First-generation somatostatin analogues (SSA) are indicated as a first-line medical treatment for acromegaly in patients with persistent disease after surgery or who are not eligible for surgery. These drugs are the largest contributor to the direct medical cost of acromegaly management worldwide. We analyze the patterns of prescription and costs of SSA for the treatment of acromegaly in Brazil.

The first-generation SSA, octreotide LAR (OCT-LAR) and lanreotide autogel (LAN-ATG), are available in Brazil to treat acromegaly through the Specialized Component of Pharmaceutical Assistance policy. The public records of the nationwide database (DATASUS) were accessed to identify the drug use patterns during the year 2022. The current values of the drug acquisition costs of each medication in 2022 were consulted at the national prices database (BPS). Results were converted in purchase power parity (PPP) dollars according to the World Bank rates.

The acquisition cost of each octreotide (OCT-LAR) ampoule was USD492.33 (10 mg), USD537.04 (20 mg), and USD703.79 (30 mg); for lanreotide (LAN-ATG), each ampoule was USD394.74 for the 60 mg dosage, and USD418.48 for the 90 mg and 120 mg dosages. The average annual cost of a monthly 30 mg dosage of OCT-LAR would be estimated as USD8,445,48 against an average annual cost of USD5,021.76 for a monthly 120 mg dosage of LAN-ATG. Thus, we estimate that USD15,520,747.59 was spent on first-generation SSA to treat acromegaly in Brazil’s Unified Health System (SUS) in the year 2022; of this, 79 percent was attributed to OCT-LAR.

Life-long treatment with SSA is related with a high economic burden in Brazil. There is a predominance of OCT-LAR prescription, where expanding the use of LAN-ATG may help reduce costs to the SUS. Nevertheless, studies and investments in other treatments, such as pituitary surgery and radiotherapy access, at a national level are also essential to improve acromegaly treatment costs.

Asthma affects individuals of all ages and is the most common chronic disease among children. The Brazilian Unified Health System (SUS) reimburses the use of mepolizumab as an additional maintenance treatment for severe eosinophilic asthma in adults. This study aimed to evaluate the cost-effectiveness of expanding the reimbursement of mepolizumab to patients aged six years and older.

The model included patients aged six years and older refractory to inhaled corticosteroid (ICS) plus long-acting beta-agonist (LABA) treatment. Baseline characteristics and outcomes were based on clinical trials and national hospitalization data. A Markov model with monthly cycles considered the health states without exacerbation, with exacerbation (including the need for oral steroids, emergency room admission, or hospitalization), and death. Direct medical costs (rate: USD1=BRL4.93) associated with all treatments were estimated from public health reimbursement sources. Quality-adjusted life years (QALY) was the primary outcome. Deterministic (Tornado) and probabilistic (Monte Carlo simulations) sensitivity analyses were conducted using Microsoft Excel.

Considering the current mepolizumab price (USD964.49) reimbursed by the Ministry of Health (MOH), the incremental cost-effectiveness ratio (ICER) was USD137,384.92/QALY (95% confidence interval: USD92,625.64, USD208,542.44), exceeding the USD24,333.86 threshold proposed by the National Committee for Health Technology Incorporation (Conitec). Using the average price from local public purchases (USD417.74), the ICER was USD96,673.51/QALY. Considering the past proposed discount from the manufacturer (USD390,93) and dose fractionation in children up to 12 years, the ICER was estimated in USD50,574.06/QALY. The most impactful parameters were the utility without exacerbation, mepolizumab cost, and relative reduction in exacerbation rate.

Considering current reimbursement prices, mepolizumab use in children and adolescents with refractory severe eosinophilic asthma is not cost effective in Brazil. Alternative scenarios with price discounts and dose fractionation may alter these conclusions.

Dengue virus is a significant public health threat in Brazil. It is transmitted by Aedes aegypti mosquitoes and causes severe symptoms such as hemorrhagic fever and dengue shock syndrome. This study explored the economic evaluation of Wolbachia mosquito replacement as a promising dengue virus control strategy.

The model considered Wolbachia mosquito replacement in seven Brazilian cities: Belo Horizonte, Campo Grande, Fortaleza, Goiânia, Manaus, Niterói, and São Paulo. A mathematical microsimulation model tracked 23 million residents over 20 years and considered transitions between five health states (susceptible, asymptomatic, ambulatory, hospitalized, and death). The current dengue control strategy and the incorporation of Wolbachia mosquito replacement were analyzed from both the health sector (public and private) and the Unified Health System perspectives. Direct costs included local dengue control program resources, Wolbachia replacement implementation, and care of patients with dengue virus. The primary outcome was disability-adjusted life-years (DALYs) averted. Sensitivity analyses were also performed.

The model projected 1,762,688 dengue cases over 20 years without Wolbachia replacement. Implementing Wolbachia replacement would prevent at least 1,295,566 cases, demonstrating a high benefit in all simulated cities. Except for Manaus and São Paulo, Wolbachia replacement was dominant (lower costs and higher effectiveness) over current control strategies. Nevertheless, estimated incremental cost-effectiveness ratios for Manaus and São Paulo, which ranged from BRL1,747.11 to BRL5,072.21 (USD309.51 to USD898.56), were well below the Brazilian cost-effectiveness threshold of BRL120,000 (USD21,258.50) for neglected diseases. Furthermore, all incremental net monetary benefit values remained positive for both scenarios in all cities—from BRL41.37 to BRL1,852.42 (USD7.33 to USD328.16).

Wolbachia replacement is a highly cost-effective option in the Brazilian context, aligning with previous international and diverse perspective studies. Sensitivity analysis and alternative scenarios confirmed the robustness of the findings.

Health technology assessment (HTA), by investigating clinical, economic, and social consequences of technologies in a country, enhances health system equity and sustainability. In low- and middle-income countries (LMICs), economic constraints and inadequate access to specialized human resources present challenges. Therefore, strategies to optimize resource allocation in the health sector are necessary.

A literature review was carried out, with studies that directly identified barriers or facilitators for the use of artificial intelligence (AI) in HTA being considered eligible. The texts were analyzed from the perspective of LMIC. The searches were carried out on 8 August 2023 using the following databases: MEDLINE via PubMed, Web of Science, and Google Scholar. The selection was performed in two stages: (i) screening by title and abstract and (ii) evaluation of the eligibility criteria in full text.

After conducting the search, five studies were selected for narrative synthesis. Evidence of the potential benefits of using AI in HTA in low- and middle-income countries includes rationalization of resources; reduction of the burden on health systems and minimization of human workload; efficiency in data analysis, including clinical data; prediction of economic impact; and support for managerial decision-making. However, important challenges were also raised, such as the deficiency of local infrastructure; the training and education of professionals; the lack of ethical regulation; and the organizational and political considerations of these countries.

There are few studies in the literature that provide scientific support on the use of AI in HTA decision-making in LMIC. The evidence points to increasing the efficiency and rationality of resources, enhancing the results arising from HTA. With this, it is expected to expand access to health technologies and enable more sustainable health systems.

Recently, there has been considerable emphasis on survival curves for data extrapolation, especially in the field of economic evaluation in oncology. Common methods for adjusting survival curves are complex and heavily reliant on individual patient data (IPD), which may not be feasible for health technology assessment (HTA). We propose an alternative method for survival curve extrapolation with direct adjustment to aggregated data.

Common parametric survival analysis models were tested: exponential, Weibull, log-normal, log-logistic, generalized gamma, and Gompertz. We had access to the IPD from a published randomized clinical trial (n=694) testing therapies (anastrozole and fulvestrant) for metastatic breast cancer with 10 years of follow-up on progression-free survival (PFS) and overall survival (OS) outcomes. After adjusting the original IPD, we sought to fit models to published aggregated data (Kaplan–Meier curves) using nonlinear regressions and optimization algorithms. Both methods were compared in terms of visual inspection and statistical fit quality (Akaike information criterion [AIC] and Bayesian information criterion [BIC]).

Survival curves directly adjusted to aggregated data showed a visually similar profile compared to IPD adjustments. According to AIC/BIC values, Weibull and generalized gamma distributions best fit OS data, both in individualized and aggregated approaches. For PFS, log-logistic and log-normal curves were the best choices for the anastrozole arm, and for fulvestrant, the best choices were log-normal and generalized gamma for individualized data, and Gompertz and generalized gamma for the aggregated method. The proposed R language code proved to be reproducible and amenable to automation in future HTA applications.

Directly adjusting survival curves to aggregated data is a simple and useful alternative in situations where access to IPD is not feasible.