School Disengagement and Mental Health Service Intensity Need Among Clinically Referred Students Utilizing the interRAI Child and Youth Mental Health Assessment Instrument

Although mental health challenges are widespread, impacting 1 in 5 children and youth, only 25% of these young people receive the required mental health supports. Unmet mental health needs are strongly associated with functional impairments including poor self-care, interpersonal challenges, and school difficulties among young people. School disengagement, or a student's lack of involvement in education through interest, curiosity, motivation, and active participation, is associated with a wide array of detrimental outcomes including chronic mental health difficulties, conduct and delinquent behaviors, criminal justice involvement, and unemployment in adolescence and adulthood. Disengagement observed within the school setting may be indicative of underlying mental health challenges and reflective of service intensity need. The current study extends the literature by examining the relationship between school disengagement and mental health service intensity need among 14,750 clinically referred students across elementary and secondary school utilizing the interRAI Child and Youth Mental Health instrument. Findings indicated that more than 25% of clinically referred students were at heighted risk for school disengagement and required high-intensity services. Further, mental health service intensity need was positively associated with risk of school disengagement among students, along with the specific reason for referral (i.e., psychiatric symptoms, harm to self, harm to others, or addiction or dependency), after controlling for sex and age. Implications of the findings are explored within the context of the school setting and future directions are suggested.

An Evaluation of the Service Intensity Add-On Payment Policy Reform in the Medicare Hospice Benefit

In 2016, the Centers for Medicare & Medicaid Services (CMS) implemented the Service Intensity Add-On (SIA) payment, which incentivized skilled nurse and medical social worker (SN/MSW) visits in the last seven days of life. Little is known about the impact of this initiative. Using 100% Medicare hospice claims, we identified a 10% random sample of Medicare hospice beneficiaries utilizing routine home care service during calendar years 2012-2018. We compared the provision of SN/MSW visits on service dates before and after the SIA’s implementation relative to beneficiaries’ date of death. We also determined hospice providers’ success in providing SN/MSW visits in the last days of life and categorized all providers into quintiles according to the average rate of these visits in the period prior to the SIA’s implementation. Cumulative over the last seven days of life, we calculated an increase of 15.7 SN/MSW minutes (95% confidence internal [CI] 14.9-16.5 minutes) per beneficiary after the SIA was implemented. The per-minute increase was greatest on days nearer to death (4.0 minutes day of death, 95% CI 3.6-4.2). There was no detectable visit increase on days which were ineligible for the SIA. Additionally, those providers in the quintile providing the lowest rate of SN/MSW visits pre-SIA exhibited a 14-percentage point increase in rates of these visits, the third, fourth, and fifth quintiles exhibited little change over time. Further monitoring is needed to ensure beneficiaries receive adequate end-of-life care.

GIS-Based Spatial Autocorrelation Analysis of Housing Prices Oriented towards a View of Spatiotemporal Homogeneity and Nonstationarity: A Case Study of Guangzhou, China

In the past decades, the booming growth of housing markets in China triggers the urgent need to explore how the rapid urban spatial expansion, large-scale urban infrastructural development, and fast-changing urban planning determine the housing price changes and spatial differentiation. It is of great significance to promote the existing governing policy and mechanism of housing market and the reform of real-estate system. At the level of city, an empirical analysis is implemented with the traditional econometric models of regressive analysis and GIS-based spatial autocorrelation models, focusing in examining and characterizing the spatial homogeneity and nonstationarity of housing prices in Guangzhou, China. There are 141 neigborhoods in Guangzhou identified as the independent individuals (named as area units), and their values of the average annual housing prices (AAHP) in (2009–2015) are clarified as the dependent variables in regressing analysis models used in this paper. Simultaneously, the factors including geographical location, transportation accessibility, commercial service intensity, and public service intensity are identified as independent variables in the context of urban development and planning. The integration and comparative analysis of multiple linear regression models, spatial autocorrelation models, and geographically weighted regressing (GWR) models are implemented, focusing on exploring the influencing factors of house prices, especially characterizing the spatial heterogeneity and nonstationarity of housing prices oriented towards the spatial differences of urban spatial development, infrastructure layout, land use, and planning. This has the potential to enrich the current approaches to the complex quantitative analysis modelling of housing prices. Particularly, it is favorable to examine and characterize what and how to determine the spatial homogeneity and nonstationarity of housing prices oriented towards a microscale geospatial perspective. Therefore, this study should be significant to drive essential changes to develop a more efficient, sustainable, and competitive real-estate system at the level of city, especially for the emerging and dynamic housing markets in the megacities in China.