Big Data Health Big Health Improvements? Dr Kerry Bailey MBBS BSc MSc MRCGP FFPH Dr Kelly Nock MPhys PhD

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1 Big Data Health Big Health Improvements? Dr Kerry Bailey MBBS BSc MSc MRCGP FFPH Dr Kelly Nock MPhys PhD

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5 Epidemiology Infection 2006 Dec;134(6): Epub 2006 Apr 20. Risk factors for hospital-acquired methicillin-resistant Staphylococcus aureus bacteraemia: a case-control study. Carnicer-Pont D 1, Bailey KA, Mason BW, Walker AM, Evans MR, Salmon RL. 1 National Public Health Service for Wales, Communicable Disease Surveillance Centre, Abton House, Cardiff, UK. Abstract A case-control study was undertaken in an acute district general hospital to identify risk factors for hospital-acquired bacteraemia caused by methicillin-resistant Staphylococcus aureus (MRSA). Cases of hospital-acquired MRSA bacteraemia were defined as consecutive patients from whom MRSA was isolated from a blood sample taken on the third or subsequent day after admission. Controls were randomly selected from patients admitted to the hospital over the same time period with a length of stay of more than 2 days who did not have bacteraemia. Data on 42 of the 46 cases of hospital-acquired bacteraemia and 90 of the 92 controls were available for analysis. There were no significant differences in the age or sex of cases and controls. After adjusting for confounding factors, insertion of a central line [adjusted odds ratio (aor) 35.3, 95% confidence interval (CI) ] or urinary catheter (aor 37.1, 95% CI ) during the admission, and surgical site infection (aor 4.3, 95% CI ) all remained independent risk factors for MRSA bacteraemia. The adjusted population attributable fraction, showed that 51% of hospital-acquired MRSA bacteraemia cases were attributable to a urinary catheter, 39% to a central line, and 16% to a surgical site infection. In the United Kingdom, measures to reduce the incidence of hospital-acquired MRSA bacteraemia in acute general hospitals should focus on improving infection control procedures for the insertion and, most importantly, care of central lines and urinary catheters.

6 In the UK 70% of the NHS budget is spent on chronic diseases 10% of NHS budget is spent on Diabetes We are not currently using the existing data to improve health and health services

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8 Predictive analytics needs to look like the future

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10 ABMU Challenge 1 How can we improve the health and wellbeing of our population through the better use of population health information?

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12 Descriptive Population Animated representation of data changes over time BMI Calculated from multiple sources 4.5 million records Data coverage represented

13 Diabetes: Case-Control Analysis

14 Case-Control Analysis with SAIL Data Comparison of mortality rates between Diabetics and non- Diabetics All ABMU patients used ~ 500k Matched on age and gender

15 Mortality by Age & Gender Males Females 16% of Diabetic males aged die within 20 years 3% of non-diabetic males aged die within 20 years

16 Diabetes: 10-Year Follow-Up Analysis

17 10-Year Follow-Up Analysis with SAIL Survival time to Diabetes 10-year follow-up (31 Dec ) Patients have BMI measurement taken within +/- 1 year of start date

18 Survival to Diabetes - Males Years to Diabetes Cumulative Survival % of males aged with a Normal BMI get Diabetes within 5 years 20% of males aged with a Morbidly Obese BMI get Diabetes within 5 years

19 Predicting Diabetes Cox models generated based on BMI controlled for age, gender and deprivation Predictions made on test set with reduction of input BMI to simulate weight-loss intervention e.g. all patients with BMI>25 have 5% weight-loss Average cost of Diabetes per patient per year ~ 4,800* * Kanavos, van den Aardweg and Schurer: Diabetes expenditure, burden of disease and management in 5 EU countries, LSE (Jan 2012) * *

20 Predicting Diabetes in ABMU In-year cost of Diabetes ( million) Do Nothing # Diabetics: 42,664 In-year cost: 202M BMI > 25 with 3% wgt.loss # Diabetics: 41,400 In-year cost: 196 BMI > 25 with 5% wgt.loss # Diabetics: 40,618 In-year cost: 193M BMI > 25 with 10% wgt.loss # Diabetics: 38,862 In-year cost: 184M

21 Summary Patients are suffering because we re not using the data. Carol, Patient

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