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The Burden of Disease, Injury and Risk Factors New Zealand 2006 - 2016 MANATÜ HAUORA.

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Presentation on theme: "The Burden of Disease, Injury and Risk Factors New Zealand 2006 - 2016 MANATÜ HAUORA."— Presentation transcript:

1 The Burden of Disease, Injury and Risk Factors New Zealand MANATÜ HAUORA

2 Overview  Context  NZBDS: objectives and methods  NZBDS: key results (provisional)  Next steps

3 CONTEXT

4 Outline  Background to burden of disease studies (BDS)  The DALY metric  Comparative risk assessment (CRA)

5 What is a BDS?  Standardised framework for measuring health loss (‘nonfinancial health accounts’)  Health loss estimated for a comprehensive set of disease, injury and risk factor causes  Fatal and nonfatal health losses integrated via the disability- adjusted life-year (DALY) metric  Comprehensive and internally consistent

6 What can you learn from a BDS?  Estimates and projections of level and distribution of unmet need for health services (health need analysis)  Estimates of effectiveness for cost effectiveness analysis (priority setting)  Epidemiological estimates and projections for other studies  Identification of gaps in health information system

7 History of BDS  Methodology developed early 1990s by Chris Murray (World Bank)  First Global Burden of Disease Study (GBDS 1990) published 1996, second in 2004 (GBDS 2000), third currently in progress (GBDS 2005)  More than 50 national BDS completed or in progress, including two Australian BDS, most recently 2003 (published 2007)  Previous NZBDS (1996 data, published 2001) – relied heavily on ABDS

8 The DALY (disability-adjusted lifeyear) metric  Time based health gap measure  Gap between population’s current state of health and that of an idealised population in which everyone lives long lives free from disability  Integrates fatal with nonfatal health outcomes - combines years of life lost to untimely death (YLL) with equivalent years of healthy life lost because of time lived in health states less than full health (YLD)  Preference for nonfatal health states expressed on a 0 – 1 scale where 0 = full health and 1 = dead,  So YLD is commensurate with YLL and the two can be summed: DALY = YLL + YLD  1 DALY = 1 year of healthy life lost  DALY is a standardised, inverted QALY

9 YLL  Number of deaths from each cause in each subpopulation in reference year  Multiplied by weight which varies with age but nothing else  Variation with age reflects preference to live long life  Weights are remaining life expectancies at each age taken from a reference lifetable  Reference lifetable constructed by taking lowest mortality rate at each age already achieved by any national population  Formula:YLL = Σ (d x w) where d = count of deaths from specified cause at specified age w = life expectancy remaining at that age (from reference lifetable)

10 YLD: incidence vs prevalence perspective  Incidence perspective captures only the nonfatal burden resulting from incident cases in reference year (cohort approach) YLD = I x D x DW where I = incident cases of condition in cell, D = duration (survival before recovery or death), DW = disability weight (preference for health state)  Prevalence perspective captures the nonfatal burden arising from all prevalent cases alive in reference year (irrespective of when they were incident) (cross sectional approach) YLD = P x DW where P = prevalent cases in cell  Provided duration is short, both yield similar YLD values ( since P = I x D if D is short)  Incidence perspective better suited to CEA, prevalence perspective better suited to health need analysis  Condition may involve multiple health states - YLD calculated for each separately, then summed

11 DISMOD epi software State 2 (eg diseased) Dead State 1 (eg not diseased) m  ir m (duration) { Prevalence

12 Disability weights  Preference for being in health state – ‘disability severity’ metric  Measurement strategies: standard gamble, person trade-off (PTO), time trade-off (TTO) 0 – 1 scale (full health =0, dead = 1)  Stable over duration of health state (separate DW for each health state)  Current values derived from GBDS 2004 (mainly based on Dutch focus group study, using PTO) – may vary by age and sex but nothing else  Will substitute values from current GBDS when available (mainly based on internet survey of people, plus 6 country surveys)

13 Comorbidity adjustment (YLDs)  People often experience more than one condition simultaneously  Disability will typically be less than sum of disabilities from each condition  Simple model is applied to adjust DW downward: DWc = 1 - Π(1- DWi)  Note independence assumption – we have shown this is reasonable

14 The art of YLD measurement  Condition model (outcome tree) – ‘sequelae’  Prevalence (or incidence and duration) by 5 year age group, sex, ethnicity – may need to use DISMOD  Distribution of cases across sequelae  Severity distribution by sequela  DWs for each severity level of each sequela (= health state)  Consistent thresholds and case definitions for all nonfatal health states and their DWs

15 CRA: counterfactual modelling approach Risk factor exposure Current distribution (2006) Counterfactual distribution (TMRED) Risk factor – disease relationship (Hazard ratio or relative risk) Total disease burden Attributable burden in 2006 Potential impact fraction

16 CRA: PIF model Potential impact fraction = proportional reduction in burden that would occur if exposure to a risk factor were reduced to the theoretical minimum risk exposure distribution Attributable burden = total burden of linked condition x PIF

17 CRA: TMRED

18 CRA: Hazard ratio

19 NZBDS: Objectives and Methods

20 Provenance of the NZBDS 2006  MRG report 2010: more needs-driven, better priority setting  Approved by ELT and Minister of Health mid 2010  Work commenced September 2010  Two year time frame  Small core team (with 1 person seconded from ACC)  Project Advisory Group  Clinical advisors (including advisors from ACC)  Link to BODE

21 Specific objectives of NZBDS  Generate an internally consistent set of epidemiological estimates for a comprehensive set of ~220 conditions  Using this database, estimate the burden of disease and injury (in DALYs) in 2006, for Maori and non-Maori  Project the burden (at condition group level) to 2011 and 2016  Estimate the burden attributable to major biological and behavioural risk factors in 2006 and project to 2016

22 NZBDS methodology: BDS  Condition selection and classification: 16 Level I categories including Injury, 217 Level II conditions, ~600 sequelae (health states)  Reference year is 2006 (in principle)  YLL: redistribution of implausible and inadequately specified causes of death (5 different redistribution algorithms)  YLD: data from NZ Health Tracker where possible, other data sources where necessary (disease registers, unlinked administrative data, health survey & related surveys, research studies)  Prevalence approach (I x D used where D < 1 year)  DISMOD for incidence to prevalence modelling and consistency checking  DWs from GBDS 2004 provisionally, adjusted for independent comorbidity  Uncertainty quantified by simulation, sensitivity analysis added where necessary  Projections (trend extrapolation for YLL, fixed relational model for YLD based on compression assumption)

23 NZBDS methodology: CRA  Risk factor selection and classification: metabolic and physiological (SBP, TBC, FPG, BMI, 25(OH)D, osteoporosis); dietary (vegetable, fruit, sodium, saturated fat); behavioural (physical activity, tobacco use, alcohol use, illicit drug use, unsafe sex); adverse health care events; diseases as risk factors (diabetes, depression etc); injury ‘E’ codes as risk factors (eg transport)  Counterfactual modelling (continuous or discrete risk factor distributions); direct estimation; mixed model  Exposure distributions from Adult Nutrition Survey 2008/09 or other data sources (depending on the risk factor)  TMREDs and HRs from GBDS or other sources (eg APCSC)  Standard formula for joint risk factor distributions: PIFc = 1 - Π(1- PIFi)  Uncertainty quantification by simulation, plus sensitivty analysis where required  Projections mainly demographic (trend analysis for tobacco, TBC, BMI)

24 KEY RESULTS (PROVISIONAL)

25 NEXT STEPS

26 Outline  Strengths and weaknesses of study  Policy and research applications  Next steps

27 Strengths of NZBDS  First to include non-hospitalised injury  First to report injury burden from both a ‘cause of injury’ (E code) and a ‘nature of injury’ (N code) perspective  First to include sleep disorders  First to include chronic pain syndromes  First to include stilbirths (albeit intrapartum only)  First to include adverse health care events (albeit in-hospital only)  First to make systematic use of data linkage to estimate epi parameters (mainly prevalence)  Improved analysis of infant conditions and birth defects, attributing health losses to ‘correct’ age groups  Transparent assumptions underlying methods for GC redistribution, comorbidity adjustment, uncertainty quantification and burden projection

28 Weaknesses of NZBDS  Reliance on ‘medical model’ –fails to capture wider welfare impacts  Does not capture clinical workload impact or health sector costs  Modelling sometimes heavily assumption driven  Not all conditions (or health states) and only selected risk factors included  Not all epi data of high quality (eg 1.6% of injury deaths were ‘unknown intent’)  Not all DWs as robust as we would like (new GBDS weights may be better)  Simulation plus sensitivity analysis may not fully capture ‘model uncertainty’ (but our method conservative)  Projections based on overly simplistic assumptions (but short-term only)

29 Potential policy applications for ACC and MOH – what DALYs tell us  Understand relative importance of injury / disease as a cause of health loss  Guide needs-based investment decisions for injury prevention (E lens) and trauma care (N lens) – both wrt services and research  And similarly for different diseases – CRA (prevention), BDS (treatment)  Understand equity issues in relation to injury / disease outcomes – and design policy accordingly  Monitor progress over time – and take corrective action accordingly

30 What DALYs don’t tell us  DALYs do not correlate strongly with clinical workload – not very useful for workforce planning  DALYs do not correlate strongly with costs to the health care system – not very useful for short-term budgeting  Burden of disease & injury studies are only one tool for analysing need (or demand) as an input into evidence- informed policy – not a panacea

31 Next steps  Complete analytic phase, including substitution of ‘new’ for ‘old’ DWs  Implement report writing / KT phase  Extend to regional / district level  Extend to other ethnic groups  Extend to analysis by NZDep  Extend to time series

32 Institutionalising the NZBDS: f rom initial study to ongoing information system  Regular updating (and progressive improvement - including comprehensiveness of risk factors, conditions and sequelae, extension to ethnic minorities, deprivation quintiles and DHBs, time series analysis)  Substitution of Tracker indicators for other data sources to the fullest extent possible (necessary to make updating cost effective)  Stabilisation of data sources, condition selection and classification, thresholds, case definitions, condition models, modelling and statistical methods, outputs  Regular production of outputs (including updated epi tables and burden analyses) and application to surveillance, needs assessment, monitoring of unmet need / scope for health gain, monitoring of inequalities, inputting to CEA (priority setting - BODE) and resource allocation more genearally

33 Acknowledgements  MOH: Martin Tobias, Maria Turley, Terry Quirke, Ann Sears, Nadia Bartholomew, Michelle Liu, Li-Chia Yeh, Craig Wright, Anna Davies, Mishra Suryaprakash, Jane Wang, Jo Wall, Robert Templeton, Stephen Salzano, Kylie Mason, Deepa Weerasekera, Lisa Lankshear  ACC: Zhi-Ling Zhang, John Wren, Zeeman Van Der Merwe, Charlotte Kieft  Collaborators: Don Schopflocher (University of Alberta), Barbara Hjalsted (NIPH, Denmark), Tony Blakely (BODE)  More than 30 clinical / epidemiological advisors  NZBDS Steering Group (project advisors)  GBDS 2005 Steering Group (Chris Murray, Alan Lopez, Theo Vos, Majid Ezzati, Colin Mathers, Gretchen Stephens)


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