Buchan, Iain

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The University of Manchester ResearchHealth e-records and the Obesity EpidemicLink to publication record in Manchester Research ExplorerCitation for published version (APA):Buchan, I. (2006). Health e-records and the Obesity Epidemic. British Computer Society Seminar, Manchester.Citing this paperPlease note that where the full-text provided on Manchester Research Explorer is the Author Accepted Manuscriptor Proof version this may differ from the final Published version. If citing, it is advised that you check and use thepublisher's definitive version.General rightsCopyright and moral rights for the publications made accessible in the Research Explorer are retained by theauthors and/or other copyright owners and it is a condition of accessing publications that users recognise andabide by the legal requirements associated with these rights.Takedown policyIf you believe that this document breaches copyright please refer to the University of Manchester’s TakedownProcedures [http://man.ac.uk/04Y6Bo] or contact uml.scholarlycommunications@manchester.ac.uk providingrelevant details, so we can investigate your claim.Download date:08. Jun. 20221Health e-records and the obesity epidemicIain BuchanThe University of Manchesterbuchan@manchester.ac.ukBCS Seminar May 2006Presentation• Obesity – ‘heads up’ the evidence production line is out of date• Health e-records feeding discoveries• Need e-records to support metabolic health– Population– Clinical– PersonalHealth evidence journeyCirca 1800: Laplace then Louis apply probability 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 fireMid-late 1800s: Lister uses statistical arguments and Pasteur’s germ theory to revolutionise surgery with carbolic sprayMid 1600s: John Graunt used death and tax data to inform civil action as plague swept LondonHealth evidence journeyCirca 1900: Galton, Pearson, Edgeworth and Yule establish Statistics as a disciplineEarly/mid 1900s: Fisher advances statistical method and experimental philosophy as Greenwood and Bradford-Hill push Statistics into medical researchMid-late 1900s: Cochrane pushes for the routine application of randomised clinical trials and leaves  the evidence based medicine movement in his wakeEvidence limits showing• Epidemiology has exhausted the big simple causes of ill health• Many trials have weak external validity• Public health interventions unstudiedMany patterns of ill health in society remain unexplained via conventional studiesMeanwhile…• Biology becoming an information science• E-Science (integration of data, methods and investigators)• More data, with more structure• Inductive ‘mining’ approaches rising• Triage & brain vs. silicon priorities2Solution?: Health Informatics partnership to build a new biomedical evidence production lineLets consider the obesity epidemic…Body Mass Index (BMI)• Weight for height• An easy measure of adiposity• BMI = Weight (Kg) / Height (m²)• Not ideal for measuring individual adiposity but good for monitoring change over time in populations.Quetelet, 1796-1874Early warning of rising adiposity of English adultsTrends in Body Mass Index from Health Survey for England 1993 to 200225.625.82626.226.426.626.82727.2Jan-93 Jan-94 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00 Jan-01 Jan-02month of measurement by nurse3-month rolling average BMI (Kg/m²)CluesActionsComplex biology• big mass à heart works harder à changes of hypertension• high cholesterol and low HDL:LDL ratio• insulin resistance• altered hormone metabolism e.g. oestrogens• altered liver function• damaging stresses on joints, ligaments, tendons and muscles• breathing impairment• altered intestinal function• Just starting to understand the interaction of these factors in pathologies such as metabolic syndrome:– central obesity + dyslipidaemia + hypertension + diabetes– causes early death & strongly predicted by childhood obesityPersonal health impact• Early death ‘from all causes’– obesity takes around a decade off your life• more if you smoke• less if you take regular exercise• Suffering and disability through:– cardiovascular disease– diabetes– osteoarthritis– low self-esteem and social functioning– some cancers (colorectal, ovarian, endometrial)– liver and intestinal disorders– sexual dysfunction– sleep apnoeaPopulation health impactEstimate for the obesity impact in the Northwest:Bringing everyone in the Northwestto a body mass index less than 30,i.e. eliminating obesity,would result in 7,000 fewer deaths per year(95% CI = 2,000 to 14,000)3Extraordinary social problemEnergy balancein (eating)out (physical activity)depends on families, friends, co-workers etc.can’t be isolated and treated in individuals‘passive obesity’Discovery from e-recordsWhere are the signals coming from?Research archivesHealth Survey for England…Early warning of rising adiposity of English adultsTrends in Body Mass Index from Health Survey for England 1993 to 200225.625.82626.226.426.626.82727.2Jan-93 Jan-94 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00 Jan-01 Jan-02month of measurement by nurse3-month rolling average BMI (Kg/m²)CluesActions12345MenWomen2525.52626.52727.5BMIIncome fifth (low to high)Women and not men from low-income householdsare fatter in EnglandData from Health Survey for England412345MenWomen2525.52626.52727.5BMIIncome fifth (low to high)Women from low-income householdsand men from high-income householdsare fatter in Greater ManchesterData from Health Survey for EnglandInformation utility• E-records in UK Data Archive– But: wait 2+ years after collection• Large national sample– But: only 0.02% of population therefore  difficult to study small areas/sub-groups– sampling of children not random• Some objective measurements by nurse– But: non-validated questionnaires lead to unexpected results that might reflect response bias more than real trends  Intelligence from routinely-collectedbut not routinely analysed data:Obesity in Wirral 3-yr-olds, 88 to 03…Rising fatness of Wirral 3yr olds from 1988 to 2003Body Mass Index (BMI) trend in Wirral 3y-olds from 1988 to 2003-0.4-0.3-0.2-0.100.10.20.30.40.5Mar-88 Jul-89 Nov-90 Apr-92 Aug-93 Jan-95 May-96 Sep-97 Feb-99 Jun-00 Nov-01 Mar-03 Aug-04Month of measurement by Health VisitorThree-monthly rolling average BMI SDSSDS = standard deviation score from 1990 British Growth Reference charts – adjusts for age and sex of the childCluesActionsInformation Utility• Large samples–Approx. 30-75% coverage of population• Data quality varies–Health visiting and school sources–Careful cleaning & interpretation–Corroborate findings across localitiesThere are similar child health datain other localities…5International Obesity Taskforce classification applied toManchester school children 2000 to 20040%10%20%30%40%50%60%70%80%90%100%5-yr-olds 11-yr-oldsNot fatOverweightObeseTypical current picture: Rochdale 5yr olds 20050%10%20%30%40%50%60%70%80%90%100%Boys Girls AllGenderIOTF categoryNot FatOverweightObeseGirls were 1.55 (95% CI 1.29 to 1.87) times more likely than boysto be overweight or obese0%10%20%30%40%50%60%70%80%90%100%98/99 99/00 00/01 01/02 02/03 03/04YearIOTF categoryNot FatOverweightObeseTypical recent trend: Liverpool 10yr oldsRoutine data - stimulating science:At what age do signs ofthe obesity epidemic appear?-0.2-0.100.10.20.30.40.51985198619871988198919901991199219931994199519961997199819992000Birth YearStandard Deviation ScoreBirthweightBMI at 3 yearsBirth-weight and BMI at 3 years for children born on Wirral between 1985 and 2000-0.100.10.20.30.40.50.60.71990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000Birth YearStandard Deviation ScoreBirth-weightWeight gain to 8mLength at 8mBMI at 2yBMI at 3yStrongest trends: Rise in BMI at 2 & 3 years and infant length  for children born on Wirral between 1990 and 20006Are some social groupsaffected more than others?Babies of affluent families are born heavier in Manchester still310031503200325033003350340034501984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006YearBirth-weightLeast deprived (1) 2 3 4 Most deprived (5)Vanishing social trends in early infant weight gain in Manchester from 1986 to 2004-0.5-0.4-0.3-0.2-0.100.10.20.30.40.51984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006YearSDS gain from birth to around 8wLeast deprived (1) 2 3 4 Most deprived (5)Decreasing inequalities in BMI at 3 years on Wirral-0.3-0.2-0.100.10.20.30.40.51988 1990 1992 1994 1996 1998 2000 2002YearSDS BMIQ1 (most affluent) Q2 Q3 Q4 Q5 (most deprived)1988-1998: r = 0.039 (95% CI: 0.028 to 0.050)1998-2003: r = 0.008 (95% CI: -0.008 to 0.024)Rochdale 5yr old obesity by deprivation in 20050%10%20%30%40%50%60%70%80%90%100%Q1 Q2 Q3 Q4 Q5 AllDeprivation Fifth (low to high)IOTF categoryNot FatOverweightObeseNo correlation with deprivation (IDAC 2004): r = -0.04 (-0.08 to 0.05)…was there a spatial as well as asocial diffusion pattern of obesityacross the 1990s?…7Fifths of IDAC 2004Proportion of households with childrenclaiming benefitsRed (light) = most deprivedRed (dark)PurpleBlue (dark)Blue (light) = most affluentChild Deprivation(2001 Census)BMI of 3 yr olds1988 - 1989Fifths of BMISDS BMI fifthRed (light) = fattestRed (dark)PurpleBlue (dark)Blue (light) = thinnestBMI of 3 yr olds1990 - 1991Fifths of BMISDS BMI fifthRed (light) = fattestRed (dark)PurpleBlue (dark)Blue (light) = thinnestBMI of 3 yr olds1992 - 1993Fifths of BMISDS BMI fifthRed (light) = fattestRed (dark)PurpleBlue (dark)Blue (light) = thinnestBMI of 3 yr olds1994 - 1995Fifths of BMISDS BMI fifthRed (light) = fattestRed (dark)PurpleBlue (dark)Blue (light) = thinnestBMI of 3 yr olds1996 - 1997Fifths of BMISDS BMI fifthRed (light) = fattestRed (dark)PurpleBlue (dark)Blue (light) = thinnest8BMI of 3 yr olds1998 - 1999Fifths of BMISDS BMI fifthRed (light) = fattestRed (dark)PurpleBlue (dark)Blue (light) = thinnestBMI of 3 yr olds2000 – 2001Fifths of BMISDS BMI fifthRed (light) = fattestRed (dark)PurpleBlue (dark)Blue (light) = thinnestBMI of 3 yr olds2002 - 2003Fifths of BMISDS BMI fifthRed (light) = fattestRed (dark)PurpleBlue (dark)Blue (light) = thinnest…height and linear growth aresocially sensitive,so how did they change withBMI across the 1990s?…Increasing correlation between height and weight in Wirral 3-yr-olds 88 to 03 à ? Growth effect0.550.570.590.610.630.650.670.690.710.730.751988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003YearHeight-weight correlation (95% CI)Source: Buchan et al. pre-publication0.000.200.400.600.801.001.201.401.601 2 3 4 5 6 7 8 9 10Tenth of height (smallest to tallest)BMI (kg/m²) increase between 1988 and 2003Secular trend to increasing BMI is much greater in taller childrenSource: Buchan et al. pre-publication9Faster infant growth and slower early child growth13.51414.51515.51616.5171990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000Birth YearMean (95% CI) length/height gain (cm)6 weeks to 8 months8 months to 2 yearsAdiposity fluctuates less in young children than it used to0.30.350.40.450.50.550.60.650.71990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000Birth yearPearson correlation (95% CI) BMI at 8 months vs. 3 yearsBMI at 2 years vs. 3 years…if fat is sticking more through childhoodwhat is happening to physical fitness?…-0.5-0.4-0.3-0.2-0.100.10.20.30.498/99 99/00 00/01 01/02 02/03 03/04Yearz score FitnessBMIRise in BMI and fall in cardio-respiratory endurance of Liverpool 10 year olds from 1998 to 2004Data from G Stratton, Liverpool Sportlinx152025303540455098/99 99/00 00/01 01/02 02/03 03/04School YearShuttle runs completed (n)Obese Overweight Not fatCardio-respiratory endurance levels of  Liverpool 10 year olds fell in all BMI groups1516171819202198/99 99/00 00/01 01/02 02/03 03/04School YearBMI (kg/m²)1 (unfittest)23 (fittest)BMI of Liverpool 10 year olds rose irrespective of cardio-respiratory enduranceData from G Stratton, Liverpool Sportlinx10Discoveries from service e-recordsThe obesity epidemic in children:• Concurred with accelerating infant growth• Showed a temporo-spatial spread pattern• Ignored socio-economic groups• Affected the physically fit and unfit alikeEvidence Based PracticeNeedsPractice Based EvidenceInformatics challenges in tackling obesity…Intelligence to support decisionsNoiseData Information Knowledge WisdomClean PackDecisionRelyAccuracyCompletenessRelevanceTimelinessExperiencePhysical SocialCollateCollect Analyse UseFuture decision support• Personal– “what activities and diet do I need to combat the past 48h of metabolic stress my biosensors report?”• Clinical– “what dose adjustment should I make to match my patient’s adiposity?”• Population– “what is the obesity-related health gain for every pound we invest in green transport?”Information culture shifting• ↓ Report cycles → ↑ surveillance• ↑ tactical service responses• More personal health systems:↑ co-ordination (individual & societal) of preventive healthcare11Individual carePersonal recordMessagePopulation monitoringService record(of time, place, population)MessagePublic Health Decisions(policy & implementation)Automatic, routinedata processesScientific researchFindingWhite literatureEvaluations & investigationsFindingGrey literatureRoutine outputsQuery usingpublic health expertiseAd hocdata processes ToolMakersResearchPartnersGPsHospitalPCTLocal Health Intelligence FrameworkClinical Information GovernanceLocal NHS repositoryDHResearch Information GovernancePseudonymisedrepositoryLA etc.Population info-lab processesGPsHospitalLifestylePopulationData inputs PreparationRecord linkageCleaningRapid recruitmentResearchBiobanksStudiesIndexingEvolving determinantsVigilance / SafetyTrials (feasibility)Clinical effectivenessPolicy / PopulationExploratoryQuality taggingPrivacy Protecting Record LinkagePersonalRecordsUnique IDPostcodeDate of birthDate of measurementPseudonymisedRepositoryPseudonymDeprivation scoreLL SOAYear of birthMonth of birthDecimal ageOne-way cipherData integrationRule-based transformationDepersonalisation(Health Topic) Intelligence SystemData cleaningStandardisationRecord linkageStatistical analysisChartingMappingQueriesReportsAlertsDatabaseSecurityWorkflow engineService-orientedhealth intelligence architectureWorkflow to produce the Wirral mapsNHSODPMReferenceClean (rules)VisualiseClean (unanticipated)StandardiseClean (rules)VisualiseClean (unanticipated)Record cleaningSummariseDepersonaliseSummarise by SOASOA boundariesThematic mapsOS / ONSPostcode à SOASOA à deprivationVisualiseSummariseModelStatistical findingsPost Office12… Turning bio-health information into discoveriesM.C. EscherOrder and Chaos, 1950

Health e-records and the Obesity Epidemic

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Health e-records and the Obesity Epidemic

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