1.0 IntroductionThis is amultidisciplinary research, which the aim is to investigate mental healthinequalities in Ireland through diet and lifestyle. To achieve this researchthere will be an integration between environmental sciences to determine thesocial determinants of mental health in Ireland and nutrition as it is one ofthe key mediators describing deprivation and well-being. The project will usethe data gathered from the Trinity, Ulster and Department of Agriculture (TUDA)Ageing Cohort Study, this database has collected data from 5,186 participantsfrom North and South of Ireland. This is a unique database with information onnutrition and diseases of ageing on each individual since 2008. The resultsgathered in this research will provide the evidence needed to inform futurehealth policy aimed at better health in older age reducing mental healthinequalities in Ireland.
2.0 BackgroundMental healthdisorders are currently affecting 15% of older people worldwide (WHO,2017) andit’s the leading cause of disability and ill health in older age. The threemost common disorders in ageing as highlighted by WHO are, dementia, depressionand anxiety. The prevalenceof dementia worldwide is estimated at 35.6 million, of which 60,000 are livingin the island of Ireland (Prince et al, 2014; Connolly et al, 2014) and thesefigures are expected to triple by 2050. Economically the total cost of managingdementia is estimated at €1.
69 billion annually in the Republic of Ireland and£23 billion in the UK (Cahill et al., 2012; Prince et al, 2014; Connolly et al,2014). Depression, often accompanied by anxiety, affects an estimated 10% ofthe Irish population and the economic cost of treating depression is estimatedat €3 billion annually in Ireland and £7.5 billion in the UK (NationalCollaborating Centre for Mental Health 2010). Globally, the total number ofpeople with depression was estimated to exceed 300 million in 2015. Nearly thatnumber again suffers from a range of anxiety disorders (WHO, 2017).Promoting bettermental health is key to promote healthier ageing and address the challengesthat individuals will encounter in later life, not only in health buteconomically and societal also.
Health and socioeconomic position are both partof the seven dimension of human well-being concept (Bhuiya et al., 1995).Nutrition has been suggested as one of the key mediating factors to explainhealth inequalities observed across the social gradient (Darmon et al, 2008).Additionally, optimal nutrition it is essential for maintaining mental health(Moore et al, 2016).
Another study showed that lower socioeconomic positionindividuals are more likely to have an unhealthy diet, eat less fruit andvegetables and be obese (Maguire et al, 2015). There is someevidence to suggest that food access inequalities are prevalent within societyand that older people are particularly vulnerable as they are more likely tohave mobility and fragility concerns that may limit their ability to bypass thelocal food environment (Darmon et al, 2008). This is an everyday concern forolder people, from a lower socioeconomic background and dealing with mentalhealth.There are manyapproaches to measure health inequalities, the challenge is to measure themaccurately. Therefore, evidence-based policy making would be more effective tocompare results from different areas or countries. Deprivation is a ‘state ofobservable and demonstrable disadvantage relative to the local community or thewider society to which an individual, family or group belongs’ (Pornet et al.
,2012). As stated by Townsend, ‘the concept of deprivation covers the variousconditions, independent of income, experienced by people who are poor’.Therefore, deprivation is a vast concept, closely linked with poverty(Townsend, 1987). By measuringdeprivation would give an insight of the socioeconomic background from anindividual, a family, a country and even a continent (Guillaume et al., 2015).Since individual data are often poorly collected in routine GP databases, thiscan be assessed by using socioeconomic characteristics of the place ofresidence (Guillaume et al.
, 2015). To measure area-based socioeconomicbackground the census, surveys and questionnaires are often used to gather thisinformation. Mental healthdisorders are determined by a combination of biological, genetic, environmentand social factors which needs further investigation. The main focus of thisresearch is to investigate health inequalities as we age in the island ofIreland and provide evidence that this is of direct relevance to policy makers. 3.0 Research objectives The overall aimof this research is to promote better ageing for the island of Ireland and toinvestigate health inequalities during the ageing process.
To inform futurehealth policy makers aimed at addressing mental health inequalities. 1) To investigate the role of area-levelsocioeconomic deprivation in mental health in older adults from the TUDA studyliving in the island of Ireland, using geo-referencing technologies to map datafrom two jurisdictions. 2) To investigate the EDI (European DeprivationIndex) and score Ireland in addition to create a model of deprivationcomparable to Europe. 3) Compare and contrast the influence of nutritionand lifestyle factors with area-level socioeconomic deprivation measures oncognitive and mental health outcomes within both health systems on the islandof Ireland (Qualitative and Quantitative)4) Apply the state-of-art brain imagingtechnologies to provide more in depth investigation of the link between brainfunction and social determinants factors. 5) Publish findings and inform media of outcomes inIreland and internationally. 4.
0 MethodologyIt is essentialto use the Trinity, Ulster and Department of Agriculture (TUDA) cohort study toachieve the aims mentioned above. It was designed to investigate nutritionalfactors and health, including mental health in ageing adults. TUDA Study design (existing data) – Designedto assess nutritional, genetic and lifestyle factors to prevent age-relateddisease in 5,186 adults over 60 years old. All participants underwent detailedneuropsychiatric evaluation that measured cognition, depression and anxiety.
Physical Self- Maintenance Scale (PSMS) and Instrumental Activities of DailyLiving scale (IADL) were also assessed. Anthropometry, blood pressure, bonehealth (by DEXA scan), metabolic measures, comprehensive questionnaire data andinformation on 33 single nucleotide polymorphisms (SNPs) involved inmicronutrient dependent metabolic pathways and brain health. Mental Health Assessment (existing data)– Cognitive function was comprehensively assessed using a battery of tests,including the Folstein Mini-Mental State Examination (MMSE), most commonly usedclinical tool worldwide to assess global cognitive function and examinesorientation, registration, attention and concentration, recall and language(Folstein et al., 1975). The maximum score is attainable is 30, with a score<24 indicative of cognitive dysfunction and a score <20 indicative ofdementia. The Repeatable Battery for the Assessment of NeuropsychologicalStatus (RBANS) is an age-adjusted tool that is able to assess specificcognitive domains including immediate and delayed memory, visual-spatial,language, and attention, with a score <80 indicative of cognitivedysfunction (Randolph et al.
, 1998). The Frontal Assessment battery (FAB) toolspecifically assesses frontal lobe function, with particular emphasis on conceptualisation,mental flexibility, programming, sensitivity to interference, inhibitory controland environmental autonomy (Dubois et al., 2000). The maximum score is 18, witha score ? 15 indicative of cognitive dysfunction. Anxiety was assessed using theHospital Anxiety and Depression Scale (HADS), with a maximum attainable scoreof 21, a score ? 11 is indicative of the presence of probable anxiety.
Depression was assessed using the Centre of Epidemiological Studies Depression(CES-D) Scale, with a maximum obtainable score of 60, a score ? 16 is theclinical cut-off for depression. Use of medications used to mental Healthdisorders were also recorded. Objective 1: To investigate the role ofarea-level socioeconomic deprivation in mental health in older adults from theTUDA study living in the island of Ireland, using geo-referencing technologiesto map data from two jurisdictions. Rationale: Area deprivation is linkedto chronic disease and mortality however few studies have examined itspecifically in relation to mental health, a leading cause of disability andpoor quality of life in old age. Preliminary work has shown that higherarea-level socio-economic deprivation is predictive of poorer cognitiveperformance. Further investigation is needed to confirm the assumption. Method: Geographic Information Systemstechnology, using ESRI ArcGIS (V10.3.
1) software, will be used to enhance theTUDA individual case health record data, Northern Ireland and Republic ofIreland, through geo-referencing and appending additional small area-basedcensus data (e.g. education attainment, employment and tenure) and compositemeasures of relative deprivation (In N. Ireland – the 2010 Multiple DeprivationMeasure MDM (NISRA, 2010) and the Republic of Ireland POBAL Deprivation Indexfor Small Areas (Haase and Pratschke,2012). This was achieved by matchingindividual address information from the health record database (e.g.
housenumber, street name and unit postcode) with X, Y co-ordinates via, in the caseof Northern Ireland, the POINTER geo-referencing database provided by Land andProperty Services (LPS) and the Maynooth University Irish Grid address matchingprotocols. The place of residence of all cases are geocoded at the small areacensus unit level or, for most of the cases, below that (e.g. the unit postcodeor individual household centroid). Statisticalanalysis will be performed using the Statistical Package for Social Sciences(SPSS, version 23, SPSS UK Ltd). Objective 2: To investigate the EDI(European Deprivation Index) and score Ireland in addition to create a model ofdeprivation comparable to Europe. Rationale: To contrast the area of deprivation in Europe and compare resultsfrom TUDA in the island of Ireland to other countries in Europe.
Once the modelis created add variables, such as food poverty to analyse the foodaccessibility in older age and urban/rural to investigate if influenceswithin. Method: First step is to construct ofan individual deprivation indicator using EU-SILC, which is a cross-sectionaland longitudinal sample survey providing data on income, poverty, socialexclusion and living conditions in the European Union (Guillaume et al., 2015).In this database it will be selected the fundamental needs for survival at theindividual level, such as goods/services. The selection of fundamental needswas assessed by EU-SILC by the ‘Ability to make ends meet’ question. Thevariables were with great difficulty to very easily, the threshold which aperson felt ‘poor’ was determined by the best fit (highest Wald tests) ofrelationship between income poverty and subjective poverty by univariablelogistic regression.
Selected fundamental needs are those for which p value wassignificant at the 5% level for both levels (Guillaume et al, 2015). Second step isto identify the variables available both at individual level (EU-SILC survey)and aggregate levels (census). This will be achieved by logistic regression(Guillaume et al., 2015).
Third step is toconstruct ecological deprivation index (the EDI), the univariable logisticregression model selected the variables of step 2, which explained theindividual indicator (p<0.05). These variables were then grouped together ina new model (Guillaume et al., 2015). After the threesteps are carried out for Ireland, it will contribute to the EuropeanDeprivation Index (EDI) and creating a model to the island of Ireland willfacilitate comparison between two jurisdictions (Figure 1). This will help theresearch with all the other variables such as food poverty and urban/rural. Figure 1: Guillaume, Elodie et al."Development of a Cross-Cultural Deprivation Index in Five EuropeanCountries.
” Journal of Epidemiology and Community Health 70.5(2016): 493–499. PMC. Web.
4 Dec. 2017.Objective 3: Compare and contrast theinfluence of nutrition and lifestyle factors with area-level socioeconomicdeprivation measures on cognitive and mental health outcomes within both healthsystems on the island of Ireland. Quantitative Rationale: To understand the complexinteractions of biological and environmental factors on the ageing brain.Geo-referencing would integrate nutritional information with geographicallyscalable environmental and socioeconomic data, gathering of potentialinformation to be explored in mental health inequalities.
Method: Using the mental healthassessments as previously described.GIS foodscapes,will be created for sample areas from food retail data compiled from governmentand commercial sources and, if required, ground tested and enhanced withprimary data collection. Accessibility measures will be calculated by traveldistance and travel time (walking and driving) using the large scale digitalroad network as well as public transport network data. Mapping the physicalavailability and accessibility of specific food products such as fresh fruitand vegetables provides important data that aligns with and can be examinedagainst nutrition lifestyle behaviours, biomarkers and brain activityassessments (MEG – Magnetoencephalography) as determinants of mental health inolder adults.
Area-based socioeconomic indicators will be supplemented withdata such as population and building infrastructure densities and crime datafrom Land and Property Services NI, Ordnance Survey Ireland and other publiclyavailable sources (e.g. Northern Ireland Statistical Research Agency).Conventionalspatial statistical analysis of the combined health, socio-economic andenvironmental data (e.g. correlation, regression) using SPSS software (Version23 SPSS UK, Ltd, Chersey, UK) will be supplemented with spatial analysis acrossthe rural-urban/ settlement-size continuum using Anselin Local Moran’s I (foridentifying geographic clusters), Getis Ord Gi* (For identifying geographic hotspots) and Geographically Weighted Regression (for identifying spatialrelationships) using Arc GIS (V.10.
3.1). Logistic regression models will beused to assess the independent and synergic effects of nutritional, social andenvironmental factors on mental health in ageing. QualitativeRationale: Whilst there are numerouslarge scale studies investigating aspects of nutrition and cognitivedysfunction, there has been less attention paid to the decision-making andcapabilities of older adults in terms of what they eat.
Individuals makechoices in terms of their diet, which can be influenced along a pathway fromdeciding what to eat, where it can be sourced (shopping opportunities), andwhat cooking facilities can be used. Forexample, an older person who does not have regular access to a car may berestricted as to which retail outlets can be used, how much can be carried, andwhether he or she has the knowledge and confidence to prepare a hot meal.Recruitment: Potential participantsliving in rural and urban environments in residing in Northern Ireland andRepublic of Ireland will be identified from the TUDA Ageing Cohort study and beinvited to participate in this qualitative investigation (n=20). Writteninformed consent will be obtained from all participantsCognitive Spatial Visualisation: Thisprojects aims to better understand the factors which may influence, andpotentially restrict, the dietary choices an older person can make.
As peopleage, their daily activity spaces tend to shrink. This project will take an innovativeapproach to understanding people’s decision-making and capabilities byemploying a spatial video technology. This enables data capture of a person’sactivities, filming movements which are geographically referenced so the extentof the activity space can be mapped. At the same time, associated audio andvideo data is used to record why those decisions are made e.g. being unable topark a car near the home, uneven pavements, food only sold in amounts which aretoo large/heavy.
A qualitative study will follow participants through theirdaily decision-making processes, to gain insight into a wide range of factorswhich influence their food intake. Analysis: the video aspects of therecordings will be mapped using a dedicated web service (www.ubpix.com) forinternal use only, which will examine activity spaces and identify physicalbarriers to fulfilling choices.
Audio recording will be transcribed andanalysed for themes, but also to provide a narrative for the spatial choices. Objective 4: Apply the state-of-artbrain imaging technologies to provide more in depth investigation of the linkbetween brain function and social determinants factors. Rationale: Despite major advances inbrain imaging technology in recent years, nutritional research in relation tobrain disease has traditionally relied on questionnaire-based assessment ofneuropsychological function. There is a need to integrate nutrition and brainimaging in order to provide robust evidence on how nutritional factors canaffect brain function. Method: Magnetoencephalographyscanning: Brain activity assessment will be using Magnetoencephalography (MEG;Elekta Neuroimag Triux), an imaging modality which passively measures themagnetic fields produced by neural activity. MEG is a non-invasive techniquethat measures electro-physiological responses in terms of brain magneticactivities recorded from extra-cranial sensors.
It will be facilitated by theMEG system at Northern Ireland Brain Mapping (NIFBM) facility of ISRC, the onlysystem of its kind on the island of Ireland. Brain activity will be measured ina subset of TUDA participants (n=48) identified in objective 1. MEG will beused to record brain activity in participants while in a resting state (withclosed eyes) and while completing delayed recognition working memory paradigms.The workingmemory paradigms and subsequent neural activity responses will be recordedusing E-RPIME 1.3 (Psychology Software Tools, Inc.) Analysis of thedelayed-recognition paradigm performance will be based on correct responses andreaction times using SPSS software (Version 23 SPSS UK Ltd, Chersey, UK).Repeated measures ANOVA will be performed using within-group factors ofparadigm responses and a between-group factor of normal and impaired cognitive function.