The aim of this study was to investigate the health and nutritional status of older people living in sheltered accommodation. The objectives were to determine if health was poorer in rented rather than owned accommodation or in more deprived areas.

The increasing numbers of older people means that the number of people with disabilities and chronic diseases associated with ageing will also increase (Khaw, 1999). As people find it difficult to continue to live in their own homes, more people are likely to move into sheltered accommodation to enable them to lead as normal lives as possible. By investigating the health of individuals living in sheltered accommodation by socioeconomic status, it may be possible to determine which individuals may be subject to more health risks than others. This information could then be used to identify ways to improve their health and potentially reduce the risk of disability and chronic disease in old age and improve their quality of life.

Aim

The aim of this study is to investigate the health and nutritional status of older people living in sheltered accommodation. This will be done by comparing participants who live in different housing tenures (renter or owner-occupier) and in different neighbourhoods using Index of Multiple Deprivation (IMD).

Objectives

The objective is to determine if participants in higher socioeconomic groups have improved health and nutritional status in comparison with lower socioeconomic groups. This will be done by examining:

        

• Smoking prevalence
• Alcohol consumption
• BMI
• Waist circumference
• MUAC
• Fruit and vegetable consumption
• Meat consumption

Hypotheses

The health and nutritional status of owner-occupiers will be better than those living in rented accommodation.

The health and nutritional status of participants living in areas with high deprivation will be poorer than those living in areas of with less deprivation.

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2. Method

Ethical approval was obtained from Liverpool John Moores University. An internet search was carried out to identify warden assisted or sheltered accommodation in Liverpool. Eligible schemes included accommodation which were rented or owned by people aged 60 years and over. A selection of schemes were identified from different postal districts of Liverpool. Where possible, this included a minimum of one rented and one owned scheme in the same postal district. A letter was sent to the warden of these schemes explaining about the study and asking if it would be possible to arrange a visit.

The letter included a contact phone number for interested wardens. The response to the letters was very limited: only two wardens expressed interest in the study, of which only one allowed access to the people living in the scheme. Follow up phone calls were made to the wardens of the schemes that had not been in contact. Some difficulties were encountered in arranging visits due to non-contact of wardens and wardens advising that people would not be interested in taking part. This meant that only five schemes were visited in total. As participation was voluntary, the wardens notified people in advance of the researcher’s visit so they could volunteer to take part.  Purposive sampling was used, as the study sought information from specific predefined groups: people aged over 60; owner-occupiers and renters; living in sheltered accommodation in different areas of Liverpool. The final sample consisted of 43 participants.

1. Questionnaire

A questionnaire was developed to provide quantitative data to enable comparisons to be made between participants living in different housing tenures and neighbourhoods. Where possible the questionnaire was administered in a communal area of the housing scheme. Where participants had restricted mobility, the warden provided the researcher with the names and flat number of the participants who wanted to take part so they could complete the questionnaire in their own home.  

Each participant was informed about the study and provided with a participation information sheet. Participants were asked to sign a consent form and then fill in the questionnaire, which took about 10 minutes to complete. The questionnaire consisted of a series of closed questions which included age, gender, anthropometric measurements, smoking habits, alcohol consumption and dietary practices (Appendix A). To keep the questionnaire short and minimize the burden on the participants, the questionnaire only sought information about the usual frequency of intake for different foods. The size of meal usually eaten was estimated by the participant using a food atlas as a visual aid (Nelson, 1997). After the pilot of the questionnaire on the first visit it was decided the researcher would need to be present when the questionnaire was completed to obtain the anthropometric measurements, specifically waist circumference and MUAC.

2. Anthropometry

Measurements to assess nutritional status included self-reported weight and height, waist circumference and MUAC. These measurements were chosen because they were inexpensive, safe and simple to carry out (Mann & Truswell, 2007).

1. Body Mass Index (BMI)

Height (cm) and Weight (kg) were self-reported by the participants. Body Mass Index was calculated by dividing weight in kg by the square of height in metres (kg/m2). The classifications used in the NDNS of people aged 65 and over and the English Longitudinal Study of Ageing were chosen for this analysis (Table 2.1).

Table 2.1: BMI Classification

Source: (Zaninotto et al, 2008)

2. Waist Circumference

This measurement was used to identify participants whose health was at risk from being overweight. Abdominal obesity (a waist circumference greater than 102 cm for men and 88 cm for women) is a risk factor for the metabolic syndrome which is associated with an increased risk of developing type 2 diabetes and CVD (NIH, 2002). In order to estimate abdominal obesity a tape measure was used to measure waist circumference. Table 2.2 shows the defined level of risk for men and women.

Table 2.2: Risk of Metabolic Complications

Source: (Zaninotto et al, 2008)

3. Mid-Upper Arm Circumference (MUAC)

Originally, triceps skin-fold thickness was to be assessed. Taking the age of the participants into consideration and the invasiveness of the procedure it was decided to measure MUAC instead. The measurement of MUAC is used as an alternative when height and weight cannot be obtained when using the 'Malnutrition Universal Screening Tool' (MUST). This tool is used by care workers in hospitals and community care settings to identify adults who are malnourished, at risk of under-nutrition or are obese (Press, 2004). The measurement of MUAC is used to estimate the participants BMI and reflects the muscle and fat in the arm. An MUAC smaller than 23.5 cm indicates that the participants BMI is likely to be less than 20 kg/m2. An MUAC greater than 32 cm indicates that the BMI is likely to be more than 30 kg/m2 (Press, 2004).

3. Index of Multiple Deprivation

After each visit, the scheme address was retrospectively post-coded, enabling the Lower layer Super Output Area (LSOA) to be determined. An LSOA is a small area of geography, there are 32,482 LSOA in England which have an average population of 1500 people living in them (LCC, 2008). The Index of Multiple Deprivation 2007 (IMD 2007), the Government's official measure of multiple deprivation, is made up of seven LSOA level domain indices which are combined into a single deprivation score for each LSOA in England. The 7 indicators are: Income Deprivation, Employment Deprivation, Health Deprivation and disability, Education, Skills and Training Deprivation, Barriers to Housing and services, Living Environment and Crime (Table 2.5 shows the deprivation indicators for each area visited).

There is an additional Income Deprivation Affecting Older People Index (IDAOPI), which is a subset of the Income Deprivation domain and represents income deprivation affecting older people (Noble, 2008). The IDAOPI is comprised of the percentage of adults aged 60 and over in the LSOA who claim Income Support, Income based Job Seekers Allowance, pension credit, child tax credit claimants and their partners (if also aged 60 or over) (Noble, 2008).

These indices are used to rank the 32,482 LSOA’s in England, the most deprived is given a rank of 1 and the least deprived a rank of 32,482 (Noble, 2008). The North West consists of 4,459 LSOA's, of which 291 are located in Liverpool. The IMD 2007 shows that Liverpool is the most deprived local authority in England (Table 2.3), 56% of Liverpool's residents live within the most deprived 10% of LSOA's (LCC, 2008).

Table 2.3: National IMD 2007 and Selected Merseyside Authority Rankings

(Sefton Council, 2008)

This study was carried out in a total of 4 LSOAs, 3 were in Liverpool and 1 was in Sefton. These areas were then ranked according to the IMD 2007 (Table 2.4). The postcodes have been removed to protect the anonymity of the participants who took park in the study.

Table 2.4: Rank applied to each visited area according to IMD 2007

(Noble, 2008)

Table 2.5: Indicators of Deprivation for visited areas

(Noble, 2008)

Compared to the whole of England, a large proportion of Liverpool local authority is in the most deprived fifth of areas in England (Figure 2.1a). Compared to the whole of Liverpool local authority, the areas visited for the study were in the least deprived areas (Figure 2.1b) (APHO, 2008a).

(Source: APHO and Department of Health.© Crown Copyright 2008)

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Blundellsands, the area visited in Sefton local authority, is in the second least deprived fifth of areas in England (Figure 2.2a) and in the least deprived fifth of areas in Sefton local authority (Figure 2.2b) (APHO, 2008b).

(Source: APHO and Department of Health.© Crown Copyright 2008)

There are inequalities in life expectancy for men and women between local authorities and between areas with different levels of deprivation. Between 2002-2006, the life expectancy in the most deprived fifth of areas in Liverpool for females, was 76 years (Figure 2.3a), compared to 77 years in Sefton (Figure 2.3b). The difference was even greater for men, 70 years in Liverpool compared to 72 years in Sefton (APHO, 2008a, APHO, 2008b)

(Source: APHO and Department of Health © Crown Copyright 2008)

4. Analysis

Analysis of the data was carried out using SPSS for Windows version 14. Results are reported using mean, median, range, ± standard deviation. Before analysis, the variables were examined for normality. Results indicated normality for age, weight, height, BMI, waist circumference and MUAC. Independent t-tests and Mann-Whitney U tests determined differences in anthropometric measurements between housing tenure. One-way between groups ANOVA with post-hoc tests and Kruskal-Wallis tests determined differences in anthropometric measurements between neighbourhoods. Chi-square tests determined differences between groups for health risks, smoking habits, alcohol consumption and eating habits. Two-way between groups ANOVA assessed the impact of age and gender on waist circumference and MUAC. For all tests the results were considered significant at p < 0.05.

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3. Results

The final study population consisted of 43 participants (n = 43), 28% were male (n = 12) and 72% were female (n = 31). The age of the participants ranged from 63 to 94 years with a mean age of 78.88 (± 8.7). For comparison, the participants were divided into three age groups: under 75, between 75 and 85 and over 85 (Figure 3.1).  

 

Figure 3.1: Age and Gender of participants

Five different sheltered accommodation schemes were visited; two were comprised of owner occupiers and three of renters. In total 37% owned their accommodation and 63% lived in rented accommodation. The interviewed participants lived in 4 different areas ranked in order of IMD 2007. Of the participants, 23.3% lived in area 1 (the most deprived area), 20.9% in area 2, 37.2% in area 3 and 18.6% in area 4 (the least deprived area). Figure 3.2 shows the distribution of the housing tenure (owned or rented) and area of where the participants lived.

Figure 3.2: Distribution of Area and Housing Tenure

The means for the group were: weight 67.06 kg (range 42-114 kg, ± 14.86), height 1.60 m (range 1.32-1.78 m, ± 0.104), BMI 25.96 kg/m2 (range 16.84-39.36, ± 4.85), waist circumference 90.9 cm (range 69-132 cm, ± 12.05), MUAC 27.8 cm (range 21-36 cm, ± 3.57). Alcohol was regularly drank by 20.9% of the participants (n = 9) and 7% smoked (n = 3). For the whole group, 74.4% of the participants consumed vegetables more than 4 times a week and 67.4% of participants consumed fruit or fruit juice more than 4 times a week.

5. Housing Tenure

Variables were examined for normality and were not significant. An Independent t-test showed no significant difference between owners and renters for age, weight, height, BMI, waist circumference and MUAC. The mean waist circumference for owners was 92.4 cm compared to 90.1 cm for renters and MUAC was 28.7 cm compared to 26.2 cm (Table 3.1). Although BMI was not significant between housing tenures (p 0.051), this could signify a trend as it is very close to 0.05. The mean BMI of the renters was 26.9 kg/m2 (± 5.5) compared to 24.3 kg/m2 (± 2.98) for the owners.

Table 3.1: Comparison of participants between housing tenure

A Mann-Whitney U Test showed no significance for weight, height, BMI or waist circumference. There was a statistical difference in MUAC between housing tenure (p 0.027). The renters had larger MUAC than the owners (Figure 3.3), the median MUAC for renters was 28.7 cm (± 3.79) versus 26.2 cm for the owners (± 2.56). Age was also statistically significant (p 0.01) between housing tenure. The median age of the renters was 75 years (± 7.7) compared to 86.5 years (± 7.5) for the owners.

Figure 3.3: MUAC of participants by housing tenure

A Chi-square test was carried out to see if owner occupiers had less health risks than those living in rented accommodation. Health risks included having a BMI <20 kg/m2 or >25 kg/m2, a waist circumference over 88 cm for women and 102 cm for men and an MUAC less than 23.5 cm. A significant association between housing tenure and BMI risk (p 0.017) was found (Figure 3.4).

Figure 3.4: BMI risk by housing tenure

Table 3.2 shows 50% of owners were overweight compared to 18.5% of renters and that 0% of owners were obese compared to 40.7% of renters.

Table 3.2: Health risks between housing tenure

Table 3.2 (cont): Health risks between housing tenure

Chi-square tests revealed no significant association between housing tenure and waist risk (p 0.283), MUAC risk (p 0.891), smoking (p 0.167) or drinking alcohol (p 0.200). Of the participants who owned their own accommodation 31.3% drank alcohol regularly and none smoked, compared to 14.8% of renters who drank alcohol and 11.1% who smoked (Figures 3.5 & 3.6).

There was no significant association between housing tenure and the frequency of consumption of different foods (Table 3.3) or meal preparation (p 0.136).

Figure 3.7: Meal preparation by housing tenure

Table 3.3: Food consumption between housing tenure

6. Neighbourhood

Variables were examined for normality and were not significant. An ANOVA compared the means of the participants between the different neighbourhoods. The Levenes test showed the assumption of homogeneity of variance was not violated for weight, height, waist circumference and MUAC, but was for age and BMI. The Brown-Forsythe test was used for age and BMI, no significant difference was shown for BMI (p 0.968) but was for age (p 0.01).

Table 3.4: Comparison of participants between neighbourhoods

The ANOVA showed a significant difference in age between participants living in areas 2 and 4 (p 0.022). In area 2, the age between participants ranged between 63 and 81 compared to 80 and 89 in area 4. The mean age of participants in area 2 was 73.1 (± 5.3) compared to 85.9 (± 2.9) in area 4 (Table 3.4).

A Kruskal-Wallis test compared weight, height, BMI, waist and MUAC between the different neighbourhoods. There was no significance for BMI (p 0.975), MUAC (p 0.819) and waist measurement (p 0.056). Although waist circumference was not significant it may signify a trend between different areas. The mean waist circumference in area 2 was 85.1 cm (range 71.12–106.68, ± 11.1) compared to 97.2 cm (range 81.28-106.68, ± 9.5) in area 4 (Table 3.4). A Mann-Whitney test revealed a significant difference in the waist circumference (p 0.037) between areas 2 (median = 81.3 cm, n = 9) and 4 (median = 101.6 cm, n = 8).

A Chi-square test was carried out to see if participants in more deprived areas had more health risks. No significant association was found for BMI risk (p 0.106) or MUAC risk (p 0.690). Areas 1 and 2 had the highest percentage of participants in the obese BMI category (Figure 3.14), whilst area 4 had more participants in the overweight BMI category (Table 3.5).

Table 3.5: Health risks between neighbourhood

A significant association was found between the area where participants lived and waist circumference (p 0.036). Area 2 had the highest percentage of participants with a low-risk waist circumference (66.7%) and areas 1 and 4 had the highest percentage of participants with a high-risk waist circumference (60% and 50%) (Figure 3.15).

Figure 3.14: BMI risk of participants according to neighbourhood

Figure 3.15: Waist risk of participants according to neighbourhood

A Chi-square test was carried out to compare participants who lived in more deprived areas with those living in less deprived areas. There was no significant association for smoking (p 0.272), drinking alcohol (p 0.151), meal preparation (p 0.173) or frequency of consumption of different foods (Table 3.6).

Table 3.6: Food consumption between neighbourhoods

7. Neighbourhood and Housing Tenure

A comparison of participants in rented and owned accommodation, who lived in the same neighbourhood (area 3) was carried out. Normality of variables was examined and was not significant for age, weight, height, BMI, waist and MUAC. An Independent t-test was used to determine differences in weight, height, BMI, waist circumference and MUAC between participants living in owned and rented housing tenures in area 3 (Table 3.7). There was a significant difference for BMI (p 0.048) and MUAC (p 0.023), the renters had a larger mean BMI and MUAC than the owners.

A Mann-Whitney U Test also revealed a significant difference in MUAC (p 0.033) between the owners and renters in area 3, with owners having a smaller MUAC (median = 25 cm, ±2.3) than renters (median = 30 cm, ±4.4). The owner’s BMI was smaller (median = 22.7 cm, ±3.7) than the renters (median = 27.1 cm, ±5.8), although not significant (p 0.059) it could signify a trend.

Table 3.7: Comparison of participants living in area 3 by housing tenure

A Chi-square test was carried out to compare participants by housing tenure who lived in area 3. No significant association was found for health risks (BMI p 0.158, waist circumference p 0.788, MUAC p 1.0). There were no obese owners compared to 37.5% of renters (Table 3.8).

Table 3.8: Health risks in area 3 by housing tenure

No significant association was found for smoking (p 1.0), drinking alcohol (p 1.0) or frequency of consumption of different foods.

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8. Renters in different Areas

An ANOVA was carried out to compare the participants who rented in different areas of Liverpool. There was no significant difference between renters and the area they lived for age, weight, height, BMI, waist circumference and MUAC (Table 3.9). This was also shown with an Independent t-test between areas 1 and 2, and areas 2 and 3.

Table 3.9: Comparison of renters by area

A Chi-square test indicated no significant association in the health risks of participants who rented in different areas of Liverpool (BMI p 0.269, waist circumference p 0.068, MUAC p 0.343).  No significant association was found for smoking (p 0.379), drinking alcohol (p 0.842) or frequency of consumption of different foods.

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9. Owners in different Neighbourhoods

An Independent t-test was carried out on the owners living in areas 3 and 4. There was no significant difference between owners and the area they lived for height, weight, BMI, waist circumference and MUAC.

Table 3.10: Comparison of owner-occupiers by area

The mean waist circumference of participants in area 4 (97.2 cm) compared to area 3 (87.6 cm) (Table 3.10), this was not statistically significant (p 0.057) but may signify a trend between these areas. A Mann-Whitney U test also revealed no significant difference in the waist circumference measurements of participants living in area 3 and 4 (p 0.056).

A Chi-square test indicated no significant association between the health risks of participants who owned their own accommodation in different areas (BMI p 0.287, waist circumference p 0.149, MUAC p 1.0).  No significant association was found for drinking alcohol (p 0.281), smoking (none of them smoked) or frequency of consumption of different foods.

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10. Age and Dietary Habits

The participants were divided into 3 age groups, under 75, between 75 and 85 and over 85. A Chi-square test was carried out to compare eating habits for all of the participants. There was no significant association between age group and frequency of food consumption (Table 3.11) except for fruit consumption (p 0.016). Only 40% of participants aged between 75 and 86 ate fruit or drank fruit juice more than 4 times a week, compared to 75% aged under 75 and 91% aged 86 years and above (Figure 3.23).

Table 3.11: Food consumption between age groups

Figure 3.23: Fruit and Fruit Juice consumption by Age

11. Age and Lifestyle

There was no association between age group and whether participants regularly drank alcohol (p 0.404) or smoked (p 0.397).

12. Age and Anthropometry

A one-way ANOVA was completed for participants between age group and BMI, waist circumference and MUAC.

Table 3.12: Measurements for age group

There was a statistically significant difference between the age groups for MUAC (p 0.047) but not for BMI (p 0.612) or waist circumference (p 0.294). Post-hoc comparisons using the Tukey HSD test indicated that the mean MUAC for the ≤ 74 age group (mean 29.44 cm, ±3.29) was significantly different from the ≥ 86 age group (mean 26.25 cm, ±2.86). The 75-85 age group did not differ significantly from the under 75 or over 86 age groups (Figure 3.24).

Figure 3.24: MUAC for participants by age group

A two-way ANOVA was carried out to assess the impact of age and gender on MUAC. There was no significant difference in the effect of age on MUAC for males and females (p 0.455). MUAC differed significantly between age groups (p 0.047) but not between gender (p 0.404). The under 75 and over 85 age groups differed significantly from one another (Figure 3.25).

Table 3.13: MUAC between age group and gender

Figure 3.25: Mean MUAC by age group and gender

A two-way ANOVA was carried out to assess the impact of age and gender on waist circumference. There was no significant difference in the effect of age on waist circumference for males and females (p 0.255). Waist circumference differed significantly between males and females (p 0.06) but not between age groups (p 0.088).

Figure 3.26: Waist circumference by gender

Table 3.14: Waist circumference between age group and gender

Figure 3.27: Mean waist circumference by age group and gender

        

A Chi-square test showed no significant association between health risks of participants and age group: BMI risk (p 0.672), waist risk (p 0.421) or MUAC risk (p 0.674).

Table 3.15: Health risks between age groups

The percentage of participants who were obese or had a low-risk waist circumference decreased with increasing age (Figures 3.28-3.29) and the percentage of participants with an ‘at-risk’ MUAC increased with increasing age (Figure 3.30).

4. Discussion

The current author’s opinion speculates that owners have higher income than renters and that income is higher in the households in the least deprived areas. The most deprived area visited in this study had the lowest ranking IDAOPI (Noble, 2008) and the least deprived area had the highest ranking IDAOPI (Table 2.4). The author also speculates that owners are equivalent to non-manual workers and that renters are equivalent to manual workers. Further study would need to be done to confirm this.

13. Smoking

Only 7% of participants reported smoking on this study; this is very low in comparison with the 2007 HSE which found 14% of men and 13% of women aged 65-74 and 10% of men and 8% of women aged 75 and over were current smokers (Craig, 2008). The 3 participants who smoked on this study were all female, under the age of 86 and renters (Figure 3.6). Although smoking was not significant between housing tenures or area, the fact that only renters smoked, 2 of them living in the most deprived area (Figure 3.16), may be reflective of the 2007 HSE which showed that smoking prevalence was highest in the lowest income households (Craig, 2008). Similarly, other studies have shown that individuals living in deprived areas are more likely to be smokers (Smith, 1998, Reijneveld, 1998). The British Women’s Heart and Health study also found local authority housing to be a strong predictor of smoking (Watt, 2009).

Longitudinal studies have shown that smoking is associated with an increased risk of death in current smokers and ex-smokers when compared with never-smokers, with the risk being greater for current smokers (Doll et al, 2004, Nazroo, 2008). The 2007 HSE has also found evidence of under-reporting of cigarette smoking status, possibly due to smoking becoming less socially acceptable (Craig, 2008). This may have prevented some of the participants in the current study from reporting their smoking behaviour, which could have affected the results. The questionnaire did not ask about previous smoking habits, so smoking prevalence could be low as it does not take ex-smokers into account, for example people who may have stopped smoking as they get older for health reasons (Kerr, 2006).

14. Alcohol

In the ELSA study, drinking alcohol occasionally has been associated with a reduced risk of death, when compared with those who never drink alcohol and those who drink alcohol everyday (Nazroo, 2008). In older people, moderate drinking of alcohol (up to 28 g of alcohol per day) has also been associated with better cognitive function, subjective well-being and fewer depressive symptoms (Lang, 2007). In the current study, alcohol was regularly consumed by 20.9% of the participants with only 3 participants regularly consuming more than 2 units a day (16 g of alcohol). Females were less likely to consume alcohol, 16.1% compared to 33.3% of men. This is in agreement with the 2007 HSE which found that 20% of men and 12% of women in the 65-74 age group had drank alcohol every day in the preceding week and that the percentage of consumers who regularly drank alcohol increased with increasing age (Craig, 2008).

The current study found that more owners regularly consumed alcohol (31.3%) than the renters (14.8%) and that consumption was more common in the least deprived area, where 4 of the 8 participants questioned regularly consumed alcohol (Figure 3.17). Comparatively, the NDNS of people aged 65 and over also found a higher proportion of participants consumed alcohol from a non-manual background than from a manual background (Finch, 1998). The 2007 HSE found that frequent alcohol consumption was most common in the highest income households; the lowest income households had the highest percentage of adults who had not drunk alcohol in the preceding week (Craig, 2008). Although the current study is limited by its small sample size, it is interesting that similarities were found with other studies.

15. Food Consumption

Older people sometimes need higher intakes of particular nutrients; this could be due to the gut not absorbing nutrients efficiently or a reduced ability to synthesise vitamin D in the skin (Khaw, 1997). This is why a nutrient dense diet is essential as people age. Although not significant, the owners reported more frequent consumption of vegetables, fruit, chicken, red meat and fish per week compared to the renters (Figures 3.8-3.13). Comparing these results with the NDNS of people aged 65 and over, the non-manual groups also had healthier eating patterns with higher consumption of oily fish, vegetables and fruit than the manual groups (Finch, 1998). Food consumption between areas was not significant although the least deprived area had the highest milk consumption per day and more frequent consumption of vegetables, fruit and fish per week (Figures 3.19-3.22). Antioxidants are protective against the oxidative damage caused by free radicals, thought to be responsible for conditions including cancer, CVD, dementia and eye diseases (Khaw, 1997). Vegetables, fruit and fruit juices have dietary importance as they are the main food source of the antioxidant vitamin C (Finch, 1998). High fruit and vegetable consumption is associated with protective benefits for conditions including macular degeneration, visual loss, cataracts (Khaw, 1997) and premature death from CVD, stroke and some types of cancer (Danaei, 2005).

In the NDNS of people aged 65 and over, the highest income groups consumed more oily fish, salad and raw vegetables, apples, pears and citrus fruits compared to the low income groups (Finch, 1998). The NDNS study also found that the manual group and the low income groups had significantly lower levels of vitamin C than the non-manual group and the higher income groups (Smithers, 1998). Vitamin C intake was also seen to significantly decrease with age in both men and women (Finch, 1998). Low intakes of vitamin C in low income groups were also found in a large investigative study of the dietary patterns of 2195 elderly Australians (Horwath, 1989). It also found higher socioeconomic groups ate a wider variety of fruit and vegetables and consumed fruit more frequently then the lower socioeconomic groups (Horwath, 1989).

Although this current study did not differentiate between different types of vegetables, fruit and fish, the results suggest slightly healthier eating patterns in the participants who owned their own home or lived in the least deprived areas. The more frequent consumption of meat, fish and poultry by the owners and the participants living in the least deprived areas may be because they have more income to spend on food. Lower income groups may have to economise on the purchase of these more expensive foods which means they are eaten less often.

These results are limited by the difficulties of participants trying to recall the number of times they eat something in a week, especially as peoples diets tend to vary on a day to day basis. Older people may also experience memory problems, as memory naturally declines with age (WHO, 2002a). There may also have been differences between participants in the interpretation of the questionnaire. For example, one participant who was asked the question “How many times a week do you eat red meat?” did not associate this with eating a “cottage pie” ready meal, hence the number of times they ate red meat would have been underestimated.

In the current study, 62.5% of owners and 74.1% of renters consumed less than half a pint of milk a day. Calcium and vitamin D in the diet are essential for good bone health. The main sources of vitamin D are from exposure to the sun and oily fish, meat and meat products, fat spread, cereals in the diet. Older people are particularly at risk of vitamin D deficiency, due to reduced exposure to sunlight if they have limited mobility or are housebound (Flint, 1998). Milk, milk products, cereal and cereal products were the main sources of calcium in the NDNS study (Flint, 1998).  A low supply of calcium in the diet is associated with increased loss of bone mass and osteoporosis, which can increase the risk of bone fracture (Gennari, 2001). Vitamin D deficiency can lead to osteomalacia (brittle bones) and increase bone loss in the presence of osteoporosis (Gennari, 2001).

Falls are a major cause of disability and mortality due to injury in older people and the risk of bone fracture is increased with osteoporosis (DOH, 2001). This is why it is important that older people have an adequate intake of both calcium and vitamin D in the diet. The reference nutrient intake (RNI) for vitamin D for those aged 65 and over is 10 μg/day (COMA, 1992), where RNI is the amount of a nutrient that is enough to meet the dietary needs of about 97% of a group of people (Henderson, 2002). The NDNS study found 97% of free-living participants had a daily intake of vitamin D below the RNI (Finch, 1998). High levels of deficiency were also seen in older people in the LIDNS; mean daily intakes of the RNI were 34% for men and 26% for women (Nelson, 2007). One of the drawbacks of the current study is that it did not ask if participants were taking vitamin supplements. Vitamin D supplements are recommended for housebound individuals and a number of participants on this study had limited mobility.

16. Meal preparation

The owners' healthier eating patterns (not significant) may have been influenced by the 31% of owners who regularly ate a main meal in the on-site restaurant at their residence. This may also have influenced the healthier eating patterns seen in the least deprived areas, as only participants in areas 3 and 4 had access to an on-site restaurant (Figure 3.18).

Of the renters in this study, 80% prepared their own meals compared to 55% of owners (Figure 3.7). This higher proportion of owners who did not prepare their own meals may be indicative that as people get older they have less desire or ability to cook. This can be due to a variety of reasons, including the loss of a partner or difficulties preparing meals. Eating in the on-site restaurant means participants do not have to worry about planning meals and purchasing large amounts of shopping.

The risk of malnutrition also increases with age due to many factors including poor appetite and insufficient dietary intake (Payette, 2005) as well as isolation, depression, loneliness and bereavement (Kim, 2007). The opportunity to socialise with others at mealtimes may therefore have a positive effect on their mental health. The provision of a meal also means they do not have to worry about functional impairments which make meal preparation difficult, such as poor eyesight, restricted mobility, poor dexterity and tremors (Kim, 2007).

There is a cost element to eating in the restaurant but this may be balanced by not having to buy large amounts of shopping, spending less money on utilities for meal preparation and less food wastage, as it is difficult to purchase portion sizes for one. The NDNS of people aged 65 and over showed that older people often rely on friends and family to do their shopping (Finch, 1998). The current study showed that older people also rely on meal provision from a variety of resources, including an on-site restaurant, meal-delivery service from supermarkets or Wiltshire Farm Foods, relying on a partner to cook or having family and friends prepare meals which can be frozen and easily reheated. This shows the importance of having social support and enough money to live on in old age.

17. Anthropometry

Compared to the participants who rented their accommodation, the owners were older then the renters, had smaller BMI and MUAC and larger waist circumference. Although there was no significance between BMI and housing tenure (p 0.051) there was a trend towards the renters having a larger mean BMI (26.9) than the owners (24.3). It is possible this would become significant if there were more participants in the study although these results should be interpreted with caution as they are based on self-reported weight and height. There was no association between BMI risk and area where participants lived, although areas 1 and 2 had the most obese participants (40% and 44.4%) and area 4 had the most overweight participants (62.5%).

An association was found between BMI risk and housing tenure (p 0.017): 50% of owners were overweight and none were obese compared to renters, 18.5% were overweight and 40.7% were obese (Figure 3.4). Similarly wave 2 of the ELSA study found BMI was associated with wealth, in the highest income groups there were less women and men in the obese categories compared to the lowest income groups. Conversely, the highest income groups had more women and men in the overweight categories (Pierce et al, 2006).

In the current study, the owners were significantly older then the renters (p 0.01), the mean age of the owners was 84 compared to 76 for renters. Between 20-70 years of age, fat mass increases and fat free mass decreases; from the age of 70 onwards both fat mass and fat free mass start to decrease (Villareal, 2005). This could account for more owners being in the overweight rather than the obese category, as fat measures decline with age, BMI also decreases. Cross-sectional studies have shown that the mean body weight and BMI tends to decrease in adults aged over 60, although these observations may have been affected by survival bias. Premature mortality of obese people at younger ages would decrease the mean weight and BMI in the survivors (Villareal, 2005). However, the longitudinal ELSA study found BMI gradually increased in the 50 to 74 age groups and then showed a small sign of reduction in the group aged 75 and over (Zaninotto et al, 2008).

In the current study this may also account for the higher percentage of owners (12.4%) than renters (7.4%) in the underweight category. In men, underweight has been associated with an increased risk of mortality (Zaninotto et al, 2008).  In total, only 9% of the group were considered underweight (BMI <20 kg/m2) whereas 55% were overweight or obese (BMI >25 kg/m2). This is similar to the NDNS of people aged 65 and over, which found just under 10% of participants to be underweight and 66% to be overweight or obese (Finch et al, 1998).

Abdominal fat is associated with increased risk of insulin resistance and of metabolic syndrome (Villareal, 2005). As people get older there tends to be an increase in abdominal fat and a decrease in fat free mass due to loss of muscle mass (Villareal, 2005). It is possible that BMI underestimates fatness in older people as it does not take fat distribution into account (Finch, 1998). Waist circumference may be a more suitable measure of body fatness and abdominal fat (Zaninotto et al, 2008). In the current study only 33% of the participants had a low- risk waist circumference, less than 94 cm for men and less than 80 cm for women.

Further analysis showed that gender had a significant effect (p 0.06) on waist circumference (Figure 3.26) with women having smaller waist circumferences than men. The ELSA study found that up to the age of 74, waist circumference increased significantly in both men and women, but the increase over time was greater among women (Zaninotto et al, 2008). Medium and high-risk waist circumferences were associated with an increased risk of death in both men and women. The greatest increase in prevalence rates of CVD were also seen in obese men and women, and women who had a high-risk waist circumference (Zaninotto et al, 2008). Two-thirds of the participants in the current study had a waist circumference which increased their risk of death.

The mean waist circumference was 92.4 cm for owners and 90.1 cm for renters. More owners had a medium-risk waist circumference (44%) and high-risk waist circumference (38%) than renters, 26% and 33% respectively. Waist circumference was significantly smaller in area 2 than in area 4 (p 0.037) and the differences in waist risk (Figure 3.15) were also significant (p 0.036). In area 2, 67% of participants compared to none in area 4 had a low-risk waist circumference. In area 2, 33% had a medium or high-risk waist circumference compared to 100% in area 4.

The participants were significantly older (p 0.022) in area 4 (86) compared to area 2 (73). As abdominal fat increases with age, this may account for participants in area 4 having larger waist circumferences (Zaninotto et al, 2008). This could also be why none of the owners were in the obese BMI category, as their fat measures have declined with age so has their BMI (Villareal, 2005). When age group was used to analyse the results, it was shown that BMI and MUAC decreased and waist circumference increased with increasing age.

The mean measurement of MUAC was 26.2 cm for owners and 28.7 cm for renters, the MUAC for owners was significantly smaller (p 0.027) than for the renters. MUAC was also significant (p 0.023) within area 3 (Table 3.7). In the NDNS of people aged 65 and over, MUAC was seen to decrease significantly with age (Finch, 1998). In the current study further analysis showed that age group had a significant effect on MUAC (p 0.047), decreasing with increasing age (Figure 3.25). This may explain the significant difference in area 3, the owners were older then the renters and had smaller MUAC, 25.4 cm compared to 29.9 cm.

One of the aims of this study was to examine differences between housing tenure in a sample of older people living in sheltered accommodation. The hypothesis that the health and nutritional status of the renters would be poorer than the owners was not supported. Although an association was found between BMI risk and housing tenure it was not supported by waist circumference which may be a better indicator of fat distribution in older people (Zaninotto et al, 2008). There was also no significance found for lifestyle choices or dietary practices.

The second aim of this study was to compare participants living in different areas, ranked according to IMD. The hypothesis that the health and nutritional status of the participants living in deprived areas would be poorer was also not supported. All of the participants in area 4, the least deprived area, had a medium or high-risk waist circumference compared to 80% in area 1, the most deprived area. There was also no significance found for lifestyle choices or dietary practices.

18. Limitations

This study may consist of healthy volunteer bias due to unwell participants not volunteering to participate or the warden not wanting to trouble those who felt unwell. The BMI of the participants may be biased as it is based on self-reported weight and height. The analysis of self-reported and measured height and weight data from participants in the Oxford cohort of the European Prospective Investigation into Cancer and Nutrition (EPIC–Oxford) found height to be overestimated in older men and women and weight to be underestimated, especially in heavier people (Spencer et al, 2002). This could lead to the calculation of a smaller BMI, which could potentially reduce the number of participants found to be obese or overweight and increase the number found to be underweight. Where possible, waist circumference and MUAC were measured by the researcher in order to reduce bias.

The sample sizes were very small for the visited areas due to difficulties obtaining access to people who live in sheltered accommodation. If the sample size had been larger, the study may have produced more significant findings. Although three of the areas visited were considered deprived in comparison to the whole of England, they were not the most deprived areas in Liverpool as a whole. It is possible that further investigation of older people living in sheltered accommodation in the most deprived areas of Liverpool would be more significant in comparison to the results obtained so far. This study showed that the age range of people living in sheltered accommodation was very large. Future studies would need to include more participants in order to control for age, specifically for the measurement of waist circumference which increases with increasing age. This study did not ask participants how active they were. As physical inactivity is a leading cause of some cancers, type 2 diabetes and heart disease (WHO, 2002b), future studies should ask about the level, frequency and duration of physical activity that participants undertake.

5. Conclusion

This study found a large proportion of older people experienced health risks regardless of housing tenure and area. The health and nutritional status of older people living in sheltered accommodation was also found to be similar to those who are free-living in the community. As people get older, decreased energy requirements and more sedentary lifestyles can contribute to weight gain and abdominal obesity; in this study 67% of the participants had a medium or high-risk waist circumference. It is important to maintain a healthy weight in order to reduce the health risks associated with being overweight including osteoarthritis of the knees (DOH, 2001), type 2 diabetes, heart disease and cancer (DOH, 2004). It may be possible to reduce these risks by keeping physically active and eating a diet high in fruit and vegetables and low in fat, salt and sugar (BNF, 2004). This study found that 30-35% of participants did not eat fruit or vegetables every day. To improve health, the Government recommends 30 minutes of moderate physical activity at least five times a week (DOH, 2001) and 5 portions of fruit and vegetables a day.

19. Recommendations

In order to improve quality of life in older age, interventions may be needed which focus on diet and physical exercise. For older people living in sheltered accommodation, this could be the provision of exercise classes for general fitness and adapted classes to improve strength, mobility and balance in frail older people (DOH, 2001). As energy intakes decline with age, it is important that older people eat a nutrient dense diet. This could be done by showing older people how to eat a healthy diet on a low income and ensuring that help is available for food purchasing and meal preparation if required.

To further investigate the health status of older people living in sheltered accommodation, additional studies with a larger sample size are warranted to establish a stronger evidence base.

6. Appendices

Appendix A – Questionnaire

1. Are you

        Male                                Female

2. How old are you?                                                  

3. Do you usually

4. How many times a day do you eat?

        None

        1

        2

        3

        4

        5 or more

5. How big is the size of meal you usually eat?

                                                                (Please refer to photos of portion sizes)

        Very small

        Small

        Medium

        Large

        Very Large

6. How much milk do you drink each day, including with tea, coffee and cereal?

7. How many times a week do you eat the following?

8. How many units of alcohol do you drink a day?

        None

        1-2

        3-5

        5 or more

1 unit = half pint of normal beer

1 unit = single shot of spirit

2 units = medium glass of 12.5% table wine

9. How many cigarettes do you smoke a day?                

10. How much do you weigh? ______________________

11. How tall are you?  _____________________

                                         

12. Approximately what size is your waist measurement? ________________

13. Approximately what size is your arm circumference?  _________________

Thank you for completing this questionnaire

Appendix B – Consent Form

A study of the health and nutritional status of people living in sheltered accommodation.

Student name

The School of Outdoors Leisure and Food

Faculty of Education Community and Leisure

1.  I confirm that I have read and understand the information provided for the above study. I have had the opportunity to consider the information, ask questions and have had these answered satisfactorily

2.  I understand that my participation is voluntary and that I am free to withdraw at any time, without giving a reason and that this will not affect my legal rights.

3.  I understand that any personal information collected during the study will be anonymised and remain confidential

4.  I agree to take part in the above study

Name of Participant                                Date                         Signature

_______________________               ____________        ___________________

Name of Researcher                        Date                        Signature

_______________________               ____________        ___________________

Name of Person taking consent                Date                        Signature

(if different from researcher)

_______________________               ____________        ___________________

Note: When completed 1 copy for participant and 1 copy for researcher

Appendix C – Participant Information Sheet

        A study of the health and nutritional status of people living in sheltered accommodation in Liverpool.

Student name

The School of Outdoors Leisure and Food

Faculty of Education Community and Leisure

“You are being invited to take part in a research study. Before you decide if you want to take part it is important that you understand why the research is being done and what it involves. Please take time to read the following information. Ask me if there is anything that is not clear or if you would like more information. Take time to decide if you want to take part or not.”

1)  What is the purpose of the study?

This is a student led research project investigating the health and nutritional status of residents living in sheltered accommodation in Liverpool. This will be done by asking individuals in different sheltered housing schemes around Liverpool to complete a short questionnaire.

2)  Do I have to take part?

No. It is up to you to decide whether or not to take part.

If you do want to take part you will be given this information sheet and asked to complete a short questionnaire.

You are still free to withdraw at any time and without giving a reason.

3)  What will happen to me if I take part?

You will be asked to complete a short questionnaire which does not contain any personal information.

4)  Are there any risks / benefits involved?

No risks.

Benefits include helping to determine the health and nutritional status of individuals living in sheltered accommodation throughout Liverpool.

5. Will my taking part in the study be kept confidential?

Your confidentiality will be safeguarded during and after the study.

All information collected during the study will be made and will remain confidential – this includes the names of the sheltered accommodation residences.

---

Appendix D - Glossary

ANOVA        ANalysis Of VAriance between groups

BMI                Body Mass Index

CHS                Cardiovascular Health Study

CVD                Cardiovascular disease

ELSA                English Longitudinal Study of Ageing

EPIC                European Prospective Investigation into Cancer and Nutrition

HSE                Health Survey for England

IDAOPI          Income Deprivation Affecting Older People Index

IMD                Index of Multiple Deprivation

LIDNS                Low Income Diet and Nutrition Survey

LSOA                Lower layer Super Output Area

MAG                Malnutrition Advisory Groups

MUAC         Mid-Upper-Arm Circumference

MUST                Malnutrition Universal Screening Tool

NDNS         National Diet and Nutrition Survey

RNI                Reference Nutrient Intake

THAW                Transport, Housing and Well being

WHO                World Health Organisation

---

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