Abstract
Objective
To study the nutritional status of nursing home residents in a multi-racial Asian society and its role in predicting short-term mortality independent of functional status and comorbidities.
Design
Crosssectional study with prospective collection of mortality data.
Setting
Nursing home facility in Singapore.
Subjects
A total of 154 patients (mean age 77 ± 12 years, 53.2% women).
Methods
We evaluated the demographic details, Mini Nutritional Assessment (MNA) scores, body mass index (BMI) and anthropometric measurements of the participants. Functional status and comorbidities were characterized by the modified Barthel Index and Charlson's comorbidity index respectively.
Results
Prevalence of undernutrition were 52% (n= 80) and 39% (n=60) when determined by BMI <18.5kg/m2 and MNA <17 respectively. Mortality was 25.3% (n= 39) over 2 years. Baseline factors associated with mortality include increased age, low Barthel's score, BMI <18.5kg/m2 and MNA <17 (OR= 1.05, 1.01, 3.08 and 3.03 respectively, all p<0.05). The association between low BMI and mortality remained significant (p=0.027) after adjustment for patient's age, gender, Barthel's and Charlson's scores, and prior nutritional intervention, but the association between MNA and mortality was diminished (p=0.106).
Conclusion
There was a high prevalence of undernutrition in this nursing home population, and the diagnosis is an important predictor of mortality. Formal nutritional screening and targeted interventions may improve important clinical outcomes.
Key words: Nutrition, mortality, MNA, nursing home
Introduction
Undernutrition is prevalent among older persons residing in long-term care facilities (1). Medical causes which predispose the elderly to undernutrition in the nursing home setting include poor dental hygiene, swallowing difficulties, malabsorption and impaired cognition, etc (2). Despite its clinical importance, undernutrition is often under-diagnosed in the frail elderly population (3). This can be at least partially attributed to the complexity of the evaluation process (4). However the early detection of poor nutritional status is important to allow for appropriate interventions which may have significant impact on patient morbidity and mortality (5, 6). Thus, nutritional assessment should be considered in any comprehensive evaluation of elderly patients (7).
Detection of malnutrition can be facilitated by the use of a simple and reliable nutritional screening tool. The Mini Nutritional Assessment (MNA) is an easily administered, validated and widely used clinical tool which can be performed in 15 minutes without the need for biochemical testing (7). While MNA has been shown previously to predict adverse clinical outcomes in hospitalized older people as well as those in a sub-acute care facility, its association with mortality in the institutionalized older persons has been less well studied ( 8., 9., 10.).
When evaluating the role of nutrition in influencing mortality of nursing home residents, it is important to acknowledge that various other factors may also contribute to the increased risk for mortality amongst these frail older persons. Previous research has identified the importance of age, gender, functional status and medical conditions, such as malignancy, cardiac diseases and dementia, in predicting short-term mortality in the nursing home setting ( 11., 12., 13.). These variables are closely related to an individual’s nutritional status and their confounding effects should be accounted for.
In our current study, we aimed to determine the nutritional status of the older persons from an Asian population residing in a nursing home facility, and the role of undernutrition (as assessed by the MNA and other nutritional parameters) in predicting the risk for short-term mortality. We further examined the influence of age, gender, comorbidities, functional status, and prior nutritional intervention on any association observed.
Methods
Subjects
This study was conducted at a 180-bedded voluntary welfare nursing home (Thong Teck Home for Senior Citizens) in Singapore, during the period commencing from 1 August 2005 to 31 July 2007. All residents staying in the home for at least 1 month were considered for enrolment in the study. We excluded patients with clinical evidence of acute illness at the time of observation, and those with a diagnosis of terminal cancer, end-stage liver or renal disease. Informed consent was obtained from the patients or their legal guardian. The study was also approved by the Institutional Review Board.
Enrolled patients were examined by members of a multi-disciplinary team, comprising one geriatrician (medical assessment), dietician (nutritional assessment), occupational therapist and physiotherapist (functional assessment). Assessors were blinded to the results of other team members’ findings. Cause and timing of death were obtained from chart review at the end of the study period.
Nutritional assessment
A dietician performed all anthropometric measurements and clinical interviews necessary for the assessment of nutritional status. Weight was recorded to the nearest kilograms (kg) while height in metres (m) was estimated from the demi-span (distance from the tip of middle finger to midline of the sternum) of all patients, using the following equations (14):
Females: Height (m) ={[1.35 x demi-span (cm)] + 60.1}/100
Males: Height (m) = {[1.40 x demi-span (cm)] + 57.8}/100
The decision to use demi-span for height approximation was made a-priori as the majority of the residents have significant spinal deformities or are chairbound, precluding the accurate assessment of standing height.
Body mass index (BMI) was calculated as weight (kg) divided by height squared (m2). The discriminate cut off point for lower body mass index in the elderly is controversial (15). For our analysis we have decided to adopt the value used by the World Health Organization (WHO) for classification of underweight in the adult population (BMI of less than 18.5 kg/m2) (16). Anthropometric measurements of triceps skin fold thickness (TSF) and mid-arm circumference (MAC) were also performed. The triceps skin fold of the left side of the body was measured to the nearest millimeter (mm) using a Harpender skin-fold caliper, according to the standard techniques. MAC was measured to the nearest centimeter (cm) at the midpoint between the acromion and the olecranon process.
A MNA score was calculated for all participants who underwent nutritional assessment. This consist of 18 items grouped in four sections: 1) anthropometric measurements (weight, height and weight loss); 2) global assessment (lifestyle, medication use and mobility); 3) dietary assessment (number of meals, food and fluid intake, and autonomy of feeding); and subjective assessment (self-perception of health and nutrition status) (17). Participants were classified as well-nourished (MNA >24), at risk of malnutrition (MNA = 17-23.5) or malnourished (MNA <17) according to the MNA score (maximum=30). The full MNA was administered in two steps, and a score for the MNA screening form (MNA-SF) was initially calculated. This consists of 6 items from the original MNA which yields a maximum score of 14. A MNA-SF score of 11 or below suggests a risk for malnutrition (7).
Finally we also accounted for the impact of nutritional supplementation on current nutritional status and subsequent mortality by documenting the presence of prescribed artificial nutrition (either via oral supplementation or enteral tube feeding), for inclusion as a variable in the multivariate analyses.
Assessment of covariates
Variables known to be associated with survival outcomes were measured and tested for their confounding effect. Predisposing factors such as age, sex, functional status and severity of comorbid illnesses were considered for in the multivariate analyses. Baseline demographic data including age, sex, race, date of admission to nursing home and medical diagnoses were obtained from medical records during the initial medical interview.
As part of the medical review, the physician recorded the Charlson’s comorbidity index as the measure for severity of coexisting medical conditions in the current nursing home population (18). The overall Charlson’s score is derived by summing the weights assigned to all health problems suffered by a patient from a predefined list of 19 medical conditions. Charlson’s index is a widely used scoring system for comorbid severity and has been previously validated for predicting mortality in a long-term care setting (19).
Functional status was assessed by both the physiotherapist and occupational therapist who then recorded the modified Barthel’s Index (Barthel’s) score for each participant. The index is a validated activity of daily living (ADL) scale in widespread clinical use, comprising 10 activities of daily living, each with five levels of dependency; the maximum score is 100 points, representing independence in daily living (20).
Statistical analysis
Our main outcome measure in the study was mortality. Since mortality was measured on a dichotomous scale, we used the logistic regression model to examine factors associated with the outcome in the univariate analysis. The variables we studied include gender, age, Barthel’s score, Charlson score, presence of feeding tube, BMI (<18.5 kg/m2), MNA-SF (<12), MNA (<17), MAC and TSF. Mean values for MAC and TSF were analyzed as single groups to improve statistical power as we looked for but did not detect any significant interaction between gender and mortality for these variables. In the multivariate analyses, we calculated the adjusted odds ratio of mortality, adjusting for age, gender, Barthel’s, Charlson’s scores and presence of nutritional supplementation, as these variables were known to be associated with mortality a-priori. Multivariate analysis for BMI and any components of the MNA were conducted separately to address the issue of collinearity. Further analyses with log-rank tests were performed to compare survival times for subjects with differential BMI (> or <18.5 kg/m2) and MNA (> or <17) values at baseline. Data analysis was carried out in Stata V9.2 (Stata Corp, College Station, Texas, USA) and all tests were conducted at the 5% level of significance.
Results
Baseline characteristics (Table 1)
Table 1.
Baseline characteristics, nutritional status and mortality
| N (%) | Mean (SD) | |
|---|---|---|
| Age, years | 154 | 77.0 (12.0) |
| Gender | ||
| Male | 72 (46.8) | |
| Female | 82 (53.2) | |
| Race | ||
| Chinese | 149 (96.8) | |
| Malay | 4 (2.6) | |
| Indian | 1 (0.6) | |
| Duration of stay, months | 148 | 56.1 (32.8) |
| Barthel | 147 | 41.0 (35.0) |
| Charlson score | 147 | 2.3 (1.7) |
| BMI, kg/m2 | ||
| BMI< 18.5 | 154 | 18.8 (3.5) |
| 80 (52) | ||
| MNA | 154 | 17.5 (3.8) |
| MNA-SF <12 | 150 (97) | |
| Total MNA< 17 | 60 (39) | |
| MAC, cm | 154 | 24.2 (3.5) |
| Males | 72 | 24.3 (3.4) |
| Females | 82 | 24.2 (3.6) |
| TSF, mm | 154 | 11.2 (4.9) |
| Males | 72 | 9.7 (4.1) |
| Females | 82 | 12.5 (5.3) |
| Nutritional Intervention | ||
| Oral supplements | 7 (4.5) | |
| Presence of feeding tube | 16 (10.4) | |
| Deaths | 39 (25.3) |
BMI = Body mass index, MNA = Mini-nutritional assessment, MNA-SF = Mini-nutritional assessment screening form, MAC = Mid-arm circumference, TSF = Triceps skin fold thickness.
All 180 residents of the nursing home were screened of which 158 agreed for participation. 4 residents with diagnoses of cancer were excluded from the study and all the remaining 154 (100%) participants underwent nutritional assessment. 149 (96.8%) participants completed the initial medical review, and 147 (95.5%) had functional assessment done. The average age of the participants was 77 ± 12 years, with the median age being 77.5 years. Women made up slightly more than half of the study population (52%), and the majority (96.8%) of the participants were Chinese. The average length of stay in nursing home was 56.1 ±32.8 months.
The average score for the modified Barthel’s Index was 41 ±35, indicating a highly dependent population of elderly. Indeed, a significant portion of the participants (37.7%) have Barthel’s scores less than 20, indicating severe impairment in function. The presence of a frail population is again suggested by a high average score for the Charlson’s (2.3±1.7). The majority of participants (n=126, 85.7%) had scores of 1 or more on the scale, implying the presence of significant comorbid conditions.
Nutritional status (Table 1)
The majority of the study population were on oral feeding, with only 16 residents (10.4%) requiring enteral feeding via nasogastric or percutaneous gastrostomy tubes. Overall only 23 residents (14.9%) had received any form of nutritional supplementation prior to study commencement. The average BMI was 18.8±3.5 kg/m2, with 52% of the population having a BMI of less than 18.5 kg/m2, indicating undernutrition. This suggestion of a high prevalence of undernutrition was again noted with nutritional assessment by the MNA. The MNA-SF detected 150 residents (97%) who are at risk of malnutrition with a score of less than 12. The average total MNA score was 17.5±3.8, and 39% were classified as malnourished with a MNA score of less than 17. The mean values for MAC were 24.3±3.4cm and 24.2±3.6cm for males and females respectively, while the average values for TSF were 9.7±4.1mm (males) and 12.5± 5.3mm (females).
Mortality data and analysis (Table 1, 2 and 3)
Table 2.
Unadjusted odds ratio for mortality associated with patients’ demography, health, functional and nutritional status
| Odds ratio | 95% confidence interval | P-value | |
|---|---|---|---|
| Age | 1.05* | 1.01- 1.09 | 0.006 |
| Gender (male) | 0.64 | 0.30- 1.33 | 0.232 |
| Race (Chinese) | 3.03 | 0.41- 22.25 | 0.277 |
| Barthel | 1.01* | 1.01-1.02 | 0.025 |
| Charlson | 1.20 | 0.96- 1.51 | 0.101 |
| BMI <18.5 kg/m2 | 3.08* | 1.40- 6.78 | 0.005 |
| MNA-SF <12 | 1.02 | 0.10- 10.1 | 0.988 |
| MNA <17 | 3.03* | 1.43- 6.41 | 0.004 |
| MAC | 0.92 | 0.82- 1.02 | 0.115 |
| TSF | 0.94 | 0.86- 1.02 | 0.117 |
| Presence of feeding tube | 2.58 | 0.89- 7.47 | 0.081 |
BMI = Body mass index, MNA = Mini-nutritional assessment, MNA-SF = Mini-nutritional assessment screening form, MAC = Mid-arm circumference, TSF = Triceps skin fold thickness.
Table 3.
Multivariate analysis of nutritional outcomes associated with mortalitya
| Adjusted OR | 95% confidence interval | P-value | |
|---|---|---|---|
| BMI <18.5 kg/m2 | 2.71* | 1.12- 6.58 | 0.027 |
| MNA <17 | 2.35 | 0.83- 6.60 | 0.106 |
Adjusted for age, gender, Barthel’s and Charlson’s scores, and presence of nutritional supplementation.
The mortality rate for all subjects was 25.3% over the 2 year study period. The most frequent cause of death was infections (n= 14, 35.9%), and other causes include ischaemic heart disease, strokes, carcinoma of the lung (diagnosed after study commencement). Baseline characteristics which showed a statistically significant, though not clinically relevant, correlation with increased mortality include participant’s age (OR= 1.05, 95% CI 1.01-1.09), and lower Barthel score (OR= 1.01 95% CI 1.01-1.02).
Nutritional parameters of MAC (p = 0.115), and TSF (p = 0.117) were both not significantly associated with mortality in univariate anaylsis. Using the pre-specified cut-offs (BMI <18.5kg/m2, MNA<17), undernutrition as defined by both the BMI (OR= 3.08, 95% CI 1.40-6.78) and MNA (OR=3.03, 95% CI 1.43-6.41) were significantly associated with mortality in univariate analysis. In the subsequent multivariate model controlling for age, sex, Barthel’s and Charlson’s scores and presence of prior nutritional intervention, the association between BMI< 18.5kg/m2 and mortality (OR= 2.71, 95% CI 1.12-6.58) remained statistically significant, while MNA< 17 was no longer significantly associated with mortality (OR= 2.35, p=0.106). Log-rank test also indicated a clear reduction in survival times for subjects with lower BMI and MNA, with p-values of 0.006 and 0.003 respectively (Figures 1 and 2).
Figure 1.
Kaplan-Meier survival curves of elderly subjects with body mass index (BMI) (kg/m2) of ≥18.5 and <18.5
Figure 2.
Mini-Nutritional Assessment score (MNA) of ≥17 or <17
Discussion
This is the first study conducted which examine, in considerable detail, the nutritional status of nursing home residents in Singapore. We noted a high prevalence of undernutrition in this population of frail older persons. 52% was classified as undernourished with BMI< 18.5 kg/m2, while 39% had a MNA score of <17, indicating a malnourished state. Such findings are not surprising, as previous studies have reported prevalence rates of malnutrition ranging from 12% to 85% in the older adults at various care settings (21). The wide range of prevalence rates can be explained at least in part by the use of different instruments for nutritional assessment in the geriatric population. In a recent systematic review of nutritional status evaluation and screening tools in the elderly, the authors conceded that there is no gold standard for nutritional status assessment; however a validated set of screening tool is still useful in the individual’s clinical practice (22).
The high prevalence of undernutrition despite the availability of nutritional supplementation further highlights the need for adequate nutritional screening in nursing home populations. Prior to study commencement, there was no formal nutritional screening program in the nursing home, and residents were referred to the dietician only when deemed necessary by the nursing or medical staff. As such, only 23 out of 154 subjects had received nutritional supplementation or intervention. The mean MNA and BMI for this group of patients (n=23) were 11.7 and 16.9 kg/m2 respectively, indicating a significant degree of undernourishment lower than the means of the study population. A screening program may have detected subjects with nutritional problems at the milder range of the spectrum, who would also benefit from active intervention. A previous study had shown improvement in the nutritional status of malnourished patients offered protein-energy supplementation in the nursing home population (23). Whether such nutritional improvements can translate into favorable mortality outcome should be determined by future research.
Our current study also helped to validate the use of MNA for the assessment of protein-calorie malnutrition in our local nursing home population. Its application in various long-term care settings has been previously published (8). Past experience has also demonstrated its utility in screening for malnutrition in the Asian community-dwelling elderly population (24, 25). We noted that a diagnosis of malnutrition based on a MNA score of less than 17 was significantly associated with mortality in univariate analysis. After adjustment for the important covariates of age, gender, functional impairment and comorbidities, the association between low MNA score and mortality became less significant. This could be explained by the fact that MNA, by its inclusion of questions for global assessment, would have significant interaction with the patient’s functional status and comorbid illnesses. Nonetheless, the MNA was effective in identifying individuals of poor nutritional status, with reduced survival times as illustrated by the Kaplan-Meier plot (Figure 2).
In our group of older persons, a BMI of less than 18.5 kg/m2 was a highly significant predictor of mortality both in univariate analysis as well as in a multivariate model adjusting for important confounders. Survival times in this group of elderly were also significantly impaired compared to those with better BMI. Several large epidemiological studies have evaluated the prognostic value of BMI in populations older than 65 years (26, 27). However the relationship between BMI and mortality risk has remained controversial. Most studies demonstrate that subjects with low BMI tend to have greater mortality than those with normal or intermediate BMI, but whether a high BMI confers a survival advantage in the older adult remains a subject of ongoing debate (28). The optimal upper limit for BMI to define obesity amongst Asian populations has also not agreed upon (29, 30). However the BMI remains a commonly measured anthropometric parameter in older persons, probably because of its ease of use in various care settings. Our current study lends further credence to its utility in identifying elderly with heightened risk for short-term mortality.
Other studies have also suggested an important role for nutrition in influencing the risk of mortality for elderly patients in various settings. Flodin and colleagues has demonstrated in a retrospective study that low BMI was associated with increased 1-year mortality in community-living geriatric patients (31). Malnutrition as measured by low MNA or BMI was also reported to be associated with increased mortality for older persons in hospital and sub-acute care (10, 28, 32, 33). For the nursing home setting, Flacker and colleague noted that BMI <23kg/m2 or reduced oral intake was independently associated with mortality in a large retrospective cohort study (13), while mid arm circumference was noted to be the best nutritional parameter predicting mortality in another study conducted in Canadian long-term care facilities (21).
Our study did not show an association between anthropometric measurements of TSF and MAC with mortality. These measurements are often performed as they are a quick, inexpensive, and noninvasive way of measuring nutritional status. TSF thickness reflects subcutaneous fat, while MAC may reflect changes in lean body mass and fat (21). However both measures may be affected by the process of aging, and no age or gender-adjusted norms are available for comparison in the local population. Their utility as nutritional measures probably lies in relating longitudinal changes in body composition to health outcomes (34).
While the majority of the subjects have significant comorbidities as evidenced by the high average Charlson’s score, we did not detect a significant association between overall Charlson’s score and mortality. This could be due to the initial exclusion of patients with cancer and end stage liver or renal disease (conditions given significant weight in the scale) from our study population. Although this may affect the results of the Charlson’s in terms of predicting mortality, the scale nevertheless serves as a useful tool for quantifying burden of illness from other conditions such as dementia and diabetes.
The strengths of our current study include a fairly large sample size, with almost all participants completing detailed medical, nutritional and functional assessments with instruments validated in nursing home populations. A limitation of the study might have been the lack of corroboration of anthropometric and clinical measures of nutrition with biochemical indicators such as serum albumin, or vitamin levels. These markers are however non-specific for malnutrition and the current study actually highlighted the utility of clinical nutritional assessment in predicting short term mortality in the elderly.
In conclusion, our study identified a high prevalence of undernutrition in this nursing home population. The diagnosis of undernutrition as defined by either BMI <18.5kg/m2 or MNA < 17 is an important predictor of short term mortality in this frail elderly population, and should be actively screened for in older adults admitted to nursing home facilities. Future research should examine the impact of nutritional intervention on morbidity and mortality of the elderly in the long term care setting.
Acknowledgements
We would like to thank the staff of Thong Teck Home for Senior Citizens, as well as Dr Low Bee Lee for their committed involvement in the study.
Source of funding
Research grant from the National Healthcare Group, Singapore
Conflict of interest
None
Financial disclosure
None of the authors had any financial interest or support for this paper.
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