Abstract
Objectives
Our goal was to evaluate the proportion of community-dwelling elderly people at risk for malnutrition and the effect of different socioeconomic status (SES) indicators as well as social, physical and leisure activities in late life on the risk for malnutrition.
Design
A cross-sectional population-based study.
Setting
A sub-urban area in Northern Italy.
Participants
698 community-dwelling older persons.
Measurements
The nutritional status of participants was assessed through the Mini Nutritional Assessment-Short Form (MNA-SF). SES was defined by means of early-life education, longest occupation, and late-life financial conditions. The following indicators were also evaluated: social contacts, and performing mental, physical and leisure activities during late-life. Chronic diseases, functional, cognitive and affective status were considered as potential confounders when examining the risk for malnutrition by logistic regression models.
Results
8% of the participants (average age 75.6 years, 408 women) were at risk for malnutrition (MNA-SF ≤ 11). Low education, poor financial condition, and lack of physical and leisure activities showed a crude association with risk for malnutrition. Multi-adjusted logistic regression models showed that only low education (OR=2.9; 95% CI=1.2–6.8) and lack of physical activity (OR=4.4;95%CI=2.0–9.7) were independently associated with the risk for malnutrition.
Conclusions
Low education and lack of physical activity in late-life may affect the risk for malnutrition in the elderly. Further studies are needed to clarify the cause-effect relationship between lack of physical activity and malnutrition.
Key words: Elderly, socioeconomic status, malnutrition
Introduction
Malnutrition is a well-recognized, common problem in the elderly, as well as an important prognostic factor of morbidity and mortality (1, 2). However, knowledge of occurrence and factors associated with malnutrition in the elderly is still limited.
One of the first issues in evaluating the nutritional status in the elderly population is the lack of consensus regarding its definition. In fact, malnutrition can be considered, according to Keller and colleagues (3), a 'general term', used to define different kind of deviations from the normal nutritional status: malnutrition can actually refer both to the state of under nutrition and to over nutrition and to an imbalance due to disproportionate intake. However, in the elderly, malnutrition commonly refers to the condition of being poorly nourished (4, 5).
The prevalence of malnutrition widely varies across studies, according to the population selected, the setting, and the diagnostic criteria used to define it, ranging from 5-10% in elderly people living in a community to 30-60% in institutionalized elderly (6, 7, 8).
Nutritional status can be assessed through a wide range of approaches: clinical evaluation, anthropometric measures, dietary evaluation, body composition analysis and laboratory investigations (9, 10, 11), but most of these assessments are complex, time-consuming and expensive (2). The Mini Nutritional Assessment (MNA) is an easy to administer, patient-friendly and inexpensive first-line-strategy developed and validated to help clinicians to identify those elderly at risk for malnutrition, who might benefit from dietary second-line specific intervention (12, 13, 14). Several studies have used the MNA to evaluate nutritional status in healthy elderly living in a community reporting a prevalence of malnutrition of 1%, whereas 29% of them were at risk for malnutrition, according to the MNA score (2, 15, 16).
Several factors have been related to malnutrition in the elderly (1), including some indicators of the socioeconomic status (SES) (17). SES is a 'composite measure that incorporates economic status, measured by income, social status measured by education, and work status measured by occupation' (18). Low levels of education and poor financial resources have been recognized as predictors of dietary intake in the elderly (19, 20, 21). Beyond SES, other factors found to be associated with malnutrition were chronic diseases (22), social isolation (23, 24, 25), and physical activity capacity (26).
The goal of our study was to estimate the proportion of elderly people in a community setting at risk for malnutrition, and to analyze the association of different SES indicators as well as social, physical and leisure activities with the risk for malnutrition in late life after adjustment for several confounders.
Methods
A cross-sectional population-based study, including all inhabitants age 65 and over, was carried out from March to June 2007 in Coccaglio, a small sub-urban town in Northern Italy. 1193 people were invited to participate in the study. The Municipality of Coccaglio funded the study in order to draw a complete picture of the resident elderly population and to evaluate the social support needs. Designers of the study were the Department of Social Services of Coccaglio, the G. Monauni Foundation, the P. e G. Mazzocchi no-profit organization and Wilhelm Reich health center of Padua (Italy). The role of the designers was the selection of the appropriate tools and scales and the organization of data collection. Everyone received a letter indicating the characteristics, methodology and goals of the study; participants were all invited to an informative conference. 495 of them (41.5%) refused to participate or died before the beginning of the study (58 subjects were institutionalized; 8 persons died during the study; 6 persons were not fluent in Italian; 40 subjects were not contactable; the remaining subjects refused to participate because of a number of different reasons, such as privacy protection).
The elderly and their next-of-kin were interviewed by trained nurses using a structured questionnaire to collect self-reported information on sociodemographic data, nutritional, health, functional and cognitive status. Informed consent was requested of all the subjects who were interviewed at their home.
Data collection on demographic and socioeconomic characteristics
Information on demographic characteristics such as age and gender was collected during the home interview. Participants were asked in detail about several SES indicators, including early-life education, longest occupation held, financial condition, along with social contacts, and mental, physical and leisure activities in late life.
Years of schooling completed were used as early-life indicator of SES. First, participants were classified in four groups, as having no education (illiterate) to 4 years of education, 5 years of education (primary school), 6-8 years of education (low-upper school) and 9+ years of education (high-upper and academic education). Due to the fact that having 6 or more years of schooling did not show any further significant effect compared to 5 years of schooling, participants were divided into two groups, those with 4 or less years of education and those with 5+ (completing primary school or more) years of education.
During the baseline interview, participants or relatives were asked about their complete work history. The subjects' lengthiest job was considered one's main occupation. Occupation was classified in the 6 following categories: blue-collar workers, farmers, housekeepers, white-collar workers, sellers, and self-employed/academic professions. These categories were then grouped in 2 main categories: manual workers (including blue-collar workers, farmers and housekeepers) and non-manual workers (including white-collar workers, sellers and self-employed/academic professions).
Information on financial condition was obtained by subjects' self-reported economic status at the time of the interview and divided in 3 main categories: very good/good, modest, and poor financial condition. Due to similar results in all analyses, the last two groups were merged in the following categories: good versus poor financial condition.
Social support was assessed through a structured questionnaire formulated in order to determine how many social contacts (with relatives and/or friends) the subject had in the last month. Having less than 2 contacts per month was considered an indicator of low social support.
Mental, physical and leisure activities were all assessed through a structured questionnaire; subjects were asked how many times a week they performed each of the following activities. Mental activities included: reading books, magazines or newspapers, listening to the radio or to music, and playing cards. Physical activities included: walking outdoor, trekking, cycling, exercising, and fishing. Leisure activities included: going to the theatre or to the movies, going to conferences, participating in clubs or civic activities, going to church, and going to pubs.
Participants were grouped according to the frequency in performing the previously mentioned activities, ranging from never versus once or more activities per week.
Nutritional status
Nutritional status was assessed using a global composite assessment, the Mini Nutritional Assessment-Short Form (MNA-SF) (2). The MNA-SF is a validated first-level nutritional screening instrument highly used in elderly people, derived from the MNA and including the 6 following items: Body Mass Index (BMI), weight loss, mobility, presence of dementia or depression, recent acute illnesses and loss of appetite. The BMI is an index linking a person's weight to their height. It's defined as a person's weight in kilograms (kg) divided by their height in meters (m) squared. Mobility was directly evaluated by the interviewers, and also obtained by questioning the subject or the informant about the person's ability to live independently in the house. Presence of dementia was verified by administrating the Mini-Mental State Examination (MMSE) (27) cognitive test as well as by medical history collected through an informant. Depression was verified by administrating the Geriatric Depression Scale-30 items (28). Information on recent acute illnesses, weight loss, and loss of appetite were asked directly to the participants, if cognitively intact, or to the informant. The total score of the MNA-SF ranges from 0 to 14. Scores of 12 or higher indicate a satisfactory nutritional status, with no requirement for further nutritional evaluation; a score = 11 suggests a risk for malnutrition (2).
Covariates
Chronic diseases
Individuals were asked about the presence of chronic diseases using the following question: “Has your doctor ever told you that you have any of the following… (diseases)?” The disease categories evaluated included: anemia, cerebrovascular diseases, dementia, diabetes, gastro-hepatic diseases, heart diseases, hypertension, lung diseases, malignancies, musculo-skeletal diseases, Parkinson's disease, peripheral vascular diseases, renal, and skin diseases.
Functional status
Functional status was investigated using the basic Activities of Daily Living (ADL) (29), a scale commonly used in multidimensional geriatric assessment to evaluate the individual's ability to manage tasks such as bathing, dressing, going to the toilet, continence, feeding, and transference. ADL is a six-point scale that ranges from 0 (independent in all activities) to 6 (totally dependent). ADL disability was defined as the need for physical assistance in more than one of the six ADL functions.
Cognitive and affective status
Cognitive status was assessed by the MMSE (27). The score of the MMSE was adjusted by age and education according to Magni (30) and collaborators who validated this test in a 65+ year-old population in Northern Italy. A score at MMSE = 24 indicates absence of cognitive impairment.
The presence of depressive symptoms was evaluated with the Geriatric Depression scale-30 items (GDS) (28). Increasing scores indicate increasing depressive symptoms, whereas scores lower than 11 indicate the absence of a depressive syndrome. The GDS scale was administered only if the MMSE score was > 16.
Statistical analysis
Demographic, clinical, functional, cognitive and socioeconomic characteristics of the population by gender and by the risk for malnutrition were described using univariate analysis. Several logistic regression models were run in order to evaluate the association of socioeconomic factors with the risk for malnutrition. First, crude and adjusted odds ratios were calculated for each SES indicator in logistic regression models: education was categorized in two groups (<5 versus 5+ years of schooling), occupation (manual vs. non manual), financial condition (poor vs. good), social contacts (0-1 vs. 2+/month), mental activities (never vs. 1+/week), physical activities (never vs. 1+/week) and leisure activities (never vs. 1+/week). Secondly, all SES indicators were included in the same models. Model 1 was adjusted for age (continuous), gender (male vs. female) and civil status (married vs. single); model 2 was adjusted for age, gender, civil status, number of diseases, functional status (2+ vs. 0-1 ADL functions lost), cognition (MMSE score < 24 vs. = 24) and affective status (GDS score 11+ vs. = 10).
Results
The study population consisted of 698 subjects; the majority being women (58.5%). The average age was 75.6 ± 6.4 years, and 25.3 % of the population was 80+ years old. Table 1 shows socio-demographic, nutritional, functional, cognitive, affective, health characteristics, and SES indicators of the whole population and by gender. Most people were independent in ADL (90.5%) and cognitively intact (81.8%); about one third of the participants suffered from depressive symptoms according to the GDS score. The mean number of self-reported chronic diseases was 2.8 ± 2.4. Men were more likely to be married and to be living with someone. Women were older, more likely to have depressive symptoms and to be independent in ADL than men. Six percent of participants had a BMI score less than 21 Kg/m2; 12% a BMI score = 21-23 Kg/m2, and 82% a BMI score >23 Kg/m2. Eight percent of the participants were at risk for malnutrition (MNA-SF<=11); the prevalence of the risk for malnutrition was higher in women than in men (9.8% vs. 5.4%; p < 0.05).
Table 1.
Characteristics of the study population by gender
| All (n=698) N(%) | Males (n=290) N(%) | Females (n=408) N(%) | p | |
|---|---|---|---|---|
| Sociodemographic characteristics | ||||
| Age, years (mean ± SD) | 75.6 ± 6.4 | 74.9 ± 5.9 | 76.1 ± 6.7 | < 0.05 |
| Civil status - Married | 426 (61.0) | 237 (81.7) | 189 (46.3) | < 0.001 |
| Cohabitation - Alone | 173 (25.1) | 29 (10.2) | 144 (35.5) | < 0.001 |
| Nutritional characteristics | ||||
| MNA-SF = 11 | 51 (8.2%) | 14 (5.4) | 37 (9.8) | < 0.005 |
| Clinical and functional status | ||||
| Number of diseases (mean ± SD) | 2.8 ±2.4 | 2.7 ± 2.5 | 2.9 ±2.4 | 0.15 |
| ADL 0-1 function lost | 618 (90.5) | 251 (88.7) | 367 (91.8) | 0.180 |
| Cognitive and affective status | ||||
| MMSE (mean ± SD) | 26.1 ± 3.5 | 26.5 ±3.4 | 25.9 ± 3.6 | < 0.05 |
| GDS score = 11 | 166 (29.7) | 40 (18) | 126 (37.4) | < 0.001 |
| SES | ||||
| Education (yrs of schooling) | ||||
| < 5 | 102 (14.8) | 33 (11.7) | 69 (17) | <0.05 |
| 5 (primary education) | 444 (64.4) | 173 (61.1) | 271 (66.7) | 0.130 |
| 6-8 (low-upper education) | 123 (17.8) | 61 (21.5) | 62 (15.2) | <0.03 |
| 9+ (high-upper education) | 20 (2.9) | 16 (5.6) | 4 (0.9) | <0.001 |
| Occupation- Manual | 403 (59.4) | 120 (43.2) | 283 (70.7) | < 0.001 |
| Financial condition - Good | 203 (29.9) | 93 (33.2) | 110 (27.6) | 0.114 |
| Social, mental, physical and leisure activities | ||||
| Social contacts < 2 contacts/month | 329 (51.0) | 117 (44.3) | 212 (55.6) | 0.005 |
| Mental activities - never | 67 (10.1) | 23 (8.4) | 44 (11.3) | <0.005 |
| Physical activities - never | 152 (22.9) | 54 (19.6) | 98 (25.2) | 0.089 |
| Leisure activities - never | 120 (18.1) | 44 (16) | 76 (19.6) | 0.231 |
The majority of the participants were at a primary school level; 59.4% were employed as manual workers, and 29.9% of the participants were well-off. Half of the population had less than 2 social contacts per month. About ten percent of the participants never engaged in mental activities, while 22.9 % and 18.1 % of them never engaged in physical and leisure activities. Men were more likely to have a higher educational level and higher occupational skills. Women were less likely to
engage in mental, physical and leisure activities.
Table 2 shows the characteristics of the population at risk for malnutrition. The risk for malnutrition was more frequent in the following categories: people aged 76+ years old, females, single persons, participants with a low level of education and in poor financial conditions, people dependent in ADL, cognitively- impaired elderly, and people with depressive symptoms. Finally, the risk for malnutrition was higher in persons who never engaged in physical or leisure activities (Table 2).
Table 2.
Sociodemographic and socioeconomic characteristics of the study population by malnutrition
| Risk for malnutrition (MNA-SF = 11) N (%) | p | |
|---|---|---|
| Age (yrs) | ||
| <75 | 12 (3.8) | < 0.001 |
| 75+ | 39 (12.4) | |
| Sex | ||
| Males | 14 (5.4) | < 0.05 |
| Females | 37 (9.8) | |
| Civil status | ||
| Married | 22 (5.6) | < 0.01 |
| Not married | 29 (11.8) | |
| Education (yrs of schooling) | ||
| <5 | 15 (16.3) | < 0.005 |
| 5+ | 36 (6.7) | |
| Occupation | ||
| Non-manual | 16 (6.4) | 0.216 |
| Manual | 34 (9.1) | |
| Financial condition | ||
| Good | 8 (4.3) | < 0.05 |
| Poor | 43 (9.7) | |
| Social contacts | ||
| = 2 contacts/month | 20 (6.9) | 0.770 |
| < 2 contacts/month | 23 (7.5) | |
| Mental activities | ||
| 1+/week | 41 (7.3) | 0.079 |
| Never | 8 (13.8) | |
| Physical activities | ||
| 1+/week | 21 (4.4) | < 0.001 |
| Never | 28 (20) | |
| Leisure activities | ||
| 1+/week | 33 (6.5) | 0.01 |
| Never | 15 (13.8) | |
| Number of diseases | ||
| <3 | 20 (5.9) | < 0.05 |
| =3 | 31 (10.8) | |
| ADL (n. of lost functions) | ||
| 0-1 | 39 (6.8) | < 0.001 |
| 2+ | 11 (20.4) | |
| MMSE score | ||
| = 24 | 28 (5.6) | < 0.001 |
| < 24 | 17 (15.2) | |
| GDS score | ||
| = 10 | 14 (3.7) | < 0.001 |
| = 11 | 22 (13.8) |
Table 3 shows the results of the logistic regression models testing the crude and multi-adjusted association of each SES indicator with the risk for malnutrition (MNA-SF = 11). A low level of education (<5 vs. 5+ years of schooling), poor financial condition, a low level of physical and leisure activities showed a crude association with the risk for malnutrition. In the model adjusted for age, gender, civil status, number of diseases, functional, cognitive and affective status, a low level of education and physical activities remained significantly associated with the risk for malnutrition (Table 3).
Table 3.
Crude and multi-adjusted odds ratio (OR) and 95% confidence interval (95% CI) for risk of malnutrition (MNA-SF = 11) due to each socioeconomic status indicator
| OR (crude) | 95 % CI | OR (multi-adjusted)∗ | 95 % CI | |
|---|---|---|---|---|
| Education (yrs of schooling) | ||||
| < 5 vs. 5+ | 2.7 | 1.4-5.2 | 2.9 | 1.2-6.8 |
| Occupation | ||||
| Manual vs. non-manual | 1.5 | 0.8-2.7 | 1.3 | 0.5-2.9 |
| Financial condition | ||||
| Poor vs. good | 2.4 | 1.1-5.3 | 2.1 | 0.8-5.3 |
| Social contacts | ||||
| < 2 vs. 2+ contacts/month | 1.1 | 0.6-2.0 | 0.6 | 0.3-1.3 |
| Mental Activities | ||||
| never vs. 1+/week | 2.0 | 0.9-4.6 | 1.8 | 0.7-4.9 |
| Physical activities | ||||
| never vs. 1+/week | 5.5 | 3.0-10.0 | 4.4 | 2.0-9.7 |
| Leisure activities | ||||
| never vs. 1+/week | 2.3 | 1.2-4.4 | 0.8 | 0.3-2.0 |
Adjusted for age, gender, civil status, number of diseases, functional, cognitive and affective status
Finally, we created further logistic regression models, including all SES indicators. Model 1 was adjusted only for age, gender and civil status and model 2 was adjusted for age, gender, civil status and number of diseases as well as functional, cognitive and affective status. In both models, a low level of education and a low level of physical activities were significantly associated with the risk for malnutrition according to MNA-SF (Table 4).
Table 4.
Odds ratio (OR) and 95% confidence interval (95% CI) for risk of malnutrition (MNA-SF = 11) due to socioeconomic status. Model 1 is adjusted for age, gender and civil status. Model 2 is adjusted for age, gender, civil status, number of diseases, functional, cognitive and affective status
| Model 1 | Model 2 | |||
|---|---|---|---|---|
| OR | 95 % CI | OR | 95 % CI | |
| Education (yrs of schooling) | ||||
| < 5 vs. 5+ | 2.5 | 1.1-5.4 | 3.3 | 1.2-8.7 |
| Occupation | ||||
| Manual vs. non-manual | 1.2 | 0.5-2.6 | 0.9 | 0.3-2.3 |
| Financial condition | ||||
| Poor vs. good | 1.9 | 0.8-4.6 | 1.5 | 0.5-4.2 |
| Social contacts | ||||
| < 2 vs. 2+ contacts/month | 0.7 | 0.3-1.4 | 0.4 | 0.2-1.0 |
| Mental Activities | ||||
| never vs. 1+/week | 1.2 | 0.5-3.2 | 1.8 | 0.6-5.6 |
| Physical activities | ||||
| never vs. 1+/week | 4.5 | 2.2-9.2 | 4.8 | 1.9-11.8 |
| Leisure activities | ||||
| never vs. 1+/week | 0.7 | 0.3-1.8 | 0.3 | 0.1-1.1 |
Stratification by gender did not show any effect modification.
Discussion
In this population-based study of Italian elderly living in a suburban area, where all participants underwent a complete assessment of their SES, a low level of education and lack of performing physical activities emerged as factors independently associated with being at risk for malnutrition.
Previous studies showed that few years of schooling in early-life can be associated with malnutrition and with dietary habits both in elderly people (31, 32) and in adults (33, 34). Van Rossum and colleagues (31) showed that socioeconomic differences affect dietary intake in elderly people, as both nutrient intake and dietary habits vary according to education; highly educated people tend to have a healthier diet than people with lower education. SES related differences in dietary knowledge may represent part of the pathway through which education exerts an influence on diet; education modifies individual's perceived benefits of a healthy and balanced diet (33) and a high level of education may lead to a greater awareness of the benefits of eating healthy products (31). Therefore education grants more autonomy and helps coping with the problems of daily living. This may also be a reason why a low level of education represents an independent risk factor of malnutrition (32).
Physical inactivity is a very common problem among older individuals (35); some studies found a correlation between the level of physical activity and nutritional status (36, 37, 38, 39). Haveman-Nies and colleagues, using figures from the cross-sectional SENECA baseline study and Framingham Heart study, found a correlation between the level of physical activity and the dietary quality in the elderly (36). Immobility can represent a risk factor for the development of malnutrition; in fact, inactivity accelerates the loss of age-associated muscle mass and it has been correlated with muscle atrophy, reduced endurance and muscle strength (39, 40). Thus, the loss of metabolic active tissue decreases energy requirements and leads both to a loss of appetite and to a reduction in food intake (41).
Differently from other studies (42, 43), showing that social isolation correlates with dietary patterns, we didn't find any association between impaired social contact and malnutrition, even if the prevalence of social isolation was quite high in our population (51%). However, comparison with other studies is difficult, due to differences in the methods, settings, and the population sample.
The major strengths of our study include the evaluation of a large-scale community population and the comprehensive assessment of economic and social status. Demographic characteristics of the sample were similar to the ones of the general Italian elderly population as reported by the Italian Longitudinal Study on Aging (44). In particular, in the ILSA study 78.8% of men were still married compared to only 37.7% of women; 30% of men and 43% of women had 3 or less years of education; and about 50% of Italian elderly have been employed as manual workers (45). However, a few limitations are worthy of mention. First, the number of refusals was high, nonetheless motivating letters and the educational conference. Secondly, the information on socioeconomic status was self-reported, thus misclassifications bias cannot be ruled out. However, potential misclassifications are unlikely to differ between those who were or not at risk for malnutrition. Finally, the cross-sectional nature of data collection does not allow establishing a cause-effect relationship between education the level of physical activity, and the risk for malnutrition.
Our study showed that education, an early-life SES indicator, and the level of physical activity during late life are factors independently associated with the risk for malnutrition in the elderly. Further studies are needed to clarify in particular the cause-effect relationship between physical activity and malnutrition, as motivating physical activity in the elderly could be the aim of future intervention programs designed to prevent malnutrition in the elderly.
Acknowledgements: The authors gratefully thank Professor Vittorio Grassi for his support in the design, in the elaboration of the study and in the interpretation of the results.
Conflict of Interests: The authors do not have any possible conflict of interest to disclose.
Author Contribution: Alessandra Marengoni contributed to the conception and the design of the study. Maria Karin Ghisa participated to the data collection. Annalisa Timpini analyzed data and wrote the manuscript. Emanuela Facchi, Giuseppe Romanelli, and Stefania Cossi reviewed the manuscript. All authors approved the manuscript for submission.
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