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The Burden of Disease, Injury and Risk Factors New Zealand

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Presentation on theme: "The Burden of Disease, Injury and Risk Factors New Zealand"— Presentation transcript:

1 The Burden of Disease, Injury and Risk Factors New Zealand 2006 - 2016

2 Overview Context NZBDS: objectives and methods
NZBDS: key results (provisional) Next steps Fix this list up later


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 i r (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) Potential impact fraction Total disease burden Attributable burden in 2006

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


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)



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: from 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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