The concentration of complexity: case mix in New Zealand general practice and the sustainability of primary care

Keywords: case mix, clinical workload, complexity, equity, general practice, multi‐morbidity, primary care, risk factors.

The New Zealand health system is currently facing significant challenges and opportunities following the impact of the coronavirus disease 2019 (COVID-19) pandemic and recent announcements about health sector change following the publication of the Health and Disability review.^1,2 A priority facing the sector is the sustainability of models of primary care, and linked to this, a core issue of the workload associated with differing levels of patient case mix seen in general practice.

There has long been discussion about whether there is a ‘concentration of complexity’ in certain primary care settings compared to others. All of general practice and primary care is recognised as ‘busy’; what is less clear is the degree to which the population health needs of patients in practices with a high proportion of patients with ‘high needs’, require substantially increased time and resourcing, such that long-term workforce sustainability in those practices can be maintained.³ Central to these discussions are concerns about the best way to provide care for under-served populations.^4,5

Although there may be diverse opinion about options to achieve a more sustainable and equitable primary care environment, there are little empirical data that describe the extent to which levels of morbidity and concomitant levels of socio-economic deprivation are distributed among primary care practices or within a primary health organisation (PHO).

This study provides that information using data from a large primary health organisation and Māori provider (PHO) of five medical practices in the same geographical area.

Routinely collected practice data from a large PHO of 57 practices and an associated Māori PHO of five medical practices were used to compare a number of population health indicators between practices that had a high proportion of high needs patients (HPHN) and those practices with a low proportion of high needs patients (Non-HPHN).

The data provider was the PHO Health Business Intelligence unit and DataCraft Analytics, a provider of Business Intelligence Services.

High proportion high needs (HPHN) practices were defined as having ≥50% of enrolled patients as Māori, Pacific (using the New Zealand prioritised ethnicity classification), or high deprivation (quintile 5), and non-high proportion high needs (Non-HPHN) where the proportion of those patients was <50%.

Beyond routinely collected demographic information about age, gender and ethnicity, data sources for practices include morbidity and illness classifications (READ codes, SnoMed codes for Indici practices), screening data, invoicing for consultations at after-hours clinics and hospital data from the District Health Boards (DHBs) for ambulatory sensitive hospitalisation (ASH) rates and emergency department (ED) attendance. Demographic data were collected at 1 July 2020.

A series of clinical indicators were collected and assessed by experienced GP clinicians and academic researchers as having significant potential impact on both health service activity and health outcomes. A comparison was made between HPHN and Non-HPHN practices using these indicators. The chosen measures were: patients with a diagnosis of diabetes and pre-diabetes, asthma, chronic obstructive pulmonary disease (COPD), gout and Stage 3b or 4 chronic kidney disease. A comparison was also made between practice groups on the basis of current smoking status, recorded body mass index (BMI) >30, a cardiovascular risk assessment (CVRA) result ≥15%, and ambulatory sensitive hospitalisation (ASH) and emergency department attendance between July 2019 and June 2020. The dataset for calculation of the comparison for clinical indicators was for the first quarter of 2019, prior to inclusion of the Māori provider PHO data.

To exclude the possibility of differential recording of morbidity or risk factors between HPHN and Non-HPHN practices, data completion was compared for completed data points routinely delivered to the PHO. No significant difference was observed between the HPHN and Non-HPHN practice groupings in terms of data completion. For example, 67.4% of patients in the HPHN grouping had ever had a BMI recorded compared to 62.4% of the Non-HPHN group.

All tabulated data were de-identified at the individual patient level. Aggregate data have been used for both HPHN and Non-HPHN groupings.

Ethics approval was obtained from the University of Otago ethics committee (reference number: HD22/056).

The combined registered population from the provider organisations is 349 782, from which these data are drawn. Eleven primary care practices in this grouping reached the definition of ‘High Needs’, being at least 50% Māori, Pacific or New Zealand Deprivation Index quintile 5.

Table 1 outlines the overall demographic summary of the practice populations according to age, ethnicity and socio-economic status. The population enrolled in HPHN practices was 39 890 (11.4% of the enrolled PHO population).

Table 1.  Demographic summary of the HPHN and Non-HPHN populations according to age, ethnicity and socio-economic status.

The demographic breakdown was not dissimilar to the overall PHO population, in terms of age and gender, though the HPHN practices had a higher proportion of younger patients. Of the high needs population, 30.5% were Māori and 32.1% were Pacific Islanders, compared to 10 and 3.6% respectively for Non-HPHN practices. The percentage of patients in deprivation quintile 5 was 53% in the HPHN group compared to 8.8% in the Non-HPHN group.

Although higher proportions of Māori, Pacific Islander and deprivation quintile 5 patients were enrolled in HPHN practices, given population numbers, more Māori and quintile 5 patients are enrolled across the Non-HPHN group. Given the smaller size of the Pacific Island patient numbers and the concentration in a small number of providers with a specific Pacific focus, there are overall greater absolute numbers of Pacific patients in the HPHN group.

Table 2 shows the difference in prevalence of long-term conditions and health outcome risk factors between the practice groups. The rates of long-term conditions and risk factors are significantly higher for patients in the HPHN group. The significance of the increase (P < 0.05) is consistent across most conditions and age groups, but for patients aged 50–79 years, there are significantly higher rates for all clinical indicators considered in this study (P < 0.01). Regardless of age, patients in the HPHN group are twice as likely to have diabetes (8.4% HPHN vs 4% Non-HPHN) or gout (5.1% vs 2.8%), three-fold more likely to smoke (18.3% vs 6.5%), and more likely to have an increased BMI (23.2% vs 16.1%).

Table 2.  Difference in prevalence of illness and risk indicators between HPHN and Non- HPHN groups. Categories with significant differences between the populations are shown as having bold P-values.

There is a clustering of increased risk factors and long-term conditions in the age groups between 30 and 70 years.

Patients in HPHN practices also have long-term conditions at a younger age. In the HPHN practices, patients are over three-fold more likely to have diabetes in the 40- to 49-year (10.6% vs 2.9%) and 50- to 59-year (19.4% vs 5.8%) age groups than those patients in the Non-HPHN practices.

There is also a marked difference between the groups in terms of interaction with secondary care services. Emergency department attendances in the study period were 10 142 (25.4%) for the high needs group versus 47 450 (15.3%) for Non-HPHN patient group. A similar pattern was observed for ASHs. Patients in the HPHN group are 2.8-fold more likely to be admitted to hospital than those in the Non-HPHN group.

A significant driver of practice workload and health outcome is the degree to which patients have multiple risk factors and morbidities. Table 3 compares the HPHN and Non-HPHN groups in terms of the likelihood of patients having multiple risk factors routinely collected by the PHO. Risk factors were common throughout both sets of practices, with 19.4% of Non-HPHN patients and 22.4% of patients in the HPHN practices having one risk factor. Patients with multiple risk factors become more prevalent in the HPHN practice grouping. Ratios for patients with three risk factors are 1.6-fold more likely in the HPHN group (6.2% vs 3.9%) and 2.3-fold more likely for patients with five risk factors (2.2% vs 0.9%).

Table 3.  Ratio of the number of long-term conditions/risk factors per patient.

These results show a marked and significant difference in the patient population characteristics between the HPHN and Non-HPHN groups. In the HPHN population group, the increased proportion of registered patients with multiple risk factors and long-term conditions creates a ‘concentration of complexity’ that poses significant questions about the workload and workforce required to maintain high-quality care to these populations.

The high concentration of risk factors and morbidities seen in the HPHN patient group with attendant medical, mental health and social needs leads to more complex appointments with resultant challenges in providing sufficient time to address these problems.^6,7

There is a danger that the inverse care law first stated by Tudor Hart in 1971 still applies, and that ‘the availability of good medical care tends to vary inversely with the need for it in the population served’.⁸ The reasons that the inverse care law holds true remain complex and involve a mixture of health system issues and social determinants.

In keeping with the findings from this study, overseas evidence shows higher levels of long-term conditions lead to increased morbidity in the most socio-economically deprived groups,⁹ and therefore, those populations require additional resources.¹⁰

As identified in overseas literature, the time requirements of acute demands of patient presentations can erode attempts at preventive care, and addressing those needs are more challenging in settings of socio-economic deprivation.^11,12 In this study, the increased time pressures of high levels of multimorbidity and the social implications of high deprivation in the HPHN practices would make it difficult, for example, to address the higher numbers of smokers in their populations.

In line with both local and overseas literature, the impact of the workload generated at these HPHN practices goes beyond their local practice environment, with higher ASH rates and potentially unnecessary attendance at EDs.^13,14

The significant concentration of Māori and Pacific patients within the HPHN group is an important factor considering the appropriateness of current service provision. The wealth of evidence indicating Māori health outcomes remain worse than that for Non-Māori, after adjusting for socio-economic and other social determinants, is another powerful indicator of the need for review of the resourcing required for practice settings where there are significant numbers of Māori and Pasifika patients.¹⁵

There are a number of reasons to explore this variation in case mix across a PHO area.

First, it is important that pressures on workload and sustainability across all of primary care and general practice can be highlighted so that appropriate changes in resourcing can be more fairly considered and quantified. Given the increasing health sector expectations, for example, on the need to reduce ASH and ED attendance, our data indicates it is unlikely that current models of care can sustainability address those issues.¹⁶

Second, although the concentration of adverse clinical and social determinants described in this paper has a direct impact on the workload and workforce in the HPHN practices, all general practices and primary health providers in the PHO will have many patients with these characteristics. Taking a focus on practice settings, where complexity is most concentrated, is also a potent reminder of the increasing workload burden faced here and overseas by all of primary care and general practice.^3,17,18 As indicated in recent Government announcements,² it is time to focus on primary care if effective health sector reform is to be achieved.

There is also an imperative to explore and understand the impact of case mix variation from a health system development perspective.

Much of health system thinking, planning and funding remains linear, whereas everyday primary care clinical experience recognises the limitations of such thinking.¹⁹

Discussion about appropriate resolution of the problems outlined in this paper requires the application of elements of complexity science.²⁰ Where multiple adverse factors are concentrated together, there will be a ‘tipping point’ beyond which new events and systems come into place, and where traditional population health approaches are unlikely to be successful. At both individual clinical and health system levels, for example, it is important to recognise that multiple co-morbidity does not increase workload through simple addition, but rather multiplies problem areas in complex and cumulative ways.^21,22

International evidence indicates that in such situations, it is important to acknowledge unpredictability and utilise available evidence from a complexity science approach to tailor solutions to local contexts.²³ It is also important to take an ‘appreciative inquiry’ approach to these system changes, recognising that within the present system, there is much that is positive and that it should be retained.^20,24

A number of evidence-based solutions will help primary care sustainability for all general practice models and teams, but particularly in settings where case mix complexity is significant. These would include lowering doctor–patient and nurse–patient ratios so that more complexity can be acknowledged,⁷ variation in the make up of the primary care team mix and appropriate utilisation of team skills,^25–27 and specific responses to local community cultural needs.^28,29

It is important to recognise that despite the challenges faced by practice teams dealing with a concentrated case mix of complex patients and problems, there are examples of how additional strategies at practice level can produce positive results. Local New Zealand examples include a weight management programme in a very low-cost access (VLCA) practice,³⁰ and gout management programmes with a specific equity focus.³¹ The same themes are seen in overseas examples where additional time for complex consultations translates into increased patient enablement.³²

We recognise as a limitation that although the participating provider organisations in this study represent a large grouping in a lower North Island context, it is not clear to what extent the same results might be reached in other areas, and where there may be other initiatives that could support practices operating in these settings.

The findings from this study demonstrate that in this PHO area population, adverse health determinants and established co-morbidities are concentrated into a low number of general practice and health provider groupings. This ‘concentration of complexity’ raises questions about current models of care and whether they are adequately resourced for quality primary care in these settings. As noted in recent commentary,³³ the current health sector reorganisation emphasises a need to address long-term underinvestment in primary care, and given the overall prevalence of adverse health indicators throughout New Zealand communities, our findings also highlight the need to develop equitable and appropriate resourcing for all patients in primary care.

The data that support this study are available in the article.

The authors declare no conflicts of interest.

This research did not receive any specific funding.