Medicare and Medicaid: conflicting incentives for long-term care

Healthcare costs for legally adjudicated elder mistreatment victims in comparison to non-mistreated older adults

BACKGROUND

Elder mistreatment (EM) is associated with adverse health outcomes and healthcare utilization patterns that differ from other older adults. However, the association of EM with healthcare costs has not been examined. Our goal was to compare healthcare costs between legally adjudicated EM victims and controls.

METHODS

We used Medicare insurance claims to examine healthcare costs of EM victims in the 2 years surrounding initial mistreatment identification in comparison to matched controls. We adjusted costs using the Centers for Medicare and Medicaid Services Hierarchical Condition Categories (CMS-HCC) risk score.

RESULTS

We examined healthcare costs in 114 individuals who experienced EM and 410 matched controls. Total Medicare Parts A and B healthcare costs were similar between cases and controls in the 12 months prior to initial EM detection ($11,673 vs. $11,402, p = 0.92), but cases had significantly higher total healthcare costs during the 12 months after initial mistreatment identification ($15,927 vs. $10,805, p = 0.04). Adjusting for CMS-HCC scores, cases had, in the 12 months after initial EM identification, $5084 of additional total healthcare costs (95% confidence interval [$92, $10,077], p = 0.046) and $5817 of additional acute/subacute/post-acute costs (95% confidence interval [$1271, $10,362], p = 0.012) compared with controls. The significantly higher total costs and acute/sub-acute/post-acute costs among EM victims in the post-year were concentrated in the 120 days after EM detection.

CONCLUSIONS

Older adults experiencing EM had substantially higher total costs during the 12 months after mistreatment identification, driven by an increase in acute/sub-acute/post-acute costs and focused on the period immediately after initial EM detection.

Temp nurses go digital: Examining gig care in US nursing homes

Nursing homes in the United States have long struggled with a shortage of nurses, which has now been exacerbated by the COVID-19 crisis. In response to the shortage, nursing homes have increasingly used temporary services to fill nursing vacancies. Existing studies have examined the care provided by temporary nurses from traditional staffing agencies; however, at the time of writing, no studies have examined care provided by their counterparts from digital gig platforms. This article reports the findings from 15 months of fieldwork (6/2019-8/2019; 6/2020-5/2021) at two US nursing homes, both characterised by high gig-nurse usage. I asked the following questions: how nurses engage in and perceive gig services, and how the use of gig services affects the work environment and everyday care. The findings indicate that gig services (1) offer gig nurses monetary incentives at the expense of exaggerating the budget deficit of nursing homes, (2) increase gig nurses' working autonomy while creating a loophole in managerial oversight, (3) lead to an uneven distribution of duties between gig nurses and nursing home staff, which demoralises the latter. Taken together, gig services, while empowering the gig nurses, have resulted in the precarisation of nursing home staffing and care environments.

Changing landscape of nursing homes serving residents with dementia and mental illnesses

OBJECTIVE

Nursing homes (NHs) are serving an increasing proportion of residents with cognitive issues (e.g., dementia) and mental health conditions. This study aims to: (1) implement unsupervised machine learning to cluster NHs based on residents' dementia and mental health conditions; (2) examine NH staffing related to the clusters; and (3) investigate the association of staffing and NH quality (measured by the number of deficiencies and deficiency scores) in each cluster.

DATA SOURCES

2009-2017 Certification and Survey Provider Enhanced Reporting (CASPER) were merged with LTCFocUS.org data on NHs in the United States.

STUDY DESIGN

Unsupervised machine learning algorithm (K-means) clustered NHs based on percent residents with dementia, depression, and serious mental illness (SMI, e.g., schizophrenia, anxiety). Panel fixed-effects regressions on deficiency outcomes with staffing-cluster interactions were conducted to examine the effects of staffing on deficiency outcomes in each cluster.

DATA EXTRACTION METHODS

We identified 110,463 NH-year observations from 14,671 unique NHs using CASPER data.

PRINCIPAL FINDINGS

Three clusters were identified: low dementia and mental illnesses (Postacute Cluster); high dementia and depression, but low SMI (Long-stay Cluster); and high dementia and mental illnesses (Cognitive-mental Cluster). From 2009 to 2017, the number of Postacute Cluster NHs increased from 3074 to 5719, while the number of Long-stay Cluster NHs decreased from 6745 to 3058. NHs in Long-stay/Cognitive-mental Clusters reported slightly lower nursing staff hours in 2017. Regressions suggested the effect of increasing staffing on reducing deficiencies is statistically similar across NH clusters. For example, 1 hour increase in registered nurse hours per resident day was associated with -0.67 (standard error [SE] = 0.11), -0.88 (SE = 0.12), and -0.97 (SE = 0.15) deficiencies in Postacute Cluster, Long-stay Cluster, and Cognitive-mental Cluster, respectively.

CONCLUSIONS

Unsupervised machine learning detected a changing landscape of NH serving residents with dementia and mental illnesses, which requires assuring staffing levels and trainings are suited to residents' needs.