Explaining spatial accessibility to high-quality nursing home care in the US using machine learning

Reddy, Brian P.; O’Neill, Stephen; O’Neill, Ciarán

doi:10.1016/j.sste.2022.100503

Cited by 2 publications

(3 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Accessibility assessment is one of the main methods of research on the rationality of the spatial distribution [26][27][28], which is widely used in the layout evaluation of public service facilities, such as education, medical treatment, elderly care facilities, parks, and green spaces [29,30]. Countries that also face serious aging problems, such as Japan [31,32], South Korea [33], Finland [34], and the United States [35,36], have also attracted extensive attention from scholars to study the accessibility of public service facilities. With the development of new urban science, multi-source data provide important support for evaluating the accessibility of elderly care facilities [37], such as POI (point of interest) [38][39][40], street-view images [41][42][43][44], smart cards [45], and mobile phones [46].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

“…Compared with the traditional small-scale sampling method, the use of multi-source data can achieve large-scale and rapid collection of the objective distribution of urban pension service resources [47]. Methods to study accessibility usually include the gravity model method, huff model, kernel density method, network analysis method, and 2-Step floating catchment area (2SFCA) method [35,48,49], among which the 2SFCA is widely favored by researchers. The 2SFCA method stems from the floating catchment area (FCA) approach, which uses spatial interaction processes in the manipulation of supply and demand [50].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

See 1 more Smart Citation

Study on Spatial Distribution Equilibrium of Elderly Care Facilities in Downtown Shanghai

Huang

Gong

White

2022

IJERPH

View full text Add to dashboard Cite

With the growing challenge of aging populations around the world, the study of the care services for older adults is an essential initiative to accommodate the particular needs of the disadvantaged communities and promote social equity. Based on open-source data and the geographic information system (GIS), this paper quantifies and visualizes the imbalance in the spatial distribution of elderly care facilities in 14,578 neighborhoods in downtown (seven districts) Shanghai, China. Eight types of elderly care facilities were obtained from Shanghai elderly care service platform, divided into two categories according to their service scale. With the introduction of the improved Gaussian 2-step floating catchment area method, the accessibility of two category facilities was calculated. Through the global autocorrelation analysis, it is found that the accessibility of elderly care facilities has the characteristics of spatial agglomeration. Local autocorrelation analysis indicates the cold and hot spots in the accessibility agglomeration state of the two types of facilities, by which we summarized the characteristics of their spatial heterogeneity. It is found that for Category−Ⅰ, there is a large range of hot spots in Huangpu District. For Category−Ⅱ, the hot-spot and cold-spot areas show staggered distribution, and the two categories of hot spot distribution show a negative correlation. We conclude that the two categories are not evenly distributed in the urban area, which will lead to the low efficiency of resource allocation of elderly care facilities and have a negative impact on social fairness. This research offers a systematic method to study urban access to care services for older adults as well as a new perspective on improving social fairness.

show abstract

Section: Introduction 1backgroundmentioning

confidence: 99%

Section: Introduction 1backgroundmentioning

confidence: 99%

Study on Spatial Distribution Equilibrium of Elderly Care Facilities in Downtown Shanghai

Huang

Gong

White

2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…The majority of studies were published between 2020 and the end of 2022. The countries in which the studies were conducted were diverse: six studies were conducted in the US [20][21][22][23][24][25], four in Australia [26][27][28][29], three in Japan [30][31][32] and China [33][34][35], two in Korea [36,37], France [38,39], Spain [40,41], one in the United Kingdom [42], the Netherlands [43], Ireland [44], Canada [45], and Belgium [46]. The number of included LTC facilities and the size of the study population varied greatly between publications.…”

Section: Characteristics Of the Included Studiesmentioning

confidence: 99%

Data science techniques to gain novel insights into quality of care: a scoping review of long-term care for older adults

Hendriks,

Hacking,

Verbeek

et al. 2024

Explor Digit Health Technol

View full text Add to dashboard Cite

Background: The increase in powerful computers and technological devices as well as new forms of data analysis such as machine learning have resulted in the widespread availability of data science in healthcare. However, its role in organizations providing long-term care (LTC) for older people LTC for older adults has yet to be systematically synthesized. This analysis provides a state-of-the-art overview of 1) data science techniques that are used with data accumulated in LTC and for what specific purposes and, 2) the results of these techniques in researching the study objectives at hand. Methods: A scoping review based on guidelines of the Joanna Briggs Institute. PubMed and Cumulative Index to Nursing and Allied Health Literature (CINAHL) were searched using keywords related to data science techniques and LTC. The screening and selection process was carried out by two authors and was not limited by any research design or publication date. A narrative synthesis was conducted based on the two aims. Results: The search strategy yielded 1,488 studies: 27 studies were included of which the majority were conducted in the US and in a nursing home setting. Text-mining/natural language processing (NLP) and support vector machines (SVMs) were the most deployed methods; accuracy was the most used metric. These techniques were primarily utilized for researching specific adverse outcomes including the identification of risk factors for falls and the prediction of frailty. All studies concluded that these techniques are valuable for their specific purposes. Discussion: This review reveals the limited use of data science techniques on data accumulated in or by LTC facilities. The low number of included articles in this review indicate the need for strategies aimed at the effective utilization of data with data science techniques and evidence of their practical benefits. There is a need for a wider adoption of these techniques in order to exploit data to their full potential and, consequently, improve the quality of care in LTC by making data-informed decisions.

show abstract

Explaining spatial accessibility to high-quality nursing home care in the US using machine learning

Cited by 2 publications

References 46 publications

Study on Spatial Distribution Equilibrium of Elderly Care Facilities in Downtown Shanghai

Study on Spatial Distribution Equilibrium of Elderly Care Facilities in Downtown Shanghai

Data science techniques to gain novel insights into quality of care: a scoping review of long-term care for older adults

Contact Info

Product

Resources

About