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Journal of Primary Health Care Journal of Primary Health Care Society
Journal of The Royal New Zealand College of General Practitioners
RESEARCH ARTICLE (Open Access)

Unmet need for primary health care and subsequent inpatient hospitalisation in Aotearoa New Zealand. A cohort study

Megan Pledger https://orcid.org/0000-0003-1669-8346 1 * , Jacqueline Cumming 1
+ Author Affiliations
- Author Affiliations

1 Te Hikuwai Rangahau Hauora - Health Services Research Centre, Te Wāhanga Tātai Hauora – Wellington Faculty of Health, Te Herenga Waka - Victoria University of Wellington, Rutherford House, Pipitea Campus, Bunny Street, Wellington 6011, New Zealand.

* Correspondence to: megan.pledger@vuw.ac.nz

Handling Editor: Felicity Goodyear-Smith

Journal of Primary Health Care 16(2) 128-134 https://doi.org/10.1071/HC24018
Submitted: 10 February 2024  Accepted: 23 May 2024  Published: 18 June 2024

© 2024 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of The Royal New Zealand College of General Practitioners. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Introduction

The inability to afford a consultation with a general practitioner may lead to delays in accessing care pathways.

Aim

This study aimed to explore the characteristics of people by their unmet need for a general practitioner consultation because of cost, and the characteristics of subsequent inpatient hospitalisations.

Methods

From the New Zealand Health Surveys (2013/14–2018/19), two groups were formed based on their unmet need for a general practitioner consultation due to cost. These groups were compared by socio-demographic factors and subsequent inpatient hospitalisation characteristics during follow-up. Time to an inpatient hospitalisation was the outcome in a proportional hazards regression model with need status as the key variable. The model was expanded to include confounding variables: sex, age group, ethnicity, the New Zealand Deprivation Index and self-rated health.

Results

The need group, characterised by having a higher proportion of females, younger adults, Māori, increased socioeconomic deprivation and poorer self-rated health experienced a greater chance of hospitalisation, a similar number of visits during follow-up, shorter stays and a quicker time to hospitalisation compared to the no-need group. Proportional hazards survival models gave a 28% higher hazard rate for the time to an inpatient hospitalisation for the need group compared to the no-need group. The inclusion of all the confounders in the model gave a similar hazard ratio.

Discussion

Although consultation fees vary across general practices, it is evident that this may not eliminate the cost barriers to accessing care for some groups. Needing multiple consultations may contribute to persistent unmet needs.

Keywords: Aotearoa New Zealand, cohort study, general practice, hospitalisation, primary health care, survey, survival analysis, unmet need.

WHAT GAP THIS FILLS
What is already known: In 2022/23, 13% of the population aged 15 and over did not have a GP consultation when needed because of cost; this percentage differs by age group, sex, ethnicity and the New Zealand Deprivation Index.
What this study adds: This paper explores the sociodemographic differences in those with met and unmet need for a GP consultation because of cost, their subsequent admissions to hospital and the characteristics of those admissions.

Introduction

Primary health care (PHC), as declared by the World Health Organization (WHO), ‘… is the first level of contact of individuals, the family and community with the national health system bringing health care as close as possible to where people live and work, and constitutes the first element of a continuing health care process.’1

Thus, PHC has two roles: to provide health care, and to act as guidance to secondary and tertiary level health care and to other health and social care. Through the former role, PHC can improve the health of patients so that hospital-level care is not needed, while the latter role channels patients towards the most suitable care pathways, possibly pre-empting unnecessary utilisation of hospital-level care. Reducing hospital-level care is important, as it holds the potential to lower overall health care expenditure.2

Primary care has been found to improve health as well as health equity.3 However, people who experience barriers to primary care may miss out on these health benefits. In turn, unmet need for primary care may affect how patients access secondary and tertiary level care. Waiting to afford a GP visit may mean it takes people longer to get a referral from their GP to access hospital care, or they may access it more quickly by bypassing primary care and self-referring to the emergency department at a hospital. In the 2022/23 New Zealand Health Survey (NZHS), it was found that 13% of the population aged 15 and over did not have a GP consultation when needed in the previous year because of cost, equating to 541,000 people.4

The New Zealand government subsidises the cost of a GP consultation with patients paying the remaining cost. There are various programs that can reduce these costs to patients: the Very-Low-Cost-Access scheme works at the level of the general practice providing lower costs for all enrollees, and the Community Services Card and the High Use Health Card schemes act at the patient level. These programs have the objective of delivering more economical health care to people facing heightened health needs and/or with limited financial means. Additionally, maternity care is free in the public health system.

This paper looks at the characteristics and primary care use of people with unmet need for a GP consultation because of cost, the time it takes them to access inpatient hospitalisations and the characteristics of such hospitalisations.

Methods

The data for this study were accessed via the Datalab at Statistics New Zealand. For a fuller account see the Supplementary material accompanying this paper.

Three datasets were combined via the encrypted National Health Index. These were six NZHSs with data collected between 2013 and 2019 which provided self-reported information about demographics, health care and health status of respondents. The Public Hospital Discharges dataset which records all inpatient discharges from public hospitals. It contains information on the start and end dates of the hospital stay and the diagnoses related to the event, coded in ICD-10-AM.5 The Mortality dataset which gives information about deaths.

The NZHS dataset contributed the primary variable of interest. Respondents were asked if during the previous year they had needed to see a GP but did not because of cost. Respondents were put into two groups, called ‘need’ and ‘no-need’, depending on whether they answered ‘yes’ or ‘no’ to this question.

Study period and follow-up

The study period was deemed to have started at the end of the survey period for the survey that a respondent was in and finished universally on 30 June 2021. Follow-up, for the purposes of proportional hazard regression modelling, started at the same point and lasted until either the respondent was hospitalised, was known to have died or 30 June 2021, with the latter two events being censoring events.

Supplementary Fig. S2 gives a visual description of the timeline, survey period and data availability.

Statistical analysis

Data from the NZHS were analysed using proportions and means. Differences between need groups were tested with two sample p-tests for differences in proportions and two sample t-tests for differences in means. The size of the dataset means that many comparisons were statistically significant when they were not practically significant. When comparing two figures, we comment when the difference is considered both practically and statistically significant or indicate otherwise. In our opinion, practical significance was 5 percentage points for percentages. For the average number of hospitalisations, the threshold was 0.5 visits, for the average first length of stay it was 0.5 days and for the average time till first stay it was 30 days.

The time to hospitalisation was modelled using proportional hazards regression with unmet need of a GP consultation due to cost being the primary variable. The model was expanded twice: model 2, with demographic variables – sex, age group, prioritised ethnicity and New Zealand Deprivation Index (NZDep); and model 3, a health variable – self-reported health.

Hospital stays were coded into one primary diagnosis and potentially multiple secondary diagnoses based on ICD-10-AM codes. The diagnoses for the first hospitalisation during follow-up were grouped into ICD chapters. Each respondent was recorded with an indicator variable if they had one or more diagnoses in a particular chapter. Where a chapter was found to show a practical and statistically significant difference between need groups, ICD-10-AM codes were investigated to see where the biggest differences occurred.

While writing this paper, the authors employed ChatGPT (https://chat.openai.com/) to synthesise text, enhance word selections, and investigate various ways of conveying concepts. The text was then refined by the authors.

Ethics approval was not sought for this research as this was a secondary analysis of administrative and survey data. The Ministry of Health received ethical approval for each survey from the New Zealand Health and Disability Multi-Region Ethics Committee. Further information can be found in the survey methodology reports.6

Results

The number of respondents in the six surveys who appear in the Datalab datasets ranged from 10,932 in the 2014/15 survey (out of 13,497 respondents in the total survey or 81%) to 12,579 in the 2018/19 survey (out of 13,572, 93%). Altogether, there were 72,243 respondents in the six surveys. Some respondents were in more than one survey and for them one random observation was chosen to be kept (516, 0.7%, observations deleted). A further 225 people died before follow-up started (0.3% deleted). This left 71,502 respondents for analysis (see Supplementary Fig. S1).

The demographic and health profile of the ‘need’ and ‘no-need’ groups appears in Table 1. We observed notable demographic differences between individuals classified as being in the need group and those in the no-need group. Specifically, a higher proportion of females were found in the need group, constituting 70% of this group, as opposed to 55% in the no-need group. Furthermore, younger adults, (25–34-year-olds and 35–44-year-olds) were most likely to be in the need group while those in the oldest age groups (65–74-year-olds and 75+ year-olds) were most likely to be in the no-need group. Māori make up 30% of the need group compared to 19% of the no-need group; conversely, European New Zealanders/Others make up 57% of the need group and 67% of the no-need group. Those in the need group were more likely to be in the most deprived quintile of NZDep (36%) compared to the no-need group (25%); conversely, 33% of the no-need group were in the top two least deprived quintiles compared to 21% of the need group.

Table 1.Demographic and health profile of the two (need and no-need) groups.

Unmet GP need due to cost
NeedNo-need
N = 11,472N = 60,030
Statistic95% CIStatistic95% CI
Sex (%)
 Female69.5(68.7, 70.4)55.0(54.6, 55.4)
 Male30.5(29.6, 31.3)45.0(44.6, 45.4)
Age group (%)
 15–2412.7(12.1, 13.3)10.9(10.6, 11.1)
 25–3424.5(23.7, 25.3)14.0(13.8, 14.3)
 35–4421.5(20.8, 22.3)15.5(15.2, 15.8)
 45–5417.4(16.7, 18.1)15.9(15.6, 16.2)
 55–6413.1(12.5, 13.7)16.8(16.5, 17.1)
 65–747.2(6.7, 7.7)15.1(14.8, 15.4)
 75+3.5(3.2, 3.8)11.8(11.6, 12.1)
Prioritised ethnicity (in priority order, %)
 Māori29.7(28.9, 30.6)19.0(18.6, 19.3)
 Pacific people7.4(6.9, 7.9)4.8(4.7, 5.0)
 Asian5.7(5.3, 6.2)8.5(8.3, 8.7)
 NZ European/Other57.1(56.2, 58.0)67.7(67.3, 68.1)
NZ deprivation quintiles (%)
 1 (least deprived)8.3(7.8, 8.8)14.9(14.6, 15.1)
 212.5(11.9, 13.1)17.6(17.3, 17.9)
 318.9(18.1, 19.6)20.1(19.7, 20.4)
 424.0(23.3, 24.8)22.5(22.2, 22.9)
 5 (most deprived)36.3(35.4, 37.2)25.0(24.6, 25.3)
Self-rated health (%)
 Excellent7.0(6.6, 7.5)14.4(14.1, 14.7)
 Very good28.3(27.5, 29.2)41.0(40.6, 41.4)
 Good39.0(38.2, 39.9)33.1(32.7, 33.5)
 Poor18.5(17.8, 19.2)9.3(9.1, 9.6)
 Very poor7.1(6.6, 7.5)2.2(2.0, 2.3)
Have you ever been told by a doctor that you have:
 Had a heart attack (%)3.8(3.4, 4.1)4.3(4.1, 4.4)
 Angina (%)4.2(3.9, 4.6)4.1(4.0, 4.3)
 Heart failure (%)2.8(2.5, 3.1)2.6(2.4, 2.7)
 Other heart disease (%)8.8(8.2, 9.3)8.7(8.5, 8.9)
 StrokeA (%)2.0(1.8, 2.3)2.2(2.1, 2.3)
 DiabetesB (%)7.8(7.3, 8.3)7.2(7.0, 7.4)
 Asthma (%)30.5(29.6, 31.3)19.2(18.9, 19.5)
 ArthritisC (%)19.1(18.3, 19.8)21.5(21.2, 21.8)
 DepressionD (%)34.4(33.5, 35.3)15.7(15.4, 15.9)
 Bipolar disorderD (%)3.0(2.7, 3.3)1.0(0.9, 1.1)
 Anxiety disorderD,E (%)22.2(21.4, 23.0)9.3(9.0, 9.5)
A Does not include transient ischaemic attacks.
B Does not include diabetes during pregnancy.
C Includes gout, lupus and psoriatic arthritis.
D Conditions that lasted or were expected to last more than 6 months.
E Includes panic attacks, PTSD, phobias and obsessive–compulsive disorders.

Respondents in the need group were more likely to be in the bottom two categories of self-rated health (26% versus 11%) and less likely to be in the top category (7% vs 14%). They were more likely to have been told by a doctor that they had depression (34% vs 16%), asthma (30% vs 19%) or an anxiety disorder (22% vs 9%).

Respondents were asked if the last visit they had made to an emergency department at a hospital could have been treated at a general practice. For those in the need group, 35% agreed with that statement compared to 24% for those in the no-need group.

Table 2 contains hospitalisation characteristics of the two groups. Compared to the no-need groups, those in need were more likely to be hospitalised (52% vs 44%), had a similar number of hospitalisations (3.4 visits vs 3.1 visits), a shorter length of stay (2.7 days vs 3.3 days) and had a shorter time to hospitalisation (608 days vs 651 days).

Table 2.Hospitalisation characteristics of the two need groups.

Unmet GP need to cost
NeedNo-need
N = 11,472N = 60,030
%95% CI%95% CI
Had an inpatient hospitalisation52.1(51.2, 53.1)43.9(43.5, 44.3)
N = 5,982N = 26,376
Stat95% CIStat95% CI
Number of hospitalisationsA (n)3.4(3.3, 3.5)3.1(3.1, 3.2)
First length of stayA (days)2.7(2.6, 2.8)3.3(3.1, 3.5)
Time to first hospitalisationA (days)608(594, 621)651(644, 658)
A For respondents who had an inpatient hospitalisation.

Table 3 presents the hazard rates for three proportional hazard survival models for time till hospitalisation. The first model contains the primary variable of interest, need status. It shows that the hazard rate is 28% higher for the need group over the no-need group. The hazard rate for the need group increased from 1.28 in the first model to 1.39 in the second model. Based on a confounder analysis, age group is the variable that accounts for this increase: the need group is younger than the no-need group and this means the unadjusted model does not show the full extent of the consequences of need.

Table 3.Proportional hazards survival model for time to hospitalisation with confounders.

Model 1Model 2Model 3
Hazard ratio95% CIHazard ratio95% CIHazard ratio95% CI
Groups
 Need1.28(1.24, 1.31)1.39(1.35, 1.43)1.25(1.22, 1.29)
 No-need111
Sex
 Female1.19(1.16, 1.22)1.21(1.19, 1.25)
 Male11
Age group
 15–24 years1.10(1.05, 1.16)1.12(1.07, 1.18)
 25–34 years1.36(1.31, 1.42)1.37(1.32, 1.43)
 35–44 years11
 45–54 years1.16(1.11, 1.21)1.13(1.08, 1.18)
 55–64 years1.50(1.44, 1.57)1.49(1.42, 1.55)
 65–74 years2.30(2.20, 2.39)2.32(2.22, 2.41)
 75+ years3.83(3.68, 4.00)3.79(3.63, 3.95)
Prioritised ethnicity
 Māori1.59(1.51, 1.68)1.55(1.47, 1.64)
 Pacific people1.53(1.43, 1.64)1.50(1.41, 1.61)
 Asian11
 NZ European/Other1.42(1.35, 1.49)1.44(1.36, 1.51)
NZ Dep quintiles
 1 (least deprived)11
 21.10(1.06, 1.15)1.09(1.04, 1.14)
 31.22(1.17, 1.27)1.18(1.14, 1.23)
 41.33(1.28, 1.38)1.27(1.22, 1.33)
 5 (most deprived)1.47(1.41, 1.53)1.37(1.32, 1.43)
Self-rated health
 Excellent1
 Very good1.14(1.10, 1.19)
 Good1.43(1.38, 1.49)
 Poor1.86(1.77, 1.94)
 Very poor2.62(2.46, 2.79)

Notes: the P-values for the difference in hazard ratios between a variable level and the reference level are all P < 0.0001.

The third model adds in the self-rated health variable, which gives a hazard ratio for the need group of 1.25. The decrease in the hazard rate for the need group from model 2 reflects that the need group is more likely to have people with lower health status.

The confounding variables act as would be expected, with those who are older, Māori or Pacific peoples, with greater socio-economic deprivation or with poorer health status having a hospital admission earlier.

On investigating the model it was seen that one particular age group behaved differently in terms of their time to hospitalisation, based on a graph of Kaplan–Meier survival curves (see Supplementary Fig. S3). The 25–34-year age group was more likely to be hospitalised earlier and were less likely to be hospitalised later relative to other age groups. Model 3 was refitted with this age group removed and the hazard rate for the difference between the need and no need groups reduced by 1.8%. This small change gives us confidence that the result is robust to this failure in the proportional hazards assumption.

Supplementary Table S1 groups the ICD codes for primary and secondary diagnoses into their respective chapters by need group for the first hospitalisation during the study period. The chapter showing the biggest difference between groups was for ICD codes involving ‘pregnancy, childbirth and the puerperium’ for the primary diagnosis (17.2% for the need group vs 9.7% for the no-need group). As pregnancy is not an avoidable condition for hospitalisation, we removed people with a pregnancy-related diagnosis and re-ran model 3. The odds ratio of the need group increased slightly by 1.5% indicating that the condition is not causing the difference between the two groups.

The biggest difference for secondary diagnoses was ‘Factors influencing health status and contact with health services’ (59.1% vs 48.4% respectively) where respondents in the need group were more likely to be current tobacco users (‘Z720 Tobacco use, current’, 26% versus 12% respectively) while the no-need group was slightly more likely to have given up tobacco products (‘Z8643 Personal history of tobacco use disorder’, 19% versus 23%).

Discussion

Those who have an unmet need for a GP consultation were more likely to be young, female, Māori or Pacific peoples, more socioeconomically deprived and in poorer health than the no-need group. While previous research has shown that consultation fees were lowest where the population surrounding a general practice was more likely to be composed of Māori, Pacific peoples or the more deprived, it appears that these lower fees are not low enough to eliminate unmet need of a GP consultation due to cost.7 It could be that it is not just the individual cost of a consultation that means an individual has unmet need but that an individual may require more consultations and the cumulative cost is an important factor for having unmet need. From the NZHS 2022/23, the average number of GP consultations per year overall was 2.4 visits; it was higher for females (2.8, 95% CI 2.3–2.5), the most deprived quintile (2.6, 95% CI 2.4–2.9) and the disabled (4.9, 95% CI 4.4–5.5).8

There may also be multiple instances of unmet need, something not asked about in the NZHS but asked in the Growing Up in NZ study. In that study, caregivers of children of 2 years of age were asked the number of times they had an unmet need for a GP consultation in the prior year. It was found that 44% of the children with unmet need had multiple unmet needs in that time, with 4% reporting more than five instances of unmet need.9

The cost of the GP consultation is not the only cost that may impose a financial barrier to care. From the NZHS 2022/23, other financial barriers felt by respondents included the cost of prescription items (4.0% of respondents), as well as transport costs (2.2%), time off work (7.4%) and owing the general practice money (1.4%).10 It is worth noting that the greatest barrier was due to the wait time for a GP appointment (21.2%).

In general, the need group was more likely to be hospitalised and to have had their first hospitalisation sooner. However, they had shorter stays in hospital on average. Given the diagnoses were generally similar between groups, this could be due to being younger so that their recuperation time is shorter, or it could be that their higher probability of having a pregnancy-related diagnosis produced a shorter stay.

The need group were more likely to have a secondary diagnosis of being current smokers and it could be that smokers in the need group are having to make choices about buying tobacco or seeing their GP. The previous Labour government had put into law a ban on buying cigarettes for those born after 2008 which was to come into force in 2024.11 This would seem an advantageous step since those with unmet need tend to be young and so it would have affected this group first. Subsequently, the National government repealed this legislation.12

Studies have looked at which characteristics of PHC providers reduce the need for hospital-level care,13 as well as interventions to reduce that need.1416 Two studies that put interventions in PHC for people identified at risk of avoidable hospital care found that there was little to no reduction in hospital level care and an increased cost or workload due to identifying people with unmet need.14,15

We cannot tell from our analysis whether the hospitalisations for the need group could have been avoided if their unmet need for a GP consultation had been met. However, the need group in this analysis had a higher probability (35%) of using the emergency department for a condition that could have treated in PHC compared to the no-need group (24%). This suggests that some in the need group appear to be using emergency departments as an alternative to a GP consultation rather than as an alternative pathway to accessing hospital-level care.

One limitation is that we do not have the interview date of the respondents in the NZHS, which means we had to choose when to start follow-up. In this case, we chose to start follow-up at the end of the survey period for the survey the respondent was in. This meant that some of the respondents could have had their first hospitalisation before follow-up started.

Another potential date for the start of the follow-up period could have been the first day in the survey period that the respondent was in. A sensitivity analysis was done where the start of follow-up was set as the first day of the survey period the respondent was in. For model 3, the hazard rate for the need group decreased by 0.02%. This shows that the hazard rate for the need group is robust to when follow-up started.

The implication from this research, and from previous research on fees,8 is that work needs to be done at a policy level so that the out-of-pocket costs to patients are better aligned with their means, especially for people who need on-going care at a general practice. Furthermore, work needs to continue on reducing tobacco use, not only to improve health but with the prospect of reducing unmet need for health care.  

Supplementary material

Supplementary material is available online.

Data availability

The data are available in the Datalab at Statistics New Zealand. Access requires a vetting process by Statistics New Zealand. Access to the data used in this study was provided by Stats NZ under conditions designed to give effect to the security and confidentiality provisions of the Statistics Act 1975 (NZ). The results presented in this study are the work of the author, not Stats NZ or individual data suppliers. These results are not official statistics. They have been created for research purposes from the Integrated Data Infrastructure (IDI) which is carefully managed by Stats NZ. For more information about the IDI please visit https://www.stats.govt.nz/integrated-data/.

Conflicts of interest

The authors declare that they have no competing interests.

Declaration of funding

This work was funded by the Health Research Council grant – ‘Enhancing Primary Health Care Services to Improve Health in Aotearoa/New Zealand’ (Programme Grant 18/667). The funders did not have any role in the design, collection, analysis, interpretation of data or writing and submission of the manuscript.

Acknowledgements

The authors thank the respondents of the New Zealand Health Surveys, 2013/14 to 2018/19, for their participation in the surveys.

Author contributions

J. C.: Funding acquisition. M. P.: Project administration, Methodology, Software, Data curation, Formal analysis, Writing – Original draft. M. P. and J. C.: Writing – Review and editing.

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