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Statistical Methods for Health Intelligence Lecture 1: Setting the scene and planning to learn

Statistical Methods for Health Intelligence Lecture 1: Setting the scene and planning to learn. Iain Buchan University of Manchester buchan@man.ac.uk. The Course. Acquire/reinforce basic knowledge of Biostatistics & Epidemiology 6 lectures, learning tasks & tests

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Statistical Methods for Health Intelligence Lecture 1: Setting the scene and planning to learn

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  1. Statistical Methodsfor Health IntelligenceLecture 1: Setting the sceneand planning to learn Iain Buchan University of Manchester buchan@man.ac.uk

  2. The Course • Acquire/reinforce basic knowledge of Biostatistics & Epidemiology • 6 lectures, learning tasks & tests • Apply knowledge to NHS questions producing reusable method objects • 6 tutorials & assignments

  3. Lecture 1:Setting the scene A journey of health information:from bills of mortality to health avatars

  4. Mid 1600s: John Graunt useddeath and tax data to inform civil action as plague swept London From: Epidemic Disease in London, ed. J.A.I. Champion (Centre for Metropolitan History Working Papers Series, No.1, 1993): pp. 35-52

  5. 1700s: Bernoulli & DeMoivre introduceprobability theory to quantifying (health) risks Early 1800s: Laplace then Louis applyprobability theory to medical data,showing some treatments to be ineffective - rebuked by medical profession– Quetelet’s concept of ‘the average man’ adds fuel to the fire Mid-late 1800s: Lister usesstatistical arguments and Pasteur’s germ theoryto revolutionise surgery with carbolic spray

  6. The Statistical Movement Circa 1900: Galton, Pearson, Edgeworth and Yule establish Statistics as a discipline Early/mid 1900s: Fisher consolidatesstatistical methods and experimental philosophy

  7. Early/mid 1900s: Greenwood, Bradford-Hill & Doll pushStatistics into medical research Evidence Based Medicine Causality Clinical Trials Mid-late 1900s: Cochrane pushes for the routine application of randomised clinical trials and leaves the evidence based medicine movement in his wake Effectiveness & Efficiency

  8. Hypothesis-driven Research

  9. Health Statistics 1600-1860 Reasoning Summarisation Knowledge Observation

  10. Health Statistics 1860-≈2000/now Reasoning Summarisation & Statistical Modelling Knowledge Observation± Experimentation

  11. def. Epidemiology “the study ofthe distributionand determinantsof diseaseand health-related statesin populations” JM Last, 2000

  12. Causal Inference Exposure Outcome Causal pathway Association Confounder

  13. Sieving Associations C = caffeine, MI = myocardial infarction (heart attack) Disciplined approach to causal inference, Bradford-Hill: Criteria (temporality, strength, dose-response,consistency, plausibility, consideration of alternatives,open to experiment, specificity, coherence)

  14. Hard to Make a Confident Causal Inference • Plausible pathway to link outcome to exposure • Same results if repeat in different time, place person • Exposure precedes outcome • Strong relationship ± dose effect • Causal factor relates only to the outcome in question • Outcome falls if risk factor removed...

  15. Exhausted Epidemiology Platform Problem 1:Dwindling hits from tools todetect independent “causes” Problem 2:Knowledge can’t be managedby reading papers any more The big public health problems e.g. Type 2 Diabeteshave “complex webs of causes” The “data-set” and structureextend beyondthe study’s observations

  16. Evidence limits showing • Epidemiology has exhausted the big simple causes of ill health • Many trials have weak external validity • Public health interventions are largely unstudied Many patterns of ill health in society remain unexplained via conventional studies

  17. Illustration Slow, fragmentedresearch and service compounding the obesity epidemic

  18. Early warning of rising fatness of English adults Trends in Body Mass Index from Health Survey for England 1993 to 2002 27.2 27 26.8 26.6 3-month rolling average BMI (Kg/m²) 26.4 26.2 Actions Clues 26 25.8 25.6 Jan-93 Jan-94 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00 Jan-01 Jan-02 month of measurement by nurse

  19. Are national analyses adequate? Another look at obesity via Health Survey for England…

  20. Mean BMI by income fifthin the rest of England 97-01 27.5 Household income affects fatness of women differently to men 27 26.5 Mean BMI (Kg/m²) 26 25.5 25 24.5 Females Lowest Males Second Middle Fourth Highest Income group Males Mean BMI by income fifthin Northwest 97-01 Females 27.5 27 Women are fatter at low incomeMen are fatter at high income in the Northwest Region but not in the rest of England 26.5 Mean BMI (Kg/m²) 26 25.5 25 24.5 Females Lowest Males Second Middle Fourth Highest Income group

  21. Why the 2 year waitfor data to be shared from Health Survey for England? Public health experts (problem holders), statistical experts, linkable datasetsand Health Survey for England data, together,could produce more timely, relevant evidenceif they were more interoperable

  22. Intelligence from routinely-collectedbut not routinely analysed data:Obesity in Wirral 3-yr-olds, 88 to 03…

  23. Rising fatness of Wirral 3yr olds from 1988 to 2003 Body Mass Index Trend in Wirral Three Year Olds from 1988 to 2003 0.5 0.4 0.3 0.2 0.1 Three-monthly rolling average BMI SDS (age, sex) 0 -0.1 Actions -0.2 Clues -0.3 -0.4 Mar-88 Jul-89 Nov-90 Apr-92 Aug-93 Jan-95 May-96 Sep-97 Feb-99 Jun-00 Nov-01 Mar-03 Aug-04 Month of measurement by Health Visitor SDS = standard deviation score from 1990 British Growth Reference charts – adjusts for age and sex of the child

  24. From short-fat to tall-fat 3-yr-olds over 16y 16.8 16.6 2003 2002 16.4 2001 2000 1999 16.2 1998 1997 BMI (kg/m²) 1996 16.0 1995 1994 1993 15.8 1992 1991 1990 15.6 1989 1988 15.4 15.2 85 90 95 100 105 Height (cm) Source: Buchan et al. submitted Oct 04

  25. Is child obesity a problem of poverty? Some clues from Wirral…

  26. Child Poverty (2001 Census) Fifths of IDAC 2004 Proportion of households with childrenclaiming benefits Red (light) = most deprived Red (dark) Purple Blue (dark) Blue (light) = most affluent

  27. Fatness of 3 yr olds 1988 - 1989 Fifths of fatness SDS BMI fifth Red (light) = fattest Red (dark) Purple Blue (dark) Blue (light) = thinnest

  28. Fatness of 3 yr olds 1990 - 1991 Fifths of fatness SDS BMI fifth Red (light) = fattest Red (dark) Purple Blue (dark) Blue (light) = thinnest

  29. Fatness of 3 yr olds 1992 - 1993 Fifths of fatness SDS BMI fifth Red (light) = fattest Red (dark) Purple Blue (dark) Blue (light) = thinnest

  30. Fatness of 3 yr olds 1994 - 1995 Fifths of fatness SDS BMI fifth Red (light) = fattest Red (dark) Purple Blue (dark) Blue (light) = thinnest

  31. Fatness of 3 yr olds 1996 - 1997 Fifths of fatness SDS BMI fifth Red (light) = fattest Red (dark) Purple Blue (dark) Blue (light) = thinnest

  32. Fatness of 3 yr olds 1998 - 1999 Fifths of fatness SDS BMI fifth Red (light) = fattest Red (dark) Purple Blue (dark) Blue (light) = thinnest

  33. Fatness of 3 yr olds 2000 – 2001 Fifths of fatness SDS BMI fifth Red (light) = fattest Red (dark) Purple Blue (dark) Blue (light) = thinnest

  34. Fatness of 3 yr olds 2002 - 2003 Fifths of fatness SDS BMI fifth Red (light) = fattest Red (dark) Purple Blue (dark) Blue (light) = thinnest

  35. Stale news due to a lack of data-level inter-operability misinforms research and policy

  36. Individual care Population monitoring Message Message Personal record Service record (of time, place, population) Scientific research Evaluations & investigations Finding Finding White literature Grey literature Automatic, routinedata processes Public Health Decisions (policy & implementation) Query usingpublic health expertise Routine outputs Ad hocdata processes

  37. 25% 20% 15% Males 10% Females 5% 0% Year Percentage excess deaths in North vs. South England Source: John Hacking & Iain Buchan, pre-publication 2009

  38. Biological Risk Factors Combined CVD Risk CVD Patient Groups Population Policies & Behaviours OUTPUTS Diabetes or IGT NON-SUDS SUDS Physical Activity Unstable Angina Chronic Angina CHD Death Combined CVD Risk Obesity (BMI) Diet Cholesterol LDL (& HDL) Early Heart Failure Acute MI From any State Smoking Blood Pressure Recurrent MI Severe Heart Failure Non-CHD Death MI survivors Deprivation Additional CVD Risk Factors Stroke PAD etc What-if we invest in 20% more statins or find 5% more people with high blood pressure? Where is the most potential health gain for this population? Outputs: Population-based incidence, prevalence; Deaths prevented; Life-Years; Life expectancy; Costs; Cost-effectiveness ratios

  39. Health Records& Knowledge Silos Health Avatars& Dynamic Models Data-intensive Paradigm -shift Open Unifying Modelling: Across mechanisms and contexts  models = Avatar Expertise Expertise Expertise • Multi-scale & • Multi-system • Health: • Research • Policy • Care e.g. Chronic obstructivepulmonary disease e.g. Lung cancer Unified Graphical Model Electronic Health Records(eHR) Large scale inference Model refinement Data Data Data

  40. Invest in Statistical Knowledge To the engineers of next generation health information systems & models

  41. Lecture 1:Planning to learn Plan your learning needs for this course now

  42. Text Book for First Part of Course • Medical Statistics, 4th EdCampbell, Machin & WaltersWiley 2007(paper or Google docs) • Statistical knowledge level:Public health practitioner

  43. Required Preparation • Please read the chapters indicatedbefore each lecture • You will be tested in each lecture • Please install R and familiarise yourself with the basics

  44. Learning Needs Please inform the tutor of: • Qualifications in Statistics or Epidemiology • Skills in probability calculus • Skills in statistical programming • Any statistical topics you want to learn about

  45. Part A Schedule 2010 • 11 Jan • 18 Jan • 1 Feb • 15 Feb • 8 Mar • 15 Mar • Mondays 11AM 306a/b (venue may vary)

  46. Lecture 2 • Basic statistical concepts & terms • Types of data • Describing/summarising data • Visualising data • Based on Chapters 1-3 of course text

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