Measuring Health and Homelessness - in Fife Summary of Results:     Jan 14 update NHS Fife hospital activity for those on the Fife Council homeless register. Data is sourced from OASIS and PIMS to provide activity in Acute, Psychiatric, Obstetrics and A&E from 2006 to 2013. Below comments referring to Fife are for 15 to 64 years of age. Population based on average number of people on HL1 on 1st day of each quarter (January, April, July and September).       A&E     HL1 Attendance Rate; Male > Female Attendance Rate; HL1 > Fife   Attendance Age; HL1 < Fife         Inpatient Daycase Admissions (IPDC) Emergency Admission Rate; HL1 > Fife Multiple Emergency Rate; HL1 > Fife % Admissions relating to Alcohol/Drug Misuse; HL1 > Fife Rate of admission relating to Alcohol/Drug Misuse; HL1 > Fife       Obstetrics (OBS)     Obstetric Admission Rate; HL1 > Fife Admission Age; HL1 < Fife         Psychiatic (PSY)     Psychiatric Admission Rate; HL1 > Fife       Outpatient Appointments (OP) Return:New Ratio; HL1 < Fife   % DNA New/Total; HL1 > Fife   % Women's Health Appointments; HL1 > Fife Dr Neil Hamlet Consultant Public Health Medicine Fife Health & Homelessness Lead neil.hamlet@nhs.net

Homelessness - a cross cutting agenda Voluntary Agencies Council Housing Departments Substance Misuse Dental Care Foot and skin care Alcohol Abuse Mental Health Issues Nutrition and diet Health & Social Care Integration Bodies Homelessness Community Health Partnerships Social Work Cross-cutting agencies Cross- cutting NHS specialties and services What is the response? Who can help? What skills are needed? Who is missing here? Need to superimpose CPP, HSCPs etc Need a 2nd map with: Inter-personal coping skills – conflict resolution Education & Skills Employment – work ready Welfare Mitigation Foodbanks Income Maximisation Fuel Poverty Home Health Checks Embed Housing Options/Adaptations teams in 24/7 services Registered Social Landlords NHS Acute Services

NHS Housing HSCP Vol Sector Multi-agency – falling through the gaps is a regular experience for clients Walking up river (upstream wherever possible) Communication, communication, communication NHS Vol Sector Housing HSCP

Linkage of Hospital data and Homelessness data in the Kingdom of Fife Bryan Archibald, Senior Information Officer barchibald@nhs.net Bryan Archibald barchibald@nhs.net

The Data Sources Fife Council NHS Fife Acute Hospitals HL1 National data set for each homeless application Based on the application (not the number of homeless individuals) NHS Fife Acute Hospitals eOASIS (patient administration system) SMR data submitted to ISD (information Services Division of NHS Scotland) Based on Patient Episodes Permission by SASPI – good relationships with Head of Housing/Homeless Service HL1 Excel spreadsheet data entry form: HL1 data collecting form: http://www.scotland.gov.uk/Topics/Statistics/15257/RevisedHL1 http://www.scotland.gov.uk/Topics/Statistics/15257/22833 http://www.scotland.gov.uk/Topics/Statistics/15257/1529 http://www.scotland.gov.uk/Topics/Statistics/15257/22833

NHS Data (OASIS) A&E (and Minor Injuries Unit [MIU]) Inpatients & Day cases Obstetrics Mental Health Inpatients Outpatients Mental Health Outpatients Linked HL1 data to NHS data obtained from OASIS (NHS Fife patient administration system) MIU – Minor Injuries Unit We interrogated the linked data set for the 6 ‘settings/departments’ Date from 2006-2014

Results: A&E Inpatients & Day cases HL1 data Year on year A&E attendances by age and trend from 2006 – 2011 by sex. Not split by flow. Only numbers IPDC SMR01

Obstetrics Outpatients Year on year hospital obstetric admission by age and trend from 2006. Not necessarily the episode of birth. Further analysis required. (get clarity on the meaning of the rate per 100 of what?) SMR02

So far so good but what does this mean? We need a comparator for the homeless population Fife’s ‘securely - housed’ population Try to compare by similar age profile Started with Fife population 15-64 as our crude method of ‘standardisation’ Further refinements planned in ‘standardisation process’ Aiming to compare ‘apples’ with ‘apples’ by security of housing as defined by HL1 registration Compare like with like by housing situation – insecurely housed vs securely-housed / and by age band (How would it look if we included older people and addressed the needs of housing for frail and elderly) Need a couple of slides explaining ‘standardisation’ of comparator NHS hospital population using European Standard Population 2013.

HL1 – age determined by the age of the ‘household applicant’ (hence no children seem to appear homeless)

‘Insecurely Housed’ ‘Securely Housed’ The value of shared data analysis Explain methodology: Fife HL1 annual extract linked to NHS PAS (patient administration system) Annonymised and graphed Can now compare the 2 populations over time, gender and by usage of various in-patient services Looking at rates not numbers (get original graph and format legend etc) (check denominator for rate / 1k popln) ‘Insecurely Housed’ ‘Securely Housed’

Abuse ? Pragmatic ? £££ How did folk get to A/E? Note the excess use of blue light / emergency services / ambulances (expensive ++) Abuse of the service or pragmatic solution for the homeless person? Get costings for a standard blue light ambulance call and calculate cost to SAS £££ Pragmatic ? 12

Over 80% are under 40 yrs Over 50% are under 30 yrs Shift to the left of the life stages – more under 30s Over 50% are under 30 Over 80% are under 40 (what if the comparator pop was 15-54 – even more skewed) Over 80% are under 40 yrs

Patients who come back time and again – 3 fold difference in the rates

£ Moving further into the hospital..... Actual admission to a hospital bed: Much higher rates of use (how would this look with an age band graph? Ie homeless is comparable to the rate experienced by the >80s? Why gone up in past 2 yrs? (when HL1 actual numbers are going down) – why sicker/more prone to admission? 15

Revolving Hospital Door effect Revolving Hospital Door effect (cf Prison situation – try and examine the multiple overlapping revolving doors of prison and temp accommodation) Rate (not absolute numbers) of folk who use hospital admission to a bed the most frequently The care is not working as it should – the system is failing 16

Drill down to specific topics/departments/causes of admission Alcohol Red bars R axis - % of total admissions for that HL1 / comparator group Blue line L axis - Rate (number/1000 population) Red Bars: In 2013 6% vs 27% for alcohol related admissions Blue Lines: In 2013 8/1000 vs 80/1000 - a 10 fold difference 17

Blue line L axis - Rate (number/1000 population) Drug Misuse Red bars R axis - % of total admissions for that HL1 / comparator group Blue line L axis - Rate (number/1000 population) Under 1.7% vs 17% 10 fold difference 18

Clear role for Community Safety Partnerships ? Injury and Poisoning – measure of violence experienced in homeless community link to Community Safety agenda – how to protect / prevent Self harm rates 4 times higher in HL1 19

Pregnancy / Fertility – why are they having so many babies? Why? Cause or consequence Cf graph of fertility in war torn society in Africa Measure of crisis or contentment Can’t tell if due to more pregnancies or difficulties in fewer pregnancies 20

Massive shift to the young – should need less medical intervention for the natural childbirth process in this age group 28% under 20 vs 8% 62% under 25 vs 31% 12% over 30 vs 40% 21

Mental distress / psychiatric illness requiring admission – very tip of the iceberg Men >> Women Is the early drop due to bed shortages keeping them out? Men rate rising past 2 yrs (unemployment)

Access / Expectation Changed address Fearful to open official mail Appointment too early No money for the bus ‘it won’t do any good’ Access / Expectation Finally – you are given an appt for OPD – but do you get there? Outpatient Services – how many folk fail to turn up for their appt as a percentage Is the service ‘accessible’ ? Is it misused or not-person focussed for the specific challenges of the homeless (eg changing address, lost internet, too early in the morning etc) 23

Inverse Care Law “The availability of good medical care tends to vary inversely with the need for it in the population served.” “Those who need healthcare least use the services more, and more effectively, than those with the greatest need and those people in the worst health receive the least services.” Julian Tudor Hart 1971 24 24

Making a Difference On call nurse manager gets a text alert on her work phone when a homeless person is seen in Accident and Emergency Dept. A daily report is now generated listing all the patients in the hospital at 8am who have a temporary homeless accommodation address . Impact of populating the NHS hospital PAS system with the addresses of council and third sector temporary accommodation: Concept of embedding HO worker in hospital acute care team – SHELTER Pilot 25 25

Admissions to Fife Hospitals with Homeless Accommodation Address Based on Current Homeless Accommodation List & Patient Address in Oasis at time of report refresh Nov-14 Dec-14 Jan-15 Feb-15 Mar-15 Apr-15 May-15 Jun-15 Jul-15 Aug-15 Sep-15 Elective Medical 2 3   Orthopaedic Surgical 1 Theatres Women & Children Total 4 Emergency 8 5 10 14 20 21 22 We now get a monthly automated listing of numbers of admissions of those with a temp accommodation at time of report analysis. This will slowly build a picture. Need a Next Steps slide: DATA Add costs Repeat for Prevent 1 Improve comparator age profile 15-55? Or 15-45? ACTIONS Embed HO expertise in acute hospitals Smoother cross-referral to both prevent and mitigate What of all the opportunities in Foodbanks / Primary Care / SW / Education / ADPs / Youth Diversion etc Employability Partnerships

Resilience Factors Supportive friends / family Strong social networks Appropriate support services Savings or access to financial help Available advice & advocacy In stable employment Personal empowerment/capacity If u have no GP then you are not recorded on SCI store CHI allocated as soon as your born

Meet needs of safety, nurture, belonging and purpose 'Houseless and Hungry' by Luke Fildes depicting homeless paupers queuing outside the casual ward of a London workhouse Back to Maslow – met needs of safety, nurture, belonging and purpose A study entitled 'Houseless and Hungry' by Luke Fildes depicting homeless paupers queuing outside the casual ward of a London workhouse. A policeman stands at the left of the scene. Luke Fildes, Houseless and Hungry, Graphic, 4 December 1869 – For Houseless Poor Act This wood engraving depicts homeless people of all ages queuing outside a police station in order to gain a ticket for admission to a casual ward. Casual wards were attached to workhouses and provided a night’s accommodation in return for a few hours work the next morning. The same scene was the subject of a later painting by Fildes, entitled Applicants for Admission to a Casual Ward (1874). 'Applicants to a Casual Ward' (1874)   In the first edition of the Graphic magazine that appeared in December 1869, Luke Fildes was asked to provide an illustration to accompany an article on the Houseless Poor Act, a new measure that allowed some of those people out of work shelter for a night in the casual ward of a workhouse. The picture produced by Fildes showed a line of homeless people applying for tickets to stay overnight in the workhouse. The engraving, entitled Houseless and Hungry, was seen by John Everett Millais who brought it to the attention of Charles Dickens, who was so impressed he immediately commissioned Fildes to illustrate 'The Mystery of Edwin Drood'. Meet needs of safety, nurture, belonging and purpose