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1 Health Informatics Centre: Using routine data to support clinical research Prof Peter Donnan, Dr Colin McCowan Population Health Sciences University.

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Presentation on theme: "1 Health Informatics Centre: Using routine data to support clinical research Prof Peter Donnan, Dr Colin McCowan Population Health Sciences University."— Presentation transcript:

1 1 Health Informatics Centre: Using routine data to support clinical research Prof Peter Donnan, Dr Colin McCowan Population Health Sciences University of Dundee

2 HIC is a collaboration between the University of Dundee, NHS Tayside and NHS Fife. HIC’s function is to provide data management support to researchers & others through: the collection, preparation and provision of researchable datasets the creation of secure infrastructures for the transmission and storage of data and data entry Our first priority is to address information governance, data security and confidentiality issues.

3 Holds patient specific datasets for entire population of Tayside (since early 90’s) & Fife (last few years) – Encashed prescribing – Hospital admissions – Demographic dataset – Cancer registry Datasets are linked, anonymised and made available for approved research projects

4 HIC Datasets Dispensed prescriptions 1993-date (variable completeness) Dental datasets – local, national Walker dataset: across 3 generations, linked via Ninewells obstetric records – 1/3 with CHI Lab data (bacteriology, haematology, biochemistry, etc) 1992 on Specialty data on patients with diabetes, cardiovascular, COPD, thyroid & liver disease; maternity, neonatal, geriatric, child health, mental health, cancer… SMR datasets from Information Statistics Division of NHS Scotland General Registrar Office data: date & cause of death Scottish Index of Multiple Deprivation

5 5/28 Community Health Index Number Date of Birth Gender Check digit 07 10 64 02 5 0

6 6/28 Drug data- CHI Lab data- CHI Drug data- CHI Lab data- CHI Data linkage and anonymisation Enter data, find CHI Drug data, lab data Fully anonymised but linked data CHI labelled data Fully identifiable data Paper prescription Lab result -ID Drug data, lab data Paper prescription -ID Lab result -ID Drug data- CHI Lab data- CHI Drug data, lab data- CHI Analysis Find CHI Link using CHI AcademiaHICNHS Drug data, lab data- CHI Delete CHI

7 7/28 Information governance and HIC Physical security: Isolation of servers holding identifiable data, of those working with it; Reliable backup and recovery mechanisms Separation of functions on NHSNet, JANET Privacy model: Inherited from NHS Scotland’s Information Systems Division Evaluated by EU Data Protection expert Petra Wilson: “the proper legal framework for the use of anonymisation techniques as demonstrated by MEMO” (BMJ 2004) Governed by Confidentiality & Privacy Advisory Committee Same pt. representative chair as ISD Privacy Advisory Committee Members include lawyer, GP, Caldecott Guardian Management tools: Standard Operating Procedure Problem reporting mechanism on intranet Project management system enforces SOP Annual external audit by information security experts + table of issues reviewed monthly by HIC Exec

8 Benefits of HIC Data Population based – No socio-economic bias – Socio-economic status – Mostly single centre treatment Outcomes data – GRO : all cause & disease specific mortality Hospital Discharge, Cancer Registry etc Specialist data sets: research & clinical Prescribing, lab results

9 Prescribing to older people

10 Aims & Research Questions To investigate if there are differences in potentially inappropriate medications between older people living in their own home compared with people living in nursing or residential homes 1.To determine if there are differences in prescribing and meeting Beers criteria guidelines between patients by place of residence for all classes and by individual criterion 2.To assess whether receiving a PIM was associated with an increased risk of death 3.To examine any differences in PIM prescribing by practice

11 Beers Criteria for potentially inappropriate medication in the elderly Limited clinical trial evidence of use of drugs in the elderly. Current guides to assess potentially inappropriate prescribing based on expert consensus e.g. Beers Criteria. The Beers criteria are one of the most widely used consensus criteria for medication use in older adults (last updated 2003), although there is increasing concern about their appropriateness DrugConcernSeverity Rating (High or Low) AmitryptylineBecause of its strong anticholinergic and sedation properties, amitryptyline is rarely the antidepressant of choice for elderly patients. High Non-Cox-Selective NSAIDS: Naproxen, Piroxicam Have the potential to produce GI bleeding, renal failure, high blood pressure and heart failure. High

12 Methods – Identifying the population Care home addresses obtained from the relevant local authorities & other sources Compared to electronic register of addresses held by NHS Tayside on all patients 377 addresses were manually checked where there was still uncertainty if they aplied to a care home Patient’s classed as living at home if address did not match any of those on the addresses of the care home list Patients classed as in care if their address matched one from the care home list

13 Methods Prescriptions Prescriptions were obtained for all patients dispensed in 2005 and 2006. Information available included, Patient Chi Number, Drug Name, Prescription Date, Formulation Code, Strength, Quantity, Directions, BNF Code and prescribing practice. BNF categories (Drug Class) BNF codes were grouped according to class of drugs e.g. 4.2.1 - Antipsychotic drugs, or - Broad-spectrum penicillins

14 Descriptive statistics of patients aged 65 -99 years, 2005-2006 At HomeIn Care Number of Patients (%)65,742 (93.5)4,557 (6.5) Mean Age (std dev)75.2 (6.8)84.5 (7.5) Age Categories n (%) 66-7020,034 (30)239 (5) 71-8031,148 (47)1,065 (23) 81-9012,934 (20)2,176 (48) 91-991,626 (2)1,077 (24) Female sex n (%)37,497 (57.0)3,296 (72.3) No. of deaths (%)5,321 (8.1)1,790 (39.3) Mean no. of prescriptions (95% CI) 66.7 (66.28-67.22)113 (110.37-115.56) Mean no. of drug classes (95% CI) 8.8 (8.73-8.82)11.6 (11.39-11.77)

15 Relationship between receiving a PIM with variables of interest Explanatory variableOdds Ratio (95% CI) UnadjustedAdjusted* Age Categories n (%) 66-701.0 71-801.16 (1.12-1.21)0.91 (0.88-0.95) 81-901.18 (1.13-1.24)0.76 (0.72-0.80) 91-990.98 (0.89-1.07)0.65 (0.58-0.72) Male1.0 Female1.37 (1.33-1.42)1.22 (1.17-1.26) Polypharmacy (No. of drug classes) 1.19 (1.18-1.19)1.19 (1.19-1.19) At home1.0 In care1.32 (1.24-1.40 )0.94 (0.87-1.01)

16 Criteria At Home % In Care % Odds Ratio (95% CI) Severity Rating UnadjustedAdjusted* Long Acting Benzodiazepines 6.3611.131.85 (1.68-2.04)1.62 (1.45-1.81)†High Nitrofurantoin 2.465.842.46 (2.15-2.81)1.52 (1.30-1.76)†High Fluoxetine 2.104.832.37 (2.05-2.74)2.25 (1.91-2.65)†High Muscle Relaxants 1.693.842.32 (1.97-2.73)1.42 (1.19-1.70)†High Amitryptyline 7.765.990.76 (0.67-0.86)0.59 (0.51-0.67)‡‡High NSAIDs 3.921.560.39 (0.31-0.49)0.42 (0.33-0.54)‡‡High Gastrointestinal antispasmodic 1.060.920.87 (0.63-1.18)0.70 (0.51-0.98) ‡‡High

17 Practice level prescribing of Beers Criteria drugs

18 Potentially Inappropriate Medications Exceptions will exist within the dataset e.g.- Patients may be on a short course of long acting benzodiazepines. - Patients may be on low doses of amitrptyline. -A patient may be on NSAIDS while awaiting a hip replacement.

19 Key Findings Older patients in care have higher numbers of prescriptions and drugs from more classes than those living at home Around 1/3 of Tayside’s older population have potentially inappropriate medications according to Beers Criteria After allowing for age, sex and number of drug classes there were no differences in overall potentially inappropriate medications between patients in care and those at home Polypharmacy is a consistent risk factor associated with potentially inappropriate medications The Beers Criteria as a screening tool may not be appropriate although some individual criteria show differences which may be important and need more investigation Barnett et al. BMJ Qual Saf 2011;20:275-281 doi:10.1136/bmjqs.2009.039818

20 Psychoactive drug use in older people Antipsychotics used for Behavioural and Psychological Symptoms of Dementia – Not very effective – Increasing evidence they are harmful – Little evidence about how commonly used Also interested in use of hypnotics, anxiolytics, anti- depressants and long-acting benzodiazepines

21 Aim The aim of this study was to examine prescribing for psychoactive medications for patients living in care homes compared to patients living at home

22 Methods Residents of care homes identified as before with recorded date of entry noted Extracted all dispensed prescriptions for psychoactive drugs 2005-2006. Examined prescribing for 1 Jan – 25 Mar 2005 – Hypnotics (BNF 4.1.1) – Anxiolytics (BNF 4.1.2) – Oral anti-psychotics (BNF 4.2.1) – Tricyclic and related antidepressants (BNF 4.3.1) – SSRI antidepressants (BNF 4.3.3) – Other antidepressants (BNF 4.3.4) Examined prescribing for patients admitted to care homes across the study period

23 Patient Demographics Of those in care, 49% in nursing homes, 39% residential homes, 12% mixed type Based on patients alive on 25 March 2005 At HomeIn Care No. of Patients66,494 (95.9)2,813 (4.1) Mean Age75.3 years84.5 years Female57.4%72.9%

24 Prescribing in 12 week period Living at homeLiving in care Mean no. of items dispensed 7.19 (7.12-7.25)15.66 (15.11-16.20) Mean no. of drug classes received 4.02 (3.99-4.04)5.65 (5.49-5.80)

25 Psychoactive prescribing in past 12 weeks Odds ratios (95% CI) adjusted for age & sex OR 3.65 (3.22-4.15) OR 1.44 (1.24-2.68) OR 12.96 (11.26-14.91) OR 2.26 (1.91-2.68) OR 1.52 (1.34-1.71) Any psychoactive medication : At home 15.5%, In Care 41.7%, OR 3.09 (2.84-3.35)

26 When are drugs started? 1,715 (2.4%) patients were admitted to a nursing home in 2005-2006 No of patients (%)Started at home Hypnotics473 (28)72% Anxiolytics343 (20)70% Oral anti-psychotics500 (29)72% Tricyclics223 (13)75% SSRI431 (25)73%

27 Oral anti-psychotics 500 patients with an admission 2005-2006 were prescribed an oral antipsychotic – 28% initiated +/- 30 days of admission – Half initiated in 30 days prior to admission – Half initiated in first 30 days after admission Median duration of use 280 days (IQR 30-613) – 299 (60%) taking oral anti-psychotics for 6 months or longer

28 No of patients Duration >= 180 days Continuous OR for stopping (%) OR (95%CI) >30 days prior to admission282 (56)215 (76)62 (22) 1.0 Within 30 days prior to admission 70 (14)29 (41)27 (39) 0.50 (0.28-0.88) Within 30 days after admission 71 (14)30 (42)25 (35) 0.53 (0.30-0.94) > 30 days after admission77 (15)25 (32)24 (31) 0.73 (0.41-1.30) Oral anti-psychotics

29 Conclusions Patients in care are more likely to be prescribed psychoactive drugs Contrary to expectation, usually initiated before admission High rates of anti-psychotic use, and once started prescribing is usually prolonged Further work should investigate why drug initiation occurs, duration of use, and whether prescribing is appropriately reviewed

30 Conclusions There is increased use of potentially harmful drugs for patients in care compared to the community Further work should investigate why drug initiation occurs, if it is based on new diagnosis and whether it is short or long term use

31 Acknowledgements Prof Bruce Guthrie, Prof Tom Fahey, Dr Stella Clark, Dougie McPhail, Dr Karen Barnett, Prof Peter Davey, Prof Frank Sullivan, Marie Pitkethley, Dr Claire Stubbings, Dr Parker Magin Alison Bell, Chris Hall & Duncan Heather at the Health Informatics Centre for supplying and managing the routine data


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