Vascular Informatics using Epidemiology & the Web: VIEW

Transcription

1 Vascular Informatics using Epidemiology & the Web: VIEW Dan Exeter on behalf of VIEW: Rod Jackson, Sue Wells, Andrew Kerr, Matire Harwood, Patricia Metcalf, Roger Marshall, Katrina Poppe, Jim Warren

2 VIEW 2020 Vascular risk Informatics using Epidemiology & the Web topic: Vascular risk prediction & risk management approach: Informatics large-scale data linkage science: Epidemiology & Biostatistics data: Web-based clinical tools were developed to generate new clinical data that we link to regional & national routine health data collections 2020

3 VIEW goals more accurate vascular risk prediction better vascular risk management reduced inequalities in vascular disease burden

4 PREDICT was designed to: Patient population Electronic medical record in primary care electronic decision support get current best evidence on risk & management into clinical practice

5 & to simultaneously generate new evidence from clinical practice Patient population Electronic medical record in primary care NHI PREDICT: electronic decision support NHI patient-specific outcomes: hospital admissions, deaths encrypted NHI patient-specific CVD risk factor profiles

6 The VIEW linked-record cohort MOH VIEW Outcomes (e.g.) Lipid testing Diabetes Hospitalisations HbA1c results Medication dispensing + Demographics Age Gender Ethnicity NZDep06 + Geographies Meshblock Area Unit Electorate District Health Board

7 A few examples of current VIEW research Developing risk equations for primary (and secondary) care Epidemiology of Acute Coronary Syndrome (ACS) Developing low information risk scores Exploring the association between residential mobility and CVD hospitalisations

8 0 5 Kaplan-Meier Observed event rate observed vs predicted risk: Framingham score Framingham score Predicted event rate: Framingham Men 1 prevention score

9 0 5 Kaplan-Meier Observed event rate observed vs predicted risk: PREDICT score PREDICT score Predicted event rate: new score Men 1 prevention score

10 ANZACS-QI: NZ IHD outcomes 60% of IHD deaths pre-hospital and, 60% of these never hospitalised for IHD 83% 3% 4% 10% IHD hospitalisation, alive >28d IHD hospitalisation, died 28d 17% overall case fatality IHD death, non-ihd hospitalisation 28d Pre-hospital IHD death Grey et al, ANZACS-QI-8, European Heart Journal 2015

11 (,000) resident population by age, according to Census and VARIANZ -22% Census 2006 VARIANZ % -15% 400-9% 300-5% 200-3% %

12 (,000) 3, Resident population distributions by ethnicity, from Census and VARIANZ Census 2006 VARIANZ 3,000 2,500-15% 2,000-9% 1,500 1, % -20% -44% -54% -33% Total Other Maori Pacific Asian Chinese Indian

13 Does residential mobility affect CVD?

14 Health data sources used to-date PREDICT equations PREDICT Web-based clinical data ANZACS-QI ANZACS-QI Web-based clinical data VARIANZ 2006 Residential Mobility PHO enrolments PHO enrolments PHO enrolments Hospitalisations Hospitalisation Hospitalisation Hospitalisations Mortality Mortality Mortality Mortality Pharmaceutical dispensing Lab test claims TestSafe Pharmaceutical dispensing Virtual diabetes register Lab test claims General Medical Subsidies NNPAC Virtual diabetes register

15 VIEW2020: Future Directions Link additional individual-level covariates (from Census, IRD, Education) & validate VARIANZ models Develop risk equations for:older people, specific ethnic groups, diabetics, post-acs, post stroke Identify under and over-treatment by: ethnicity, deprivation, location Create a national vascular risk engine & national vascular health information platform to help close evidence-practice gaps & reduce inequities in vascular disease

16 (,000) 1000 Getting the denominator right SNZ Official pop VARIANZ-IDI

17 VIEW team Rod Jackson, Matire Harwood (Tania Riddell), Sue Wells, Andrew Kerr, Dan Exeter, Katrina Poppe, Roger Marshall, Patricia Metcalf, Jim Warren, Jeff Harrison, Rob Doughty, Jinfeng Zhao, Romana Pylypchuk, Corina Grey, Josh Knight, Suneela Mehta, Billy Wu (Tadd Clayton)