What do screening tools measure? Lessons learned from SCREEN II and SNAQ65+

Borkent, Jos W.; Schuurman, Lisanne T.; Beelen, Janne; Linschooten, Joost O.; Keller, Heather; Roodenburg, Annet J. C.; Schueren, Marian A.E. de van der

doi:10.1016/j.clnesp.2020.05.008

Cited by 14 publications

(15 citation statements)

References 22 publications

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“…This could indicate that the DETERMINE tool might be more discriminatory than MUST in detecting earlier signs of nutritional risk, before there is substantial unintentional weight loss or reductions in body weight. Our findings are consistent with those of Borkent and colleagues, in their study comparing two different screening tools in the community setting, in which they highlighted the distinction between screening tools for nutrition risk and for overt malnutrition [ 34 ]. It is likely that the two tools examined in our study measure different aspects of nutritional risk.…”

Section: Discussionsupporting

confidence: 93%

“…It is likely that the two tools examined in our study measure different aspects of nutritional risk. The MUST tool, in similarity with most screening tools that have been validated in older adults, focuses on identifying risk of protein-energy malnutrition [ 34 ]. Conversely, DETERMINE is one of a few tools that focuses on identifying other aspects of nutrition risk, including risk factors for nutritional problems in later life, and could thus be used to complement the MUST, in community-living older people [ 8 , 34 ].…”

Section: Discussionmentioning

confidence: 99%

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Nutritional risk and its relationship with physical function in community-dwelling older adults

et al. 2022

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Background Malnutrition is a serious concern in older populations. Simple screening approaches are needed to identify signs of early nutritional risk in older people, to allow intervention before overt malnutrition develops, along with the poorer health outcomes associated with it, such as sarcopaenia and frailty. The main aim of this study was to compare nutrition risk scores, calculated from the DETERMINE Checklist (‘Determine Your Nutritional Health’, also known as the Nutrition Screening Initiative Checklist), with physical function variables in a group of community-dwelling older adults. Another aim was to assess the prevalence of nutrition risk using the DETERMINE and the MUST (Malnutrition Universal Screening Tool). Methods Participants of the Hertfordshire Cohort Study (HCS) were recruited and visited at home by a trained researcher. Self-reported physical function was assessed using the SF-36 PF (Short Form-36 Physical Function) scale. The Short Physical Performance Battery (SPPB) was performed, which included the assessment of gait speed, chair rise time and standing balance. Handgrip strength was measured using a Jamar dynamometer. Frailty was assessed according to the presence of at least three of the following Fried frailty criteria: unintentional weight loss, weakness, self-reported exhaustion, slow gait speed and low physical activity. Nutrition risk scores were calculated from the DETERMINE checklist (range 0–21). Nutritional risk was also assessed using the MUST. Analyses were adjusted for sex, age, age left education and number of comorbidities. Results In the study, 176 participants (94 men and 82 women), median age 83.3 (IQR 81.5–85.7) years, were assessed. Almost half (47%) scored either ‘moderate’ (score 3–5) or ‘high’ (score ≥ 6) nutritional risk (9% were at high risk), using the DETERMINE checklist, whereas 8% were at risk using the MUST. Higher nutrition risk scores, calculated from DETERMINE, were associated with poorer self-reported physical function (difference in SF-36 PF score: − 0.36, 95% CI (− 0.60, − 0.12) SD per unit increase in nutrition risk score, P = 0.004) and higher odds of being frail (odds ratio Fried frailty: 2.23, 95% CI (1.15, 4.33), P = 0.017). There were no significant associations between DETERMINE nutrition risk scores and the other variables examined. Conclusion Cross-sectional associations between higher nutrition risk scores, assessed from the DETERMINE checklist, and poorer self-reported physical function and greater likelihood of frailty suggest that this screening tool may have utility for screening older populations. Prospective studies are required to explore the ability of the tool to predict poor physical function and frailty, though these data suggest it has potential for early, simple detection of nutritional problems in community-living older adults.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Nutritional risk and its relationship with physical function in community-dwelling older adults

et al. 2022

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“…As previous studies showed that malnutrition is related to very old age (≤ 90 years vs. ≥ 91 years) (32), number of comorbidities (≤1 vs. ≥2) (33,34) and living status before admission (alone vs. together) (35), all regression analyses were adjusted for these variables (model 1). As most behavioralcognitive problems are associated with each other, a full model was created which included in addition all five determinants (CS, DRS, RISE, ABS and CPS) (full model).…”

Section: Discussionmentioning

confidence: 99%

Behavioral and Cognitive Problems as Determinants of Malnutrition in Long-Term Care Facilities, a Cross-Sectional and Prospective Study

Borkent

Hout²,

Feskens³

et al. 2022

The Journal of nutrition, health and aging

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Objectives To investigate the cross-sectional and prospective associations between behavior and cognitive problems and malnutrition in long-term care facilities (LTCF). Design Cross-sectional and prospective routine care cohort study. Setting 6874 Residents in Dutch LTCFs (period 2005–2020). Participants Data were obtained from the InterRAI-LTCF instrument. Cross-sectional analyses on prevalence of malnutrition at admission included 3722 residents. Prospective analyses studied incident malnutrition during stay (total follow-up time 7104 years) and included data of 1826 residents with first measurement on admission (‘newly-admitted’) and n=3152 with first measurement on average ∼1 year after admission (‘existing’). Measurements InterRAI scales for communication problems (CS), aggressive behavior (ABS), social engagement (RISE), depressive symptoms (DRS), cognitive performance (CPS) and the total number of behavior and cognitive problems were investigated as independent variables and malnutrition (ESPEN 2015 definition) as dependent variable in regression analyses. Results were stratified for gender and group ‘newly-admitted’ vs. ‘existing’. Results On admission, 9.5% of residents was malnourished. In men, low social engagement was associated with prevalence of malnutrition. In women, all behavior and cognitive problems except depression were associated with malnutrition in the unadjusted analyses, but this attenuated in the full model taking all problems into account. The incidence of malnutrition during stay amounted to 8.9%. No significant associations of behavior and cognitive problems with malnutrition incidence were seen in ‘newly-admitted’ male residents while in ‘existing’ male residents all determinants were significantly associated. In ‘newly-admitted’ female residents CS, ABS and CPS, and in ‘existing’ female residents CS, RISE, ABS and CPS were significantly associated with incident malnutrition. All associations slightly attenuated after adjustment. Malnutrition incidence increased with increasing number of combined behavior and cognitive problems. Conclusion Residents with behavior and cognitive problems are at an increased risk of being malnourished at admission, or becoming malnourished during stay in a LTCF, especially residents with multiple behavior and cognitive problems.

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“…Two questions addressing these determinants are ‘eating alone’ and ‘inability to perform grocery shopping’, but we do not have data to test how these two questions relate to psychological or physical health. A recent publication showed that SCREEN II was associated with food intake but only in a lesser extent to physical parameters, while the SNAQ 65+ screening tool was more related to physical health [ 20 ]. The combination of the two might provide supplementary information on undernutrition risk, covering both early stage and late stage malnutrition [ 20 ].…”

Section: Discussionmentioning

confidence: 99%

Cross-Country Differences and Similarities in Undernutrition Prevalence and Risk as Measured by SCREEN II in Community-Dwelling Older Adults

et al. 2020

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Undernutrition is highly prevalent among community-dwelling older adults. Early identification of nutrition risk is important to prevent or treat undernutrition. This study describes the prevalence rates of nutrition risk in community-dwelling older adults (aged ≥ 65) using the same validated tool across different countries and aims to identify differences in nutritional risk factors. Cross-sectional data was obtained from three datasets including participants from the Netherlands (NL), Canada (CA) and New Zealand (NZ). Seniors in the Community Risk Evaluation for Eating and Nutrition II (SCREEN II) was used to assess nutritional risk factors and prevalence of risk. Differences between countries were tested with logistic and linear regression. Sensitivity analyses were conducted to test the influence of sampling strategy. A total of 13,340 participants were included, and 66.3% were found to be at high nutrition risk. After stratifying the data for method of data sampling, prevalence rates showed some differences across countries (NL: 61.5%, NZ: 68.2%, CA: 70.1%). Risk factor items that contributed to nutrition risk also differed among countries: NZ and CA participants scored higher for weight change, skipping meals, problems with meal preparation, use of meal replacements, problems with biting and chewing, low fluid intake and problems with doing groceries, as compared to participants in NL. Low intake of fruits and vegetables and meat were more prevalent in NL. In conclusion: nutrition risk is a worldwide, highly prevalent problem among community-dwelling older adults, but risk factors contributing to nutrition risk differ by country.

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What do screening tools measure? Lessons learned from SCREEN II and SNAQ65+

Cited by 14 publications

References 22 publications

Nutritional risk and its relationship with physical function in community-dwelling older adults

Nutritional risk and its relationship with physical function in community-dwelling older adults

Behavioral and Cognitive Problems as Determinants of Malnutrition in Long-Term Care Facilities, a Cross-Sectional and Prospective Study

Cross-Country Differences and Similarities in Undernutrition Prevalence and Risk as Measured by SCREEN II in Community-Dwelling Older Adults

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