Does independent needs assessment limit use of publicly financed long-term care?

Strategic Coding in the Assessment of Long-Term Care Needs: Evidence From France

There is strong evidence of "upcoding" whereby health care providers overstate the severity of disease to increase billing revenue. Much less is known about strategic coding in the assessment of patient eligibility for long-term care. This paper takes advantage of a unique French linked survey dataset to document how patient assessment depends critically on the incentives of the assessing agents. I find that nursing homes assess their patients to be more disabled (thus increasing their revenue) compared to community assessors who seek to minimize disability payments levels. Public hospital-owned long-term care facilities are more likely to overrate disability levels; there is also evidence that cognitively impaired or socially disadvantaged patients exhibit more disability upcoding. In the context of nursing homes, upcoding might be read as "side-coding," driven by flaws in the assessment tool that does not allow the care provider to adequately fund the time they spend on these patients. Conversely, assessors of patients living in the community could downcode disability to shift some of the care tasks to informal caregivers.

Trajectories of long-term care entitlements in severely disabled older adults using nationwide Dutch data

Long-term care trajectories typically vary in care types, transitions and durations. Understanding these patterns can help optimize care provision. This study aimed at (1) identifying formal long-term care trajectories of severely disabled older individuals, and (2) assessing the relationships between individual characteristics and these trajectories.Nationwide administrative data from the Centre for Care Needs Assessment were used. The sample included all 140,973 individuals older than 65 and with institutional care entitlements for somatic or psychogeriatric problems on July 1, 2015. The study period was from January 2010 to June 2019. Latent class growth analysis was performed to derive distinct care trajectories. Multinomial logistic regression analysis was used to examine the relationships between individual characteristics and these trajectories.Five trajectories were identified, varying in complexity and duration of needs in long-term care. Two were characterized by mostly mild (somatic) institutional care and three by (psychogeriatric) institutional care with increasing duration and intensity. Men and younger individuals were more likely to require intensive (psychogeriatric) institutional care.This study highlighted clear divergences among formal long-term care trajectories of severely disabled older individuals. Though less generous entitlement criteria may result in relatively lower expenditures in formal (institutional) care, they also imply increased use of alternatives, such as informal and community care. These insights can be used to tailor the provision of long-term care services in the future, and inform policy related to the financial sustainability of long-term care systems.

Long-term care insurance purchase decisions of registered nurses: Deep learning versus logistic regression models

OBJECTIVE

The purpose of this study was to use a deep learning model and a traditional statistical regression model to predict the long-term care insurance decisions of registered nurses.

METHODS

We Prospectively surveyed 1,373 registered nurses with a minimum of 2 years of full-time working experience at a large medical center in Taiwan: 615 who already owned long-term care insurance (LTCI), 332 who had no intention to purchase LTCI (group 1), and 426 who intended to purchase LTCI (group 2).

RESULTS

After inverse probability of treatment weighting (IPTW), no statistically significant differences were identified in the study characteristics of the two groups. All the performance indices for the deep neural network (DNN) model were significantly higher than those of the multiple logistic regression (MLR) model (P<0.001). The strongest predictor of an individual's long-term care insurance decision was their risk propensity score, followed by their caregiving responsibilities, whether they live with older adult relatives, their experiences of catastrophic illness, and their openness to experience.

CONCLUSIONS

The DNN model is useful for predicting long-term care insurance decisions. Its prediction accuracy can be increased through training with temporal data collected from registered nurses. Future research can explore designs for two-level or multilevel models that explain the contextual effects of the risk factors on long-term care insurance decisions.

Differences in spatial patterns of long-term care depending on severity in Hokkaido, Japan

The increasing demand for long-term care (LTC) among the ageing population is a serious problem worldwide, which has greatly increased also in Japan since the introduction of the LTC insurance system there. Since there is a difference between insurers with respect to the proportion of people needing LTC, this study aimed at clarifying the spatial patterns of LTC. Insurer (n=156) LTC data for the period 2012-2019 were obtained from Ministry of Health, Labour, and Welfare and those needing LTC were classified into three classes: total, mild and severe with ageand sex-adjusted proportions needing LTC. Global and local Moran’s I statistics were calculated for each 2-year period to clarify the trends of global and local spatial clusters. From 2012 to 2019, the mean proportion of mild class cases increased (10.6% to 11.6%), whereas that of severe class cases decreased slightly (5.9% to 5.7%). The spatial pattern of the proportion of each class revealed positive spatial autocorrelation. Based on analysis by local Moran’s I, differences in spatial patterns were emphasised between the mild and severe classes. In Hokkaido, High-High clusters of mild cases were identified in the central and southern parts and severe ones in the northern and southern parts. Spatial patterns differed depending on the LTC class. Some insurers had distinctly higher or lower certification rates than those of their neighbourhoods.

Strategic cost-shifting in long-term care. Evidence from the Netherlands

With the reform in 2015 of the system of long-term care (LTC) in the Netherlands, responsibilities for the provision of social support and assistance were delegated from the central government to the municipalities. Unintentionally, the way municipalities are financed created incentives to shift cost from the local level back to central level. In this paper we examine whether municipalities respond to the prevailing financial incentives by shifting costs to the public LTC insurance scheme. Using data on almost all Dutch municipalities over the period 2015-2019, we estimate that municipalities with a solvency rate below 20% have a 2.5% higher admission rate to the public LTC scheme. Furthermore, we show that the tightening municipal budgets for social care since 2017 were accompanied with about 14% higher admission rates in 2018 and 2019 compared to 2015. The results point to strategic cost shifting by municipalities that can be counteracted by changing the financial incentives for municipalities and by reducing the existing overlap between the local and central care domains.