A Guide to Using Amenable Mortality as a

System Level Measure 2018

1Version: January 2018

Acknowledgements

This guide has been developed by the Ministry of Health. The authors thank the peer reviewers, both within and outside the Ministry.

Purpose of this Guide

Amenable mortality is used internationally to measure the performance of health systems. Since 1 July 2016 it has been included as one of the six System Level Measures that provide the organising framework for quality improvement and system integration by DHBs and their health system partners through their district alliances in New Zealand.

This guide explains the concept of amenable mortality, how it is measured, and how this indicator can be used to improve health system performance.

The guide also includes a brief summary of key amenable mortality statistics for the past 1-2 decades, to familiarise users of this metric with the current situation and recent trends in this performance measure.

The guide may be used by district alliances to guide the development of their System Level Measure Improvement Plans.

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ContentsIntroduction...........................................................................................................................................4

The System Level Measures Framework............................................................................................4

The Amenable Mortality construct.....................................................................................................4

Measurement and monitoring...............................................................................................................7

High level operational definition........................................................................................................7

Inclusion and exclusion criteria..........................................................................................................7

Current codelist (as at 1 July 2016)....................................................................................................8

Table 1: Current amenable mortality codelist (July 2016)..................................................................8

Mortality data....................................................................................................................................9

Population data................................................................................................................................10

Standardisation of amenable mortality rates...................................................................................10

Reporting..........................................................................................................................................11

Updating the amenable mortality indicator.....................................................................................11

Impact of the 2016 update of the amenable mortality codelist.......................................................11

Commencement date.......................................................................................................................12

Interpreting amenable mortality rates for use as a System Level Measure.........................................12

Improving amenable mortality rates: the role of contributory measures........................................13

Key findings 2006 – 2014......................................................................................................................15

Key insights.......................................................................................................................................19

Appendix 1 - technical notes on Standardisation.................................................................................20

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IntroductionThe System Level Measures FrameworkThis Framework aims to improve health outcomes for people by supporting DHBs to work in collaboration with their health system partners (primary, community and hospital) using specific quality improvement measures. It provides a foundation for continuous quality improvement and system integration.

System Level Measures align with the five themes of the Health Strategy and other national strategic priorities such as Better Public Service Targets. They have a focus on children, youth and vulnerable populations. System Level Measures are part of the DHB annual planning process and provide an opportunity for DHBs to work with their primary, secondary and community care partners to improve health outcomes of their local population. They promote better understanding and use of health information, engagement with people in the design and delivery of health services and better health investment in models of care based on local population needs.

The Ministry worked closely with the sector to co-develop the six System Level Measures (SLMs) that together provide a system-wide view of performance. The SLMs engage the health sector broadly (in terms of professions, settings and health conditions) and cover all levels of health care.

The six SLMs are:

Childhood Ambulatory Sensitive Hospitalisations rates for 0-4 year olds acute hospital bed days per capita patient experience of care amenable mortality rates babies living in smokefree homes youth access to and utilisation of youth appropriate health services.

Brief definitions and a summary of data sources and statistical methods used to calculate these measures are available in the Measures Library, accessible via the Health Quality Measures New Zealand website (www.hqmnz.org.nz). The Measures Library also lists and defines key ‘contributory measures’, which are more specific and local measures of factors that can contribute to improvement in the SLMs.

The Amenable Mortality constructAmenable mortality is widely accepted internationally as a valid and reliable indicator of health system performance. In New Zealand this metric is especially relevant to the ’value and high performance’ theme of the refreshed New Zealand Health Strategy, and can serve to identify potential areas of concern for more detailed investigation (see The Guide to using the System Level Measures Framework; and Saving Lives: Amenable mortality in New Zealand 1996 – 2006).

The idea underpinning the amenable mortality construct is that the contribution of health care to improvement in population health can be quantified by counting deaths from causes avoidable through health system intervention. Such interventions may work by reducing incidence (prevention of the underlying condition to reduce the number of new cases) or by reducing case fatality (treatment leading to cure or delay in progression of the underlying condition).

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The rate at which deaths from these selected causes continue to occur should then reflect the performance of the health system, at least in so far as avoidance of fatal outcomes is concerned.

There are two refinements to this broad concept:

Prevention can be achieved by providing clinical preventive services such as immunisation and screening to individuals, or by services directed to populations collectively, such as legislation on vehicle standards or taxation of tobacco. The healthcare system cannot directly control intersectoral interventions aimed at the social determinants of health. So only deaths amenable to clinical interventions, preventive or therapeutic and provided to individuals are included.

Death is of course inevitable, so only deaths (from avoidable causes) occurring at a younger age than expected (‘premature’ deaths) should be included.

A more casual definition of amenable mortality is ‘unnecessary, untimely deaths’.

In reality, not all deaths from any ‘amenable’ cause can be avoided – the relevant intervention may have limited efficacy; the person may have presented late in the disease process; the person may fail to respond to the intervention for genetic, immunologic or other reasons; the person may be frail or have serious co-morbidities; the person may not tolerate or may refuse or fail to adhere to the prescribed treatment. So the concept refers to the expectation that the population mortality rate from an amenable cause will be substantively reduced, not necessarily eliminated.

Similarly, the requirement that deaths be ‘premature’ is not readily operationalised. The simplest approach (which is currently the one used) is to define an arbitrary upper age limit for the death to qualify as a ‘premature’ death – acknowledging that this is inevitably ageist.

An even more basic limitation of the metric arises from the reality that much healthcare is not aimed at life extension at all, but rather at improving health related quality of life. This dimension of health system performance cannot be captured by a mortality metric.

Finally, the binary classification of causes of death as ‘amenable’ or ‘not amenable’ poses operational challenges of its own. Two widely used lists exist: Nolte & McKee and Tobias & Glover. In New Zealand the Tobias & Glover list is used.

Tobias & Glover developed their list through a combination of expert judgement (using a panel of clinicians and public health physicians), trend analysis (looking for discontinuities in mortality trends following introduction of new interventions), and systematic review of RCTs (to identify potentially effective interventions). Table 1 shows the current cause list with the rationale for each included cause.

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Table 1 Current Amenable Mortality Cause list: Rationale

Cause of death Rationale for inclusion Pulmonary tuberculosis Advances in directly observed treatments (DOTS)Meningococcal disease Advances in antibiotics and intensive carePneumococcal disease Advances in antibiotics and intensive careHepatitis C Advances in antiviral drugs HIV / AIDS Advances in antiretroviral drugs (HAART)Stomach cancer Advances in diagnosis (endoscopy), surgery and adjuvant

therapy (combination chemotherapy including fluorouracil), and advances in antibiotics to eliminate H. pylori infection

Rectal cancer Advances in radical surgery and adjuvant therapy (oxaliplatin)Bone and cartilage cancer Advances in surgery and adjuvant therapy (chemo-radiotherapy)Melanoma of skin Advances in early detection and adjuvant therapyFemale breast cancer Advances in mammographic screening, estrogen receptor

assays, and adjuvant therapy (tamoxifen and congeners)Cervical cancer Advances in: screening, surgery, adjuvant therapy (concurrent

chemo-irradiation)Uterine cancer Advances in early case detection and surgeryProstate cancer Advances in anti-androgens and other adjuvant therapy Testis cancer Advances in chemotherapy (cisplatin)Thyroid cancer Advances in diagnosis and adjuvant therapyHodgkin lymphoma Advances in high dose combination chemotherapy and peripheral

blood stem cell transplantationAcute lymphoblastic leukaemia Advances in chemotherapy (combination chemotherapy including

6-mercaptopurine, vincristine and prednisone) (<45 only)Complications of pregnancy Advances in obstetric careComplications of perinatal period

Advances in neonatal intensive care and surfactant therapy

Cardiac septal defect Advances in diagnosis, surgery (including cardiopulmonary bypass and DHCA)* and paediatric intensive care

Diabetes Advances in diagnosis, insulins, oral hypoglycemics, tight glucose and blood pressure control, and models of care

Valvular heart disease Advances in diagnosis (cardiac catheterisation), surgery, and artificial valve replacement

Hypertensive diseases Advances in antihypertensive drugs (especially ACE inhibitors and calcium antagonists)

Coronary heart disease Statins (and other drugs for secondary prevention), thrombolysis, advances in reperfusion therapy and coronary care units

Pulmonary embolism Advances in diagnosis and anticoagulationAtrial fibrillation & flutter Advances in rhythm control (drugs, pacemakers) and

anticoagulationHeart failure Advances in diagnosis, and in combined therapy including ACE

inhibitorsCerebrovascular diseases Advances in imaging, antihypertensives, atrial fibrillation (AF)

management, and dedicated stroke unitsCOPD Advances in antibiotics, bronchodilators, and physiotherapyAsthma Advances in bronchodilators, steroids and intensive careCholelithiasis Advances in lithotripsyRenal failure Advances in dialysis and renal transplantation (including

advances in immunosuppression)Peptic ulcer disease Advances in drug treatment (cimetidine and other H2 receptor

antagonists) and H. pylori eradicationLand transport accidents excluding trains

Advances in emergency transport and trauma care

Accidental falls on same level Advances in osteoporosis treatment and orthopaedic careFire (burns) Advances in early excision and skin graftingSuicide Advances in antidepressant therapy

*Deep hypothermia cardiac arrest

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Measurement and monitoringHigh level operational definitionAmenable mortality is defined as premature deaths (deaths under age 75) that could potentially be avoided given effective and timely healthcare. That is, early deaths from causes (diseases or injuries) for which effective health interventions exist and are accessible to everyone in need (in New Zealand).

Not all deaths from these causes could be avoided in practice (for example, because of co-morbidity, frailty and patient preference). However, a higher than expected rate of such deaths in a DHB (for the total population or a subgroup such as Māori) may indicate that access to, or quality of, care could be improved.

Inclusion and exclusion criteriaA specific intervention, package of interventions, or model of care (hereafter 'intervention') must be identified and linked to a specific cause of death (COD). There must be a clear ICD code for the COD and reporting and coding must be of high quality.

The intervention must be a medical or surgical intervention delivered by or under the direction of a clinician (doctor or nurse) to individuals (patients or well persons at risk) in a healthcare setting (including the home). Note that the intervention may involve screening, diagnosis or rehabilitation, as well as treatment. Public health interventions delivered collectively to populations (eg food safety laws, tobacco taxes, safe sex social marketing campaigns) are excluded so that the measure reflects access to and effectiveness of health care rather than wider social systems.

The intervention must have been introduced and become generally accessible to New Zealand patients or at risk populations within the past 40-50 years (ie post 1960). Interventions introduced many decades ago are likely to have become diffused even in poorly performing health systems so such interventions provide no comparative information regarding current health system performance.

The intervention must have either already reduced under 75 mortality (in the relevant New Zealand subpopulation or patient group) by more than 30%, or have been shown in randomized controlled trials (RCTs) or high quality observational studies to be capable of such mortality reduction within five years of universal coverage being achieved1.

The linked COD must account (currently) for >0.1% of all under 75 deaths (roughly 10 deaths per year)2.

1 Upper age limit reflects high prevalence of multi-morbidity in the very old, making valid assignment of a single underlying COD difficult. Short lag period (<5 years) is required so that the metric indicates current, not future, health system performance. Note that some amenable CODs may have age restrictions within the 0-74 age range.2 This is an optional criterion and is not strictly enforced – included only to avoid cluttering the list with CODs whose associated mortality has fallen to very low levels, with little possibility of resurgence absent total health system collapse – such CODs are ‘avoided’ rather than ‘avoidable’.

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Current codelist (as at 1 July 2016)The current list of amenable causes of death (Table 1) was updated by an expert panel in June 2016. The updated list comprises 38 conditions, grouped into six super-categories:

infections maternal and infant conditions injuries cancers cardiovascular diseases and diabetes other chronic diseases.

In analysis, it can be more useful to use these super categories as the rates of individual conditions may be too small to make much impact on the overall amenable mortality rate. This should not prevent consideration of where an intervention may make a significant impact on the rates of specific conditions.

Table 1: Current amenable mortality codelist (July 2016)

Group Condition ICD-10-AM-VI

Infections

Pulmonary tuberculosis A15-A16

Meningococcal disease A39

Pneumococcal disease A40.3, G00.1, J13

Hepatitis C (HCV) B17.1, B18.2

HIV/AIDS B20-B24

Cancers

Stomach cancer C16

Rectal cancer C19-C21

Bone and cartilage cancer C40-C41

Melanoma of skin C43

Female breast cancer C50

Cervical cancer C53

Uterine cancer C54, C55

Prostate cancer C61

Testis cancer C62

Thyroid cancer C73

Hodgkin lymphoma C81

Acute lymphoblastic leukaemia (For ages 0-44 only)

C91.0

Maternal and infant Complications of pregnancy O00-O96, O98-O99

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Group Condition ICD-10-AM-VI

disorders

Complications of perinatal period P01-P03, P05-P94

Cardiac septal defect Q21

Cardiovascular disorders and diabetes

Diabetes E10-E14

Valvular heart disease I01, I05-I09, I33-I37

Hypertensive diseases I10-I13

Coronary heart disease I20-I25

Pulmonary embolism I26

Atrial fibrillation & flutter I48

Heart failure I50

Cerebrovascular diseases I60-I69

Other chronic disorders

Chronic obstructive pulmonary disease (COPD) J40-J44

Asthma J45-J46

Cholelithiasis K80

Renal failure N17-N19

Peptic ulcer disease K25-K27

Injuries

Land transport accidents excluding trains V00-V04, V06-V14, V16-V24, V26-V34, V36-V44, V46-V54, V56-V64, V66-V74, V76-V79, V80.0-V80.5, V80.7-V80.9, V82-V86, V87.0-V87.5, V87.7-V87.9, V88.0-V88.5, V88.7-V88.9, V89, V98-V99

Accidental falls on same level W00-W08, W18

Fire (burns) X00-X09

Suicide X60-X84

Mortality dataThe numerator data for the amenable mortality metric is extracted from the Mortality Data Collection. The Mortality Data Collection uses information from a variety of sources (including death certificates, hospital separations summaries, patient records, coronial reports and police reports) to code the underlying cause of death (COD) for every death in New Zealand. The underlying cause is defined as the cause that initiated the train of events leading to the death.

Currently, COD is coded using ICD-10 amenable mortality version VI. 2014 will be the first year coded in ICD-10-AM-VIII. Note that the Ministry changes versions regularly, and at some point ICD-10 will be replaced by ICD-11.

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To calculate amenable mortality, all deaths registered in New Zealand in the relevant calendar year with underlying COD included in the current version of the amenable mortality Codelist, where the deceased was aged 0-74 years at date of death (with some exceptions within this broad age range), are extracted.

Variables extracted from the Mortality Data Collection for each relevant death include:

NHI DHB of residence (and linked NZDep decile) Date of birth (and age in five-year age bands) Date of death Sex Ethnicity (prioritised) Underlying cause of death (ICD-10-AM three or four digit code as applicable)

Population data Denominator data to calculate amenable mortality rates are the national or DHB usually resident populations derived from projections the Ministry gets annually from Statistics New Zealand (SNZ) for the PBFF3. The ethnic denominators are based on prioritised ethnicity, to align with the numerator data. Both these denominators and the NZDep decile or quintile denominators are derived internally within the Ministry of Health from the SNZ projections; they are not available directly from SNZ.

Variables extracted include:

Year DHB Age 0-74 in five-year age bands Sex Ethnicity NZDep decile

Standardisation of amenable mortality ratesCrude amenable mortality rates (along with 95% or 99% confidence intervals) are calculated by dividing the amenable mortality count by the corresponding population count (with confidence intervals estimated in the usual way). However, these crude rates do not allow fair comparison of one DHB with another, or the same DHB with itself over time, because of variation in the underlying population age and sex structure, ethnic mix or socioeconomic (ie: deprivation) distribution.

To control for confounding by these socio-demographic variables, a combination of stratification and standardisation is employed. Alternatively, regression modelling may be used. This report used the WHO World Population as the reference for the age weights (for all ages). This standardization method has its limitations (for further information, see appendix 1) but was selected to allow international comparison if needed and for being consistent with other indicators and Ministry publications.

3 SNZ populations are preferred to PHO enrolment data as the latter does not cover everyone usually resident in New Zealand. PBFF (Population-based funding formula) determines the share of funding to be allocated to each DHB based on the population living in each district.

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ReportingReports are made available to DHBs annually, in or around February of each year (subject to mortality data being available in December). DHBs are provided with a rolling five year data set, covering the period from two to seven years prior to the current calendar year.

It takes several years for some coronial cases to return verdicts. Given the significant impact these cases can have on some causes of death, estimates for the amenable mortality indicator are not available until approximately two years after the end of the year of death registration.

Updating the amenable mortality indicatorThe amenable mortality codelist requires regular updating:

Medical advances mean that some CODs not previously classified as amenable will now meet all inclusion criteria.

Mortality from some CODs currently included as amenable will fall so low that no further fall is possible; nor would any recurrence in mortality from these conditions be expected barring total collapse of the health system. While leaving such CODs on the list does no harm, it adds nothing either and increases ‘clutter’ and opportunity for error.

Any change to the codelist creates a discontinuity in the time series that makes interpreting trends in amenable mortality rates more difficult. The frequency of updating needs to balance these opposing tensions. For that reason, the codelist is updated at 5-10 year intervals.

Impact of the 2016 update of the amenable mortality codelist The codelist was updated in 2016 (using amenable mortality data up to 2013). A clinical expert panel identified potential interventions introduced since 2006. The CODs linked to these interventions were filtered through the inclusion and exclusion criteria listed above, and mortality trends were examined for the filtered causes. The expert panel then made final recommendations based on the above analyses. The opportunity was also undertaken to assess whether any CODs currently considered amenable should be dropped from the updated list. Relevant authorities in Australia (AIHW) and the UK (ONS) were also consulted to identify any new CODs they were planning to include in their amenable mortality codelists.

This exercise resulted in the identification of three new CODs and the decision to exclude one existing COD:

Inclusions: HCV infection (acute or chronic), atrial fibrillation & flutter, cancer of the uterus. Exclusions: Treatment injury (data quality inadequate).

Amenable mortality rates calculated using ‘old’ and ‘new’ lists show that the minor changes made to the codelist in 2016 have not substantively affected the DHB rates, at least at the total amenable mortality metric level. Nevertheless, it is important that the list be kept current, both for credibility and because differences may be larger at super category level, especially for some DHB population subgroups.

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Commencement dateThe previous list was updated in 2009, based on COD data up to 2006. The current list is based on 2006 – 2013 data. Arguably the new list could commence in 2006; however some of the interventions would have only been introduced after this date, or lag periods may not have fully elapsed by then. Equally, starting in 2013 may omit several years during which some of the new interventions were already exerting their full effect. It was decided to begin use of the new list at the midpoint of the update period, in this case 2010.

Interpreting amenable mortality rates for use as a System Level MeasureIt is recommended that improvement teams in district alliances look first at the amenable mortality metric per se, then consider data at super category level, and finally drill down to specific condition level. This should be done for the DHB population as a whole, but also separately for each lifecycle stage, sex, major ethnic group and NZDep quintile.

If a particular COD, or group of CODs, is identified as the cause of the concerning amenable mortality rate, then further investigation using improvement science methodology may identify quality improvement activities that the district alliance partners can undertake at system, practitioner or service level to address the issue.

When interpreting results, the improvement teams in district alliances should be aware that:

When disaggregated to specific condition level, numbers of deaths from some causes in some DHBs may be too small to permit any conclusions to be drawn. For this reason, it is better to use super category level data in developing improvement plans.

Differences between DHBs, or for the same DHB over time, may reflect residual confounding. Age standardisation, or even double standardisation for age and ethnicity, does not remove confounding by other variables, such as deprivation, rurality, or migration.

The data are two to three years old, so may be out of date and some areas of poor performance may already have been addressed.

Differences may not be large enough to be clinically or epidemiologically important, even if statistically significant at the 99% level generally used for DHB-level data.

Even when not disaggregated by cause, data may be too sparse to allow analysis at sub-district level (death – especially premature death – is a relatively rare event).

Similarly, some ethnic analyses may have insufficient volumes. Note that all subgroup analyses will be more subject to random variation from year to year than total DHB level analyses.

Studying trends over time and variation by ethnicity, deprivation, or by where people live can give useful insights to guide quality improvement. The measure does not work well for making judgements between districts but is useful to stimulate enquiry as to possible underlying causes within a district and provide rationale for activities that might improve health outcome.

Possible reasons for worsening or unwarranted variation on this measure include poor implementation of health interventions, failure to ensure timely access for all population groups, failure or deterioration in performance of funded health services.

Monitoring whether the under-75 mortality rate from a condition, and the relevant super category, and the amenable mortality indicator as a whole improves (in the DHB population as a whole and in the affected population subgroup) – allowing for the two year reporting

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delay) can be used to evaluate the effectiveness of improvement activities at the district level.

Improving amenable mortality rates: the role of contributory measuresThere is clearly a wide range of health services involved in reducing amenable mortality risks [given the large number and varied categories of CODs included in the rubric]. Furthermore, the major causes contributing to the amenable mortality rate vary by age group (within the 0-74 age range) and sex. As a consequence there is a large number of contributory measures that could potentially be used to monitor.

Rather than attempt to provide a complete listing of potential contributory measures, the table below maps the contributory measures chosen by the district alliances for their 2016/2017 and 2017/2018 System Level Measure Improvement Plans. This mapping provides a sample of what contributory measures the district alliances chose to monitor local progress on quality improvement activities relating to this System Level Measure.

Contributory measures included in System Level Measure Improvement Plans (2016/17 and 2017/18)

Category Contributory measure

Smoking cessation % PHO enrolled patients who smoke that have been offered help to quit smoking by a health professional in the last 15 months

% pregnant women who smoke who have been offered help to quit by their Lead Maternity Carer

% hospital patients who smoke that have been offered help to quit smoking by a health professional

% of registered smokers who have been referred to a smoking cessation service (disaggregated by ethnicity)

% of quit attempts amongst people with mental illness and addictions

Lifestyle management Green Prescription Plus uptake rate

Alcohol brief intervention rate

Weight management referral rate

% obese children (B4SC) referred for weight management

Cardiovascular risk management % PHO enrolled eligible population who have had a CVD risk assessment recorded in last 5 years and/or measure showing good management of cardiovascular risk (disaggregated by ethnicity)

% increase (relative) in dual therapy for those with CVD RA greater than 20%.

% increase (relative) in triple therapy for those with a prior CVD event

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LTC management % PHO enrolled eligible population with a record of a diabetes annual review during the reporting period whose HbA1c <64 mmol/mol and prescribed insulin

%PHO enrolled eligible population with microalbuminuria prescribed ACE inhibitor or angiotensin receptor blocker

% PHO enrolled eligible population served by diabetes collaborative management strategy

Number of pre-diabetes courses held in primary care

COPD hospitalisation rate (disaggregated by ethnicity)

Mental health services Suicide screening rates for at risk populations

Access to care for people seeking support for mild to moderate mental health issues

% eligible mental health clients covered by transition plans

Cancer services % patients who received their first cancer treatment within 62 days of referral with high suspicion of cancer

% PHO enrolled women aged 25-69 who have had a cervical screen in last 3 years (disaggregated by ethnicity)

% PHO enrolled women aged 50-69 who have had a breast cancer screen in last 2 years (disaggregated by ethnicity)

Reduce the equity gap for Māori women by one third for both breast and cervical screening

% of PHO enrolled Māori women aged 25 to 69 years who have had a cervical sample taken in the past three years

% of PHO enrolled eligible Māori women (50-69) who have had a breast screen in the last two years

Rollout of national bowel screening programme

Immunisation Immunisation coverage rates at 8 months, 2 years and 5 years

Influenza immunisation rate for people 65 and over

Pneumococcal immunisation rate for people 65 and over

Primary health care access and quality

Ambulatory Sensitive Hospitalisation rate

Primary care utilisation rate

Primary Health Care access measure

Measure of 3D health pathways utilisation

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Key findings 2006 – 2014Of all deaths occurring at ages <75, 48.4% were from amenable causes in 2014 (5518 out of 11 396 deaths). This percentage has again declined steadily from 54.9% (6356 out of 11 585 deaths) in 2000.

Adjusting for differences in population sizes and age structures, using pooled data 2010-2014, the age-standardised amenable mortality rate (ASR) per 100 000 people aged 0-74 was 2-3 fold higher among Māori and Pacific people than among New Zealanders of other ethnicities.

ASR per 100 000

Standardised rate ratio (SRR)

Māori 215 2.73Pacific 192 2.44Non-Māori, non-Pacific 79 1

Coronary heart disease was the leading amenable cause among adults (45-64 years) in 2014, accounting for 27.4% of all amenable deaths in this age group. Other major causes in this age group included stroke, COPD, diabetes, breast cancer and suicide.

Pulmonary

tuberculosis

Pneumococcal d

isease

HIV/AIDS

Rectal c

ancer

Melanoma of skin

Cervica

l cance

r

Prostate ca

ncer

Thyroid ca

ncer

Acute ly

mphoblastic l

eukaemia

Complications o

f perin

atal perio

d

Diabetes

Hyperte

nsive dise

ases

Heart failu

re

Pulmonary

embolism

COPD

Peptic ulce

r dise

ase

Renal failu

re

Accidental fa

lls on sa

me leve

l

Suicide

0

100

200

300

400

500

600

700

Amenable mortality deaths by condition, ages 45-64

45-64

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Injury caused almost all amenable deaths among youth (15-24 years) in 2013, accounting for 162 out of 179 deaths (90.5%). Suicide accounted for 90 of these 179 injury deaths (50.3%) and land transport accidents for almost all the remainder (65 out of 179 injury deaths, or 36.3%). Injury (including suicide) was the leading cause of amenable deaths among the 25-44 years age group accounting for 272 deaths (52.9 %); coronary disease caused 51 (9.9%) and breast cancer 44 deaths (8.6%).

Pulmonary

tuberculosis

Pneumococcal d

isease

HIV/AIDS

Rectal c

ancer

Melanoma of skin

Cervica

l cance

r

Prostate ca

ncer

Thyroid ca

ncer

Acute ly

mphoblastic l

eukaemia

Complications o

f perin

atal perio

d

Diabetes

Hyperte

nsive dise

ases

Heart failu

re

Pulmonary

embolism

COPD

Peptic ulce

r dise

ase

Renal failu

re

Accidental fa

lls on sa

me leve

l

Suicide

0

20

40

60

80

100

120

140

160

180

200

Amenable mortality deaths by condition, ages 25-44

25-44

Amenable deaths among children were dominated by complications of the perinatal period, which caused 167 out of 214 amenable deaths in this age group in 2013 (78.0%). The majority of these deaths resulted from complications of prematurity and occurred around the time of birth or in the first month of life.

Amenable deaths resulted in an average loss of 17.7 years of life in 2014

RegYear Number YLL AverageYLL

2010 5565 106936 19.22011 5510 99140 18.02012 5523 101172 18.32013 5401 95725 17.72014 5457 96640 17.7

In 2014 each amenable death accounted for 17.7 years of life (YLL) lost on average (ie: mean age at death from amenable causes was 75 - 17.7 = 57.3 years). This represents a smaller loss per death

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than in 2010, when the mean YLL was 19.2 years. Unfortunately, because of the change in the cause list in 2010, YLL for earlier years is not fully comparable.

Amenable mortality has been declining by 3.7% per year on average over the past decade (once adjusted for demographic trends)

Adjusting for change in the size and age structure of the population, the amenable mortality rate has declined steadily from 146 per 100 000 people aged 0-74 in 2000 to 93 per 100 000 in 2014. This represents an average annual growth rate of -3.2% per annum. By contrast, all-cause mortality has fallen more slowly over the same period, by approximately -1.7% per annum.

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010* 2011 2012 2013 20140.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

160.0

Age standardised amenable mortality rate per 100 000, 2000-2014

This decline is seen for all ethnic groups. The average annual rate of decline (approximately -3.2% per year) has been similar for all three ethnic groups analysed (Asian ethnic group could not be analysed separately because of small numbers of deaths in earlier years). However there still remains a significant gap between the Māori, Pacific and non-Māori non-Pacific rates.

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2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010* 2011 2012 2013 20140.0

50.0

100.0

150.0

200.0

250.0

300.0

350.0

Amenable mortality age standardised rates by ethnicity

Maori Pacific non-Maori, non-Pacific

Amenable mortality rates vary widely across the DHBs – but much of this variation is explained by demographic differences

Northland

Waitemata

Auckland

Counties Manuka

u

Waikato

Lakes

Bay of P

lenty

Tairawhiti

Hawkes B

ay

Taranaki

Midcentra

l

Whanganui

Capital &

Coast

Hutt Valley

Wairarapa

Nelson M

arlborough

West Coast

Canterbury

South Canterbury Otago

Southland0.0

20.040.060.080.0

100.0120.0140.0160.0

Age standardised amenable mortality rate per 100 000, 2010-2014

Total rate non-Maori, non-Pacific rate

Five years of data have been pooled to allow for small numbers of annual deaths in some DHBs.

After adjusting for differences in DHB population age structures, considerable variation remains in amenable mortality rates (orange bars) – from a low of 71.3 per 100 000 in Waitemata to a high of 144.9 per 100 000 in Tairawhiti. However, once stratified by ethnicity (as well as adjusted for age),

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the inter DHB variation is reduced (as shown, for example, for the non-Māori non Pacific ethnic group, blue bars).

Key insights

Approximately 50% of under-75 deaths between 2000 and 2014 were from amenable causes. In 2014 this amounted to approximately 5500 deaths.

Adjusting for differences in population size and age structure, Māori had 2.73 and Pacific people 2.44 times the rate of amenable deaths as European/Other people in 2010-2014.

Amenable mortality has been declining by 3.2% per year on average over the past decade (once adjusted for demographic trends), approximately twice as fast as non-amenable mortality. The rate of decline is about the same for all three major ethnic groups.

Coronary heart disease was the leading cause of amenable deaths among adults (25-74) over the past 1-2 decades. Injury (in particular suicide and road traffic injury) was the leading cause in youth (15-24), while amenable mortality in children was dominated by complications of the perinatal period (mainly disorders of prematurity).

Each amenable death resulted in a loss of 17.7 years of life in 2014 on average, down from 19.2 YLL in 2010.

Amenable mortality rates vary widely across the DHBs – but much of this variation is explained by socio-demographic differences.

Trends and variation in amenable mortality rates reflect health system performance and should be closely monitored and carefully investigated to establish whether, and what, improvement actions may be indicated (and whether any such action taken is successful).

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Appendix 1 - technical notes on Standardisation

When comparing DHBs overall, the usual approach is to directly standardise for age. In this case, the reference population used as the source for the age weights should be an ‘older’ population, as this will minimise distortion of DHB rates overall. A recent New Zealand population projection (see above) could be selected. Alternatively, the WHO World Population can be used as the reference for the age weights, if international comparison is needed.

Note that this controls for confounding by age, but not by other variables such as ethnic composition and socioeconomic (ie NZDep) distribution. This would generally be done by indirect standardisation, which has the advantage of being robust with low counts and does not require a standard population. However, each DHB can then be compared only with the national population, not with each other (as each acts as its own ‘reference’). Alternatively, direct double standardisation can be used if the only major confounders are age and ethnicity; the same projected New Zealand population then acts as the reference or source for both age and ethnic weighting (triple standardisation for age, ethnic mix and NZDep distribution is not recommended). However, the best way to control for confounding by multiple variables is regression modelling.

When examining indigenous or ethnic inequalities, however, the main population of interest is the Māori (or Pacific or Asian) population, so a population with a ‘young’ age structure should be used as the reference population for direct age standardisation (in order to avoid distorting the rates of interest). For this purpose, there is no need to adjust for socioeconomic variation (because deprivation is a mediator, not a confounder, of the ethnicity – mortality relationship), and the inequality estimates (age standardised rate ratios [SSR] or rate differences [SSD]) are of course already stratified by DHB. Variation in the ethnic SRR (or SRD) by DHB can then be directly assessed. For example if the Māori-non-Māori amenable mortality SRR in Northland DHB is 2.0 and that in Auckland DHB is 1.5, then the indigenous inequality in amenable mortality in Northland is twice that in Auckland (or 100% greater): [2.0 – 1.5] / [1.5 – 1].

While any population with a young age structure could serve as the standard, the 1996-2000 Māori population estimate is often used. However, the age structure of the Māori population (and Asian and Pacific populations) is undergoing rapid change (getting older). Using a population that no longer reflects the age structure of the population of interest invalidates the metric; a more appropriate reference would be a recent Māori population projection (derived for PBFF as explained above). Alternatively, Segi's World population could be used. This was the international standard for most of the last 50 years and is still used by the International Agency for Research on Cancer (IARC). While its age structure is slightly older than the current Māori population, it may not be too far from that of the Māori population in (say) 2025 – which would provide a suitable standard for the next 20 years or so.

Note that whatever the source of weights, these should be the All Ages population (to allow wider comparisons) although weights normed to the 0-74 population are also acceptable.

Also note that any form of summarisation (whether through direct or indirect standardisation or any other modelling approach) may disguise important information. For example, if Māori and non-Māori inequality in amenable mortality rates is different in children than in adults, this effect modification by age will be lost if only the age standardised rates are compared. Instead, fully

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stratified results (ie age / sex / ethnic / dep specific rates) should be reported whenever there is adequate cell size to do so.

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