Background Research has shown unilateral cochlear implants (CIs) significantly improve clinical outcomes and quality of life in adults. However, only 13% of eligible Swedish adults currently use a unilateral CI. The objective was to estimate the cost-effectiveness of unilateral CIs compared to a hearing aid for Swedish adults with severe to profound hearing loss. Methods A Markov model with a lifetime horizon and six-month cycle length was developed to estimate the benefits and costs of unilateral CIs from the Swedish health system perspective. A treatment pathway was developed through consultation with clinical experts to estimate resource use and costs. Unit costs were derived from the Swedish National Board of Health and Welfare and the Swedish Association of Local Authorities and Regions. Health outcomes were reported in terms of Quality Adjusted Life Years (QALYs). Results Unilateral CIs for Swedish adults with severe to profound hearing loss are likely to be deemed cost-effective when compared to a hearing aid (SEK 140,474 per QALY gained). The results were most sensitive to the age when patients are implanted with a CI and the proportion of patients eligible for CIs after triage. Conclusions An increase in the prevalence of Swedish adults with severe to profound hearing loss is expected as the population ages. Earlier implantation of unilateral CIs improves the cost-effectiveness among people eligible for CIs. Unilateral CIs are an efficacious and cost-effective option to improve hearing and quality of life in Swedish adults with severe to profound hearing loss.

Non‐preference‐based patient‐reported outcome measures (PROMs) are popular in health outcomes research. These measures, however, cannot be used to estimate health state utilities, limiting their usefulness for economic evaluations. Mapping PROMs to a multi‐attribute utility instrument is one solution. While mapping is commonly conducted using econometric techniques, failing to specify the complex interactions between variables may lead to inaccurate prediction of utilities, resulting in inaccurate estimates of cost‐effectiveness and suboptimal funding decisions. These issues can be addressed using machine learning. This paper evaluates the use of machine learning as a mapping tool. We adopt a comprehensive approach to compare six machine learning techniques with eight econometric techniques to map the Patient‐Reported Outcomes Measurement Information System Global Health 10 (PROMIS‐GH10) to the EuroQol five dimensions (EQ‐5D‐5L). Using data collected from 2015 Australians, we find the least absolute shrinkage and selection operator (LASSO) model out‐performed all machine learning techniques and the adjusted limited dependent variable mixture model (ALDVMM) out‐performed all econometric techniques, with the LASSO performing better than ALDVMM. The variable selection feature of LASSO was then used to enhance the performance of the ALDVMM in a hybrid model. Our analysis identifies the potential benefits and challenges of using machine learning techniques for mapping and offers important insights for future research.

Background: International humanitarian assistance is essential for disaster-affected populations, particularly in resource scarce settings. To target such assistance, needs assessments are required. According to internationally endorsed principles, donor governments should provide funding for humanitarian assistance based on need. Aim: The aim of this study is to explore a major donor’s use of needs assessment data in decision-making for allocations of funds for health-related humanitarian assistance contributions. Setting: This is a case study of the Swedish International Development Cooperation Agency (Sida), a major and respected international donor of humanitarian assistance. Methods: To explore Sida’s use of needs assessment data in practice for needs-based allocations, we reviewed all decision documents and assessment memoranda for humanitarian assistance contributions for 2012 using content analysis; this was followed by interviews with key personnel at Sida. Results: Our document analysis found that needs assessment data was not systematically included in Sida’s assessment memoranda and decision documents. In the interviews, we observed various descriptions of the concept of needs assessments, the importance of contextual influences as well as previous collaborations with implementing humanitarian assistance organizations. Our findings indicate that policies guiding funding decisions on humanitarian assistance need to be matched with available needs assessment data and that terminologies and concepts have to be clearly defined. Conclusion: Based on the document analysis and the interviews, it is unclear how well Sida used needs assessment data for decisions to allocate funds. However, although our observations show that needs assessments are seldom used in decision making, Sida’s use of needs assessments has improved compared to a previous study. To improve project funds allocations based on needs assessment data, it will be critical to develop distinct frameworks for allocation distributions based on needs assessment and clear definitions, measurements and interpretations of needs. Key words: Needs assessment, humanitarian assistance, disasters, donor decision-making

Objective The National Institute for Health and Care Excellence (NICE) updated its eligibility criteria for unilateral cochlear implants (UCIs) in 2019. NICE claimed this would not impact the cost-effectiveness results used within its 2009 technology appraisal guidance. This claim is uncertain given changed clinical practice and increased healthcare unit costs. Our objective was to estimate the cost-effectiveness estimates of UCIs in UK adults with severe to profound hearing loss within the contemporary NHS environment. Methods A cost–utility analysis employing a Markov model was undertaken to compare UCIs with hearing aids or no hearing aids for people with severe to profound hearing loss. A clinical pathway was developed to estimate resource use. Health-related quality of life, potential adverse events, device upgrades and device failure were captured. Unit costs were derived mostly from the NHS data. Probabilistic sensitivity analysis further assessed the effect of uncertain model inputs. Results A UCI is likely to be deemed cost-effective when compared to a hearing aid (£11,946/QALY) or no hearing aid (£10,499/QALY). A UCI has an 93.0% and 98.7% likelihood of being cost-effective within the UK adult population when compared to a hearing aid or no hearing aid, respectively. ICERs were mostly sensitive to the proportion of people eligible for cochlear implant, discount rate, surgery and device costs and processor upgrade cost. Conclusion UCIs remain cost-effective despite changes to clinical practice and increased healthcare unit costs. Updating the NICE criteria to provide better access UCIs is projected to increase annual implants in adults and children by 70% and expenditure by £28.6 million within three years. This increased access to UCIs will further improve quality of life of recipients and overall social welfare.

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