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| Journal of the New Zealand Medical Association,
02-March-2007, Vol 120 No 1250
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Assessing Māori/non-Māori differences in
cardiovascular disease risk and risk management in routine primary care practice
using web-based clinical decision support: (PREDICT CVD-2)
Tania Riddell, Rod Jackson, Susan Wells, Joanna Broad, Lot
Bannink
The gap in life expectancy between Māori and
non-Māori in New Zealand increased over the period 1980–1999. Most
notably, the slow decline in Māori cardiovascular disease (CVD) mortality
rates over this period contrasted with the rapid decline in non-Māori
rates.1 As a consequence, CVD remains the major
contributing cause to the widening life expectancy disparity between Māori
and non-Māori people in New Zealand.
The most recent New Zealand guideline on CVD risk
management2 recommends a systematic
evidence-based approach to assessment and management based on a patient’s
absolute 5-year risk.
The target population for CVD risk assessment is all men
over the age of 45 years, and all women over the age of 55 years. However, at
any given age, Māori have an increased prevalence of CVD compared to
non-Māori. Therefore, risk assessment is recommended a decade earlier for
Māori patients—i.e. at ages 35 years for Māori men, and 45 years
for Māori women.
PREDICT-CVD is a web-based clinical decision support
programme for CVD risk assessment and management. Its development has been
described elsewhere.3 PREDICT-CVD has been
shown to be an effective tool for increasing CVD risk assessment in routine
primary care practice. Indeed, in a before-after evaluation, investigators found
its use produced a four-fold increase in risk assessment and risk factor
documentation for both Māori and
non-Māori.4,5
This paper reports on differences at baseline between
Māori and non-Māori CVD risk assessments and management in the first
3½ operational years of PREDICT-CVD in ProCare a large primary health
organisation in Auckland, New Zealand.
MethodsThis report describes a pre-planned analysis of
Māori/non-Māori differences in a study examining CVD risk and risk
management in primary care in Auckland, New Zealand. A full description of the
study methods and data definitions can be found in the paper by Bannink et
al.3
A web-based clinical decision support programme,
PREDICT-CVD, was integrated with the patient management system MedTech for
primary care practitioners in ProCare, a large primary health organisation
(patient population approximately 650,000) in Auckland, New Zealand.
Whenever a ProCare health practitioner used
PREDICT-CVD, an anonymous electronic patient profile was generated and stored on
the PREDICT server. PREDICT-CVD generates either one or two datasets; the first
dataset is generated on all patients assessed and includes a CVD risk factor
profile and estimate of 5-year CVD risk.
A second extended dataset that includes risk assessment
and risk management is generated if the practitioner uses PREDICT to provide
individualised management advice based on a patient’s CVD risk. This is
typically requested for high-risk patients. This latter dataset includes more
extensive information on CVD risk factors and current drug and non-drug
management of CVD risk. The data from these two patient data sets formed the
basis for this study.
When clinicians use PREDICT, ethnicity data are
automatically transferred from the patient management system. The New Zealand
Health Information Service recommend the categorisations of Statistics New
Zealand for self-reported ethnicity.6 For the
purpose of these analyses, ethnicity data from PREDICT were categorised into two
groups: Māori and non-Māori (European and other, Pacific, Indian, and
other Asian peoples).
All data were analysed using SAS 9.1 statistical
software. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated
for Māori compared with non-Māori using a logistic regression model
that adjusted for age group and sex. For continuous data, mean differences and
95% CIs were calculated using a general linear model that adjusted for age group
and sex.
Subgroup analysis was undertaken for those patients
with a personal history of CVD and the smaller subgroup of those with a history
of ischaemic heart disease (IHD). A logistic regression model adjusting for age
group and sex was used to test the association of Māori compared to
non-Māori of having had a revascularisation procedure (percoronary
intervention such as a stent or angioplasty, or coronary artery bypass
graft—PCI or CABG).
Ethical approval: The PREDICT project
was approved by the Auckland Ethics Committee (AKY/03/12/314).
ResultsBetween 2002 and February 2006, PREDICT-CVD collected and
stored 20,614 CVD risk assessments. Of these, 1450 (7%) were for Māori
patients and 19,164 (93%) for non-Māori patients. The mean age of
Māori patients was 53.2 years for Māori and 46% were female. For
non-Māori the mean age was 56.5 years and 44% were female.
Table 1 presents ORs and mean differences with 95% CIs for
CVD risk factors for all Māori and all non-Māori patients adjusted by
age group and sex.
Table 1. CVD risk factor profiles for all
Māori compared to all non-Māori
Odds ratios and mean differences adjusted for age group and
sex.
Within this cohort of ProCare patients, Māori were
three times more likely to be smokers than non-Māori. Māori patients
were also twice as likely to have diabetes and a personal history of CVD (IHD,
stroke or transient ischaemic attack, and peripheral vascular disease) as
non-Māori.
The reporting of a family history of CVD was 30% higher for
Māori compared to non-Māori. On average, body mass index, systolic and
diastolic blood pressures, and total cholesterol/HDL ratios were higher for
Māori compared to non-Māori.
Of the 20,614 patients screened, 2341 (11%) had a history of
CVD. Of this group of high-risk patients, 215 (9%) were Māori and 2126
(91%) non-Māori. Management data (i.e. the second PREDICT dataset described
in the Methods section) was available for 1363 (58%) of these patients, with a
greater proportion of Māori (66%) than non-Māori (57%) patients having
both risk assessment and risk management datasets completed.
Table 2 presents ORs and mean differences with 95% CIs for
CVD risk factors and management variables for Māori compared to
non-Māori with a history of known CVD. Within this group of patients,
Māori were more likely than non-Māori to be smokers, to have diabetes,
a family history of CVD and a high BMI. However, Māori were more likely to
be prescribed antiplatelet or anticoagulant, antihypertensive, and
lipid-lowering medications. As a result, Māori and non-Māori in this
subgroup had comparable blood pressure and serum lipid measurements.
Table 2. CVD risk factor and management
profiles for Māori compared to non-Māori with a personal history of
CVD
Odds ratios and mean differences adjusted for age group and
sex; *PREDICT includes both risk assessment and risk management modules (see
Methods); The risk management module is typically used for high risk
patients
A further subset analysis of those with a history of
ischaemic heart disease (IHD) (n=1727) was undertaken to determine access to
revascularisation procedures. Only 18% of Māori (27/152) compared with 30%
of non-Māori (473/1575) patients with a history of IHD had undergone a
revascularisation procedure. Māori had approximately half (OR=0.46, 95%CI:
0.34-0.83) the revascularisation rate as non-Māori in this cohort of
patients.
DiscussionThis paper has reported differences between Māori and
non-Māori CVD risk assessments undertaken opportunistically in the first
3½ years of a large pilot of a web-based clinical decision support
programme in routine general practice.
Baseline CVD risk assessments were completed for over 20,000
patients aged 35 years and older from ProCare, a large Auckland-based primary
care organisation, establishing one of the largest cohorts of Māori and
non-Māori ever assembled in New Zealand. Of these assessments, 7% (1450)
were from Māori patients.
The mean age of Māori participants was 53 years while
non-Māori participants were on average 3 years older, complying toward (but
not meeting) current guideline recommendations and the higher age specific CVD
risk of Māori. This indicates that more can be done to risk assess
Māori at a younger age. The higher prevalence of CVD risk factors among
Māori in this study was similar to previous studies.
Tobacco consumption is more prevalent among Māori than
non-Māori, and has declined less for Māori over the last 20
years.7 Diabetes prevalence among Māori is
three and five times higher for males and females respectively compared to New
Zealand Europeans.8
The observation of greater CVD risk associated with
increased body mass index, blood pressure, and cholesterol levels for Māori
compared to non-Māori is also consistent with other New Zealand
cross-sectional surveys.9-14
Robust epidemiological and clinical trial evidence supports
the use of anticoagulant, antihypertensive, and lipid-lowering medications for
the secondary prevention of CVD.15-18
Encouragingly, for those patients with a history of CVD, Māori were more
likely than non-Māori to be receiving (and taking) these pharmacotherapies
given the comparable systolic blood pressure; and total, HDL, and LDL
cholesterols.
This may indicate that primary healthcare practitioners are
intensifying their efforts given the highlighted Māori health disparities.
However, (unless contraindicated) all those with known CVD should be receiving
antiplatelet or anticoagulant, antihypertensive, and lipid-lowering treatment in
the secondary prevention of coronary heart disease.
Disappointingly, we found that only 58% of those with known
CVD had risk management data available, and of those less than half were
receiving all three drug therapies described above. This represents a
significant gap between evidence-based secondary prevention and the clinical
reality in primary care. Therefore, targeting risk assessment to those groups
most in need is not enough.
Systematic risk management must follow risk assessment and
be subject to a quality-driven implementation
programme.5
Of concern, this study suggests that Māori with known
IHD receive significantly fewer revascularisation procedures than
non-Māori. Indeed, this is consistent with the findings of other
studies.19-22
After controlling for differences in age, sex, and
deprivation, one study found that CABG and PCI intervention rates for Māori
patients were about half those of
non-Māori.23 It is unacceptable that
Māori have greater exposure to CVD health risks and less access to
high-quality secondary healthcare services in New Zealand.
Differential access to health services is a likely
contributor to Māori CVD inequalities.24
The health sector has a statutory responsibility and key role to ensure that
access to healthcare for Māori is
equitable.25
This study has highlighted that there are a number of
opportunities to optimise management for Māori with CVD. They include the
revision of coronary scoring and surgical prioritisation methods; providing full
funding for drug therapies, including plant sterol and stanol-fortified
spreads;26 and more intensive management and
monitoring of risk factors at the whānau (extended family) level.
In conjunction with drug treatment, non-drug interventions
for Māori at high risk should include intensive lifestyle advice about
cardioprotective diets and physical activity as well as ready access to smoking
cessation programmes that are Māori specific. These opportunities could
become realities with increased funding for healthcare practitioners who
actively manage and monitor high risk Māori patients and their whānau.
In addition, cardiovascular health research investment that
is weighted to Māori and aimed at improving Māori CVD inequalities is
needed.
Possible misclassification bias associated with ethnicity
data collected by PREDICT-CVD was not addressed in these analyses. An ethnicity
validation substudy is currently being analysed (A. Lindsay, personal
communication, 2006). This cross-sectional analysis will compare patient
ethnicity data recorded in PREDICT-CVD to self-reported ethnicity recorded via a
postal questionnaire (using the standard ethnicity question recommended by the
2004 report on Ethnicity Data Protocols for the Health and Disability
Sector27).
Over time, it will be possible to link this large and
continually expanding PREDICT dataset with national data on hospital admissions
and deaths. Linkage will enable us to generate New Zealand population CVD-risk
prediction equations.
Māori-specific equations will replace the Framingham
equations based on a white, largely middle-class, North American population. New
Zealand will then have a world class CVD and diabetes data repository from which
information on the burden and management of these chronic conditions can be
generated and acted upon.
Note: PREDICT-CVD was developed by the
University of Auckland and Enigma Publishing Ltd in collaboration with ProCare
Health Ltd, Counties Manukau District Health Board, Ministry of Health, National
Heart Foundation, New Zealand Guidelines Group, and MedTech Global Ltd.
Conflict of interest statement: There
are no conflicts of interest.
Author information: Tania Riddell, Senior
Lecturer; Rod T Jackson, Professor of Epidemiology; Sue Wells, Senior Lecturer;
Joanna Broad, Research Fellow; Lot Bannick, Visiting Scholar; Section of
Epidemiology and Biostatistics, School of Population Health, University of
Auckland, Auckland
Acknowledgements: This study was supported
by funds from the Health Research Council of New Zealand. SW is the recipient of
a National Heart Foundation Research fellowship. We are also grateful to Sue
Furness and Pritibha Singh for their help with analysis.
Correspondence: Dr Tania Riddell,
Section of Epidemiology and Biostatistics, School of Population Health,
University of Auckland, Private Bag 92019, Auckland. Fax: (09)
3737 624; email: t.riddell@auckland.ac.nz
References:
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