Unplanned Hospital Visits and Poor Oral Health With Undernutrition in Nursing Home Residents

Aishima, Miya; Ishikawa, Takako; Ikuta, Kasumi; Noguchi-Watanabe, Maiko; Nonaka, Sayuri; Takahashi, Kunihiko; Anzai, Tatsuhiko; Fukui, Sakiko

doi:10.1016/j.jamda.2023.07.013

Cited by 5 publications

(4 citation statements)

References 35 publications

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“…Easily applicable research like this is expected to continue to be widely conducted. Only one study has utilized LIFE data, collected by the Ministry of Health, Labor and Welfare since 2021, and this study used data from facilities (60). Although currently underutilized, this rich dataset holds potential for broader application in future research, thereby enhancing studies related to LTC (61).…”

Section: Discussion Of Current Status and Perspectives Of Studies Usi...mentioning

confidence: 99%

Utilization of Japanese long-term care-related data including Kaigo-DB: An analysis of current trends and future directions

Watanabe,

Tamiya

2024

GHM

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Despite high expectations from the government and researchers regarding data utilization, comprehensive analysis of long-term care (LTC)-related data use has been limited. This study reviewed the use of LTC-related data, including Kaigo-DB, in Japan after 2020. There was an increase in studies using LTC-related data in Japan between 2020 and 2021, followed by a stabilization period. The national government provided 13.5% of this data (6.5% from Kaigo-DB), while prefectures and municipalities contributed 85.2%, and facilities provided 1.3%. The linked data used in 90.4% of the studies primarily consisted of original questionnaire or interview surveys (34.6%) and medical claims (34.0%). None of the studies based on Kaigo-DB utilized linked data. In terms of study design, cohort studies were the most common (84.6%), followed by descriptive (5.1%), cross-sectional (3.2%), and case-control studies (1.3%). Among the 138 individual-based analytical descriptive studies, the most frequently used LTC-related data as an exposure was LTC services (26.8%), and the most common data used as an outcome was LTC certification or care need level (43.5%), followed by the independence degree of daily living for the older adults with dementia (18.1%). To enhance the use of LTC-related data, especially the valuable national Kaigo-DB, insights can be gleaned from how researchers effectively utilize municipal and prefectural data. Streamlining access to Kaigo-DB and enabling its linkage with other datasets are promising for future research in this field.

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Section: Discussion Of Current Status and Perspectives Of Studies Usi...mentioning

confidence: 99%

Utilization of Japanese long-term care-related data including Kaigo-DB: An analysis of current trends and future directions

Watanabe,

Tamiya

2024

GHM

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“…Therefore, the present study suggested that older adults with poor perception of oral health were more likely to have an unfavorable BMI. Moreover, a recent retrospective cohort study showed that poor oral health also with undernutrition could be used as an indicator to identify older nursing home residents at high risk for unplanned hospital visits [ 44 ]. The application of OHRQoL tools together with objective clinical oral indicators needs to be emphasized as it could be utilized as oral health predictors that might lead to impaired nutrition in older populations.…”

Section: Discussionmentioning

confidence: 99%

The Relationship between Oral Health-Related Quality of Life and Body Mass Index in an Older Population from Southern Italy: The Salus in Apulia Study

Dibello,

Lobbezoo,

Sardone

et al. 2023

JPM

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Background: The assessment of oral health-related quality of life (OHRQoL) evaluated the impact of an individual’s oral health on the patient’s physical and psychosocial status. We evaluated the association between subjective OHRQoL, measured with the Oral Health Impact Profile-14 (OHIP-14) questionnaire, and unfavorable body mass index (BMI) (i.e., too high or too low) in a large population-based study on older adults from Southern Italy. Moreover, we assessed which of the seven OHIP-14 domains was the most strongly associated with an unfavorable BMI. Methods: We used data on a subpopulation of the Salus in Apulia Study, including 216 older adults. BMI < 18.4 kg/m2 and >30 kg/m2 were classified as unfavorable, while values between 18.5 and 30 kg/m2 were classified as ideal. Results: A higher OHIP-14 total score increased the risk of an unfavorable BMI (odds ratio (OR): 1.08, 95% confidence interval (CI): 1.01–1.15). In the model adjusted for age, sex, education, hypertension, carbohydrate consumption, and alcohol consumption, this finding was confirmed with a higher OHIP-14 total score increasing the risk of an unfavorable BMI (OR: 1.10, 95% CI: 1.01–1.22), and higher age linked to a decreased risk of an unfavorable BMI (OR: 0.89, 95% CI: 0.82–0.97). In a random forest regression model, the most important predictive domains/sub-scales of OHIP-14 in the mean decrease in the Gini coefficient for unfavorable BMI were, in order of decreasing importance, physical pain, functional limitation, psychological discomfort, physical disability, social disability, psychological disability, and handicap. Conclusions: In older age, negative OHRQoL, particularly linked to the physical pain domain, increased the risk of being underweight or overweight and obesity.

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“…The details of the LIFE have been reported previously. [17][18][19][20] In brief, the Japanese Ministry of Health, Labor, and Welfare launched the LIFE long-term care insurance service database in April 2021. This database stores information on diseases, physical and cognitive functions, rehabilitation goals and interventions, ADL, instrumental ADL, and nutrition.…”

Section: Methodsmentioning

confidence: 99%

“…The Longterm care Information system For Evidence (LIFE) database collects data on nutrition to improve nursing care. [17][18][19][20] The data collected by the LIFE include motivation to eat, appetite and food satisfaction, and food consciousness. However, the potential differing effects of these items on dietary intake and nutritional status are unknown.…”

Section: Introductionmentioning

confidence: 99%

Impact of Motivation for Eating Habits, Appetite and Food Satisfaction, and Food Consciousness on Food Intake and Weight Loss in Older Nursing Home Patients

Wakabayashi,

Kinoshita,

Isowa

et al. 2024

Ann Geriatr Med Res

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Background: This study analyzed data from the Long-term care Information system For Evidence (LIFE) database to examine the effects of motivation to eat, appetite and food satisfaction, and food consciousness on food intake and weight loss.Methods: Of the 748 nursing home residents enrolled in the LIFE database, 336 met the eligibility criteria for this cross-sectional study. Motivation to eat, appetite and food satisfaction, and food consciousness were rated on five-point Likert scales (e.g., good, fair, normal, not so good, and not good). We applied Spearman rank correlation coefficient and multiple regression analyses to analyze the relationships between these three items, daily energy and protein intake, and body weight loss over 6 months.Results: The mean participant age was 87.4±8.1 years and 259 (77%) were female. The required levels of care included—level 1, 1 (0%); level 2, 4 (1%); level 3, 107 (32%); level 4, 135 (40%); and level 5, 89 (27%). The mean daily energy intake was 28.2±7.8 kcal/kg. The mean daily protein intake was 1.1±0.3 g/kg. The mean weight loss over six months was 1.2±0.7 kg. We observed strong positive correlations among motivation to eat, appetite and food satisfaction, and food consciousness (r>0.8). These three items were significantly associated with higher daily energy intake but not with daily protein intake. Only appetite and food satisfaction were significantly associated with lower weight loss over six months.Conclusion: The observed associations of appetite and food satisfaction suggest that these factors may be more important to assess than motivation to eat or food consciousness among older adult residents of long-term care facilities.

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Unplanned Hospital Visits and Poor Oral Health With Undernutrition in Nursing Home Residents

Cited by 5 publications

References 35 publications

Utilization of Japanese long-term care-related data including Kaigo-DB: An analysis of current trends and future directions

Utilization of Japanese long-term care-related data including Kaigo-DB: An analysis of current trends and future directions

The Relationship between Oral Health-Related Quality of Life and Body Mass Index in an Older Population from Southern Italy: The Salus in Apulia Study

Impact of Motivation for Eating Habits, Appetite and Food Satisfaction, and Food Consciousness on Food Intake and Weight Loss in Older Nursing Home Patients

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