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Unmet need of GP services in Pacific people and other
New Zealanders
Megan J Pledger, Jacqueline Cumming, Mili Burnette, Jacob
Daubé
General practitioners (GPs) are an important gateway into
the New Zealand health system. While providing care themselves, they also
provide a pathway to more specialised services. When people are unable to access
GP services, their health can be compromised.
Numerous studies in New Zealand have found that the health
status of Pacific peoples (mostly of Samoan, Tongan, Niuean, or Cook Islands
origin) to be worse than that of other New
Zealanders.1–4 Part of the reason for
this disparity could be due to delays or avoidance in seeking GP services when
they are needed.
Published information derived from 2006/07 New Zealand
Health Survey has already identified that there is clear divergence in unmet
need for GP services between ethnicities. The Ministry of Health’s (2008)
A Portrait of Health identified that reported unmet need for GPs among
New Zealand adults, before and after adjustment for age, is significantly
greater for Pacific peoples than
European/Other.5 Amongst children, however, a
further Ministry of Health report regarding the health of Māori and Pacific
children found that after adjusting for age, there was no significant difference
between the prevalence of unmet need for Pacific children and non-Pacific
children.6
Non-Ministry research surrounding the usage of GPs by
Pacific peoples in New Zealand has largely focussed on rates of GP visitation
(use) rather than the presence of unmet need.
Crampton and colleagues (2007) conducted a nationally
representative survey of GPs and patient visits. Observational results showed
that after adjusting for socioeconomic deprivation scores (NZDep2001), age,
gender, and organisation type, average annual exposure to primary health care
was higher among those selecting the ‘European’ ethnic group than
the ‘Māori’, ‘Pacific’, or ‘Asian’
ethnic groups.7
Similarly, an analysis of data routinely collected from GP
practices in the Wellington region found that after adjusting for socioeconomic
deprivation scores, age and gender, Māori, Pacific Island and Asian
populations had lower (although Māori only slightly) doctor consultation
rates than Europeans.8 Such data, however, only
include those who used GP services over the study period and not the general
population. It may be that some people are not using GP care at all.
National literature has previously linked some of the ethnic
disparity in use of services to high cost. In the Health Utilisation Research
article (2006), GP visitation figures were compared between groups with higher
subsidy (children under six years of age) and those with lower subsidy (children
six and over). Utilisation rates for children under 6 years of age were slightly
higher for Pacific Islanders than Europeans, both before and after adjusting for
deprivation, while for children over six rates of attendance were considerably
lower for Pacific Islanders than Europeans, before and after deprivation
adjustment. Similar results were shown for Māori and Asian
populations.8 Cost therefore appears to limit
access for Pacific peoples.
This paper looks at the level of unmet GP need in Pacific
peoples and Other New Zealanders, the variables associated with this unmet need,
and whether there are any differences between Pacific peoples and Other New
Zealanders. It also looks at the reasons why people do not go to a GP when they
need to and what they do instead.
MethodsConfidentialised, unit record data from the 2006/2007
National Health Survey were supplied by the Ministry of
Health.5 This data set contains 12,488
respondents, aged 15 years and over, who were living in a private dwelling in
New Zealand. The survey over-sampled Māori, Pacific and Asian peoples
through a complex method of sampling; however, the survey has been weighted to
produce a representative sample.
Estimates produced by these weights form unbiased
estimates of the corresponding population values. The dataset also includes a
set of 100 replicate weights which were created using the delete-group
method.5 Each of these weights creates an
estimate. The variance of these 100 estimates around the unbiased estimate gives
the sampling variance of the unbiased estimate. For the purpose of this paper,
SUDAAN software was used to do these calculations.
9
Respondents were asked a range of questions about
doctor-diagnosed health conditions, health service use, risk factors and
sociodemographics factors. Except for height and weight which were measured, the
responses given were self-reported.
Respondents were asked which ethnic group/s that they
belonged too. For the purposes of this study, anyone who reported any Pacific
ethnicities were recorded as a Pacific Person and everyone else was recorded as
an Other New Zealander.
Respondents were also asked whether they had needed to
go to a GP in the last year but did not go. This was coded as 1 for those who
said they had unmet need and 0 for those who did not and analysed using logistic
regression. A selection of sociodemographic, health status and risk factors were
selected as explanatory variables for the model.
The model was constructed in four steps. At the first
step, a base model was constructed with age, sex and ethnicity and their
interactions. The form of the model was found by using backwards selection. At
the second step, all the explanatory variables were considered individually with
the base model from step 1 in four ways:
These
results are displayed in a table to show which variables are important by
themselves even if they are not part of the final model. At the third step, all
the variables that were significant at the second step were put into the final
model and backwards selection was used to reduce the model to its final form.
Results from the final model are reported as odds ratio
and adjusted probabilities for each variable. Adjusted probabilities use the
model to form probabilities based on the value of the coefficients in the model
and the distributions of the explanatory variables. Variables that had
interactions with age or sex were displayed graphically.
Finally, in the fourth step, a final series of models
were run to look at what variables affect the difference in unmet GP need
between Pacific peoples and Other New Zealanders. For each variable that had
main effect only, the final model was run but with that variable removed and the
change in the ethnic difference was recorded. If the ethnic difference changed
by more than 10% then the variable removed was said to be a confounder for the
ethnic difference and was therefore said to explain or accentuate some of the
difference seen between Pacific peoples and Other New Zealanders.
ResultsThe variables considered in the model and their distribution
by ethnicity appear in Table 1. The two groups, Pacific peoples and Other New
Zealanders, appear to be most dissimilar for the variables age, household size,
New Zealand Individual's Deprivation Index (NZiDep), Urban/Rural living, AUDIT
score for alcohol consumption and body mass index (BMI).
At the first step, the base model formed was a main effects
model with age, sex and ethnicity.
The results from the second step, the selected variables
fitted individually with the base model, appear in Table 2. This shows which
variables were significant by themselves, in interactions with age and with sex.
Some of the variables that are important at this stage may not survive the model
building process but can offer insight into unmet need.
Table 3 gives the results from the third step which gives
the final form of the model. The variable which shows the greatest range in
adjusted probabilities is NZiDep, with the most deprived having the greatest
probability of unmet GP need. This is followed by age and then self-rated
health; older people and those who are the most unwell have the greatest
probability of unmet need.
In the model, higher education is also associated with a
higher probability of unmet need.
Table 1. The variables considered in the model
and their distribution with Pacific people and Other New
Zealanders
![]() Table 2. Significance results for variables
fitted individually and in different combinations of age and sex with the base
model
![]() Table 3. Results from the final model of unmet
GP need
![]() Figure 1. The interaction between ethnicity and
asthma in the model for unmet GP need
![]() The variables asthma and BMI have interactions with
ethnicity. For Other New Zealanders, having asthma leads to a greater
probability of unmet need, while having asthma leads to a lower probability of
unmet need for Pacific peoples. For Other New Zealanders, BMI appears to cause
no difference to unmet need, whereas for Pacific peoples, being overweight has a
greater probability of unmet need than being underweight or normal weight.
The adjusted probability for Pacific peoples having unmet GP
need is 0.07 and for Other New Zealanders it is 0.05 with a p-value for the
difference of 0.0487.
Variables were removed from the final model one at a time to
see which variables had the greatest effect on this ethnic difference. Removing
NZiDep had the greatest effect on the ethnic difference as Pacific peoples are
more concentrated in the higher levels of NZiDep where there is greater unmet
need. Removing educational qualifications had the next biggest effect but in the
opposite direction. Other New Zealanders have a greater concentration of people
at the higher levels of education qualifications, where there is greater unmet
need, and this makes the difference between ethnicities smaller than would
otherwise be unexpected.
Respondents were asked the reason why they did not go to the
GP when they needed to. For Pacific peoples with unmet need, the most common
reason was “that the GP cost too much” (33%; 95%CI 22–44) and
for Other New Zealanders, the most common reason was “Couldn't get an
appointment soon enough/at a suitable time” (29%; 95%CI 24–33).
However, there were no significant differences between the two different ethnic
groups for any of the 17 reasons given.
Those with higher educational levels had a higher
probability of unmet need than those with lower levels. Those with no
qualifications were more likely to say “that the GP cost too much”
(11%; 95%CI 6–16) compared to those with university qualifications (2%;
95%CI 0–5). Those with university qualifications were more likely to say
“Couldn't spare the time” (21%; 95%CI 14–28) compared to those
with no qualifications (7%; 95%CI 3–11). These were the only two reasons
out of 17 given where there were significant differences between these
qualification levels.
Those with higher levels of deprivation had a higher
probability of unmet need than those with lower levels. Twenty nine percent
(95%CI 24–33) of those with none or one deprivation characteristic who did
not go to a GP gave as a reason that they “Couldn't get an appointment
soon enough/at a suitable time” compared to 25% (95%CI 21–33,
p=0.5767) for those with two or more deprivation characteristics.
The next most common reason for those with none or one
deprivation characteristic for not going to a GP was that they “Didn't
want to make a fuss” (20%, 95%CI 20–30) compared to 16% (95%CI
12–20) for those with two or more deprivation characteristics. For those
with two or more deprivation characteristics, the most common reason for not
going to a GP when there was a need was that it “Costs too much”
(44%, 95%CI 38–51) compared with 16% (95%CI 12–21) of those with
none or one deprivation characteristic.
Respondents were asked what they did instead of going to the
GP when they needed to. The most common answer was “Nothing” for
both Pacific peoples (36%; 95%CI 26–47) and Other New Zealanders (47%;
95%CI 42–51). The second most common reason was “Went to see the GP
at a later date” for Pacific peoples (20%; 95%CI 9–32) and Other New
Zealanders (12%; 95%CI 9–15). There was only one significant difference
between Pacific peoples and Other New Zealanders in the reasons given which was
“Took it easy/rested and relaxed more/got more sleep” which 8% of
Pacific peoples identified (95%CI 1–16) compared to 3% of Other New
Zealanders (95%CI 1–4).
Figure 2. The interaction between ethnicity and
BMI in the model for unmet GP need
![]() DiscussionThere were three main themes associated with unmet GP need
in the last year – there was higher unmet need in people who were unwell,
people who had higher levels of individual deprivation, and people with higher
educational qualifications. The latter two reflect different reasons for not
being able to get to a GP—people with more deprivation characteristics are
more likely to give financial reasons then those with fewer deprivation
characteristics, while people with higher education levels are more likely to
say they had time constraints.
These two variables also explained some of the differences
between Pacific peoples and Other New Zealanders: Pacific peoples are more
likely to have more deprivation characteristics which are associated with more
unmet GP need. This concurs with pre-existing identified relationships between
ethnicity, deprivation scores and avoidable
mortality,10, 11 and the over-representation of
Pacific peoples in regions of high
deprivation.12
On the other hand, Other New Zealanders are more likely to
be in the higher educational categories where there is higher unmet need also.
There is much pre-existing research into the positive relationship between
higher education and better health status.13
That higher education levels could negatively impact on GP utilisation appears
to be largely unstudied.
Previous international studies have shown that time
constraints impact on use of GP services for certain populations: urban women
have been shown to have greater unmet need due to time constraints than rural
women in Australia.14 Such time constraints
leading to unmet need have also been shown to not vary between disparate groups
despite educational disparity. A study of unmet need for health care in
immigrant communities in Canada found that the proportion of immigrants
selecting ‘Too busy’ as a reason for unmet need was not
statistically significantly different to non-immigrants, despite there being a
(slightly) lower mean educational achievement ranking for non-immigrants than
immigrants.15
There are two variables where Pacific peoples and Other New
Zealanders have different patterns of unmet GP need. First, Pacific peoples have
more unmet need if they are overweight compared to normal/underweight people,
while BMI does not appear to affect Other New Zealanders’ unmet GP need.
Second, Pacific peoples have more unmet need when they do not have asthma but
the converse is true for Other New Zealanders.
Prevalence of high BMI among Pacific peoples has been an
identified issue for some time 1,2,6,12,
largely due to the presence of weight-related illness such as diabetes. Pacific
peoples with diabetes have been shown to have a higher number of GP
consultations than Europeans with the illness and have been shown to possess
more of the adverse risk factors for diabetes complications than Europeans: such
as being a smoker, having an HbA1c greater than 8%, and having
microalbuminuria.4
Diagnosed asthma in New Zealand has been associated with
greater utilisation of GP services without factoring in
ethnicity.16 The ethnic variation is somewhat
counterintuitive as, in the past, it appears Pacific peoples have had greater
unmet asthma need.17
Confusingly, asthma rates among Māori and Pacific
Islanders in New Zealand have been deemed considerably greater than for Other
New Zealanders,17 while Ministry of Health
data, however, have shown that medicated asthma rates are considerably lower for
Pacific peoples than European/Other.5
Furthermore, the lack of longitudinal data from previous Health Surveys makes
tracking changing rates of prevalence difficult.
ConclusionThis statistical study shows, using nationally
representative data, variations in unmet GP need rates, for several reasons and
affected by several variables, between Pacific peoples and Other New Zealanders.
Health need has been shown to both positively and negatively affect the unmet GP
need of Pacific peoples. Financial constraints predictably contribute to unmet
need, while less predictably higher education and the associated time
constraints (more prevalent in Other New Zealanders) also contribute to unmet GP
need.
Competing interests: None.
Author information: Megan Pledger, Senior
Research Fellow; Jacqueline Cumming, Director; Mili Burnette, Research
Assistant; Jacob Daubé, Research Assistant; Health Services Research
Centre, School of Government, Victoria University of Wellington,
Wellington
Acknowledgements: We thank the respondents
of the New Zealand Health Survey 2006/07 for their participation in the survey.
Correspondence: Megan Pledger, Health
Services Research Centre, School of Government, Victoria University of
Wellington, PO Box 600, Wellington 6140, New Zealand. Fax: +64 (0)4
4636568; email: Megan.Pledger@vuw.ac.nz
References:
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