Obesity among Nursing Home Residents: Association with Potentially Avoidable Hospitalizations

Relationship between nutritional status and clinical outcomes among older individuals using long-term care services: A systematic review and meta-analysis

BACKGROUND & AIMS

Nutritional status is a significant issue in an aging society; however, the impact of the nutritional status of older individuals using long-term care services on the caregiving burden remains unclear. This systematic review and meta-analysis aimed to investigate the impact of nutritional issues on adverse outcomes in older individuals using long-term care services.

METHODS

We used data from the Cochrane Central Register of Controlled Trials, MEDLINE, Embase, Web of Science, CINAHL, and Ichu-shi Web databases. Original articles published in English or Japanese between January 2000 and July 2022 were included. The inclusion criteria were interventional and observational studies on individuals using long-term care services with aged ≥65 years and a focus on body weight or weight loss. Data on adverse outcomes related to caregiving burden, including the number of people requiring care, mortality, complications, activities of daily living (ADL), and quality of life, were collected.

RESULTS

The literature search yielded 7873 studies, of which 35 were ultimately included. Seven observational studies investigated mortality outcomes, and seven examined ADL outcomes. The meta-analysis revealed significantly higher mortality rates in individuals classified as underweight (BMI <18.5 kg/m2) than in those with BMI ≥18.5 kg/m2 (risk ratio [RR] 1.49; 95 % confidence interval [CI] 1.31 to 1.73, 0.22; I2 93 %). Further, on categorising the participants based on a BMI cutoff of 25 kg/m2, those with a BMI of <25 kg/m2 had a significantly increased mortality rate (RR 1.21; 95 % CI 1.04-1.40; I2 = 98 %). BMI and weight loss did not affect ADL.

CONCLUSIONS

Our findings indicate that underweight and weight loss are significantly associated with increased mortality in older individuals using long-term care services. Therefore, appropriate weight management is recommended for this population. However, further research is necessary owing to the high heterogeneity observed in this study.

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

BACKGROUND

The term "data science" encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications.

OBJECTIVES

This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature.

METHODS

We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care-acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture.

RESULTS

Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing.

CONCLUSION

This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.