1. Together, we can help everyone to love later life Viviane Degbe-Agbeko and Marcus Green Age UK Research Department Who is at risk of undernourishment in later life?

2. 01 Definitions and prevalence of undernourishment

3. How is undernourishment (malnutrition) defined? Indications for nutrition support in hospital and the community a BMI of less than 18.5 kg/m 2 unintentional weight loss greater than 10 per cent within the last 3–6 months a BMI of less than 20 kg/m 2 and unintentional weight loss greater than 5 per cent within the last 3–6 months. Extract from Nutrition Support in Adults. CG32. NICE (2006) Definitions and prevalence of undernourishment

4. Undernourishment prevalence in the UK 93 per cent of undernourished individuals aged 65 and over live in the community 5 per cent of undernourished individuals aged 65 and over live in care homes 2 per cent of undernourished individuals aged 65 and over are in hospital BAPEN (2009); Report on the Advisory Group on Malnutrition, led by BAPEN

5. What is the prevalence of undernourishment in the UK? Geographical areaProportion of those aged 65 and over with a Body Mass Index of less than 20 Number of people aged 65 and over with a Body Mass Index of less than 20 (estimates rounded to nearest thousand) UK4.5%470,000 England4.6%404,000 North5.7%94,000 Midlands, Yorkshire and Humber and the East 3.7%133,000 London5.6%51,000 South4.3%126,000 Wales4.1%23,000 Scotland3.8%34,000 Northern Ireland3.5%9,000 Definitions and prevalence of undernourishment Sources: Understanding Society (2011) and Office for National Statistics (2013)

6. 02 Data and methodology

7. Data Data and methodology Understanding Society Multi-purpose UK longitudinal study of households in the community Wave 1 – 8,992 individuals aged 65 and over Wave 2 (nurse assessments) – 3,803 individuals aged 65 and over BMI measures Richness of data to build individual profiles

8. Methodology Data and methodology Literature review to identify factors found to be associated with being undernourished. Interactive community risk profile tool Firth logistic regression analysis used to investigate the relationship between BMI score and socio-demographic, health and attitudinal characteristics. Tool produced using characteristics found to be associated with having a BMI score of less than 20. Logit function used to derive the estimated probability (risk) of being undernourished. Measuring changes in BMI Wave one and wave two (nurse assessments) allowed us to captured BMI score changes of varying magnitude.

9. 03 Identifying individuals at risk of undernourishment in the community at the national level

10. What are the findings? Identifying individuals at risk of undernourishment in the community at the national level CovariatesResponse values Parameter estimates P-value chi- square Odds ratio Sex1.Female (r)0 1.0000 2.male-0.8508<.00010.4271 Age groupB:65-69 (r)01.0000 C:70-74-0.25760.24740.7729 D:75-790.08090.71341.0843 E:>=800.7906<.00012.2047 Single in household1.No (r)01.0000 2.Yes0.6888<.00011.9913 Felt downhearted and depressed1.Most of the time0.52090.25441.6835 2.Some of the time-0.07160.84650.9309 3.A little of the time0.46020.08681.5844 4.None of the time (r)0 1.0000

11. More findings Identifying individuals at risk of undernourishment in the community at the national level CovariatesResponse values Parameter estimates P-value chi- square Odds ratio General health1.Very good (r)0 1.0000 2.Good-0.2170.09740.8050 3.Fair-0.06380.65040.9380 4.Poor0.04530.79961.0460 GHQ: problem overcoming difficulties1.Not at all-0.1610.40010.8510 2.No more than usual-0.12760.47670.8800 3.Rather more than usual (r)0 1.0000 Satisfaction with amount of leisure time1.Dissatisfied-0.05340.80820.9480 2.Neither satisfied nor dissatisfied0.19830.30981.2190 3.Satisfied (r)0 1.0000 Satisfaction with life overall1.Dissatisfied-0.03390.87450.9670 2.Neither satisfied nor dissatisfied-0.08340.69240.9200 3.Satisfied (r)0 1.0000

12. And some more findings… Identifying individuals at risk of undernourishment in the community at the national level CovariatesResponse values Parameter estimates P-value chi- square Odds ratio Local friends mean a lot1.Agree (r)0 1.0000 2.Neither agree nor disagree0.00420.97681.0040 3.Disagree0.0110.96491.0110 Arthritis1.not mentioned (r)0 1.0000 2.mentioned-0.5709<.00010.5650 government office region1.North East0.24840.3211.2820 2.North West0.12270.521.1310 3.Yorkshire and the Humber0.0480.82851.0490 4.East Midlands-0.09440.68451.0490 5.West Midlands-0.08580.6960.9180 6.East of England-0.01670.93630.9830 7.London-0.06490.77830.9370 8.South East (r)0 1.0000

13. And just a few more. Identifying individuals at risk of undernourishment in the community at the national level CovariatesResponse values Parameter estimates P-value chi- square Odds ratio 9.South West0.0840.67691.0880 10.Wales-0.20690.42070.8130 11.Scotland-0.28580.20570.7510 12.Northern Ireland-0.32320.4180.7240 Total monthly personal income - grossA:Low income (r)0 1.0000 B:Median income-0.10660.36110.8989 C:High income-0.05110.79020.9502

14. Key findings Females are over twice as likely as males to be undernourished. Individuals aged 80 and over are two times more likely to be undernourished than those aged 65 to 69. Those who live on their own are almost twice as likely to be undernourished as those who are not sole residents. Surprisingly it was found that individuals with arthritis were half as likely as those who did not have arthritis to be undernourished. There is little significant variation in the factors associated with varying levels of decrease in BMI score. Identifying individuals at risk of undernourishment in the community at the national level

15. 04 Going local

16. How can this research be applied? Going local Targeting people at risk of undernourishment at the local level. There is a lack of local data through surveys and clinical datasets however we can use 2011 Census data to produce neighbourhood level analysis. Using the findings to understand a typology of undernourishment. Testing for local and regional variation in the importance of risk factors – to be undertaken in Spring 2014. We are adding modules to our tools that we provide to local partners to help them target older people at risk of undernourishment in their area. The risk profile tool could be useful to healthcare professionals, commissioners and those bidding or delivering health services.