of 1818 patients participated in this study (1016 men [55.9%]; median age, 33 years [IQR, 24-46 years]; 266 Hispanic patients [14.6%]; 1221 non-Hispanic White patients [67.2%], 142 non-Hispanic Black patients [7.8%], 64 non-Hispanic Asian patients [3.5%], and 125 non-Hispanic patients of other race and ethnicity [6.9%]). A total of 137 of 1102 patients (12.9%; weighted prevalence) attempted suicide within 1 month, and a total of 268 of 1220 patients (22.0%; weighted prevalence) attempted suicide within 6 months. Clinicians’ assessment alone was little better than chance at predicting suicide attempts, with externally validated area under the receiver operating characteristic curve (AUC) of 0.67 for the 1-month model and 0.60 for the 6-month model. Prediction accuracy was slightly higher for models based on EHR data (1-month model: AUC, 0.71; 6 month model: AUC, 0.65) and was best using patient self-reports (1-month model: AUC, 0.76; 6-month model: AUC, 0.77), especially when patient self-reports were combined with EHR and/or clinician data (1-month model: AUC, 0.77; and 6 month model: AUC, 0.79). A model that used only 20 patient self-report questions and an EHR-based risk score performed similarly well (1-month model: AUC, 0.77; 6 month model: AUC, 0.78). In the best 1-month model, 30.7% (positive predicted value) of the patients classified as having highest risk (top 25% of the sample) made a suicide attempt within 1 month of their ED visit, accounting for 64.8% (sensitivity) of all 1-month attempts. In the best 6-month model, 46.0% (positive predicted value) of the patients classified at highest risk made a suicide attempt within 6 months of their ED visit, accounting for 50.2% (sensitivity) of all 6-month attempts. Conclusions and Relevance This prognostic study suggests that the ability to identify patients at high risk of suicide attempt after an ED visit for psychiatric problems improved using a combination of patient self-reports and EHR data., This prognostic study assesses methods of identifying patients at high risk of suicide attempts within 1 and 6 months after patients presented at an emergency department for psychiatric problems using clinician assessments, a brief patient self-report scale, and electronic health records.

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.

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.

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.

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.

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

BACKGROUND: Several cluster-randomized HIV prevention trials aim to demonstrate the population-level preventive impact of antiretroviral therapy (ART). 2013 World Health Organization (WHO) guidelines raising the ART initiation threshold to CD4 METHODS: We used a computational model to simulate strategies from a hypothetical cluster-randomized HIV prevention trial. The primary model outcome was the relative reduction in 24-month HIV incidence between control (ART offered with CD4 below threshold) and intervention (ART offered to all) strategies. We assessed this incidence reduction using the revised (CD4 RESULTS: With a control ART initiation threshold of CD4 CONCLUSIONS: Implementing the 2013 WHO HIV treatment threshold could substantially diminish the incidence reduction in HIV population prevention trials. Alternative HIV testing frequencies and trial horizons can bolster this incidence reduction, but they could be logistically and ethically challenging. The feasibility of HIV population prevention trials should be reassessed as the implementation of treatment guidelines evolves.

Importance Anti–vascular endothelial growth factor (VEGF) medicines have revolutionized diabetic macular edema (DME) treatment. A recent randomized clinical trial comparing anti-VEGF agents for patients with decreased vision from DME found that at 1 year aflibercept (2.0 mg) achieved better visual outcomes than repackaged (compounded) bevacizumab (1.25 mg) or ranibizumab (0.3 mg); the worse the starting vision, the greater the treatment benefit with aflibercept. However, aflibercept and ranibizumab, respectively, are approximately 31 and 20 times more expensive than bevacizumab. Objective To examine the incremental cost-effectiveness ratios (ICERs) of aflibercept, bevacizumab, and ranibizumab for the treatment of DME. Design, Setting, and Participants Post hoc analysis of efficacy, safety, and resource utilization data at 1-year follow-up from the Diabetic Retinopathy Clinical Research Network Comparative Effectiveness Trial. Patients were enrolled from August 22, 2012, through August 28, 2013, and analysis was performed from August 21, 2014, through November 7, 2015. Main Outcomes and Measures The ICERs for all trial participants and subgroups with baseline vision of approximate Snellen equivalent 20/32 to 20/40 (better vision) and baseline vision of approximate Snellen equivalent 20/50 or worse (worse vision). One-year trial data were used to calculate cost-effectiveness for 1 year for the 3 anti-VEGF agents; mathematical modeling was then used to project 10-year cost-effectiveness results. Results The study included 624 participants (mean [SD] age, 60.6 [10.5] years; 45.7% female; 65.5% white), 209 in the aflibercept group, 207 in the bevacizumab group, and 208 in the ranibizumab group. For all participants, during 1 year, the ICERs of aflibercept and ranibizumab compared with bevacizumab were $1 110 000 per quality-adjusted life-year (QALY) and $1 730 000 per QALY, respectively. During 10 years, they were $349 000 per QALY and $603 000 per QALY, respectively. Compared with ranibizumab, aflibercept’s ICER was $648 000 per QALY at 1 year and $203 000 per QALY at 10 years. For the subgroup with worse baseline vision, the 10-year ICERs of aflibercept and ranibizumab compared with bevacizumab were $287 000 per QALY and $817 000 per QALY, respectively. In eyes with decreased vision from DME, treatment costs of aflibercept and ranibizumab would need to decrease by 69% and 80%, respectively, to reach a cost-effectiveness threshold of $100 000 per QALY compared with bevacizumab during a 10-year horizon; for the subgroup with worse baseline vision, the costs would need to decrease by 62% and 84%, respectively. Conclusions and Relevance Aflibercept (2.0 mg) and ranibizumab (0.3 mg) are not cost-effective relative to bevacizumab for treatment of DME unless their prices decrease substantially. These results highlight the challenges that physicians, patients, and policymakers face when safety and efficacy results are at odds with cost-effectiveness results.

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.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."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.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.

Background The cost-effectiveness of early antiretroviral therapy (ART) in persons infected with human immunodeficiency virus (HIV) in serodiscordant couples is not known. Using a computer simulation of the progression of HIV infection and data from the HIV Prevention Trials Network 052 study, we projected the cost-effectiveness of early ART for such persons. Methods For HIV-infected partners in serodiscordant couples in South Africa and India, we compared the early initiation of ART with delayed ART. Five-year and lifetime outcomes included cumulative HIV transmissions, life-years, costs, and cost-effectiveness. We classified early ART as very cost-effective if its incremental cost-effectiveness ratio was less than the annual per capita gross domestic product (GDP; $8,100 in South Africa and $1,500 in India), as cost-effective if the ratio was less than three times the GDP, and as cost-saving if it resulted in a decrease in total costs and an increase in life-years, as compared with delayed ART. Results ...

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.