POPULATION HEALTH MANAGEMENT - Ageing Well

POPULATION HEALTH MANAGEMENT -

Ageing Well

Copyright © NHS Nottingham City Clinical Commissioning Group 2019. This document can be used or reproduced freely for non-commercial purposes. If all or part of the documents be reproduced, we request that the source and Copyright owners be acknowledged.

“ It matters not how long we live but how”

Philip James Bailey

Page Electronic Frailty Index (eFI) 28 Electronic Frailty Index (eFI) Mild Moderate Frailty 29 Electronic Frailty Index (eFI) Severe Frailty 30 Impact of Mulitple Co-morbidities 31 Multiple Co-morbidities: Number of LTC’s 32 COVID-19 Vaccination – 65+ Age Group 33 Emergency Admissions 34 Social Factors 35 Public Transport – Time to KMH 36 Public Transport - Time to NUH 37 Public Transport – Time to Nottm City (NUH) 38 Public Transport – Tine to GP Practice 39 Pension Credit 40 Employment for people aged 65 and Over 41 Ageing Well – Risk Factors 42 Multivariate Regression Analysis 43 Flu & Pneumonia – Logistic Regression 44 Flu & Pneumonia Admissions – Confounding Variables 45 Flu & Pneumonia Admissions – eFI 46 Flu & Pneumonia Admissions – eFI Severe Frailty 47 Flu & Pneumonia Admissions – eFI Moderate Frailty 48 Predictors of a Fall, Injury or Fracture 49 Falls, Injuries Fractures Admissions – Logistic Regression 50 Falls, Injuries Fractures Admissions – Logistic Regression – Ethnicity 51 Predictors of a Fall, Injury or Fracture 52 Predictors of a Fall, Injury or Fracture - Care Home Residents 53 Predictors of a Fall, Injury or Fracture – Lives Alone 54

Page

Contents 2/3 Glossary 4 Introduction 5 Strategic Context – Ageing Well 6 Strategic Context – Ageing Well 7 Executive Summary 1 8 Executive Summary 2 9 Executive Summary 3 10 Executive Summary 4 11 Executive Summary 5 12 Ageing Well 13 Develop Outcomes for Each Population Segment 14 Identifying Priority Cohorts 15 Impactable Interventions 16 Ageing Well Population Profile 17 Population Profile: Age 18 Population Profile: Age/Gender 19 Population Profile: Deprivation 20 Female Life Expectancy 21 Male Life Expectancy 22 Healthy Life Expectancy 23 Hospital Admission – COVID-19 24 Deprivation Deciles 25 Deprivation Deciles Nottingham City 26 Ethnicity 27

Contents

Page Predictors of Fall, Injury or Fracture – Diagnosis of Dementia - 55 Care Home Bed Capacity 56 Care Home v’s Home Care Capacity 57 Healthy and Independent Ageing – Nottm City 58 Healthy and Independent Ageing – Nottm County 59 Polypharmacy Prescribed 15 or more Unique Medicines 60 Polypharmacy Prescribed 8/10 or more Unique Medicines 61 ICP /PCN localised Data and Intelligence 62 Mid Notts Statistics 63 Mid Notts Age 65+ - Ethnicity 64 Mid Notts Age 65+ - Deprivation 65 Mid Notts Age 65+ - Co-morbidities 66 Mid Notts Age 65+ - Social Factors 67 Mid Notts Age 65+ - Medication 68 Mid Notts Age 65+ - Demographics 69 Mid Notts Age 65+ Clinical Information 70 Mid Notts Age 65+ In Summary 71 Nottingham City Statistics 72 Nottingham City Age 65+ - Ethnicity 73 Nottingham City Age 65+ - Deprivation 74 Nottingham City Age 65+ - Co-morbidities 75 Nottingham City Age 65+ - Social Factors 76 Nottingham City Age 65+ - Medication 77 Nottingham City Age 65+ - Demographics 78 Nottingham City 65+ Clinical Information 79 Nottingham City Age 65+ In Summary 80

Page South Nottingham Statistics 81 South Nottingham Age 65+ - Ethnicity 82 South Nottingham Age 65+ - Deprivation 83 South Nottingham Age 65+ - Co-morbidities 84 South Nottingham Age 65+ - Social Factors 85 South Nottingham Age 65+ - Medication 86 South Nottingham Age 65+ - Demographics 87 South Nottingham 65+ Clinical Information 88 South Nottingham Age 65+ In Summary 89 Bassetlaw Statistics 90 Bassetlaw Age 65+ - Ethnicity 91 Bassetlaw Age 65+ - Deprivation 92 Bassetlaw Age 65+ - Co-morbidities 93 Bassetlaw Age 65+ - Social Factors 94 Bassetlaw Age 65+ - Medication 95 Impactable interventions 96 No Service/Self Care 97 Universal Contact 98 Target Contact 99 High Intensity 100 Appendix 101 PHM Looks Beyond the Health System 102 Our Vision and Aims 103 Our Vision, Aims and PHM Approach 104 Our PHM Approach 105 Outcomes Metrics Part 1 106 Outcome Metrics Part 2 107 Supporting Information 108 Acknowledgements 109

Contents

Glossary

3I’s Infrastructure, Intelligence, Intervention BMI Body Mass Index BNF British National Formulary CCG Clinical Commissioning Group CCM Chronic Care Model CHD Chronic Heart Disease CIS Clinical Information System CKD Chronic Kidney Disease CQC Care Quality Commission DWP Department of Work and Pensions e-Healthscope Electronic Health (technology) eFI Electronic Frailty Index EoL End of Life ePACT Electronic prescribing data GP General Practitioner GPRCC General Practice Repository for Clinical Care HLE Healthy Life Expectancy ICS Integrated Care System ICP Integrated Care Provider IMD Index of Multiple Deprivation

JSNA Joint Strategic Needs Assessment KMH King’s Mill Hospital LTC Long Term Condition LSOA Lower Level Super Output Area NECS North of England Commissioning Support Unit NIMS National Immunisation Management System NHS National Health Service NHSE National Health Service Executive NICE National Institute of Clinical Excellence NUH Nottingham University Hospital Trust ONS Office National Statistics PCN Primary Care Network PH Public Health PHE Public Health England PHM Population Health Management QALY Quality Adjusted Life Year QOF Quality and Outcomes Framework QoL Quality of Life T2DM Type 2 Diabetes Mellitus

This document aims to provide organisations with our planned approach in enabling our systems to achieve and deliver the visions and aims of fully integrating health and care across Nottingham/Nottinghamshire. Using localised data, evidence based literature reviews and offering impactable interventions to enable deliver of high quality services with the best possible outcomes to our population. Significant work has been undertaken to identify, articulate and quantify the specific gaps in health and wellbeing; care and quality; and our baseline financial position. Creating a healthier population is at the heart of our plan. Our vision is for our community to be happier, healthier and to live longer in good health. To do this we must embrace the opportunities that working together can deliver. We must look to emerging technologies and finding new and better ways of working that can eliminate duplication and waste and we must develop and support a motivated, highly skilled and professional workforce to serve Nottingham and Nottinghamshire. In order to meet the ambitious vision of the ICS, we must also look at the social determinants of health and wellbeing. Our aim is to help people to be, stay or regain good health and wellbeing. To do this we must take a preventative approach and build strong and joined-up community services. Working together in this way will allow us to look across the system at how services are provided and identify opportunities to add value, improve outcomes and eliminate duplication and reduce costs. The Population Health Management programme will be at the heart of driving this transformational approach in Nottingham and Nottinghamshire. The programme will bring key partners together to use local, joined-up data to help identify gaps in pathways for different segments of our population, and agree outcomes and design interventions tailored to our local people and communities.

Our current approach is underpinned by a rigorous programme structure, utilising a wide range of experts, internal and external, both clinical and non-clinical, to understand our population’s current needs, activity, cost and outcomes. The group has agreed that the initial focus will be on the population segment of people with Long Term Conditions. Through further sub-segmentation and risk stratification of this cohort, the programme will lead the delivery of standardised, evidence-based pathway redesign approach, with appropriate interventions to achieve the aims of the ICS outcomes framework, and in turn to meet the needs of our population at a Primary Care Network level. There will be a clear process for monitoring and evaluating change within the programme framework. We will quantify the financial impact of the interventions proposed by the programme as part of the evaluation criteria for agreeing these. The approach taken will identify opportunities to address gaps in care, reduce acute emergency activity which is avoidable and which does represents the optimal value-for-money, and shift resource into proactive, targeted out-of-hospital interventions to keep our population well. Ultimately this will underpin our system strategy to achieve financial sustainability and reduce pressure with the hospitals acute sector. This Population Health Management project and approach will focus on our Ageing Well population.

Introduction

PHM Team Nottingham/Nottinghamshire

The DHSC white paper ‘Integration and innovation: working together to improve health and social care for all’ supports the foundations of the NHS Long Term Plan that sets out in some detail a vision of what the future will look like in the context of new models of care. Population Health Management, and our local approach to PHM, underpins the strategic direction at both Local and National levels through fully understanding the health and care needs of our population; size, demographics, socio economic factors, etc. which in turn becomes the engine that feeds all ICS work streams in delivering the priorities and improving services, health and wellbeing of our population. National strategic drivers NHSE - Long Term Plan PHE Strategy – 2020-25 NHS Priorities and Operational Planning Guidance 2021/22 Local strategic drivers ICS Priorities Health Inequality Strategies Health and Well Being Boards JSNA’s Our response to the NHS Long Term Plan and the five ICS priority areas is where we believe we can make the biggest impact on improving services and improving the health and wellbeing of the population are as follows: • Promote wellbeing, prevention, independence and self-care • Support people to stay healthy and independent, and prevent

avoidable illness • Support stronger communities that can share responsibility for the

people who live there • Signpost people to good advice and information • Strengthen primary, community, social care and carer services.

Strategic Context – Ageing Well

‘People in England can now expect to live for far longer than ever before – but these extra years of life are not always spent in good health, with many people developing conditions that reduce their independence and quality of life’. NHS Long Term Plan

What we know Nationally: We have a growing and ageing population. Over the next 20 years the population in England is expected to grow by almost 10%. The number of people aged 75+ is expected to grow by almost 60% – an additional 2.7 million people. Social care has seen activity grow In 2019/20, there were 1.9 million requests for adult social care support from new clients, an increase of 6% since 2015/16 Growing morbidity and complexity of disease Around 20% of our lives are spent in poor health, which has been increasing in recent years and is likely to continue in future. The proportion of people aged 65+ with four or more diseases is set to almost double by 2035, with around a third of these people having a mental health problem.

What we know locally: Overall the age structure of Nottinghamshire is slightly older than the national average, with 20% of the population aged 65+ in 2016 compared with 18% in England. Our population is predicted to continue to age and over the next ten years with the number of 75-84 year olds increasing by 44% and 85+ year olds by 39%.

• In general people are living longer but with greater levels of ill

health and disability.

• Men spend around 18 years of their life in poor health and for women it is 21 years.

• The proportion of 85+ who need care and support is increasing. 1 in 8 people have caring responsibilities at some stage in their lives.

• It is estimated that care provided by adult children will increase by 90%.

• The majority of carers who provide over 50 hours of care are aged 65+ looking after their partners.

• These carers are more likely to have poorer health than those who do not provide care.

• The number of older people who live alone will increase.

• Those living in rural areas without access to accessible transportation are particularly vulnerable.

Our PHM approach to ageing well required ambition and collaboration across the system with health, social care, voluntary and third sector organisations being integral to our success. Our success will be underpinned by our ability to support our ICS communities and carers to improve outcomes for our population to age well. The programme: • Has a system-wide, outcomes-based focus driven by data

intelligence and need, not by existing services.

• Considers the whole life course and addresses early intervention, primary, secondary and tertiary disease prevention.

• Considers factors much wider than health and care services, as the wider determinants have a wider impact on health outcomes.

• Aims to understand and address any health and/or care inequalities to have a positive impact on our outcomes overall.

Strategic Context – Ageing Well

Executive Summary 1

This Ageing Well blue print pack forms part of the PHM program, supported and sponsored by the Nottingham and Nottinghamshire ICS. This document presents the population health management data and intelligence driven approach, to our Ageing Well population across Nottingham/Nottinghamshire Integrated Care System (ICS) and follows the 6 step approach.

The PHM analytical team have utilised numerous data sources; health/care and wider determinant data to understand our ageing well populations health and care needs. Our aim is to identify variants, addresses any health inequalities and produce our ageing well local population profiles. It offers evidence based impactable intervention recommendations to assist our system in fulfilling both local and National priorities and to achieve further improved outcomes for our ageing population. In order to undertake this program we have engaged collaboratively with, and received a wealth of professional support from across our system partners, clinical and non – clinical, and through a series of task and finish group meetings we have collectively agreed, and enabled the development of this Ageing Well Blueprint pack. The following pages offer an executive summary of the key high level data and intelligence findings, multivariate regression analysis, combined with a high level overview of some of the headline areas where impactable interventions can be applied. Further detailed data intelligence led findings at a more localised level (ICP and PCN), also forms part of this blue print pack.

Executive Summary 2

As an ICS we have in excess of 190,160 people aged 65 and over - key risk factors:

• Over 40% of the population aged 65 and over in Nottingham City are classed as living in an area with high deprivation (source ONS)

• 7,560 people are identified as housebound (source GPRCC)

• 26,205 of the population is identified as living alone (source GPRCC)

• 15,405 people are identified as having a carer (source GPRCC)

• 20,500 people have a shielded status (source GPRCC)

• 37,755 are identified as being socially vulnerable (source GPRCC)

• 2,600 people receive carer services from Notts County Council

• 9,100 people are on long term support for Notts County Council • 5,130 people are a care home resident (source GPRCC)

• 33,290 people have an anxiety diagnosis (source GPRCC)

• 93,520 of our 65 and overpopulation have hypertension included

within their GP record (source GPRCC)

• 34,275 people have a diagnosis of depression (source GPRCC)

• 8,410 people have a diagnosis of dementia (source GPRCC)

• 32,455 people are diagnosed with T2 DM (source GPRCC)

• 19,645 people are identified as being Pre Diabetic (source GPRCC)

• 13,985 people have an Osteoporosis diagnosis (source GPRCC)

• Approximately 23% of the Nottinghamshire 65+ population rent their accommodation (source: POPPI).

• Around 35% of the 65+ population in Nottingham City rent their accommodation (source: POPPI).

• Over 8,800 people have had an emergency admissions in the last 3 months (source Sus)

• Over 1,750 people have had two emergency admissions in the last 3 months

(source Sus)

ELECTRONIC FRAILTY INDEX The electronic frailty index (eFI) is a calculation to identify the population aged 65 and over who may be living with varying degrees of frailty. It takes into account clinical signs, symptoms, diseases, disabilities and abnormal test values. eFI is used to stratify the population aged 65 and over into four groups: (Fit - Mild frailty - Moderate frailty - Severe frailty). Across the 65 and over population eFI data identified the following at PCN level: People identified as Fit varies from 31% - 52% People identified with Mild Frailty varies between 28% and 33% People identified with Moderate Frailty varies between 12% and 21% People identified with Severe Frailty varies between 10% - 18% (excluding Bassetlaw) * Note: eFI is calculated using information captured within the GP record and only includes the population in contact with, and known to healthcare services.

A high level view of our initial findings

Executive Summary 3

• 5 of our PCN’s have 24% of their population aged 65 and over and for the most elderly age brackets (75+) there are a higher number of females than males

• Strong correlation between higher deprivation levels and lower life expectancy for the 65 and over population, and leads to a PCN variation of 5 years for females and 7 years for males (PCN level).

• Correlation between higher deprivation levels and higher Covid – 19 hospital admissions for the 65 and overs

• ONS data shows our most deprived 65 and over population is within Nottingham City, however there are significant pockets of higher deprivation for this cohort in Worksop, Sutton, Mansfield (Ravensdale, Oak Tree), Kirkby and Newark.

• Our population on pension credits that are outside of the 45 minute public transport boundary to KMH include Ollerton, Bilsthorpe, Hucknall and parts of Clipstone and Blidworth.

• Bulwell, Bestwood and Carlton as identified as having high numbers of people on pension credit that are outside of the 45 minute NUH public transport boundary.

• Co-morbidities data shows Nottingham City populations ‘ long term conditions are beginning at a much younger age compared to the rest of the ICS.

• 21.5 % of the following two areas accounts for all over 65 emergency admissions (based on 2019 SuS data).

• 7,800 admissions for falls, Injuries and fractures equating to approximately 70,000 bed days.

• 5,100 Flu and pneumonia emergency admissions equating to 43,000 bed days.

• 360 care homes with more than 10,500 beds. Beeston has the

highest proportion of care home residents.

• Comparing care home workers (9,200) across the ICP’s show more care home beds than care workers in Bassetlaw, Mid Notts and South Notts. City have more care workers than care home beds.

Polypharmacy • Radford and Mary Potter PCN is seen to have the highest rate for

three of the four poly-pharmacy measures. BACHs PCN was also noted to be high within these measures.

• Hypertension is the most prevalent diagnosis across our 65 and over population (included on the GP record)

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Executive Summary 4

Multivariate regression analysis is a statistical method used to understand how a number of independent variables can have an impact on a dependent variable (outcome). It uses existing data to predict an ‘odds ratio’ potential outcomes using a number of predictor variables.

Logistic Regression Modelling Findings:

Flu and Pneumonia: • People classed with severe frailty are 5.9 times more likely to have a flu

and pneumonia admission than those people not classed with severe frailty.

• People classed with moderate frailty are 1.9 times more likely to have a flu and pneumonia admission than those people not classed with moderate frailty.

Fall, injury or fracture

• Data also identifies the odds ratio predictor for the population with and

eFI calculation of severe are 13.8 times more likely of someone having a fall, injury or fracture.

• The 65 and over population residing within a care home are predicted to be 9.0 more likely of having a fall, injury or fracture. Beeston is identified as having the highest number of care home residents and the highest 65 and over population.

Headline Interventions : • Target and increase 95% flu vaccination for the 65 and overs (Utilise PHM Flu Vaccination Blueprint Pack) • Work collaboratively with providers to signpost/advice and

guidance on: • Accommodation (LA) • Finance (CAB/DWP), PCB’s etc • Cold weather planning (Heating)- home checks etc

• Increase annual medication reviews

• Regular falls assessments

• Provide advice and guidance on any home adaptations/home check requirements

• Increased uptake in Comprehensive Geriatric Assessment (CGA) and individualised care and support planning.

• Work in collaboration with LA, SC, housing and environmental teams

• Work collaboratively with care homes to reduce the risk of falls

Executive Summary 5

Living Alone • The 65 and over population living alone are 8.8 times more

likely to be admitted to hospital than those not living alone. Mid Nottinghamshire has the largest PCN proportion of people living alone (17%)

Headline Interventions: • Promote/sign post to improve/maintain good diet/nutrition • Signpost the population to maintain/increase cognitive and social

activities and participation: • One to one activities (e.g. befriending) • Group based activities • Volunteering opportunities

• Target eFI Fit population to prevent/reduce escalation to Mild Frailty

(Use every opportunity to signpost/promote, increase access/maintain good levels of physical activity or decrease sedentary life styles or maintain balance, strength and weight bearing functions) • Provide advice and guidance on any home adaptations/home check

requirements


• Work in collaboration with LA, SC, housing and environmental teams

• Actively target uptake of: • Health checks/screenings • Vaccinations

• Provide signposting/advice and guidance for: • Accommodation (LA) • Finance (CAB/DWP), PCB’s • Cold weather planning (Heating) • Accessibility to services/Transport/Appointment Booking

Times

Logistic Regression Modelling Findings Continued:

13

• People with dementia are predicted to be 8.2 times more likely to be admitted into hospital. By PCN the North of the County have the highest numbers whilst the highest prevalence of dementia is in Beeston, Clifton and the Meadows.

Headline Interventions: • Involve our 65 and over people and their families in planning and co-ordinating their

own care • Utilise joint needs assessments to support care planning that reflects persons own

preferences and circumstances

• Raise awareness of the risk of developing some types of dementia can be reduced, or the onset or progression delayed, through lifestyle changes.

• Annual medication reviews, Regular falls assessments


• Ensure accessible treatment for ‘minor’ needs that limit independence

• Increasing uptake of community-based strength and balance programmes

• Undertake holistic medical reviews and consider Comprehensive Geriatric Assessment in a community setting

• Consider using Smart technologies, such as tailored internet programs, to help older people better manage and understand various health conditions

• Proactively Identify and support carers through routine contact with services in all settings, taking into account that many people do not identify with the label ‘carer’

• Providing support, signposting and education for family and volunteer carers

• Increased ageing well training for our community workforce;

earlier identification of needs Signposting for support/services Increase information shared across services about individuals Increase/promote health checks, vaccinations/screening etc Increase training and awareness of digital/telehealth support (NHS App

etc)

Logistic Regression Modelling Findings Continued:

Executive Summary 6

Based on the data intelligence and initial findings there are a number of headline areas where targeted interventions can be done to achieve better outcomes for our ageing well population. More targeted interventions are included within this pack for ICP, PCNs based on their localised data intelligence. The full ageing well intervention literature review can be found in the Appendix.

Ageing Well

PHM requires developing population health outcomes and measures that address the relationships between epidemiological measures (such as risk exposures, incidence, and mortality rates) and multi-domain measures of population health status. Many health care delivery systems are shifting from a focus on the diagnosis and treatment of disease to a population health management approach that emphasises wellness. To achieve the potential of the ageing well population, we established local goals that align to the ICS outcomes framework that clearly define the ’purpose’ with a set of measures, distinguishing between outcomes for which all share responsibility and actions to improve health for which the health care sector, public health agencies, and others should be held accountable. An agreed set of local outcomes developed in partnership with the Ageing Well Task and Finish Group has been undertaken and prioritised to meet both strategic system level and local health and care priorities.

• The population feel more supported on how to manage their health and well-being

• The population live in safe stable accommodation • The population feel financially secure • The population have regular social contact, and do activities that are meaningful

and enjoyable

• Our population receive expert co-ordinated management of any long-term conditions

• Our population will be encouraged to address health difficulties (e.g. mobility, continence)

• The population feel supported and empowered to recover and regain their independence in there own home with dignity

• Our population will have shared decision-making to make important choices about the right care and treatment

• Reduce inappropriate admissions to hospital • EoL care plans in place for those in the last 12 months of life • Experience end of life according to their personal and individual wishes • Reduction in delayed transfers of care

• Our population will be supported to live independently in their own home for as long as possible with dignity

• Carers have improved well-being and life satisfaction (unpaid/paid) • Our population has Improved access to evidence based services dependant on

the level of need • Older people with dementia and cognitive impairment will be known to the

system as early as possible • Our workforce are trained on how to support aging with dignity

Develop outcomes for each population segment

No Services Self care

Universal Contact

Targeted Contact

High Intensity

Identifying and establishing a process to assign a risk status to our ageing well population, then using this information to direct care and improve overall health outcomes. Our ultimate goal of stratifying and segmenting patients into distinct groups of similar complexity and care needs. A "one-size-fits-all" model, where the same level of resources is offered to every patient, is clinically ineffective and prohibitively expensive. To maximize efficiency and improve outcomes it is best practice to analyse the patient population and customise care and interventions based on identified risks and costs. Approaching our population in this manner creates the framework to begin identifying the data requirements needed to support our population at each level. Identifying and prioritising these cohorts has taken place through a series of Task and Finish Group meetings

Identifying Priority Cohorts

• Age 65> • Severely frail • Severe challenging behaviour • Terminally ill - Identified as having <12 months to live • Recent hospital admission • Poor physical health • Loss of independence • Preferred place of death not known/communicated

• Age 65> • Moderately frail with a physical health condition. • Recent life events, e.g. bereavement • Depression/bipolar affective disorder • Poor physical health • Diagnosed with Dementia • Living alone • Lack of good family infrastructure

• Those within Mental Health Crisis Care and Liaison

• Co-existing neurodevelopmental conditions

• History of falls • Dementia • History of stroke • Socially vulnerable – not physically

active, amputee/wheelchair

• Age 65> • Mildly frail • Shielded/shielding • Carers • Deaf , visually impaired, language barriers, interpreters • Social Prescribing • Recent life events, e.g. bereavement, esp. loss of partner • Shielded status

• Age 65> • Managing Well • Retired • *Experiencing financial challenges • Veteran • Recent retirement • Not able to drive / availability of public transport

• Not financially stable - pensions, savings

• Housing status (owner, social, rented etc)

• No access to open space • Physical/mental health conditions

No Services Self care

Universal Contact

Targeted Contact

High Intensity

To deliver impactable interventions, It is imperative that we build from the learning of the analytics to align this with qualative information to make decisions on the services provided to the public; identifying effective, evidence-based interventions and implementing them. Our approach has been complimented with help from: Library & Knowledge Service Sherwood Forest Hospitals NHS Foundation Trust And Research and Evidence team at NHS Nottingham and Nottinghamshire CCG We undertook a full literature review and evidence-based development of localised impactable interventions across the ICS, ICPs and PCNs Ageing Well demography (Nottingham/Nottinghamshire). Consideration has been taken in to all aspects of the health and care economy, based on the agreed outcomes framework and aligned locally to patient need. *A full copy of the evidence based research can be found in the Appendix

Rather than identifying patients based solely on previous cost or utilization history, the concept of introducing impactable interventions is to consider how patients will likely respond to interventions and what strategies are needed to effectively engage them in care. This principle not only helps programs refine how they identify high-need, high-cost populations, but also for identifying those who are defined as “rising risk” — individuals who are not yet considered “high-risk,” but are on a trajectory to becoming so. The key to understanding and realise opportunities which population health management can address across Nottingham/Nottinghamshire will be high quality analysis of Local and National information (JSNAs, insight teams, police, housing, social care, Public Health etc) aggregated from individual patient-level data sets and our ability to break this down into local levels; community, PCN etc, and the insight that this can provide. This will enable evidence-based interventions to be identified, designed and implemented at an appropriate scale, and tailored and targeted to specific cohorts of the segmented population. It will also allow ongoing regular monitoring and evaluation of the achievement of outcomes.

Impactable Interventions

AGEING WELL POPULATION PROFILE The population profile presented in this report is defined as our local population aged 65 and over. This report covers the following topics:

• Overview of the population characteristics for the ICS (including Bassetlaw)

• Impact of deprivation on life expectancy and Covid admission rates.

• Introduction to the use of regression analysis for estimating the relationships between dependent variables (outcomes) and independent variables

• Case study into the risk factors for an admission into hospital for a fall, injury or fracture

• Care home and home care capacity

• Benchmarking outcomes for independent living and risks measures for polypharmacy

• ICP level population characteristics

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Population Profile: Age

• There are five PCNs (shaded in dark blue) that have 24% of their population aged 65 and over.

• Radford and Mary Potter PCN and Unity PCN have the lowest percentage with less than 5% of their population aged 65 and over. This is shown in the lightly shaded area on the map.

• The 7 PCNs with the lowest % are all based in Nottingham City.

• At ICP level the % of the population aged 65 and over are:

Bassetlaw: 22%

Mid-Notts: 20%

Nottm City: 11%

South Notts: 21%

19

Population Profile: Age & Gender

• For the most elderly age brackets (over 75s) there are a higher number of females than males

• In all but one PCN there are more females aged 65 and over than there are males.

• The data shows there is not much variation between PCNs and ICPs on this gender split.

The ICS population aged 65 and over – Age / Gender

The ICP population aged 65 and over – Age / Gender

20

Population Profile: Deprivation

• The deprivation quintiles for the population aged 65 and over shows significant variation across ICPs.

• Higher deprivation is associated to higher morbidity and lower life expectancy

• Over 40% of the population aged 65 and over in Nottingham City are within the most deprived quintile.

The ICS population aged 65 and over – Deprivation 1 = Very deprived 5 = Least deprived

21

Female Life Expectancy

It is important for us to understand the levels of deprivation in our local area as the female life expectancy between PCNs differs by more than 5 years across our ICS.

The correlation can be observed by plotting the data points for each PCN.

Higher deprivation scores correlate to fewer years of life with a R squared value of 0.75.

This means that in our statistical model 75% of the variance in life expectancy can be explained by the level of deprivation.

Source: PH PCN data packs 22

Male Life Expectancy

For male life expectancy the difference is more than 7 years in PCNs across the ICS.

A similar correlation is observed when plotting the life expectancy of males against the deprivation score for each PCN.

Once again a higher deprivation score correlates to fewer years of life with a R squared value of 0.75.


Healthy Life Expectancy

• Healthy life expectancy does not show large differences between males and females but does vary significantly across the ICS.

• There is a 15 year age gap in healthy life expectancy between Rushcliffe South (70 years) and Radford and Mary Potter (55 years)

• Healthy life expectancy is lower for those PCNs that have the highest levels of deprivation.


Hospital Admission - Covid

Other outcomes that are shown to have strong correlation to deprivation score are hospital admissions for certain types of diseases.

The plot shows the correlation between an age standardised rate of hospital admissions related to Covid against the deprivation score.

A correlation can be observed between a higher deprivation score and a higher admissions rate.

The R squared value in this case is 0.68.

Source: SUS hospital data 25

Deprivation Deciles

• ONS have published data on deprivation deciles for older people (age 60 and over).

• The most deprived areas for older people are clustered in Nottingham City. Further detail is shown on the following map.

• There are also pockets of high deprivation for older people in Worksop, Sutton, Mansfield (Ravensdale and Oak Tree), Kirkby and Newark.

26

Deprivation Deciles - Nottingham City

• Areas falling into the most deprived decile for older people in Nottingham City are identified in the map.

• A large part of Nottingham City Centre is part of the most deprived decile for England.

Source - IMD data

27

Ethnicity

The ICS population aged 65 and over – Ethnicity

28

electronic frailty index (eFI) - Fit The electronic frailty index (eFI) is a calculation to identify the population aged 65 and over who may be living with varying degrees of frailty. The eFI is calculated using information captured on the GP record and takes into account clinical signs, symptoms, diseases, disabilities and abnormal test values. The eFI can be used to stratify the population aged 65 and over into groups and different interventions may be targeted at each group. The four groups are: Fit, Mild frailty, Moderate frailty and Severe frailty.

The percentage of the population aged 65 and over identified as fit varies from 52% in Rushcliffe North to 31% in Radford and Mary Potter.

Source: GPRCC 29

electronic frailty index (eFI) Mild Frailty

For an eFI of mild frailty the percentage varies between 28% and 33% with PCN percentages shown below.

The numbers of people with mild frailty are shown below.

Source: GPRCC 30

electronic frailty index (eFI) Moderate Frailty

For an eFI of moderate frailty the percentage varies between 12% and 21% with PCN percentages shown below.

The numbers of people with moderate frailty are shown by PCN below.

31 Source: GPRCC

electronic frailty index (eFI) Severe Frailty

Source: GPRCC 32

For an eFI of severe frailty the percentage varies between 5% and 18% with PCN percentages shown below.

The numbers of people with severe frailty are shown by PCN below. Note that the 3 Bassetlaw PCNs may be under reported.

The Impact of Multiple Co-morbidities

We have observed that having multiple co-morbidities has a significant impact on odds of having a hospital admissions. The plot below shows the population of the ICS and the number of people aged 65 and over who have certain co-morbidities.

93,510 people in our ICS have hypertension included on the GP record. There are a number of co-morbidities where the prevalence is greater than 10,000 people.

33

Multiple Co-morbidities: Number of Long Term Conditions

Another good variable for predicting outcomes are the proportion of the population aged 65 and over who have multiple long term conditions.

The charts show there is little variation across ICPs.

34

COVID 19 Vaccinations – 65+ age group

• As of the 26th April 2021, 94% of the 65+ population had received their first dose of Covid vaccination.

• This varied across GP practices between 75% and 98% with the map identifying variation across the ICS.

• The areas that have had the lowest % uptake are also areas with higher levels of deprivation and where ethnic minorities make up a higher proportion of the aged 65+ population .

Source: NIMS 35

Emergency Admissions

A further outcome considered within the population aged 65 and over are the number of emergency admissions into hospital.

In 2019 there were over 7,800 emergency admissions for falls, injuries and fractures accounting for nearly 70,000 bed days.

For flu and pneumonia there were 5,100 emergency admissions accounting for 43,000 bed days.

These two areas cover 21.5% of all emergency admissions for the 65 and over population. It is therefore important to understand the key risk factors leading to these two outcomes.

36

Social Factors

The ICS population aged 65 and over – Social factors

37

Public Transport – Time to King’s Mill Hospital

• The areas in green show those areas of Mid-Nottinghamshire where it is possible to reach King’s Mill Hospital within 45 minutes by public transport

• Areas identified as having high numbers of people on pension credit that are outside of the 45 minute boundary include Ollerton, Bilsthorpe, Hucknall and parts of Clipstone and Blidworth.

Source: SHAPE

Public transport time to Kings Mill Hospital

38

Public Transport – Time to Queen’s Medical Centre (NUH)

• The areas in green show those areas of Nottingham and South Nottinghamshire where it is possible to reach Queen’s Medical Centre within 45 minutes by public transport

• Areas identified as having high numbers of people on pension credit that are outside of the 45 minute boundary include Bulwell, Bestwood and Carlton.

Source: SHAPE

Public transport time to Queens Medical Centre

39

Public Transport – Time to Nottingham City Hospital (NUH)

• The areas in green show those areas of Nottingham and South Nottinghamshire where it is possible to reach Nottingham City Hospital within 45 minutes by public transport

• Areas identified as having high numbers of people on pension credit that are outside of the 45 minute boundary include Eastwood and Stapleford.

Source: SHAPE

Public transport time to Nottm City Hospital

40

Public Transport – Time to a GP Practice

• The are relatively few areas where it is not possible to reach a GP practice within 45 minutes by public transport. These are mostly rural areas in Newark and Sherwood, Bassetlaw and Rushcliffe.

• The public transport travel times to a GP practice are represented in the green shaded areas of the map with residents of Nottingham City mostly having access to a GP practice within 10 minutes on public transport.

Source: SHAPE

Public transport time to a GP practice

41

Pension Credit

• The Department of Work and Pensions have published LSOA detail on the number of people claiming pension credit.

• The LSOAs within the ICS where more than 50 people claim pension credit are shown in the orange shaded areas of the map.

• These orange shaded areas are distributed more evenly across the districts as this is a combination of both the % population aged 65 and over and the deprivation deciles.

42

Employment for People Aged 65+

Employment for people aged 65 and over dropped from 11,700 in 2019 to just 7,700 in 2020.

Source: Annual Population Survey Nottingham County Council

Ageing Well Risk Factors

Has a carer 15,475

Type 2 Diabetes 32,455

Dementia 8,425

Housebound patients

7,645

Pre-Diabetes 19,705

Depression 34,311

Osteoporosis 13,990

Lives Alone 26,245

Below are the number of people who are known to have one the key risk factors for not ageing well.

44

Multivariate Regression Analysis

Multivariate regression analysis is a statistical method used to understand how a number of independent variables can have an impact on a dependent variable (outcome). In this section we are concerned with the binary outcome of whether a person has had a hospital admission in the last 12 months or not. For predicting binary outcomes a commonly used technique is logistic regression analysis. A tool within eHealthscope has been developed to allow the user to carry out logistic regression analysis on a number of outcomes using a number of predictor variables. This is found on the profiling tool and uses the ‘Analyse’ button on this page once a outcome indicator and population group have been selected. https://ehsweb.nnotts.nhs.uk/Default.aspx?tabid=410 In the following example we look at hospital admissions related to flu and pneumonia to demonstrate how logistic regression works within the tool. In depth analysis has then been carried out for the outcome of a hospital admissions for falls, injuries and fractures. We have already shown that people living in more deprived areas have both a lower life expectancy and are also more likely to be admitted into hospital for certain diseases such as Covid-19. The next section looks to use logistic regressions to help quantify these associations between a predictor variable and the outcome of a hospital admission. This is known as the odds ratio and can use a number of predictor variables such as deprivation, social factors and co-morbidities.

45

https://ehsweb.nnotts.nhs.uk/Default.aspx?tabid=410

https://ehsweb.nnotts.nhs.uk/Default.aspx?tabid=410

Flu and Pneumonia Admissions Logistic Regression

These results are from a logistic regression model developed within GPRCC and show the impact of a predictor variable against a binary outcome. In this case it measures the odds ratio of how deprivation quintiles are associated to the outcome of a hospital admission for flu and pneumonia. The odds ratio (similar to relative risk) is compared to the population group that sits on the ‘1’ line to the left of the chart. This plot confirms that higher deprivation is associated with an increase in the odds ratio for the outcome of a flu and pneumonia admission. IMD quintile one contains the most deprived areas and people living in these areas are 2.5 times more likely to have a hospital admission than people living in the least deprived quintile (quintile 5) The results also show that people in a care home are 5.3 times more likely to have a flu and pneumonia hospital admission than our population group not resident in a care home.

46

Flu and Pneumonia Admissions Confounding Variables

A confounding variable occurs where a perceived association between a predictor and binary outcome is actually caused by another (third party) variable. Age tends to be a confounding variable in most medical outcomes and skew results if the age profile is not consistent across each of your predicting variable groups. Logistic regression modelling allows you to remove the confounding factor by including it in your model. An example of this is shown on this page. By including 5 year age bands, gender and deprivation quintiles in the model then you are adjusting for these additional variables which can be confounding. In the case of care homes residents it has reduced the odds ratio to 4.6 where is what 5.3 before on the previous page. Note that the odds ratios for deprivation are also different to the previous page as we are now adjusting for gender and 5 year age bands within our model.

47

Flu and Pneumonia Admissions electronic frailty index (eFI)

Source: GPRCC

Logistic regression analysis supports the assumption that we would observe higher odds ratios as we move along the electronic frailty index.

People classed with severe frailty are 5.9 times more likely to have a flu and pneumonia admission than those people not classed with severe frailty.

People classed with moderate frailty are 1.9 times more likely to have a flu and pneumonia admission than those people not classed with moderate frailty.

48

Flu and Pneumonia Admissions eFI – Severe Frailty

The largest number of people identified as severely frail are in the PCNs within Mid-Nottinghamshire.

However, as a percentage of the aged 65 and over population Clifton and Meadows have the highest percentage of people with an eFI of severe frailty.

The relatively low % values in the 3 Bassetlaw PCNs should be taken with caution and this may relate to data recording.

Source: GPRCC 49

Flu and Pneumonia Admissions eFI – Moderate Frailty

The PCNs with the most people identified as moderately frail are within Mid-Nottinghamshire and Bassetlaw.

However, the four PCNs with the highest percentage of people who are moderately frail are within Nottingham City.

The PCN percentages range from 12% to 21%.

Source: GPRCC 50

Predictors of a Fall, Injury or Fracture

Analysis can be undertaken for any outcome that is captured within the profiling tool of e-Healthscope. For this section of the pack we are going to focus on emergency admissions for a falls, injury or fracture. Work has been undertaken with the ICS falls prevention group to list common risk factors related to a risk of a fall. Our analysis will allow us to use local data to generate the odds ratio for each of the predictors where data is available. For all of the analysis we will include age, gender and deprivation within our logistic regression model so to adjust for these variables. The common risk factors that lead to a risk of a fall are as follows. • 1. Age. • 2. Cognitive impairment. • 3. Previous history of falls. • 4. Previous history of fractures or at risk of fractures. • 5. Frailty. • 6. Visual impairment. • 7. Polypharmacy. • 8. Impaired activities of daily living ADLS and poor mobility Other risk factors such as transport links, housing conditions, financial stability , place of residence and lifestyle choices will also be considered where the data is available For those predictors with high odds ratios we then show the percentage of the population with this risk factor in each PCN to investigate the association with high admission rates.

51

Falls, Injuries and Fracture Admissions Logistic Regression

The odds ratios (O.R.) for common risk factors are shown within these charts. Confidence intervals (C.I. are also included). A ‘0’ flag indicates negative and a ‘1’ flag indicates positive.

Source: GPRCC 52

Falls, Injuries and Fracture Admissions Logistic Regression - Ethnicity

If we model the odds ratios for ethnicity then the model sets the baseline of 1 for Ethnicity:White as this has the largest number of observations.

In comparison to Ethnicty:White the odds ratios for other populations are either lower or statistically similar.

Both Ethnicity: Asian/Asian British and Ethnicity: Black/Caribbean/ Black British are less likely to have an admissions for a fall, injury or fracture.

Source: GPRCC 53

Predictors of a Fall, Injury or Fracture

The results of the logistic regression analysis identified those predictors that have a high odds ratio for a hospital admission relating to a fall, injury of fracture. All odds ratios were calculated as part of a model that adjusted for age, gender and deprivation.

The table below orders the odds ratio modelled on a random sample of our population.

Predictor Variable Odds Ratio

eFI calculation – severe frailty 13.8

In a care home 9.0

Lives alone 8.8

Dementia diagnosis 8.2

Stroke diagnosis 5.3

Deprivation (IMD quintile 1) 3.4

Housebound 3.4

Sight Impaired 3.3

Deprivation (IMD quintile 2) 2.4

BMI under 20 1.9

Alcohol misuse 1.7

An eFI calculation of severe frailty has the highest odds ratio within our model.

The PCN breakdown of people classified with severe frailty was shown earlier in the report.

The next two predictors with the highest odds ratios were both linked to residency factors, either living in a care home or living alone.

Source: GPRCC 54

Predictors of a Fall, Injury or Fracture Care Home Residents

The logistic regression modelling identified people resident in a care home are 9 times more likely to have a hospital admission than those people that are non residents.

Beeston has both a high number of care home residents and the highest percentage per population aged 65 and over.

Source: GPRCC 55

Predictors of a Fall, Injury or Fracture Lives Alone

The logistic regression modelling identified people who live alone are 8.8 times more likely to have a hospital admission than those not living alone.

The PCNs with the highest percentage of older people living alone are predominantly found in Mid-Nottinghamshire. The top 4 PCNs have more than 17% of older people living alone.

Source: GPRCC 56

Predictors of a Fall, Injury or Fracture Diagnosis of Dementia

The logistic regression modelling identified people with dementia are 8.2 times more likely to have a hospital admission than people without dementia.

By PCN the highest numbers are predominantly in the north of the county but the highest prevalence rates for dementia are in Beeston and in Clifton & Meadows.

Source: GPRCC 57

Care Home Bed Capacity

Source: CQC 58

The map identifies the location of our local care homes for the elderly and shows the number of beds at each home.

There are approximately 205 care homes for the elderly with more than 8,500 care home beds in Nottingham and Nottinghamshire.

The breakdown by nursing and residential care home beds is shown in the bar chart.

Care Home vs Home Care Capacity

Source: CQC & NECS capacity tracker

59

There are approximately 205 care homes for the elderly with more than 8,500 care home beds in Nottingham and Nottinghamshire.

In comparison there are approximately 9,200 care workers providing home care services.

The breakdown by ICP shows that there are more care home beds for the elderly than home care workers in Bassetlaw and South Nottinghamshire.

There are more home care workers than care home beds in Nottingham City.

The differences in age profile for each ICP could be one factor for these results.

Health and Independent Ageing – Nottingham City

These indicators are taken for the fingertips tool provided by Public Health England and is based on 2018/19 data.

They allow us to benchmark a number of outcome measures for Nottingham City compared to England and the midlands region.

Source: PHE fingertips 60

These indicators are taken for the fingertips tool provided by Public Health England.

They allow us to benchmark a number of outcome measures for Nottinghamshire County compared to England and the midlands region.

Source: PHE fingertips

Health and Independent Ageing – Nottinghamshire County

61

Polypharmacy Prescribed 15 or more unique medicines -

An anticholinergic burden score of 6 or more

These indicators are taken from the ePACT system.

The charts provide polypharmacy measures that could be perceived as risk factors and are aggregated up to PCN.

This is for the period October 2020 to December 2020.

Source: ePACT 62

Polypharmacy Prescribed 10 or more unique medicines -

Prescribed 8 or more unique medicines

Source: ePACT

These indicators are taken from the ePACT system.

The charts provide polypharmacy measures that could be perceived as risk factors and are aggregated up to PCN.

This is for the period October 2020 to December 2020.

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The following pages show overall statistics (numbers and percentages) for the 65 and over population PCN/Neighbourhood and ICP level for the three Nottingham/Nottinghamshire ICPs and includes some Bassetlaw data.

• Ethnicity • Deprivation • Co-morbidities • Social Factors • Medication • Demographics • Clinical Information • Summary ‘Headlines’ • Interventions

The statistical data and intelligence provided throughout this pack is intended to compliment the local knowledge of the ageing well population. It is envisaged that the data and intelligence provided will facilitate future planning and commissioning intentions and assist ICP’s and PCN’s to prioritise and target specific cohorts of the population in achieving better outcomes for this population.

A full research literature review of evidence based impactable interventions has been undertaken as part of this work, a summary of this is provided in the following pages (please see the Appendix for the full ageing well literature review documentation).

*The Heat Maps are meant to highlight patterns (high to low) rather than provide any form of RAG (red/amber/green) ratings

ICP/PCN Localised Data and Intelligence

64

Mid Nottinghamshire’s Statistics

65

Mid - Notts aged 65 and over - Ethnicity

Source: GPRCC 66

Mid-Notts aged 65 and over - Deprivation

In Mid-Nottinghamshire the population is spread relatively evenly across all 10 deprivation deciles.

Mid-Notts aged 65 and over – Co-morbidities

The chart show how many people aged 65 and over have certain co-morbidities. Hypertension is clearly the most prevalent co-morbidity for the Mid-Notts 65 and over population.

Mid-Notts aged 65 and over – Social Factors

The chart shows how many people aged 65 and over are also impacted by certain social factors.

Mid-Notts aged 65+ medication in the last 3 months

The charts for people aged 65 and over which medication has been taken in the last three months. Lipid lowering drugs for lowering cholesterol are most common medication taken for this population.

Demographics Mid Nottinghamshire

72

Clinical Information Mid Nottinghamshire

73

Headlines 67,435 patients aged 65 and over

• Hypertension is the most prevalent co-morbidity for the Mid-Notts 65 and over population

• 11,589 live alone

• 14,345 are socially vulnerable (eFI)

• 11,335 are housebound (eFI)

• 17% Mansfield North 65 an over population have an eFI score of Moderate

• 31,470 are prescribed Lipid lowering drugs for lowering cholesterol

• 23,695 are using ulcer healing drugs

• Between 17% – 20% live alone

• 22% of Ashfield North population 65 and over are pre diabetic

• 30% of Mansfield North population 65 and over are recorded as obese

• 24% of Ashfield South 65 and over population are prescribed 8 or more unique medications

• Approximately only 5% of the 65 and over population have a BMI under 20

• Areas identified as having high numbers of people on pension credit that are outside of the 45 minute boundary include Ollerton, Bilsthorpe, and parts of Clipstone and Blidworth

Mid Nottinghamshire In Summary

Nottingham City Statistics

75

Nottingham City aged 65 and over - Ethnicity

Source: GPRCC 76

Nottingham City aged 65 and over - Deprivation

More than 40% of the population for Nottingham City are in the two most deprived deciles.

Nottingham City aged 65 and over – Co-morbidities

The chart shows how many people aged 65 and over have certain co-morbidities. Hypertension is clearly the most prevalent co-morbidity for the 65 and over population.

78

Nottingham City aged 65 and over – Social Factors

The chart shows how many people aged 65 and over are also impacted by certain social factors. The chart also allows a count of the 65 and over population.

Nottingham City aged 65+ medication in the last 3 months

The chart shows for people aged 65 and over which medication has been taken in the last three months. Lipid lowering drugs for lowering cholesterol are most common medication taken for the 65 and over population.

Demographics Nottingham City

81

Clinical Information Nottingham City

82

Nottingham City In Summary

Headlines: 42,820 65 and over population

• Hypertension is the most prevalent co-morbidity for the Nottm City 65 and over population

• 9,920 65 and over are socially vulnerable (eFI)

• 6, 435 65 and over are housebound (eFI)

• 4,770 65 an over live alone (eFI)

• 16% of Clifton and Meadows 65 and over population live alone

• 2,435 are diagnosed as sight impaired

• 26% BACHS, 32% Mary Potter 65 and over patients are prescribed 8 or more unique medications

• 29% of Radford and Mary Potter 65 and over population have a T2 diabetes diagnosis

• 4 PCN’s have 20% of the 65 and over population with a diagnosis of depression

• With exception to Unity and City South, all other PCN’s have a 65 and over population between 25% -29% recorded as obese

• 22% of Radford and Mary Potter 65 and over population are recorded as moderately frail (eFI)

• Across Nottingham City less than 5% of the 65 and over population have a BMI below 20

• With exception to Unity and City South 65 and over population all other PCN’s have a 11-17% smoking prevalence

• Unity, Radford Mary Potter, BACHS and City South 65 and over population have the highest % of alcohol consumption 19%-25%

• Areas identified as having high numbers of people on pension credit that are outside of the 45 minute boundary to NUH (QMC) are Bulwell and Bestwood

83

Heat Maps

South Nottingham Statistics

84

South Nottinghamshire aged 65 and over - Ethnicity

Source: GPRCC 85

South Notts aged 65 and over - Deprivation

More than 40% of the population of South Nottinghamshire are within the two least deprived deciles.

South Notts aged 65 and over – Co-morbidities

The chart shows how many people aged 65 and over have certain co-morbidities. Hypertension is the most prevalent co-morbidity for the 65 and over populations.

South Notts aged 65 and over – Social Factors

The chart shows how many people aged 65 and over are also impacted by certain social factors. The chart also allows a count of the 65 and over population

South Notts aged 65+ medication in the last 3 months

The chart shows for people aged 65 and over which medication has been taken in the last three months. Lipid lowering drugs for lowering cholesterol are most common medication taken for the 65 and over population.

Demographics South Nottingham

90

Clinical Information South Nottingham

91

South Nottingham In Summary

Headlines 79,900 65 and over population

• Hypertension is the most prevalent co-morbidity for the 65 and over population

• Beeston has 5% of the 65 and over population within a care home

• 13% of the Eastwood 65 and over population have a carer

• 15% of the Stapleford 65 and over population live alone

• 18% of the Byron 65 and over population are prescribed 8 or more unique medications

• Byron, Arnold and Calverton have between 32% - 33% of the 65 and over population identified as having mild frailty (eFI)

• 9% of the Stapleford 65 and over population have a sight impairment diagnosis

• 18% of Byron 65 and over population have T2 diabetes

• Between 14%-16% of Byron, Eastwood and Beeston 65 and over population are diagnosed as pre diabetic

• Byron, Arnold and Calverton, Synergy Health and Eastwood have between 20%-22% of the 65 and over population diagnosed with depression

• South Nottinghamshire ICP’s 65 and over population have less than 5% of the population with a BMI under 20

• An area identified as having high number of people on pension credit outside of the 45 minute boundary to NUH (QMC) is Hucknall. With Eastwood and Stapleford having high numbers of people on pension credit that are outside of the 45 minute boundary to Nottingham City Hospital

92

Bassetlaw Statistics

93

Bassetlaw aged 65 and over - Ethnicity

94

Bassetlaw aged 65 and over - Deprivation

Bassetlaw 65 and over population is spread relatively evenly across all 10 deprivation deciles. Bassetlaw have few older people aged 65 and over in the least deprived decile compared with other ICP’s.

Bassetlaw aged 65 and over – Co-morbidities

This chart shows how many people aged 65 and over have certain co-morbidities . Hypertension is the most prevalent co-morbidity for these populations.

Bassetlaw aged 65 and over – Social Factors

These charts show how many people aged 65 and over are also impacted by certain social factors. The chart also allow a count of the 65 and over population.

Bassetlaw aged 65+ medication in the last 3 months

The chart shows people aged 65 and over which medication has been taken in the last three months. Lipid lowering drugs for lowering cholesterol are most common medication taken for this population.

The following pages are a summary of the evidence based interventions for Ageing Well, undertaken by the Library & Knowledge Service Sherwood Forest Hospitals NHS Foundation Trust and the Research and Evidence team at NHS Nottingham and Nottinghamshire CCG, in conjunction with the PHM team on behalf of Nottingham/Nottinghamshire ICS.

The full systematic literature review and supporting documents are available to download :

https://www.connectednottinghamshire.nhs.uk/population-health-management

Identify Impactable Interventions

99






No Services (Self Care)

Population Segment Outcome

• Intervention

- Any person who has not been identified as having received physical or mental health services or pharmaceutical products related to frailty

• The population feel more supported on how to manage their health and well-being

• The population live in safe stable accommodation

• The population feel financially secure

• The population have regular social contact, and do activities that are meaningful and enjoyable

• Use every opportunity to signpost/promote, increase access/maintain good levels of physical activity or decrease sedentary life styles or maintain balance, strength and weight bearing functions

• Use every opportunity to promote/sign post and improve/maintain good diet/nutrition

• Use every opportunity to promote/sign post the population to reduce/prevent/stop tobacco consumption • Use every opportunity to promote/sign post the population to decrease and prevent excessive alcohol

consumption • Promote and signpost the population to maintain/increase cognitive and social activities and participation:

• One to one activities (e.g. befriending) • Group based activities • Volunteering opportunities

• Actively target uptake of:

• Health checks/screenings • Vaccinations

• Utilise the role of pharmacy teams working in different healthcare settings to promote productive health ageing

• Provide signposting/advice and guidance for: • Accommodation (LA) • Finance (CAB/DWP), PCB’s • Cold weather planning (Heating) • Accessibility to services/Transport/Appointment Booking Times

• Employer engagement and awareness of ageing well for the ageing workforce (particularly labour intensive

roles)

No Services (Self Care)

Universal Contact Population Segment Outcome

Intervention

• Community setting - Any person who is supported of received community based services

• Pharmacy - Any person who received pharmaceutical products related to frailty

• Carer supported – Any person known to be supported by carers, are receiving the support they need with the aim of them staying at home (paid or unpaid)

• Residential Care – Any person residing and receiving mental health community support in a residential setting

• Early intervention – any person known to engage with LA early intervention services and or voluntary sector

• Our population will be supported to live independently in their own home for as long as possible with dignity

• Carers have improved well-being and life satisfaction (unpaid/paid)

• Our population has Improved access to evidence based services dependant on the level of need

• Older people with dementia and cognitive impairment will be known to the system as early as possible

• Our workforce are trained on how to support aging with dignity

• Involve our 65 and over people and their families in planning and co-ordinating their own care

• Utilise joint needs assessments to support care planning that reflects persons own preferences and circumstances

• Raise awareness of the risk of developing some types of dementia can be reduced, or the onset or progression delayed, through lifestyle changes.

• Annual medication reviews

• Regular falls assessments


• Ensure accessible treatment for ‘minor’ needs that limit independence

• Increasing uptake of community-based strength and balance programmes

• Undertake holistic medical review s and consider Comprehensive Geriatric Assessment in a community setting

• Consider using Smart technologies, such as tailored internet programs, to help older people better manage and understand various health conditions

• Proactively Identify and support carers through routine contact with services in all settings, taking into account that many people do not identify with the label ‘carer’

• Providing support, signposting and education for family and volunteer carers

• Increased ageing well training for our community workforce;

earlier identification of needs Signposting for support/services Increase information shared across services about individuals Increase/promote health checks, vaccinations/screening etc Increase training and awareness of digital/telehealth support (NHS App etc)

Universal Contact

Targeted Contact Population Segment Outcome

Intervention

• Physical Health Services

- Any person diagnosed

as frail who has a

physical or mental

health condition.

• Crisis Services – Any

person known to or in

contact with crisis

services and or

community crisis

services

• Supported

accommodation

rehabilitation and re-

ablement Any person

requiring additional

support from services

• Our population receive expert co-ordinated management of any long-term conditions

• Our population will be encouraged to address health difficulties (e.g. mobility, continence)

• The population feel supported and empowered to recover and regain their independence in there own home with dignity

• Our population will have shared decision-making to make important choices about the right care and treatment


• increased use of community based urgent response services for crisis episodes • Increase PCN hubs - community services, voluntary sector, mental health and social

services to discuss patients whose care is particularly complex and requires advice or support from different disciplines

• Increase joint needs assessment (early discharge planning, discharge to place of choice)

• Increased referrals to community re-ablement services

• Increase opportunity to provide support, advice and guidance on ADL’s (cross sector)

• Increased offer of the WELD program to carers

• Increase use of social prescribers for signposting to cross sector services

Targeted Contact

High Intensity Population Segment Outcome

Intervention

• Inpatient Setting - Any person who

received care in a hospital

inpatient setting

• EOL Pathway – Any person who is

on an end of life pathway

• Liaison Psychiatry/Out

patients/Community – Any person

receiving mental services outside

of an inpatient setting Residential

Care – Any person residing and

receiving care and support in a

specialist residential setting

• Emergency Department Setting -

Any person over 65 presenting in

an emergency department setting

– change to crisis

• Crisis Services – Any person know

to or in contact with crisis services

and or community crisis services

• Reduce inappropriate admissions to hospital

• EoL care plans in place for those in the last 12 months of life

• Experience end of life according to their personal and individual wishes

• Reduction in delayed transfers of care

• Increase individualised care and support plan, including Advanced Care Plan (ReSPECT)

• Increased uptake and cross sector sharing of EoL plans

• Provide EoL support, advice and guidance to family/carers; • Bereavement Services • Financial Services etc

• Increase joint needs assessment (early discharge planning, discharge to place

of choice)

• Increase cross sector working - Acute, community services, voluntary sector, mental health and social services to discuss patients whose care is particularly complex and requires advice or support from different disciplines

High Intensity

Appendix

• PHM Looks Beyond the Health System to Consider Wider Determinants of Health • Our Vision and Aims • Our Vision, Aims and Population Health Management (PHM) Approach • Our Population Health Management (PHM) Approach • Outcome Metrics – part 1 • Outcome Metrics – part 2 • Supporting Information

104

PHM Looks Beyond the Health System to Consider Wider Determinants of Health

‘Population Health Management improves population health (the health of an entire population) by data-driven planning and deliver of proactive care to achieve maximum impact’ . Andi Orlowski ICHP London.

Population Health Management vs previous approaches: • Public Health has looked at promoting, protecting and

prolonging healthy life through coordinated programmes (normally offered to the whole population)

Population health management focuses on: • Key outcomes for identified groups or segments (age,

morbidity, ethnicity, gender, deprivation)

• Healthy populations as much as those who are sick

• Resource Planning that includes the wider determinants of health

• Risk management approach promoting well-being, preventing ill health

In light of the challenges we face as a health and care system we have set an

ambitious vision, adopted the triple aim framework and embraced a Population Health Management (PHM) approach

Our Triple Aim

To help us address the challenges we face and optimise the performance of our health and care system, we have adopted the triple aim framework - the guiding principles for a truly integrated health and care system:

• Improving the health and wellbeing of our population

• Improving the overall quality of care and life our service users and carers are able to have and receive

• Improving the effective utilisation of our resources

Our Vision

Across Nottinghamshire, we seek to both increase the duration of

people’s lives and to improve those additional years, allowing people to live longer, happier, healthier

and more independently into their old age.

Our Vision and Aims

Our Vision, Aims and Population Health Management (PHM) Approach

Our Population Health Management (PHM) Approach

Our PHM approach to ageing well will: • Understanding the current ageing well population baseline, agreeing the

scope, inclusive of all health, care and socio economic factors to segment the population

• Segmenting the population by the utilisation, needs and desired outcomes of the population with consideration of national and local requirements

• Base lining the activity on the population once segmented.

• Base lining the spend on the population once segmented.

• Identifying true variation based on the segmentation and developing infrastructure, intelligence and interventions that supports the mitigation of unwarranted variation.

• Agreeing and recommending system standard cohort outcomes (health and care), ensuring these align and meet the whole ICSs system outcomes framework and best practice guidance.

• Developing and agree risk stratification/algorithms (low, medium and high) criterion that can be adopted to meet the whole diabetes population health’s system outcomes framework.

• Identifying and recommending system level, measurable indicators.

• Develop and agree a fully informed, segmented, stratified blue print prototype approach for the diabetes work stream that can be adopted, with a set of agreed impacable interventions based on literature reviews and localised data evidence that enables informed commissioning intentions across our populations.

Outcome Metrics – part 1

109

Outcome Metrics – part 2

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Supporting Information Additional resources can be found on the following link: https://www.connectednottinghamshire.nhs.uk/population-health-management 1. The full Ageing Well evidence based impactable intervention literature review 2. PHM approach to the Flu Vaccination Programme 3. PHM approach to Diabetes 4. PHM Rapid Response to Mental Health (COVID-19)

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This Ageing Well pack has been produced in conjunction with and on behalf of Nottingham/Nottinghamshire Integrated Care System. This work has only been possible with the support of system partners , data management teams and the invaluable analytical support. The Ageing Well Task and Finish Group has been integral to the development of this and we would like to thank everyone involved for their time, effort and patience. Core Task and Finish Team: Alice Kilby - Nottinghamshire Healthcare Foundation Trust Caitriona Kennedy - GP Claire Williamson- Nottingham/Nottinghamshire CCG Duncan Hanslow – Nottingham University Hospital Trust Jack Rodber – Nottingham/Nottinghamshire ICS Jackie Hewlett-Davies- Nottingham/Nottinghamshire ICS James Rhodes- Public Health Jane Cashmore- Nottinghamshire County Council Jane Betha - Nottinghamshire County Council Wendy Lipmann - Nottinghamshire County Council Julie Barker - Nottingham/Nottinghamshire ICS Julie Hankin - Nottinghamshire Healthcare Foundation Trust Julie Theaker – Nottingham/Nottinghamshire CCG Laura James - GP Suzanne Avington - Nottinghamshire Healthcare Foundation Trust Literature Review and Research Teams: • Library & Knowledge Service, Sherwood Forest Hospitals NHS Foundation Trust • Research and Evidence team at NHS Nottingham and Nottinghamshire CCG

Joanne Taplin – GP Mandy Clarkson – Public Health Marcus Pratt – Nottingham/Nottinghamshire ICS Michael Azad – Consultant Geriatrician NUH Mike O’Neil - GP Mindy Bassi - Nottingham and Nottinghamshire CCG Maria Principe - PHM/ICS Amanda Robinson - PHM/ICS Rob Disney - Nottinghamshire County Council Ryan Cope - PHM/ICS Sergio Pappalettera -PHM/ICS Simon Draycon - Mid-Notts ICP Stephen Wormall -GP Terry Dafter - Public Health Steve Rutter - Consultant Geriatrician SFHT

Acknowledgements

POPULATION HEALTH MANAGEMENT - Ageing Well

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