Residential aged care providers rely on refundable accommodation deposits (RADs) to finance capital expenditure, but changing consumer accommodation payment preferences may reduce their access. Our study evaluated the potential impact of a significant reduction in provider RAD balances. We surveyed 300 providers across Australia in 2020 and conducted focus groups and interviews with stakeholder executives, to develop key themes using an inductive constant comparative method couched within grounded theory. We found preferences for RADs vary across providers, with those seeking to undertake capital expenditure mostly preferring RADs. Stakeholder views suggested RADs facilitate low cost capital investment for some providers and allow banks to offer more debt to more providers. However, stakeholders suggested RADs may also create a more volatile financial structure and impose barriers to entry for equity. Stakeholders suggested a significant and sustained reduction in RADs would negatively impact capital expenditure and increase provider financial risk. Our study proposes that government intervention to stop a significant reduction in provider RAD balances should only occur if access to care is threatened. Intervention options could include enforcing liquidity and capital adequacy requirements, increasing investor returns to attract more equity and facilitate more commercial debt and establishing a government backed accommodation capital facility.JEL Classification: D14, G41, G51, G53, I18

Each year, many frail older people in Australia and their informal carers, are faced with the complex choice of how to pay for nursing home accommodation (through a refundable, one-off lump sum payment on entry, daily 'rent-style' interest payments or a combination), a choice interconnected with several other financial decisions. The payment choice often needs to be made suddenly after a health shock and within tight timeframes, and impacts an older person's income, consumption, and long-term wealth. To explore what factors potentially bear on this complex choice, we modelled payment choices in online survey data using multinomial and fractional logistic regression. We found payment choices to be associated with an older person's finances, family situation and health situation, supporting that individual budgets and access to liquidity constrains choice. Informal carer characteristics including educational attainment, English-speaking background, investment risk appetite and perceived stress were associated with choices, suggesting informal carers may directly or indirectly exert influence. Nursing home providers' payment preferences were also associated with choices, suggesting potential influence due to providers' capital financing requirements. We offer policy suggestions on reducing the complexity of payment choices, and better aligning the interests of consumers and providers.

Introduction The COVID-19 pandemic has raised concerns about the persistence of symptoms after infection, commonly referred to as ‘post-COVID’ or ‘long-COVID’. While countries in high-resource countries have highlighted the increased risk of disadvantaged communities, there is limited understanding of how COVID-19 and post-COVID conditions affect marginalised populations in low-income and middle-income countries. We study the longitudinal patterns of COVID-19, post-COVID symptoms and their impact on the health-related quality of life through the IndiQol Project.Methods and analysis The IndiQol Project conducts household surveys across India to collect data on the incidence of COVID-19 and multidimensional well-being using a longitudinal design. We select a representative sample across six states surveyed over four waves. A two-stage sampling design was used to randomly select primary sampling units in rural and urban areas of each State. Using power analysis, we select an initial sample of 3000 household and survey all adult household members in each wave. The survey data will be analysed using limited dependent variable models and matching techniques to provide insights into the impact of COVID-19 pandemic and post-COVID on health and well-being of individuals in India.Ethics and dissemination Ethics approval for the IndiQol Project was obtained from the Macquarie University Human Research Ethics Committee in Sydney, Australia and Institutional Review Board of Morsel in India. The project results will be published in peer-reviewed journals. Data collected from the IndiQol project will be deposited with the EuroQol group and will be available to use by eligible researchers on approval of request.

Objectives While the economic burden imposed by dementia is well-documented, findings are mixed on health care use for those with mild cognitive impairment (MCI). Our objective was to analyse annual, non-hospital medical and pharmaceutical use patterns for older people with undiagnosed MCI and diagnosed dementia, living in the Australian community. Methods We analysed panel data from a community sample, the Sydney Memory and Ageing Study (Australia), linked to administrative data on health care use, using two-part models to estimate the probability of using health care and the annual costs incurred by study participants. Results People with MCI, unaware of their diagnoses, were significantly less likely to incur annual pathology and diagnostic imaging costs relative to cognitively normal individuals. This effect was concentrated in individuals with MCI who had non-amnestic symptoms, lived alone, or had limited carer support. Compared to cognitively normal individuals, people with MCI were predicted to have slightly lower annual costs for broad medical care categories related to the management and diagnosis of cognitive impairment, and people with dementia, substantially higher professional attendances, and pharmaceutical costs. These findings were consistent across estimation models adjusting for attrition over the study. Policy implications Diagnosis and symptom management in primary care may enable individuals with MCI to improve their quality of life and prevent more costly future health care use. However, our study found potential gaps in medical service use for people with undiagnosed MCI in the community, especially when they had less support or did not have memory symptoms. Primary care services may need to better diagnose and target these individuals.

Informal caregivers are accepting more responsibility for making financial decisions for older people with cognitive impairment. Paying for nursing home accommodation is a complex financial decision in Australia. Residents can make a lump sum payment, daily payment, or a combination of both. Due to government means-testing, the choice can impact the resident's income, assets, bequest values, and how much they pay for their aged care services. We examined the relationship between the financial literacy of informal caregivers and the accommodation payment decision. We collected data using an Australia-wide survey of informal caregivers that either made or substantially influenced the accommodation payment decision. Using the 'Big Three' questions and self-assessed financial literacy, we found that less than half of respondents were financially literate. Greater financial literacy was significantly associated with reduced decision complexity and greater decision certainty, but this relationship was moderated by nursing home behaviour and perceived time available for decision-making. Whether a nursing home suggested consulting a financial adviser, expressed a payment type preference or informed caregivers about their decision-making time period, predicted financial adviser use and the perceived complexity and certainty of decision-making.

Background and Objective The Patient-Reported Outcomes Measurement Information System (PROMIS-29) is gaining popularity as healthcare system funders increasingly seek value-based care. However, it is limited in its ability to estimate utilities and thus inform economic evaluations. This study develops the first mapping algorithm for estimating EuroQol 5-Dimension 5-Level (EQ-5D-5L) utilities from PROMIS-29 responses using a large dataset and through extensive comparisons between econometric models. Methods An online survey was conducted to collect responses to PROMIS-29 and EQ-5D-5L from the general Australian population (N = 3013). Direct and indirect mapping methods were explored, including linear regression, Tobit, generalised linear model, censored regression model, beta regression (Betamix), the adjusted limited dependent variable mixture model (ALDVMM) and generalised ordered logit. The most robust model was selected by assessing the performance based on average ten-fold cross-validation geometric mean absolute error and geometric mean squared error, the predicted mean, maximum and minimum utilities, as well as the fitting across the entire distribution. Results The direct approach using ALDVMM was considered the preferred model based on lowest geometric mean absolute error and geometric mean squared error in cross-validation (0.0882, 0.0299) and its superiority in predicting the actual observed mean, full health states and lower utility extremes. The robustness and precision in prediction across the entire distribution of utilities with ALDVMM suggest it is an accurate and valid mapping algorithm. Moreover, the suggested mapping algorithm outperformed previously published algorithms using Australian data, indicating the validity of this model for economic evaluations. Conclusions This study developed a robust algorithm to estimate EQ-5D-5L utilities from PROMIS-29. Consistent with the recent literature, the ALDVMM outperformed all other econometric models considered in this study, suggesting that the mixture models have relatively better performance and are an ideal candidate model for mapping.

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.

The International Journal of Integrated Care (IJIC) is an online, open-access, peer-reviewed scientific journal that publishes original articles in the field of integrated care on a continuous basis.IJIC has an Impact Factor of 5.120 (2020 JCR, received in June 2021)The IJIC 20th Anniversary Issue was published in 2021.

In many countries LTC providers rely on government contributions to fund capital expenditure. Providers in the US, the UK and Canada mostly rely on debt and equity sourced from financial markets. Australia has a unique financing approach to LTC capital expenditure. Aged care residents can choose to pay a lump sum to providers, known as a refundable accommodation deposit (RAD), with the accommodation payment returned to the resident or estate on leaving care. Alternatively, residents can pay for their accommodation using a daily accommodation payment (DAP) that acts like a rent, or by choosing to pay any combination of RAD and DAP.

Non-preference-based measures cannot be used to directly obtain utilities but can be converted to preference-based measures through mapping. The only mapping algorithm for estimating Child Health Utility-9D (CHU9D) utilities from Strengths and Difficulties Questionnaire (SDQ) responses has limitations. This study aimed to develop a more accurate algorithm. We used a large sample of children (n = 6898), with negligible missing data, from the Longitudinal Study of Australian Children. Exploratory factor analysis (EFA) and Spearman’s rank correlation coefficients were used to assess conceptual overlap between SDQ and CHU9D. Direct mapping (involving seven regression methods) and response mapping (involving one regression method) approaches were considered. The final model was selected by ranking the performance of each method by averaging the following across tenfold cross-validation iterations: mean absolute error (MAE), mean squared error (MSE), and MAE and MSE for two subsamples where predicted utility values were < 0.50 (poor health) or > 0.90 (healthy). External validation was conducted using data from the Child and Adolescent Mental Health Services study. SDQ and CHU9D were moderately correlated (ρ = − 0.52, p < 0.001). EFA demonstrated that all CHU9D domains were associated with four SDQ subscales. The best-performing model was the Generalized Linear Model with SDQ items and gender as predictors (full sample MAE: 0.1149; MSE: 0.0227). The new algorithm performed well in the external validation. The proposed mapping algorithm can produce robust estimates of CHU9D utilities from SDQ data for economic evaluations. Further research is warranted to assess the applicability of the algorithm among children with severe health problems.