Dataset Information

Cost-utility Analysis of Opportunistic and Systematic Diabetic Retinopathy Screening Strategies from the Perspective of the Brazilian Public Healthcare System.

ABSTRACT:

Objective

To perform a cost-utility analysis of diabetic retinopathy (DR) screening strategies from the perspective of the Brazilian Public Healthcare System.

Methods

A model-based economic evaluation was performed to estimate the incremental costs per quality-adjusted life-year (QALY) gained between three DR screening strategies: (1) the opportunistic ophthalmology referral-based (usual practice), (2) the systematic ophthalmology referral-based, and (3) the systematic teleophthalmology-based. The target population included individuals with type 2 diabetes (T2D) aged 40 years, without retinopathy, followed over a 40-year time horizon. A Markov model was developed with five health states and a 1-year cycle. Model parameters were based on literature and country databases. One-way and probabilistic sensitivity analyses were performed to assess model parameters' uncertainty. WHO willingness-to-pay (WHO-WTP) thresholds were used as reference (i.e. one and three times the Brazilian per capita Gross Domestic Product of R$32747 in 2018).

Results

Compared to usual practice, the systematic teleophthalmology-based screening was associated with an incremental cost of R$21445/QALY gained ($9792/QALY gained). The systematic ophthalmology referral-based screening was more expensive (incremental costs?=?R$4) and less effective (incremental QALY?=?-0.012) compared to the systematic teleophthalmology-based screening. The probability of systematic teleophthalmology-based screening being cost-effective compared to usual practice was 0.46 and 0.67 at the minimum and the maximum WHO-WTP thresholds, respectively.

Conclusion

Systematic teleophthalmology-based DR screening for the Brazilian population with T2D would be considered very cost effective compared to the opportunistic ophthalmology referral-based screening according to the WHO-WTP threshold. However, there is still a considerable amount of uncertainty around the results.

SUBMITTER: Ben AJ

PROVIDER: S-EPMC6978298 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Publications

Cost-utility Analysis of Opportunistic and Systematic Diabetic Retinopathy Screening Strategies from the Perspective of the Brazilian Public Healthcare System.

Ben Ângela J ÂJ Neyeloff Jeruza L JL de Souza Camila F CF Rosses Ana Paula O APO de Araujo Aline L AL Szortika Adriana A Locatelli Franciele F de Carvalho Gabriela G Neumann Cristina R CR

Applied health economics and health policy 20200201 1

<h4>Objective</h4>To perform a cost-utility analysis of diabetic retinopathy (DR) screening strategies from the perspective of the Brazilian Public Healthcare System.<h4>Methods</h4>A model-based economic evaluation was performed to estimate the incremental costs per quality-adjusted life-year (QALY) gained between three DR screening strategies: (1) the opportunistic ophthalmology referral-based (usual practice), (2) the systematic ophthalmology referral-based, and (3) the systematic teleophthal ...[more]

PMID: 31674001

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Project description:Aims/hypothesisIn this study we examined the cost-effectiveness of three different screening strategies for diabetic retinopathy: using a personalised adaptive model, annual screening (fixed intervals), and the current Dutch guideline (stratified based on previous retinopathy grade).MethodsFor each individual, optimal diabetic retinopathy screening intervals were determined, using a validated risk prediction model. Observational data (1998-2017) from the Hoorn Diabetes Care System cohort of people with type 2 diabetes were used (n = 5514). The missing values of retinopathy grades were imputed using two scenarios of slow and fast sight-threatening retinopathy (STR) progression. By comparing the model-based screening intervals to observed time to develop STR, the number of delayed STR diagnoses was determined. Costs were calculated using the healthcare perspective and the societal perspective. Finally, outcomes and costs were compared for the different screening strategies.ResultsFor the fast STR progression scenario, personalised screening resulted in 11.6% more delayed STR diagnoses and €11.4 less costs per patient compared to annual screening from a healthcare perspective. The personalised screening model performed better in terms of timely diagnosis of STR (8.8% less delayed STR diagnosis) but it was slightly more expensive (€1.8 per patient from a healthcare perspective) than the Dutch guideline strategy.Conclusions/interpretationThe personalised diabetic retinopathy screening model is more cost-effective than the Dutch guideline screening strategy. Although the personalised screening strategy was less effective, in terms of timely diagnosis of STR patients, than annual screening, the number of delayed STR diagnoses is low and the cost saving is considerable. With around one million people with type 2 diabetes in the Netherlands, implementing this personalised model could save €11.4 million per year compared with annual screening, at the cost of 658 delayed STR diagnoses with a maximum delayed time to diagnosis of 48 months.

Project description:This study investigated the diagnostic performance, feasibility, and end-user experiences of an artificial intelligence (AI)-assisted diabetic retinopathy (DR) screening model in real-world Australian healthcare settings. The study consisted of two components: (1) DR screening of patients using an AI-assisted system and (2) in-depth interviews with health professionals involved in implementing screening. Participants with type 1 or type 2 diabetes mellitus attending two endocrinology outpatient and three Aboriginal Medical Services clinics between March 2018 and May 2019 were invited to a prospective observational study. A single 45-degree (macula centred), non-stereoscopic, colour retinal image was taken of each eye from participants and were instantly screened for referable DR using a custom offline automated AI system. A total of 236 participants, including 174 from endocrinology and 62 from Aboriginal Medical Services clinics, provided informed consent and 203 (86.0%) were included in the analysis. A total of 33 consenting participants (14%) were excluded from the primary analysis due to ungradable or missing images from small pupils (n = 21, 63.6%), cataract (n = 7, 21.2%), poor fixation (n = 2, 6.1%), technical issues (n = 2, 6.1%), and corneal scarring (n = 1, 3%). The area under the curve, sensitivity, and specificity of the AI system for referable DR were 0.92, 96.9% and 87.7%, respectively. There were 51 disagreements between the reference standard and index test diagnoses, including 29 which were manually graded as ungradable, 21 false positives, and one false negative. A total of 28 participants (11.9%) were referred for follow-up based on new ocular findings, among whom, 15 (53.6%) were able to be contacted and 9 (60%) adhered to referral. Of 207 participants who completed a satisfaction questionnaire, 93.7% specified they were either satisfied or extremely satisfied, and 93.2% specified they would be likely or extremely likely to use this service again. Clinical staff involved in screening most frequently noted that the AI system was easy to use, and the real-time diagnostic report was useful. Our study indicates that AI-assisted DR screening model is accurate and well-accepted by patients and clinicians in endocrinology and indigenous healthcare settings. Future deployments of AI-assisted screening models would require consideration of downstream referral pathways.

Project description:BackgroundCommunity-based telemedicine screening for diabetic retinopathy (DR) has been highly recommended worldwide. However, evidence from low- and middle-income countries (LMICs) on the choice between artificial intelligence (AI)-based and manual grading-based telemedicine screening is inadequate for policy making.ObjectiveThe aim of this study was to test whether the AI model is more worthwhile than manual grading in community-based telemedicine screening for DR in the context of labor costs in urban China.MethodsWe conducted cost-effectiveness and cost-utility analyses by using decision-analytic Markov models with 30 one-year cycles from a societal perspective to compare the cost, effectiveness, and utility of 2 scenarios in telemedicine screening for DR: manual grading and an AI model. Sensitivity analyses were performed. Real-world data were obtained mainly from the Shanghai Digital Eye Disease Screening Program. The main outcomes were the incremental cost-effectiveness ratio (ICER) and the incremental cost-utility ratio (ICUR). The ICUR thresholds were set as 1 and 3 times the local gross domestic product per capita.ResultsThe total expected costs for a 65-year-old resident were US $3182.50 and US $3265.40, while the total expected years without blindness were 9.80 years and 9.83 years, and the utilities were 6.748 quality-adjusted life years (QALYs) and 6.753 QALYs in the AI model and manual grading, respectively. The ICER for the AI-assisted model was US $2553.39 per year without blindness, and the ICUR was US $15,216.96 per QALY, which indicated that AI-assisted model was not cost-effective. The sensitivity analysis suggested that if there is an increase in compliance with referrals after the adoption of AI by 7.5%, an increase in on-site screening costs in manual grading by 50%, or a decrease in on-site screening costs in the AI model by 50%, then the AI model could be the dominant strategy.ConclusionsOur study may provide a reference for policy making in planning community-based telemedicine screening for DR in LMICs. Our findings indicate that unless the referral compliance of patients with suspected DR increases, the adoption of the AI model may not improve the value of telemedicine screening compared to that of manual grading in LMICs. The main reason is that in the context of the low labor costs in LMICs, the direct health care costs saved by replacing manual grading with AI are less, and the screening effectiveness (QALYs and years without blindness) decreases. Our study suggests that the magnitude of the value generated by this technology replacement depends primarily on 2 aspects. The first is the extent of direct health care costs reduced by AI, and the second is the change in health care service utilization caused by AI. Therefore, our research can also provide analytical ideas for other health care sectors in their decision to use AI.

Project description:IntroductionThe MONALEESA-7 trial compared ribociclib plus endocrine therapy (ET) with placebo as first-line treatment of advanced luminal/HER2-negative breast cancer (ABC) in premenopausal and perimenopausal women (age <50 years) and showed significant benefits to progression-free survival and overall survival. This study aimed to compare the cost-effectiveness of ribociclib + ET versus ET alone in patients with ABC from the perspective of the Brazilian public national health system.MethodsWe calculated the incremental cost-effectiveness ratio (ICER) using a Markov model with progression-free survival, post-progression survival, and death states. We expressed ICER as incremental costs per progression-free life-year (PFLY) and quality-adjusted life-year (QALY) gained in a 10-year time horizon. We used parametric survival distributions fit to MONALEESA-7 data to generate survival distributions for progression-free and post-progression survival. The largest British preference study in breast cancer served as the basis to estimate health-state utilities. We estimated direct costs (ABC treatment, follow-up, monitoring, and adverse events) using Brazilian-specific values from public sources. An expert consensus panel determined the resource patterns required. We applied annual discounts of 5% to costs and QALYs.ResultsRibociclib + ET resulted in an incremental gain of 1.03 PFLYs and 0.80 QALYs at a cost of $37,319.31. The ICER of ribociclib + ET versus ET was $36,379.41 per PFLY gained and $46,590.79 per QALY gained. In deterministic sensitivity analysis, results were primarily affected by the annual discount rate, followed by the cost of ribociclib. In probabilistic sensitivity analysis, simulations agreed with the base-case.ConclusionRibociclib increased PFLYs and QALYs in patients with HR+/HER2- ABC when added to ET. Because Brazil does not have a formally defined cost-effectiveness threshold, other domains need to be considered for incorporation decisions, such as disease burden and humanistic impact on this young, economically active population. These findings may be useful in discussions for incorporation of ribociclib into the Brazilian public health system.

Dataset Information

Cost-utility Analysis of Opportunistic and Systematic Diabetic Retinopathy Screening Strategies from the Perspective of the Brazilian Public Healthcare System.

Objective

Methods

Results

Conclusion

Publications

Cost-utility Analysis of Opportunistic and Systematic Diabetic Retinopathy Screening Strategies from the Perspective of the Brazilian Public Healthcare System.

Similar Datasets

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