Financial social protection and individual out-of-pocket costs of long-term care in the USA and Europe: An observational study.

Background: Empirical evidence informing policies aiming at ensuring affordability of long-term care (LTC) costs is limited. Combining system-level with individual-level data, we quantify the burden of out-of-pocket costs of LTC services on households in 13 European countries and the USA and explore how social protection systems impact affordability of care. Methods: In this observational study, we use harmonised data from the Health and Retirement Study (HRS), collected between 2012 and 2016, and from the Survey of Health, Ageing and Retirement in Europe, collected between 2013 and 2017. We assess the severity of LTC needs of older adults (65+) on the basis of self-reported limitations in activities of daily living (ADLs) and instrumental activities of daily living (IADLs). We classify countries' social protection systems in terms of affordability and progressivity using information obtained from country officials. We examine variation in individual-level out-of-pocket LTC costs by social protection systems' affordability and progressivity. Findings: Out-of-pocket LTC costs are heterogeneous across countries and increase with individuals' needs. In countries where LTC is more affordable and social protection systems less progressive, older adults incur significantly lower levels of LTC costs. Within Europe, not only are costs lower where systems are characterized by higher affordability and lower progressivity, but they also represent a lower share of households' disposable income. Interpretation: Our findings indicate that the social protection systems significantly affect the level of out-of-pocket costs faced and reported by older adults with LTC needs as well as the share of their income that is devoted to pay for care. Funding: We received funding from the National Institute on Aging (grant number R01 AG030153). The OECD programme of work on ageing and long-term care is partly funded by the European Union.

A comparative assessment of action plans on antimicrobial resistance from OECD and G20 countries using natural language processing.

Following the launch of the Global Action Plan on antimicrobial resistance (AMR-GAP) in 2015, most OECD and G20 countries developed their own national action plans (AMR-NAPs). This is the first paper that deploys natural language processing (NLP) techniques to systematically measure and compare the extent to which AMR-NAPs from 21 OECD and G20 countries align with the AMR-GAP in terms of the strategic objectives and interventions. We quantify the extent of alignment based on two NLP metrics: term-frequency (TF) and term-frequency-inverse document frequency (TF-IDF). Quantifying TF allows us to compare the relative prominence of strategic objectives and interventions, whereas quantifying TF-IDF enables us to identify interventions that occur more frequently in each AMR-NAP. Similar to the AMR-GAP, in our sample, terms associated with optimizing antimicrobial use in human and animal health have the highest frequency (TF = 0. 287), whereas terms linked to raising AMR awareness and education have the lowest frequency (TF = 0.066). Substantial cross-country variation exists in the distribution of interventions that are distinctly frequent in each AMR-NAP. We also report new evidence on the selected policy design and monitoring and evaluation features of these documents. Our results suggest a high degree of congruence between the AMR-GAP and AMR-NAPs, with notable diversity in the spate of interventions that OECD and G20 countries discuss in their action plans.

Fulfilling the Promise of Artificial Intelligence in the Health Sector: Let's Get Real.

OBJECTIVES: This study aimed to showcase the potential and key concerns and risks of artificial intelligence (AI) in the health sector, illustrating its application with current examples, and to provide policy guidance for the development, assessment, and adoption of AI technologies to advance policy objectives. METHODS: Nonsystematic scan and analysis of peer-reviewed and gray literature on AI in the health sector, focusing on key insights for policy and governance. RESULTS: The application of AI in the health sector is currently in the early stages. Most applications have not been scaled beyond the research setting. The use in real-world clinical settings is especially nascent, with more evidence in public health, biomedical research, and "back office" administration. Deploying AI in the health sector carries risks and hazards that must be managed proactively by policy makers. For AI to produce positive health and policy outcomes, 5 key areas for policy are proposed, including health data governance, operationalizing AI principles, flexible regulation, skills among health workers and patients, and strategic public investment. CONCLUSIONS: AI is not a panacea, but a tool to address specific problems. Its successful development and adoption require data governance that ensures high-quality data are available and secure; relevant actors can access technical infrastructure and resources; regulatory frameworks promote trustworthy AI products; and health workers and patients have the information and skills to use AI products and services safely, effectively, and efficiently. All of this requires considerable investment and international collaboration.

Financial strategies targeting healthcare providers to promote the prudent use of antibiotics: a systematic review of the evidence.

Improving prudent use of antibiotics is one way to limit the spread of antimicrobial resistance (AMR). The objective of this systematic review was to assess the effects of financial strategies targeting healthcare providers on the prudent use of antibiotics. A systematic review of the literature was conducted searching PubMed, Embase and Cochrane databases, and the grey literature. Search terms related to antibacterial agents, drug resistance, financial strategies, and healthcare providers and/or prescribers. Twenty-two articles were included in the review, reporting on capitation and salary reimbursement, cost containment interventions, pay-for-performance initiatives, penalties, and a one-off bonus payment. There was substantial variation in the reported outcomes describing prescribing behaviours, including proportion of patients prescribed antibiotics, antibiotic prescriptions per patient, and number of cases treated with recommended antibiotic therapy. All financial strategies were associated with improvements in the appropriate prescription of antibiotics in the short-term, although the magnitude of observed effects varied across financial strategies. Financial penalties were associated with the greatest decreases in inappropriate antibiotic prescriptions, followed by capitation models and pay-for-performance schemes that paid bonuses upon achievement of performance targets. However, the risk of bias across studies must be noted. Findings point to the viability of financial strategies to promote the prudent use of antibiotics. Measuring the downstream impact of prescriber behaviour changes is key to estimating the true value of such interventions to tackle AMR. Research efforts should continue to build the evidence on causal mechanisms driving provider prescribing patterns for antibiotics and the long-term impact on antibiotic prescriptions.